WeightNet
This repository provides MegEngine implementation for "WeightNet: Revisiting the Design Space of Weight Network".
Requirement
- MegEngine 0.5.1 (https://github.com/MegEngine/MegEngine)
Citation
If you use these models in your research, please cite:
@inproceedings{ma2020weightnet,
title={WeightNet: Revisiting the Design Space of Weight Networks},
author={Ma, Ningning and Zhang, Xiangyu and Huang, Jiawei and Sun, Jian},
booktitle={Proceedings of the European Conference on Computer Vision (ECCV)},
year={2020}
}
Usage
Train:
python3 train.py --dataset-dir=/path/to/imagenet
Eval:
python3 test.py --data=/path/to/imagenet --model /path/to/model --ngpus 1
Inference:
python3 inference.py --model /path/to/model --image /path/to/image.jpg
Trained Models
- OneDrive download: Link
Results
- Comparison under the same #Params and the same FLOPs.
| Model | #Params. | FLOPs | Top-1 err. |
|---|---|---|---|
| ShuffleNetV2 (0.5Γ) | 1.4M | 41M | 39.7 |
| + WeightNet (1Γ) | 1.5M | 41M | 36.7 |
| ShuffleNetV2 (1.0Γ) | 2.2M | 138M | 30.9 |
| + WeightNet (1Γ) | 2.4M | 139M | 28.8 |
| ShuffleNetV2 (1.5Γ) | 3.5M | 299M | 27.4 |
| + WeightNet (1Γ) | 3.9M | 301M | 25.6 |
| ShuffleNetV2 (2.0Γ) | 5.5M | 557M | 25.5 |
| + WeightNet (1Γ) | 6.1M | 562M | 24.1 |
- Comparison under the same FLOPs.
| Model | #Params. | FLOPs | Top-1 err. |
|---|---|---|---|
| ShuffleNetV2 (0.5Γ) | 1.4M | 41M | 39.7 |
| + WeightNet (8Γ) | 2.7M | 42M | 34.0 |
| ShuffleNetV2 (1.0Γ) | 2.2M | 138M | 30.9 |
| + WeightNet (4Γ) | 5.1M | 141M | 27.6 |
| ShuffleNetV2 (1.5Γ) | 3.5M | 299M | 27.4 |
| + WeightNet (4Γ) | 9.6M | 307M | 25.0 |
| ShuffleNetV2 (2.0Γ) | 5.5M | 557M | 25.5 |
| + WeightNet (4Γ) | 18.1M | 573M | 23.5 |