codes-kzhan / ProxylessNAS

ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware. https://arxiv.org/abs/1812.00332

ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware

@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.

Performance

Mobile settings	GPU settings
Model Top-1 Top-5 Latency MobilenetV1 70.6 89.5 113ms MobilenetV2 71.8 91.0 75ms MNasNet(our impl) 74.0 91.8 79ms ProxylessNAS (mobile) 74.6 92.2 78ms	Model Top-1 Top-5 Latency MobilenetV2 71.8 91.0 6.1ms ShufflenetV2(1.5) 72.6 - 7.3ms ResNet-34 73.3 91.4 8.0ms MNasNet(our impl) 74.0 91.8 6.1ms ProxylessNAS (GPU) 75.1 92.5 5.1ms
2.6% better than MobilenetV2 with same speed.	3.1% better than MobilenetV2 with 20% faster.

ProxylessNAS consistently outperforms MobileNetV2 under various latency settings.

Specialization

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.

How to use / evaluate

• 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