Awesome

ThiNet: A Filter Level Pruning Method for Deep Neural Network Compression

Pretrained caffe model of ICCV'17 paper:

"ThiNet: A Filter Level Pruning Method for Deep Neural Network Compression"

For more details, please see our project page: [ThiNet Project Page]

Code

Models

224x224 center crop validation accuracy on ImageNet, tested on one M40 GPU with batch_size=32.

Model	Top-1	Top-5	#Param.	#FLOPs	f./b. (ms)
ThiNet-GAP	67.34%	87.92%	8.32M	9.34B	71.73/145.51
ThiNet-Tiny	59.34%	81.97%	1.32M	2.01B	29.51/55.83

Note: These two models are trained with different image cropping method, see trainval.prototxt for more details.

Citation

If you find this work useful for your research, please cite:

@CONFERENCE{ThiNet_ICCV17,
  author={Jian-Hao Luo, Jianxin Wu, and Weiyao Lin},
  title={ThiNet: A Filter Level Pruning Method for Deep Neural Network Compression},
  booktitle={ICCV},
  year = {2017},
  pages={5058-5066},
}

Contact

Feel free to contact me if you have any question (Jian-Hao Luo luojh@lamda.nju.edu.cn or jianhao920@gmail.com).