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This is the authors' demo (single-GPU-version) code for the DAVIS 2016 dataset as described in the above paper. Please cite our paper if you find it useful for your research.
@inproceedings{Cheng_favos_2018,
author = {J. Cheng and Y.-H. Tsai and W.-C. Hung and S. Wang and M.-H. Yang},
booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
title = {Fast and Accurate Online Video Object Segmentation via Tracking Parts},
year = {2018}
}
Download DAVIS 2016 dataset, trained models, tracked parts and pre-computed results
sh download_all.sh
Test our model
We provide an example testing script test_davis16.sh.
# Please run download_all.sh first
# Usage: sh test_davis16.sh <GPU-id> <sequence-name>
sh test_davis16.sh 0 blackswan
The results would be saved in results-demo/res_favos/<sequence-name>.
You can replace the sequence name with others in the DAVIS 2016 validation set to obatin results for other videos.
Train your own ROISegNet
Download ResNet-101 model and save it in the folder "models" as "init.caffemodel"
cd ROISegNet
python solve.py ../models/init.caffemodel solver_davis16.prototxt 0
Note that, we are currently working on a stable version to combine part tracking and ROISegNet for practical usage on any videos. We will update the code in a near future.
Download our segmentation results on the DAVIS datasets