Awesome
Contrastive Transformation for Self-supervised Correspondence Learning
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}
}