Tensorflow Implementation of DenseNets
Two types of Densely Connected Convolutional Networks (DenseNets) are available:
- DenseNet - without bottleneck layers
- DenseNet-BC - with bottleneck layers
Each model can be tested on such datasets:
- Cifar10
- Cifar10+ (with data augmentation)
- Cifar100
- Cifar100+ (with data augmentation)
- ImageNet
Example run:
python train_densenet_cifar.py
There are also many other implementations - they may be useful also.
Citation:
@article{Huang2016Densely,
author = {Huang, Gao and Liu, Zhuang and Weinberger, Kilian Q.},
title = {Densely Connected Convolutional Networks},
journal = {arXiv preprint arXiv:1608.06993},
year = {2016}
}
Dependencies
- Model was tested with Python 2.7 with and without CUDA.
- Model should work as expected with TensorFlow >= 1.4.
Repo supported with requirements file - so the easiest way to install all just run pip install -r requirements.txt
.