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AMC Compressed Models

This repo contains some of the compressed models from paper AMC: AutoML for Model Compression and Acceleration on Mobile Devices (ECCV18).

Reference

If you find the models useful, please kindly cite our paper:

@inproceedings{he2018amc,
  title={AMC: AutoML for Model Compression and Acceleration on Mobile Devices},
  author={He, Yihui and Lin, Ji and Liu, Zhijian and Wang, Hanrui and Li, Li-Jia and Han, Song},
  booktitle={Proceedings of the European Conference on Computer Vision (ECCV)},
  pages={784--800},
  year={2018}
}

Download the Pretrained Models

Firstly, download the pretrained models from here and put it in ./checkpoints.

Models

Compressed MobileNets

We provide compressed MobileNetV1 by 50% FLOPs and 50% Inference time, and also compressed MobileNetV2 by 70% FLOPs, with PyTorch. The comparison with vanila models as follows:

Models	Top1 Acc (%)	Top5 Acc (%)	Latency (ms)	MACs (M)
MobileNetV1	70.9	89.5	123	569
MobileNetV1-width*0.75	68.4	88.2	72.5	325
MobileNetV1-50%FLOPs	70.5	89.3	68.9	285
MobileNetV1-50%Time	70.2	89.4	63.2	272
MobileNetV2-width*0.75	69.8	89.6	-	300
MobileNetV2-70%FLOPs	70.9	89.9	-	210

To test the model, run:

python eval_mobilenet_torch.py --profile={mobilenet_0.5flops, mobilenet_0.5time, mobilenetv2_0.7flops}

Converted TensorFLow Models

We converted the 50% FLOPs and 50% time compressed MobileNetV1 model to TensorFlow. We offer the normal checkpoint format and also the TF-Lite format. We used the TF-Lite format to test the speed on MobileNet.

To replicate the results of PyTorch, we write a new preprocessing function, and also adapt some hyper-parameters from the original TF MobileNetV1. To verify the performance, run following scripts:

python eval_mobilenet_tf.py --profile={0.5flops, 0.5time}

The produced result is:

Models	Top1 Acc (%)	Top5 Acc (%)
50% FLOPs	70.424	89.28
50% Time	70.214	89.244

Timing Logs

Here we provide timing logs on Google Pixel 1 using TensorFlow Lite in ./logs directory. We benchmarked the original MobileNetV1 (mobilenet), MobileNetV1 with 0.75 width multiplier (0.75mobilenet), 50% FLOPs pruned MobileNetV1 (0.5flops) and 50% time pruned MobileNetV1 (0.5time). Each model is benchmarked for 200 iterations with extra 100 iterations for warming up, and repeated for 3 runs.

AMC

You can also find our PyTorch implementation of AMC Here.

Contact

To contact the authors:

Ji Lin, jilin@mit.edu

Song Han, songhan@mit.edu