The official PyTorch implementation of SwiftNet:Real-time Video Object Segmentation, which has been accepted by CVPR2021.
- Python >= 3.6
- Pytorch 1.5
- Numpy
- Pillow
- opencv-python
- scipy
- tqdm
- The training pipeline of Swiftnet is similar with the training pipeline of STM, which can be found in our reproduced STM training code.
Usage
python eval.py -g 0 -y 17 -s val -D 'path to davis'
Performance on Davis-17 val set.
| backbone | J&F | J | F | FPS | weights |
|---|---|---|---|---|---|
| resnet-18 | 77.6 | 75.5 | 79.7 | 65 | link |
Note: The FPS is tested on one P100, which does not include the time of image loading and evaluation cost.
This repository is partially founded on the official STM repository.
If you find this repository helpful and want to cite SwiftNet in your own projects, please use the following citation info.
@inproceedings{wang2021swiftnet,
title={SwiftNet: Real-time Video Object Segmentation},
author={Wang, Haochen and Jiang, Xiaolong and Ren, Haibing and Hu, Yao and Bai, Song},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={1296--1305},
year={2021}
}