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Channel Pruning for Accelerating Very Deep Neural Networks (ICCV'17)
| Date | Stars |
|---|---|
| 2026-07-24 | 1089 |
| 2026-07-25 | 1089 |
| 2026-07-28 | 1089 |
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| 2026-08-06 | 1088 |
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# Channel Pruning for Accelerating Very Deep Neural Networks  [GitHub - yihui-he/channel-pruning: Channel Pruning for Accelerating Very Deep Neural Networks (ICCV'17)](https://github.com/yihui-he/channel-pruning) [Channel Pruning for Accelerating Very Deep Neural Networks](https://arxiv.org/abs/1707.06168) **ICCV 2017**, by [Yihui He](http://yihui-he.github.io/), [Xiangyu Zhang](https://scholar.google.com/citations?user=yuB-cfoAAAAJ&hl=en&oi=ao) and [Jian Sun](http://jiansun.org/) Please have a look our new works on compressing deep models: - [AMC: AutoML for Model Compression and Acceleration on Mobile Devices](http://openaccess.thecvf.com/content_ECCV_2018/html/Yihui_He_AMC_Automated_Model_ECCV_2018_paper.html) **ECCV’18**, which combines channel pruning and reinforcement learning to further accelerate CNN. [code](https://github.com/mit-han-lab/amc-release) and [models](https://github.com/mit-han-lab/amc-compressed-models) are available! - [AddressNet: Shift-Based Primitives for Efficient Convolutional Neural Networks](https://arxiv.org/abs/1809.08458) **WACV’19**. We propose a family of efficient networks based on Shift operation. - [MoBiNet: A Mobile Binary Network for Image Classification](https://arxiv.org/abs/1907.12629) **WACV’20** Binarized MobileNets. In this repository, we released code for the following models: | model | Speed-up | Accuracy | | --- | --- | --- | | https://github.com/yihui-he/channel-pruning/releases/tag/channel_pruning_5x | 5x | 88.1 (Top-5), 67.8 (Top-1) | | https://github.com/yihui-he/channel-pruning/releases/tag/VGG-16_3C4x | 4x | 89.9 (Top-5), 70.6 (Top-1) | | https://github.com/yihui-he/channel-pruning/releases/tag/ResNet-50-2X | 2x | 90.8 (Top-5), 72.3 (Top-1) | | https://github.com/yihui-he/channel-pruning/releases/tag/faster-RCNN-2X4X | 2x | 36.7 ([email protected]:.05:.95) | | https://github.com/yihui-he/channel-pruning/releases/tag/faster-RCNN-2X4X | 4x | 35.1 ([email protected]:.05:.95) | 3C method combined spatial decomposition ([Speeding up Convolutional Neural Networks with Low Rank Expansions](https://arxiv.org/abs/1405.3866)) and channel decomposition ([Accelerating Very Deep Convolutional Networks for Classification and Detection](https://arxiv.org/abs/1505.06798)) (mentioned in 4.1.2) ### Citation If you find the code useful in your research, please consider citing: ``` @InProceedings{He_2017_ICCV, author = {He, Yihui and Zhang, Xiangyu and Sun, Jian}, title = {Channel Pruning for Accelerating Very Deep Neural Networks}, booktitle = {The IEEE International Conference on Computer Vision (ICCV)}, month = {Oct}, year = {2017} } ``` ### requirements 1. Python3 packages you might not have: `scipy`, `sklearn`, `easydict`, use `sudo pip3 install` to install. 2. For finetuning with 128 batch size, 4 GPUs (~11G of memory) ### Installation (sufficient for the demo) 1. Clone the repository ```bash # Make sure to clone with --recursive git clone --recursive https://github.com/yihui-he/channel-pruning.git ``` 2. Build [my Caffe](https://github.com/yihui-he/caffe-pro) fork (which support bicubic interpolation and resizing image shorter side to 256 then crop to 224x224) ```bash cd caffe # If you're experienced with Caffe and have all of the requirements installed, then simply do: make all -j8 && make pycaffe # Or follow the Caffe installation instructions here: # http://caffe.berkeleyvision.org/installation.html # you might need to add pycaffe to PYTHONPATH, if you've already had a caffe before ``` 3. Download ImageNet classification dataset http://www.image-net.org/download-images 4. Specify imagenet `source` path in `temp/vgg.prototxt` (line 12 and 36) ### Channel Pruning *For fast testing, you can directly download pruned model. See [next section](about:blank#pruned-models-for-download)* 1. Download the or
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Ethan He · @xai-org · United States
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:310f7cd7e789fcac, topic:object-detection, topic:image-classification, readme:computer vision
matched fp:310f7cd7e789fcac, topic:model-compression, readme:model compression, name:pruning