DGPNet - Dense Gaussian Processes for Few-Shot Segmentation
Welcome to the public repository for DGPNet. The paper is available at arxiv: https://arxiv.org/abs/2110.03674 .
How to run
Download data
- Download and unzip PASCAL and COCO images
- Download and unzip PASCAL and COCO annotations (we provide link here)
- Change
local_config.py
to point out the images and annotations. Also changeslurm_launch.sh
if using slurm. - Download and unzip PASCAL and COCO data splits (we provide link here)
- Make sure that the data splits are at
DGPNet/data_splits
Install dependencies
The dependencies are listed in DGPNet/singularity/Dockerfile21.09
Train and test model
We typically run via slurm, using
sbatch singularity/slurm_launch.sh runfiles/dgp_5shot_pascal_resnet50.py --train --test --dataset pascal --fold 0 --add_packages_to_path
Code layout
checkpoints
- Checkpoints will be stored here at the end of training.data_splits
- Defines the different folds.fss
- Code is here.local_config.py
- Used to set up pathslogs
- Used to store slurm checkpointsrunfiles
- Any experiment we run is defined in a runfile. The runfile is launched as main to start the experiment.singularity
- We use singularity/slurm and any files related to that are stored here.visualization
- During training and testing, our code stores some visualizations. They go here.