/OICSR

OICSR: Out-In-Channel Sparsity Regularization for Compact Deep Neural Networks

Primary LanguagePython

OICSR Pruned Models

This repo contains some of the pruned models from paper OICSR: Out-In-Channel Sparsity Regularization for Compact Deep Neural Networks (CVPR 2019).

Reference

If you find the models useful, please kindly cite our paper:

@inproceedings{li2019oicsr,
  title={OICSR: Out-In-Channel Sparsity Regularization for Compact Deep Neural Networks},
  author={Li, Jiashi and Qi, Qi and Wang, Jingyu and Ge, Ce and Li, Yujian and Yue, Zhangzhang and Sun, Haifeng},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  pages={7046--7055},
  year={2019}
}

Download the pruned models

Download the pretrained models from here and put it in ./checkpoints.

Models

Pruned ResNet-50

We provide ResNet-50 model with various FLOPs pruned percents. The channel pruning results are showed as follows:

Models Top1 Acc (%) Drop Top1 Acc (%) Top5 Acc (%) Drop Top5 Acc (%) FLOPs (M)
resnet50 76.32 0.00 93.00 0.00 4089
resnet50-37.3%FLOPs 76.53 -0.21 93.16 -0.16 2563
resnet50-44.4%FLOPs 76.30 0.02 92.92 0.08 2274
resnet50-50.0%FLOPs 75.95 0.37 92.66 0.34 2046

To test the model, run:
python eval_prune_model.py --test_data /mnt/cephfs_wj/cv/common/datasets/ImageNet/ILSVRC2012_img_val --fpp 50.0

Contact

To contact the author:
Jiashi Li, lijiashi@bupt.edu.cn