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https://gitlab.com/EAVISE/lightnet

Darknet got illuminated by PyTorch ~ Meet Lightnet
https://gitlab.com/EAVISE/lightnet

CNN computer vision darknet deep learning object detection pytorch single shot detection yolo

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Darknet got illuminated by PyTorch ~ Meet Lightnet

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README 

        
Logo  



Building blocks to recreate Darknet networks in Pytorch  

[![Version][version-badge]][release-url]

[![Documentation][doc-badge]][documentation-url]

[![Pipeline][pipeline-badge]][pipeline-url]

Ko-Fi  

[![Python][python-badge]][python-url]

[![PyTorch][pytorch-badge]][pytorch-url]  

[![VOC][voc-badge]][voc-url]

[![COCO][coco-badge]][coco-url]



## Why another framework

[pytorch-yolo2](https://github.com/marvis/pytorch-yolo2) is working perfectly fine,

but does not easily allow a user to modify an existing network.  

This is why I decided to create a library,

that gives the user all the necessary building blocks, to recreate any darknet network.

This library has everything you need to control your network,

weight loading & saving, datasets, dataloaders and data augmentation.



Where it started as library to recreate the darknet networks in PyTorch,

it has since grown into a more general purpose single-shot object detection library.



## Installing

First install [PyTorch and Torchvision](https://pytorch.org/get-started/locally).  

Then clone this repository and run one of the following commands:

```bash

# If you just want to use Lightnet

pip install brambox   # Optional (needed for training)

pip install .



# If you want to develop Lightnet

pip install -r develop.txt

```

> This project is python 3.6 and higher so on some systems you might want to use 'pip3.6' instead of 'pip'



## How to use

[Click Here](https://eavise.gitlab.io/lightnet) for the API documentation and guides on how to use this library.  

The _examples_ folder contains code snippets to train and test networks with lightnet. For examples on how to implement your own networks, you can take a look at the files in _lightnet/models_.

>If you are using a different version than the latest,

>you can generate the documentation yourself by running `make clean html` in the _docs_ folder.

>This does require some dependencies, like Sphinx.

>The easiest way to install them is by using the __-r develop.txt__ option when installing lightnet.



## Cite

If you use Lightnet in your research, please cite it.

```

@misc{lightnet18,

  author = {Tanguy Ophoff},

  title = {Lightnet: Building Blocks to Recreate Darknet Networks in Pytorch},

  howpublished = {\url{https://gitlab.com/EAVISE/lightnet}},

  year = {2018}

}

```



## Main Contributors

Here is a list of people that made noteworthy contributions and helped to get this project where it stands today!



- [Tanguy Ophoff](https://gitlab.com/0phoff)

- [Jon Crall](https://gitlab.com/Erotemic)

- [Cedric Gullentops](https://github.com/CedricGullentops)



[version-badge]: https://img.shields.io/pypi/v/lightnet.svg?label=version

[doc-badge]: https://img.shields.io/badge/-Documentation-9B59B6.svg

[python-badge]: https://img.shields.io/badge/Python-min%203.6%20%7C%20rec%203.9-FFD43B.svg

[pytorch-badge]: https://img.shields.io/badge/PyTorch-1.9.0-F05732.svg

[pipeline-badge]: https://gitlab.com/EAVISE/lightnet/badges/master/pipeline.svg

[release-url]: https://gitlab.com/EAVISE/lightnet/tags

[documentation-url]: https://eavise.gitlab.io/lightnet

[python-url]: https://python.org

[pytorch-url]: https://pytorch.org

[pipeline-url]: https://pypi.org/project/lightnet

[voc-badge]: https://img.shields.io/badge/repository-Pascal%20VOC-%2300BFD8

[voc-url]: https://gitlab.com/eavise/top/voc

[coco-badge]: https://img.shields.io/badge/repository-MS%20COCO-%2300BFD8

[coco-url]: https://gitlab.com/eavise/top/coco