semvec/pypi-packages huggingface dataset:
DenseNet-armhzjz 0.1.2
A DenseNet implementation
Requires Python: >=3.6

## About this DenseNet implementation

After reading the [DenseNet paper](https://arxiv.org/abs/1608.06993) I was very surprised for it being so simple and yet so powerfull. So I decided to make my own implementation of it and give it a try.<br>
This implementation of DenseNet was done under python version 3.6.10.

I used [this Cifar-10 datase](https://www.kaggle.com/emadtolba/cifar10-comp) from kaggle to test the performance of my implementation. For that purpose, I trained a total of 4 DenseNet networks to the data; 2 of them were BC variants and the other to were none BC networks. The quick comparison I did on these four networks can be found in [this jupyter notebook](https://github.com/armhzjz/DenseNet/blob/master/performance_Analysis/Cifar-10_performanceTest.ipynb).

A test script may be found [here](https://github.com/armhzjz/DenseNet/tree/master/tests/Cifar-10). I used this test sctipt to ensure the implementation works also out of the context of kaggle notebooks. It downloads the Cifar-10 dataset directly from its official webpage, prepares the training, validation and test data sets, trains a DenseNet model and evaluates it using the best parameters produced during its training. The execution of this script will take a considerable amount of time depending on the GPU hardware you use, so beware of this and don't get puzzled if it seems to take forever until the script is completely executed.

<br>Finally, a [kaggle kernel is found in here](https://www.kaggle.com/ahernandez1/mydensenet-implementation) in case the reader is interested in a cifar-10 evaluation (i.e. not only a rough comparison as the one provided in this repository).

Note: In order to use this module, pytorch must be install in your system.
systemize 0.0.1
Systemize automatically organizes all of your files into the grouping of a similar kind together, getting rid of clutter

# Systemize


![Twitter Follow](https://img.shields.io/twitter/follow/ayushman_17?label=Follow&style=social)

Project name is a `Systemize` that `Helps` you to `organize your PC by automatically organizes all of your files into the grouping of a similar kind together` such as images, PDFs, presentations, videos, and others, getting rid of clutter. So itâ€™s easy to keep it tidy and find exactly what  youâ€™re looking for.

``With Systemize, a cluttered desktop/folders are a Thing of the Past.``
When you run Systemize, it expands to show the grouped files.


## Prerequisites

Before you begin, ensure you have met the following requirements:

* âœ…Be sure You have installed the latest version of `Python 3.x+` and `pip` (optional) by running these on your command prompt:
---
> - `python --version`
> - `pip -V`
---

* âœ…You have a `Windows/Linux/Mac` machine.
* âœ…And `the desktop/folder that get very cluttered`.

## Installing Systemize

To install Systemize, follow these steps:

Linux and macOS:
```
pip install systemize
```

Windows:
```
pip install systemize
```
## Using Systemize

To use `Systemize`, follow these steps:

---
> **Note:**
> - âœ…Be sure to be inside the folder you want to organize the disorderðŸ§½and running python on your command prompt
---
```
import systemize
systemize.here() 
```


## Contributing to Systemize

To contribute to Systemize, follow these steps:

1. Fork this [repository](https://github.com/ayushman17/Systemize).
2. Create a branch: `git checkout -b <branch_name>`.
3. Make your changes and commit them: `git commit -m '<commit_message>'`
4. Push to the original branch: `git push origin <project_name>/<location>`
5. Create the pull request.

Alternatively see the GitHub documentation on [creating a pull request](https://help.github.com/en/github/collaborating-with-issues-and-pull-requests/creating-a-pull-request).


## Contact

If you want to contact me you can reach me at <ascb508@gmail.com>.

## License
<!--- If you're not sure which open license to use see https://choosealicense.com/--->

This project uses the following license: [MIT License](https://github.com/ayushman17/Systemize/blob/master/LICENSE.txt).