Dense Gaussian Processes for Few-Shot Segmentation

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Deep LearningDGPNet
Overview

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.
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Comments
  • how to visualise the Gaussian process?

    how to visualise the Gaussian process?

    Hi there, I think your idea of using GP is very interesting. May I know how you visualize the Gaussian mean and covariance in the overview figure (Figure2 in the latest Arvix version)? Thanks in advance.

    opened by ry-jojo 4
  • torch.linalg.cholesky warnings

    torch.linalg.cholesky warnings

    Hi Joakim, When training on 10shot, I am facing with warnings like the below:

    WARNING batched routines are designed for small sizes. It might be better to use the Native/Hybrid classical routines if you want good performance.

    I think this warning comes from the torch.linalg.cholesky(K_ss), when K_ss's largest size() > 2048.

    May I know how you deal with this warning during training? Thanks in advance!

    opened by ry-jojo 2
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