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 (0.5×)	1.4M	41M	39.7
+ WeightNet (1×)	1.5M	41M	36.7
ShuffleNetV2 (1.0×)	2.2M	138M	30.9
+ WeightNet (1×)	2.4M	139M	28.8
ShuffleNetV2 (1.5×)	3.5M	299M	27.4
+ WeightNet (1×)	3.9M	301M	25.6
ShuffleNetV2 (2.0×)	5.5M	557M	25.5
+ WeightNet (1×)	6.1M	562M	24.1

• Comparison under the same FLOPs.

Model	#Params.	FLOPs	Top-1 err.
ShuffleNetV2 (0.5×)	1.4M	41M	39.7
+ WeightNet (8×)	2.7M	42M	34.0
ShuffleNetV2 (1.0×)	2.2M	138M	30.9
+ WeightNet (4×)	5.1M	141M	27.6
ShuffleNetV2 (1.5×)	3.5M	299M	27.4
+ WeightNet (4×)	9.6M	307M	25.0
ShuffleNetV2 (2.0×)	5.5M	557M	25.5
+ WeightNet (4×)	18.1M	573M	23.5