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Network Slimming (Pytorch) (ICCV 2017)
| Date | Stars |
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| 2026-07-24 | 919 |
| 2026-07-25 | 919 |
| 2026-07-28 | 919 |
| 2026-07-30 | 919 |
| 2026-08-06 | 919 |
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# Network Slimming (Pytorch)
This repository contains an official pytorch implementation for the following paper
[Learning Efficient Convolutional Networks Through Network Slimming](http://openaccess.thecvf.com/content_iccv_2017/html/Liu_Learning_Efficient_Convolutional_ICCV_2017_paper.html) (ICCV 2017).
[Zhuang Liu](https://liuzhuang13.github.io/), [Jianguo Li](https://sites.google.com/site/leeplus/), [Zhiqiang Shen](http://zhiqiangshen.com/), [Gao Huang](http://www.cs.cornell.edu/~gaohuang/), [Shoumeng Yan](https://scholar.google.com/citations?user=f0BtDUQAAAAJ&hl=en), [Changshui Zhang](http://bigeye.au.tsinghua.edu.cn/english/Introduction.html).
Original implementation: [slimming](https://github.com/liuzhuang13/slimming) in Torch.
The code is based on [pytorch-slimming](https://github.com/foolwood/pytorch-slimming). We add support for ResNet and DenseNet.
Citation:
```
@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}
}
```
## Dependencies
torch v0.3.1, torchvision v0.2.0
## Channel Selection Layer
We introduce `channel selection` layer to help the pruning of ResNet and DenseNet. This layer is easy to implement. It stores a parameter `indexes` which is initialized to an all-1 vector. During pruning, it will set some places to 0 which correspond to the pruned channels.
## Baseline
The `dataset` argument specifies which dataset to use: `cifar10` or `cifar100`. The `arch` argument specifies the architecture to use: `vgg`,`resnet` or
`densenet`. The depth is chosen to be the same as the networks used in the paper.
```shell
python main.py --dataset cifar10 --arch vgg --depth 19
python main.py --dataset cifar10 --arch resnet --depth 164
python main.py --dataset cifar10 --arch densenet --depth 40
```
## Train with Sparsity
```shell
python main.py -sr --s 0.0001 --dataset cifar10 --arch vgg --depth 19
python main.py -sr --s 0.00001 --dataset cifar10 --arch resnet --depth 164
python main.py -sr --s 0.00001 --dataset cifar10 --arch densenet --depth 40
```
## Prune
```shell
python vggprune.py --dataset cifar10 --depth 19 --percent 0.7 --model [PATH TO THE MODEL] --save [DIRECTORY TO STORE RESULT]
python resprune.py --dataset cifar10 --depth 164 --percent 0.4 --model [PATH TO THE MODEL] --save [DIRECTORY TO STORE RESULT]
python denseprune.py --dataset cifar10 --depth 40 --percent 0.4 --model [PATH TO THE MODEL] --save [DIRECTORY TO STORE RESULT]
```
The pruned model will be named `pruned.pth.tar`.
## Fine-tune
```shell
python main.py --refine [PATH TO THE PRUNED MODEL] --dataset cifar10 --arch vgg --depth 19 --epochs 160
```
## Results
The results are fairly close to the original paper, whose results are produced by Torch. Note that due to different random seeds, there might be up to ~0.5%/1.5% fluctation on CIFAR-10/100 datasets in different runs, according to our experiences.
### CIFAR10
| CIFAR10-Vgg | Baseline | Sparsity (1e-4) | Prune (70%) | Fine-tune-160(70%) |
| :---------------: | :------: | :--------------------------: | :-----------------: | :-------------------: |
| Top1 Accuracy (%) | 93.77 | 93.30 | 32.54 | 93.78 |
| Parameters | 20.04M | 20.04M | 2.25M | 2.25M |
| CIFAR10-Resnet-164 | Baseline | Sparsity (1e-5) | Prune(40%) | Fine-tune-160(40%) | Prune(60%) | Fine-tune-160(60%) |
| :---------------: | :------: | :--------------------------: | :-----------------: | :-------------------: | :----------------:| :--------------------:|
| Top1 Accuracy (%) | 94.75 | 94.76 | 94.58 | 95.05 | 47.73 | 93.81Excerpt of 6,797 characters
Read on GitHub
Mingjie Sun · CMU CS PhD · United States
12
3
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:cadee81c61813d4b, topic:deep-learning, topic:pytorch
matched fp:cadee81c61813d4b, topic:sparsity, readme:pruning, readme:sparsity