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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 (ICLR’19)
AMC: AutoML for Model Compression and Acceleration on Mobile Devices (ECCV’18)
HAQ: Hardware-Aware Automated Quantization (CVPR’19)
Defenstive Quantization: When Efficiency Meets Robustness (ICLR'19)
The repo contains the residual-SqueezeNet, which is obtained by adding bypass layer to SqueezeNet_v1.0. Residual-SqueezeNet improves the top-1 accuracy of SqueezeNet by 2.9% on ImageNet without changing the model size(only 4.8MB).
If you find residual-SqueezeNet useful in your research, please consider citing the paper:
@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}
}
$CAFFE_ROOT/build/tools/caffe test --model=trainval.prototxt --weights=SqueezeNet_residual_top1_0.6038_top5_0.8250.caffemodel --iterations=1000 --gpu 0
I0422 14:07:39.810755 32299 caffe.cpp:293] accuracy_top1 = 0.603759
I0422 14:07:39.810775 32299 caffe.cpp:293] accuracy_top5 = 0.824981
I0422 14:07:39.810792 32299 caffe.cpp:293] loss = 1.76711 (* 1 = 1.76711 loss)
The building block:
