export CAFFE_ROOT=$your_caffe_root
python decode.py /ABSOLUTE_PATH_TO/SqueezeNet_deploy.prototxt /ABSOLUTE_PATH_TO/compressed_SqueezeNet.net /ABSOLUTE_PATH_TO/decompressed_SqueezeNet.caffemodel
note: decompressed_SqueezeNet.caffemodel is the output, can be any name.
$CAFFE_ROOT/build/tools/caffe test --model=SqueezeNet_trainval.prototxt --weights=decompressed_SqueezeNet.caffemodel --iterations=1000 --gpu 0

@article{SqueezeNet,
  title={SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and< 0.5MB model size},
  author={Iandola, Forrest N and Han, Song and Moskewicz, Matthew W and Ashraf, Khalid and Dally, William J and Keutzer, Kurt},
  journal={arXiv preprint arXiv:1602.07360},
  year={2016}
}
@article{DeepCompression,
  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}
}
@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{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}
}