mit-han-lab / Amc Models
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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, [email protected]
Song Han, [email protected]