Funnel Activation for Visual Recognition
This repository provides MegEngine implementation for "Funnel Activation for Visual Recognition".
Requirement
- MegEngine 0.5.1 (https://github.com/MegEngine/MegEngine)
Citation
If you use these models in your research, please cite:
@inproceedings{ma2020funnel,
title={Funnel activation for visual recognition},
author={Ma, Ningning and Zhang, Xiangyu 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 on ImageNet dataset:
| Model | Activation | Top-1 err. |
|---|---|---|
| ResNet50 | ReLU | 24.0 |
| ResNet50 | PReLU | 23.7 |
| ResNet50 | Swish | 23.5 |
| ResNet50 | FReLU | 22.4 |
| ShuffleNetV2 0.5x | ReLU | 39.6 |
| ShuffleNetV2 0.5x | PReLU | 39.1 |
| ShuffleNetV2 0.5x | Swish | 38.7 |
| ShuffleNetV2 0.5x | FReLU | 37.1 |