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}
}
- Python 2 or 3
- TensorFlow >= 1.0
- [Tensorpack] (https://github.com/ppwwyyxx/tensorpack)
- OpenCv-Python
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).
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.
Please drop me a line if you have any questions!