AMC Compressed Models

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


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

  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)},

Download the Pretrained Models

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


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 --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 --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.


You can also find our PyTorch implementation of AMC Here.


To contact the authors:

Ji Lin,

Song Han,