@article{cai2018proxylassnas,
    title={ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware},
    author={Cai, Han and Zhu, Ligeng and Han, Song},
    journal={arXiv preprint arXiv:1812.00332},
    year={2018}
}  

Without any proxy, directly search neural network architectures on your target task and hardware!

Note: our code is developed in PyTorch 0.3, and has been tested in both 0.3 and 0.4.

ProxylessNAS consistently outperforms MobileNetV2 under various latency settings.

People used to deploy one model to all platforms, but this is not good. To fully exploit the efficiency, we should specialize architectures for each platform.

Please refer to our paper for more results.

• Use

  from proxyless_nas import proxyless_cpu, proxyless_gpu, proxyless_mobile
  net = proxyless_cpu(pretrained=True) # Yes, we provide pre-trained models!

• Evaluate

  python eval.py --path 'Your path to imagent' --arch proxyless_cpu