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Scribble-OSVOS

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Scribble-supervised version of OSVOS, a baseline for DAVIS Interactive Challenge

// repository documentation

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# Scribble-OSVOS

This repository contains a baseline for the interactive track of the [DAVIS Challenge on Video Object Segmentation Wokshop](http://davischallenge.org/challenge2019/index.html) held in CVPR 2018 and CVPR 2019, and is provided to familiarize with the DAVIS interactive track.

This PyTorch code is based on the original [OSVOS-Pytorch](https://github.com/kmaninis/OSVOS-PyTorch) implementation. It adapts [OSVOS](http://vision.ee.ethz.ch/~cvlsegmentation/osvos) to train only on scribbles instead of the full mask.

### Installation:
The code was tested with [Miniconda](https://conda.io/miniconda.html) and Python 3.6. After installing the Miniconda environment:

0. Clone the repo:
    ```Shell
    git clone https://github.com/kmaninis/Scribble-OSVOS
    cd Scribble-OSVOS
    ```
 
1. Install dependencies:
    ```Shell
    conda install pytorch=0.3.1 torchvision -c pytorch 
    conda install matplotlib opencv pillow scikit-learn scikit-image
    ```
2. Install the DAVIS interactive package following [these](http://interactive.davischallenge.org/user_guide/installation/) instructions ('PyPi Install' section), and download the scribbles ('DAVIS Dataset' section). 
  
3. Download the model by running the script inside ```models/```:
    ```Shell
    cd models/
    chmod +x download_osvos_parent.sh
    ./download_osvos_parent.sh
    cd ..
    ```
4. Edit the path to [DAVIS 2017](http://davischallenge.org/davis2017/code.html) in mypath.py

5. Modify any parameters in ``demo_interactive.py`` (for example the gpu_id).

6. To run the interactive session (with the default parameters it takes ~10 hours on a Titan Xp):
    ```Shell
    python demo_interactive.py
    ```
7. A CSV report with all results, and a json file with summarized metrics will be generated under ``results/``.
The expected output after running all sequences can be found at `results/summary.json`.
You can use `analyze_report.py` to generate a figure of performance over time.

Enjoy!

### Citation:
	@InProceedings{Caelles_arXiv_2018,
	  Title          = {The 2018 DAVIS Challenge on Video Object Segmentation},
	  Author         = {Sergi Caelles and Alberto Montes and Kevis-Kokitsi Maninis and Yuhua Chen and Luc {Van Gool} and Federico Perazzi and Jordi Pont-Tuset},
	  journal        = {arXiv:1803.00557},
	  Year           = {2018}
	}
If you encounter any problems with the code, want to report bugs, etc. please open an issue or contact us at {kmaninis, scaelles}[at]vision[dot]ee[dot]ethz[dot]ch.

Scribble-OSVOS

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