InsCorr

Code for [Self-supervised Learning of Visual Correspondence via Instance Discrimination]. The code is developed based on the PyTorch framework.

Model and Result

Our trained model can be downloaded from here. The tracking performance on DAVIS-2017 for this model (without training on DAVIS-2017) is:

J_mean	J_recall	J_decay	F_mean	F_recall	F_decay
0.560	0.669	0.012	0.577	0.685	0.059

Requirements

torch==1.1.0

torchvision==0.2.2.post3

scikit-image==0.14.2

Dataset Preparation

Please read DATASET.md for downloading and preparing the VLOG dataset for training and DAVIS dataset and JHMDB dataset for testing.

Training

Replace the input list in train.py in the home folder as:

    params['filelist'] = 'YOUR_DATASET_FOLDER/vlog_frames_12fps.txt'

Then run the following code:

    python3 train.py  --checkpoint ./pytorch_checkpoints/release

Testing

Replace the input list in test_davis.py in the home folder as:

    params['filelist'] = 'YOUR_DATASET_FOLDER/davis/DAVIS/vallist.txt'

Set up the dataset path YOUR_DATASET_FOLDER in run_test*.sh . Then run the testing and evaluation code together:

    sh run_test_davis.sh

    sh run_test_davis_texture.sh

    sh run_test_PCK.sh

Acknowledgements

TimeCycle by Xiaolong Wang and Allan Jabri and Alexei A. Efros.

SFNet by Lee, Junghyup and Kim, Dohyung and Ponce, Jean and Ham, Bumsub.

PCGAN by Dong Liang, Rui Wang, Xiaowei Tian, Cong Zou.