AutoML for Model Compression (AMC)

This repo contains the PyTorch implementation for paper AMC: AutoML for Model Compression and Acceleration on Mobile Devices.


If you find the repo 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)},

Other papers related to automated model design:

  • HAQ: Hardware-Aware Automated Quantization [link]

  • ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware [link]

Training AMC

Current code base supports the automated pruning of MobileNet on ImageNet. The pruning of MobileNet consists of 3 steps: 1. strategy search; 2. export the pruned weights; 3. fine-tune from pruned weights.

To conduct the full pruning procedure, follow the instructions below (results might vary a little from the paper due to different random seed):

  1. Strategy Search

    To search the strategy on MobileNet ImageNet model, first get the pretrained MobileNet checkpoint on ImageNet by running:


    It will also download our 50% FLOPs compressed model. Then run the following script to search under 50% FLOPs constraint:


    Results may differ due to different random seed. The strategy we found and reported in the paper is:

    [3, 24, 48, 96, 80, 192, 200, 328, 352, 368, 360, 328, 400, 736, 752]
  2. Export the Pruned Weights

    After searching, we need to export the pruned weights by running:


    Also we need to modify MobileNet file to support the new pruned model (here it is already done in models/

  3. Fine-tune from Pruned Weightsa

    After exporting, we need to fine-tune from the pruned weights. For example, we can fine-tune using cosine learning rate for 150 epochs by running:


AMC Compressed Model

We also provide the models and weights compressed by our AMC method. We provide compressed MobileNet-V1 and MobileNet-V2 in both PyTorch and TensorFlow format here.

Detailed statistics are as follows:

Models Top1 Acc (%) Top5 Acc (%)
MobileNetV1-width*0.75 68.4 88.2
MobileNetV1-50%FLOPs 70.494 89.306
MobileNetV1-50%Time 70.200 89.430
MobileNetV2-width*0.75 69.8 89.6
MobileNetV2-70%FLOPs 70.854 89.914


Current code base is tested under following environment:

  1. Python 3.6.5
  2. PyTorch 0.4.1
  3. torchvision 0.2.1
  4. NumPy 1.14.3
  5. SciPy 1.1.0
  6. scikit-learn 0.19.1
  7. tensorboardX
  8. ImageNet dataset