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pytorch-slimming

This is a PyTorch re-implementation of algorithm presented in "Learning Efficient Convolutional Networks Through Network Slimming (ICCV2017)." . The official source code is based on Torch. For more info, visit the author's webpage!.

CIFAR10-VGG16BN Baseline Trained with Sparsity (1e-4) Pruned (0.7 Pruned) Fine-tuned (40epochs)
Top1 Accuracy (%) 93.62 93.77 10.00 93.56
Parameters 20.04M 20.04M 2.42M 2.42M
Pruned Ratio 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7
Top1 Accuracy (%) without Fine-tuned 93.77 93.72 93.76 93.75 93.75 93.40 37.83 10.00
Parameters(M) / macc(M) 20.04/ 398.44 15.9/ 349.22 12.28/ 307.78 9.12/ 272.94 6.74/ 247.86 4.62/ 231.86 3.14/ 222.17 2.42/ 210.84
Pruned Ratio architecture
0 [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 256, 'M', 512, 512, 512, 512, 'M', 512, 512, 512, 512]
0.1 [60, 64, 'M', 128, 128, 'M', 256, 255, 253, 245, 'M', 436, 417, 425, 462, 'M', 463, 465, 472, 424]
0.2 [58, 64, 'M', 128, 128, 'M', 256, 255, 250, 233, 'M', 360, 336, 329, 398, 'M', 420, 412, 435, 341]
0.3 [56, 64, 'M', 128, 128, 'M', 256, 254, 249, 227, 'M', 284, 239, 244, 351, 'M', 369, 364, 384, 255]
0.4 [52, 64, 'M', 128, 128, 'M', 256, 254, 247, 218, 'M', 218, 162, 166, 294, 'M', 317, 315, 318, 165]
0.5 [52, 64, 'M', 128, 128, 'M', 256, 254, 245, 214, 'M', 179, 117, 116, 229, 'M', 228, 220, 210, 111]
0.6 [51, 64, 'M', 128, 128, 'M', 256, 254, 245, 213, 'M', 165, 85, 92, 153, 'M', 83, 86, 87, 111]
0.7 [49, 64, 'M', 128, 128, 'M', 256, 254, 234, 198, 'M', 114, 41, 24, 11, 'M', 14, 13, 19, 104]

Baseline

python main.py

Trained with Sparsity

python main.py -sr --s 0.0001

Pruned

python prune.py --model model_best.pth.tar --save pruned.pth.tar --percent 0.7

Fine-tuned

python main.py -refine pruned.pth.tar --epochs 40

Reference

@InProceedings{Liu_2017_ICCV,
    author = {Liu, Zhuang and Li, Jianguo and Shen, Zhiqiang and Huang, Gao and Yan, Shoumeng and Zhang, Changshui},
    title = {Learning Efficient Convolutional Networks Through Network Slimming},
    booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
    month = {Oct},
    year = {2017}
}

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Learning Efficient Convolutional Networks through Network Slimming, In ICCV 2017.

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