Contrastive Transformation for Self-supervised Correspondence Learning
December 10, 2020 ยท View on GitHub
Ning Wang, Wengang Zhou, and Houqiang Li
To appear in AAAI 2021
Prerequisites
The code is tested in the following environment:
- Ubuntu 16.04
- Pytorch 1.1.0, tqdm, scipy 1.2.1
Training on the TrackingNet
Dataset
We use the TrackingNet dataset for model training.
Training command
python train_trackingnet.py
Testing on DAVIS2017
To test on DAVIS2017 for instance segmentation mask propagation, please run:
python test.py -d /workspace/DAVIS/ -s 560
Important parameters:
-c: checkpoint path.-o: results path.-d: DAVIS 2017 dataset path.-s: test resolution, all results in the paper are tested on 560p images, i.e.-s 560.
Please check the test.py file for other parameters.
Testing on the VIP dataset
To test on VIP, please run the following command with your own VIP path:
python test_mask_vip.py -o results/VIP/category/ --scale_size 560 560 --pre_num 1 -d /DATA/VIP/VIP_Fine/Images/ --val_txt /DATA/VIP/VIP_Fine/lists/val_videos.txt -c weights/checkpoint_latest.pth.tar
and then:
python eval_vip.py -g DATA/VIP/VIP_Fine/Annotations/Category_ids/ -p results/VIP/category/
Citation
If you find this work useful for your research, please consider citing our work:
@inproceedings{Wang_2021_Contrastive,
title={Contrastive Transformation for Self-supervised Correspondence Learning},
author={Wang, Ning and Zhou, Wengang and Li, Houqiang},
booktitle={AAAI},
year={2021}
}