# Deep-Compression-AlexNet

**Repository Path**: william_gzl/Deep-Compression-AlexNet

## Basic Information

- **Project Name**: Deep-Compression-AlexNet
- **Description**: Deep Compression on AlexNet
- **Primary Language**: Unknown
- **License**: BSD-2-Clause
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-04-25
- **Last Updated**: 2020-12-20

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

- March 15, 2019: for our most updated work on model compression and acceleration, please reference: 

	[ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware](https://arxiv.org/pdf/1812.00332.pdf) (ICLR’19)

	[AMC: AutoML for Model Compression and Acceleration on Mobile Devices](https://arxiv.org/pdf/1802.03494.pdf) (ECCV’18)

	[HAQ: Hardware-Aware Automated Quantization](https://arxiv.org/pdf/1811.08886.pdf)  (CVPR’19)
	
	[Defenstive Quantization: When Efficiency Meet Robustness](https://openreview.net/pdf?id=ryetZ20ctX) (ICLR'19)


# Deep Compression on AlexNet
This is a demo of [Deep Compression](http://arxiv.org/pdf/1510.00149v5.pdf) compressing AlexNet from 233MB to 8.9MB without loss of accuracy. It only differs from the paper that Huffman coding is not applied. Deep Compression's video from [ICLR'16 best paper award presentation](https://youtu.be/kQAhW9gh6aU) is available. 

# Related Papers
[Learning both Weights and Connections for Efficient Neural Network (NIPS'15)](http://arxiv.org/pdf/1506.02626v3.pdf)

[Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding (ICLR'16, best paper award)](http://arxiv.org/pdf/1510.00149v5.pdf)

[EIE: Efficient Inference Engine on Compressed Deep Neural Network (ISCA'16)](http://arxiv.org/pdf/1602.01528v1.pdf)

If you find Deep Compression useful in your research, please consider citing the paper:

	@inproceedings{han2015learning,
	  title={Learning both Weights and Connections for Efficient Neural Network},
	  author={Han, Song and Pool, Jeff and Tran, John and Dally, William},
	  booktitle={Advances in Neural Information Processing Systems (NIPS)},
	  pages={1135--1143},
	  year={2015}
	}
	
	
	@article{han2015deep_compression,
	  title={Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding},
	  author={Han, Song and Mao, Huizi and Dally, William J},
	  journal={International Conference on Learning Representations (ICLR)},
	  year={2016}
	}
	
**A hardware accelerator working directly on the deep compressed model:**
	
	@article{han2016eie,
	  title={EIE: Efficient Inference Engine on Compressed Deep Neural Network},
	  author={Han, Song and Liu, Xingyu and Mao, Huizi and Pu, Jing and Pedram, Ardavan and Horowitz, Mark A and Dally, William J},
	  journal={International Conference on Computer Architecture (ISCA)},
	  year={2016}
	}



# Usage:

    export CAFFE_ROOT=$your caffe root$

    python decode.py bvlc_alexnet_deploy.prototxt AlexNet_compressed.net $CAFFE_ROOT/alexnet.caffemodel 

    cd $CAFFE_ROOT

    ./build/tools/caffe test --model=models/bvlc_alexnet/train_val.prototxt --weights=alexnet.caffemodel --iterations=1000 --gpu 0


# Test Result:
	I1022 20:18:58.336736 13182 caffe.cpp:198] accuracy_top1 = 0.57074
	I1022 20:18:58.336745 13182 caffe.cpp:198] accuracy_top5 = 0.80254