Channel Pruning for Accelerating Very Deep Neural Networks

GitHub - yihui-he/channel-pruning: Channel Pruning for Accelerating Very Deep Neural Networks (ICCV'17)

ICCV 2017, by Yihui He, Xiangyu Zhang and Jian Sun

Please have a look our new works on compressing deep models:

AMC: AutoML for Model Compression and Acceleration on Mobile Devices ECCV’18, which combines channel pruning and reinforcement learning to further accelerate CNN. code and models are available!
AddressNet: Shift-Based Primitives for Efficient Convolutional Neural Networks WACV’19. We propose a family of efficient networks based on Shift operation.
MoBiNet: A Mobile Binary Network for Image Classification WACV’20 Binarized MobileNets.

In this repository, we released code for the following models:

model	Speed-up	Accuracy
VGG-16 channel pruning	5x	88.1 (Top-5), 67.8 (Top-1)
VGG-16 3C	4x	89.9 (Top-5), 70.6 (Top-1)
ResNet-50	2x	90.8 (Top-5), 72.3 (Top-1)
faster RCNN	2x	36.7 ([email protected]:.05:.95)
faster RCNN	4x	35.1 ([email protected]:.05:.95)

3C method combined spatial decomposition (Speeding up Convolutional Neural Networks with Low Rank Expansions) and channel decomposition (Accelerating Very Deep Convolutional Networks for Classification and Detection) (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

Python3 packages you might not have: scipy, sklearn, easydict, use sudo pip3 install to install.
For finetuning with 128 batch size, 4 GPUs (~11G of memory)

Installation (sufficient for the demo)

Clone the repository

# Make sure to clone with --recursive
 git clone --recursive <https://github.com/yihui-he/channel-pruning.git>

Build my Caffe fork (which support bicubic interpolation and resizing image shorter side to 256 then crop to 224x224)

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