cv-ip / agame-vos

PyTorch implementation of the paper "A Generative Appearance Model for End-to-End Video Object Segmentation".

agame-vos

PyTorch implementation of the paper A Generative Appearance Model for End-to-End Video Object Segmentation, including complete training code and trained models.

Dependencies:

python (>= 3.5 or 3.6)
numpy
pytorch (>= 0.5 probably)
torchvision
pillow
tqdm

Datasets utilized:

DAVIS

YouTubeVOS

How to setup:

1. Install dependencies
2. Clone this repo:

git clone https://github.com/joakimjohnander/agame-vos.git

3. Download datasets
4. Set up local_config.py to point to appropriate directories for saving and reading data
5. Move the ytvos_trainval_split/ImageSets directory into your YouTubeVOS data directory. The directory structure should look like

/...some_path.../youtube_vos
-- train
---- Annotations
---- JPEGImages
-- valid
---- Annotations
---- JPEGImages
-- ImageSets
---- train.txt
---- train_joakim.txt
---- val_joakim.txt

How to run method on DAVIS and YouTubeVOS with pre-trained weights:

1. Download weights from https://drive.google.com/file/d/1lVv7n0qOtJEPk3aJ2-KGrOfYrOHVnBbT/view?usp=sharing
2. Put the weights at the path pointed out by config['workspace_path'] in local_config.py.
3. Run

python3 -u runfiles/main_runfile.py --test

How to train (and test) a new model:

1. Run

python3 -u runfiles/main_runfile.py --train --test

Most settings used for training and evaluation are set in your runfiles. Each runfile should correspond to a single experiment. I supplied an example runfile.