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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

  1. Download and unzip PASCAL and COCO images
  2. Download and unzip PASCAL and COCO annotations (we provide link here)
  3. Change local_config.py to point out the images and annotations. Also change slurm_launch.sh if using slurm.
  4. Download and unzip PASCAL and COCO data splits (we provide link here)
  5. 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 paths
  • logs - Used to store slurm checkpoints
  • runfiles - 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.