Awesome
Carrying out CNN Channel Pruning in a White Box (IEEE TNNLS 2022) (Paper Link) 
Requirements
- python3.7.4, pytorch 1.5.1, torchvision 0.4.2, thop 0.0.31
Reproduce the Experiment Results
Run the following scripts to reproduce the results reported in paper (change your data path in the corresponding scripts).
- VGGNet-16-CIFAR10 ./scripts/vgg.sh
- ResNet-56-CIFAR10 ./scripts/resnet56.sh
- ResNet-110-CIFAR10 ./scripts/resnet110.sh
- MobileNet-v2-CIFAR10 ./scripts/mobilenetv2.sh
- ResNet-50-ImageNet(FLOPs:2.22B) ./scripts/resnet50-1.sh
- ResNet-50-ImageNet(FLOPs:1.50B) ./scripts/resnet50-2.sh
Evaluate Our Pruned Models
Run the following scripts to test our results reported in the paper (change your data path and input the pruned model path in the corresponding scripts. The pruned model can be downloaded from the links in the following table).
- VGGNet-16-CIFAR10 ./scripts/test-vgg.sh
- ResNet-56-CIFAR10 ./scripts/test-resnet56.sh
- ResNet-110-CIFAR10 ./scripts/test-resnet110.sh
- MobileNet-v2-CIFAR10 ./scripts/test-mobilenetv2.sh
- ResNet-50-ImageNet(FLOPs:2.22B) ./scripts/test-resnet50-1.sh
- ResNet-50-ImageNet(FLOPs:1.50B) ./scripts/test-resnet50-2.sh
CIFAR-10
| Full Model | Flops ↓ | Accuracy | Pruned Model |
|---|---|---|---|
| VGG16 | 76.4% | 93.47% | Modellink |
| ResNet56 | 55.6% | 93.54% | Modellink |
| ResNet110 | 66.0% | 94.12% | Modellink |
| MobileNet-V2 | 29.2% | 95.28% | Modellink |
ImageNet
| Network | Flops ↓ | Top-1 Acc. | Top-5 Acc. | Pruned Model |
|---|---|---|---|---|
| ResNet50 | 45.6% | 75.32% | 92.43% | Modellink |
| ResNet50 | 63.5% | 74.21% | 92.01% | Modellink |