semvec/pypi-packages huggingface dataset:
swachand-helloworld 0.0.3
Say Hello!

# Hello World

This is an example project demonstrating how to publish a python module to PyPI. 

## Installation

Run the following to install:

```python
pip install swachand_helloworld
```

## Usage

```python
from helloworld import say_hello

# Generate "Hello, World!"
say_hello()

# Generate "Hello, Everybody!"
say_hello("Everybody")
```
# Developing Hello World

To install helloworld, along with the tools you need to develop and run tests, run the following in your virutalenv:

```bash
$ pip install -e .[dev]
$ pip install -e .'[dev]' # for ZSH 
$ pip install -e ."[dev]" # for Windows 
```
enochecker-async 0.5.0
Library to build async checker scripts for the EnoEngine A/D CTF Framework in Python
Requires Python: >=3.7

# EnoChecker Async [![Build Status](https://dev.azure.com/ENOFLAG/ENOWARS/_apis/build/status/enochecker_async%20CI?branchName=master)](https://dev.azure.com/ENOFLAG/ENOWARS/_build) ![](https://tokei.rs/b1/github/enowars/enochecker_async)

This is the asynchronous python variant of the ENOWARS checkerlib.

### Implementing an asynchronous checker
Consumers must extend the BaseChecker class:
```python
class DemoChecker(BaseChecker):
    port = 8000

    def __init__(self):
        super(DemoChecker, self).__init__("Demo", 8080, 2, 0, 0) # 2 flags, 0 noises, 0 havocs

    async def putflag(self, logger: LoggerAdapter, task: CheckerTaskMessage, collection: MotorCollection) -> None:
        pass

    async def getflag(self, logger: LoggerAdapter, task: CheckerTaskMessage, collection: MotorCollection) -> None:
        pass

    async def putnoise(self, logger: LoggerAdapter, task: CheckerTaskMessage, collection: MotorCollection) -> None:
        pass

    async def getnoise(self, logger: LoggerAdapter, task: CheckerTaskMessage, collection: MotorCollection) -> None:
        pass

    async def havoc(self, logger: LoggerAdapter, task: CheckerTaskMessage, collection: MotorCollection) -> None:
        pass
```

For a full example, check out the [WASP checker](https://github.com/enowars/service-wasp/blob/master/checker/checker.py).

### Testing your checker

[enochecker_cli](https://github.com/enowars/enochecker_cli) is a nice cli tool that you can use to send tasks to your checker.
cv-models 0.1.0
Implementations of common Computer Vision models
Requires Python: >=3.6

# Computer Vision models
Implementations of common computer vision deep learning models.


**There are better versions already out there with pretrained weights, why bother?**

First and foremost, it's a bit for fun and a bit for learning purposes. \
Moreover, while it is true that there is a plethora of ready-to-use implementations in the wild, many repositories I encountered often lacked documentation and organization, while in other cases even provided *not-so-standard* implementations, to say the least. \
This is an attempt to overcome these problems, by rolling my own broken versions (ironic I know).

## Classification
CV models aimed at classification tasks, with a standard classifier head.

### Current models:
- ResNet variations (18, 34, 50, 101, 152)
- Xception (8 mid-flow blocks)

| Model                | pretrained  | Score (TODO)      |
|----------------------|-------------|-------------------|
| ResNet18             | ✔           |                   |
| ResNet34             | ✔           |                   |
| ResNet50             | ✔           |                   |
| ResNet101            | ✔           |                   |
| ResNet152            | ✔           |                   |
| Xception (8 mid flow)| ✘           |                   |

## Segmentation
Models for semantic and/or instance segmentation, including backbones when necessary.

### Current backbones:
| Model                 | pretrained  | Score (TODO)      |
|-----------------------|-------------|-------------------|
| ResNet18              | ✔           |                   |
| ResNet34              | ✔           |                   |
| ResNet50              | ✔           |                   |
| ResNet101             | ✔           |                   |
| ResNet152             | ✔           |                   |
| Xception (8 mid flow) | ✘           |                   |
| Xception (16 mid flow)| ✘           |                   |


### Current models
| Model                 |
|-----------------------|
| UNet                  |
| DeepLabV3             |
| DeepLabV3+            |