DenseNet-tensorflow

This repository contains the tensorflow implementation for the paper Densely Connected Convolutional Networks.

The code is developed based on Yuxin Wu's implementation of ResNet (https://github.com/ppwwyyxx/tensorpack/tree/master/examples/ResNet).

Citation:

 @inproceedings{huang2017densely,
      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}
  }

Dependencies:

Python 2 or 3
TensorFlow >= 1.0
[Tensorpack] (https://github.com/ppwwyyxx/tensorpack)

Train a DenseNet (L=40, k=12) on CIFAR-10+ using

python cifar10-densenet.py

In our experiment environment (cudnn v5.1, CUDA 7.5, one TITAN X GPU), the code runs with speed 5iters/s when batch size is set to be 64. The hyperparameters are identical to the original [torch implementation] (https://github.com/liuzhuang13/DenseNet).

Training curves on CIFAR-10+ (~5.77% after 300 epochs)

Training curves on CIFAR-100+ (~26.36% after 300 epochs)

Differences compared to the original [torch implementation] (https://github.com/liuzhuang13/DenseNet)

Preprocessing is not channel-wise, instead we use mean and variances of images.
There is no momentum and weight decay applied on the batch normalization parameters (gamma and beta), whereas torch vertison uses both momentum and weight decay on those.

Questions?

Please drop me a line if you have any questions!