Awesome

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.

• Comparison under the same FLOPs.