Awesome
PyTorch Implementation of CURL
- Neural Network Pruning with Residual-Connections and Limited-Data, CVPR 2020, Oral.
- [CVF open acess]
Requirements
PyTorch environment:
- Python 3.6.9
- PyTorch 1.2.0
- torchsummaryX
- yaml 3.12
Prune on ImageNet
- clone this repository.
- download the ImageNet dataset and organize this dataset with train and val folders.
- select subfolder:
cd ImageNet/CURL - start pruning and fine-tuning:
./run_this.sh
Note:
The training log files on ImageNet are missing. We provide the pruned model: ImageNet/released_model/ResNet50-CURL-1G.pth. You can run ImageNet/released_model/run_this.sh to test its accuracy.
Prune on CUB200
- clone this repository.
- download the CUB200 dataset and organize this dataset with train and val folders.
- expand the small dataset using
CUB200/expand_dataset.py - select subfolder:
cd CUB200/mobilenetv2/ - edit the configuration file
config.yaml. - calculate the importance score for each filter:
./evaluate_importance.sh - fine-tune the pruned model:
./run_this.sh
Note: The training log files are provided in corresponding folders.
Results
We prune the ResNet50 on ImageNet dataset:
| Architecture | Top-1 Acc. | Top-5 Acc. | #MACs | #Param. |
|---|---|---|---|---|
| ResNet-50 | 76.15% | 92.87% | 4.09G | 25.56M |
| CURL | 73.39% | 91.46% | 1.11G | 6.67M |
The results of MobileNetV2 on CUB200:
| Architecture | Top-1 Acc. | #MACs |
|---|---|---|
| MobileNetV2-1.0 | 78.77% | 299.77M |
| MobileNetV2-0.5 | 73.96% | 96.12M |
| CURL | 78.72% | 96.07M |
The results of ResNet50 on CUB200:
| Architecture | Top-1 Acc. | #MACs |
|---|---|---|
| ResNet50 | 84.76% | 4.09G |
| ResNet50-CURL | 81.33% | 1.11G |
| CURL | 83.64% | 1.10G |
Citation
If you find this work is useful for your research, please cite:
@InProceedings{Luo_2020_CVPR,
author = {Luo, Jian-Hao and Wu, Jianxin},
title = {Neural Network Pruning With Residual-Connections and Limited-Data},
booktitle = {The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2020},
pages = {1458-1467}
}
Contact
Feel free to contact me if you have any question (Jian-Hao Luo luojh@lamda.nju.edu.cn or jianhao920@gmail.com).