Dense Gaussian Processes for Few-Shot Segmentation

Last update: Jan 07, 2023

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Deep Learning DGPNet

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

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 change slurm_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 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?

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

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

Dense Gaussian Processes for Few-Shot Segmentation

Related tags

Overview

DGPNet - Dense Gaussian Processes for Few-Shot Segmentation

How to run

Download data

Install dependencies

Train and test model

Code layout

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