This repository contains the code for DenseNet introduced in the following paper

Densely Connected Convolutional Networks (CVPR 2017, Best Paper Award)

Gao Huang*, Zhuang Liu*, Laurens van der Maaten and Kilian Weinberger (* Authors contributed equally).

Now with much more memory efficient implementation! Please check the technical report and code for more infomation.

The code is built on fb.resnet.torch.

If you find DenseNet useful in your research, please consider citing:

@inproceedings{DenseNet2017,
  title={Densely connected convolutional networks},
  author={Huang, Gao and Liu, Zhuang and van der Maaten, Laurens and Weinberger, Kilian Q },
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  year={2017}
}

Our [Caffe], Our memory-efficient [Caffe], Our memory-efficient [PyTorch], [PyTorch] by Andreas Veit, [PyTorch] by Brandon Amos, [PyTorch] by Federico Baldassarre, [MXNet] by Nicatio, [MXNet] by Xiong Lin, [MXNet] by miraclewkf, [Tensorflow] by Yixuan Li, [Tensorflow] by Laurent Mazare, [Tensorflow] by Illarion Khlestov, [Lasagne] by Jan Schlüter, [Keras] by tdeboissiere,
[Keras] by Roberto de Moura Estevão Filho, [Keras] by Somshubra Majumdar, [Chainer] by Toshinori Hanya, [Chainer] by Yasunori Kudo, [Torch 3D-DenseNet] by Barry Kui, [Keras] by Christopher Masch, [Tensorflow2] by Gaston Rios and Ulises Jeremias Cornejo Fandos.

Note that we only listed some early implementations here. If you would like to add yours, please submit a pull request.

0. Multi-Scale Dense Convolutional Networks for Efficient Prediction
1. DSOD: Learning Deeply Supervised Object Detectors from Scratch
2. CondenseNet: An Efficient DenseNet using Learned Group Convolutions
3. Fully Convolutional DenseNets for Semantic Segmentation
4. Pelee: A Real-Time Object Detection System on Mobile Devices

1. Introduction
2. Usage