Dense Gaussian Processes for Few-Shot Segmentation

Related tags

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.
You might also like...
Code for 'Self-Guided and Cross-Guided Learning for Few-shot segmentation. (CVPR' 2021)'

SCL Introduction Code for 'Self-Guided and Cross-Guided Learning for Few-shot segmentation. (CVPR' 2021)' We evaluated our approach using two baseline

null 34 Oct 8, 2022
Official code for
Official code for "Simpler is Better: Few-shot Semantic Segmentation with Classifier Weight Transformer. ICCV2021".

Simpler is Better: Few-shot Semantic Segmentation with Classifier Weight Transformer. ICCV2021. Introduction We proposed a novel model training paradi

Lucas 103 Dec 14, 2022
PFENet: Prior Guided Feature Enrichment Network for Few-shot Segmentation (TPAMI).

PFENet This is the implementation of our paper PFENet: Prior Guided Feature Enrichment Network for Few-shot Segmentation that has been accepted to IEE

DV Lab 230 Dec 31, 2022
The code is for the paper
The code is for the paper "A Self-Distillation Embedded Supervised Affinity Attention Model for Few-Shot Segmentation"

SD-AANet The code is for the paper "A Self-Distillation Embedded Supervised Affinity Attention Model for Few-Shot Segmentation" [arxiv] Overview confi

cv516Buaa 9 Nov 7, 2022
CharacterGAN: Few-Shot Keypoint Character Animation and Reposing
CharacterGAN: Few-Shot Keypoint Character Animation and Reposing

CharacterGAN Implementation of the paper "CharacterGAN: Few-Shot Keypoint Character Animation and Reposing" by Tobias Hinz, Matthew Fisher, Oliver Wan

Tobias Hinz 181 Dec 27, 2022
Few-shot Learning of GPT-3

Few-shot Learning With Language Models This is a codebase to perform few-shot "in-context" learning using language models similar to the GPT-3 paper.

Tony Z. Zhao 224 Dec 28, 2022
Ready-to-use code and tutorial notebooks to boost your way into few-shot image classification.

Easy Few-Shot Learning Ready-to-use code and tutorial notebooks to boost your way into few-shot image classification. This repository is made for you

Sicara 399 Jan 8, 2023
git《FSCE: Few-Shot Object Detection via Contrastive Proposal Encoding》(CVPR 2021) GitHub: [fig8]
git《FSCE: Few-Shot Object Detection via Contrastive Proposal Encoding》(CVPR 2021) GitHub: [fig8]

FSCE: Few-Shot Object Detection via Contrastive Proposal Encoding (CVPR 2021) This repo contains the implementation of our state-of-the-art fewshot ob

null 233 Dec 29, 2022
Library of various Few-Shot Learning frameworks for text classification

FewShotText This repository contains code for the paper A Neural Few-Shot Text Classification Reality Check Environment setup # Create environment pyt

Thomas Dopierre 47 Jan 3, 2023
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
Owner
GitHub Repository
SmallInitEmb - LayerNorm(SmallInit(Embedding)) in a Transformer to improve convergence

SmallInitEmb LayerNorm(SmallInit(Embedding)) in a Transformer I find that when t

PENG Bo 11 Dec 25, 2022
Pytorch Performace Tuning, WandB, AMP, Multi-GPU, TensorRT, Triton

Plant Pathology 2020 FGVC7 Introduction A deep learning model pipeline for training, experimentaiton and deployment for the Kaggle Competition, Plant

Bharat Giddwani 0 Feb 25, 2022
Simple Baselines for Human Pose Estimation and Tracking

Simple Baselines for Human Pose Estimation and Tracking News Our new work High-Resolution Representations for Labeling Pixels and Regions is available

Microsoft 2.7k Jan 05, 2023
Ludwig Benchmarking Toolkit

Ludwig Benchmarking Toolkit The Ludwig Benchmarking Toolkit is a personalized benchmarking toolkit for running end-to-end benchmark studies across an

HazyResearch 17 Nov 18, 2022
Geneva is an artificial intelligence tool that defeats censorship by exploiting bugs in censors

Geneva is an artificial intelligence tool that defeats censorship by exploiting bugs in censors

Kevin Bock 1.5k Jan 06, 2023
library for nonlinear optimization, wrapping many algorithms for global and local, constrained or unconstrained, optimization

NLopt is a library for nonlinear local and global optimization, for functions with and without gradient information. It is designed as a simple, unifi

Steven G. Johnson 1.4k Dec 25, 2022
The AugNet Python module contains functions for the fast computation of image similarity.

AugNet AugNet: End-to-End Unsupervised Visual Representation Learning with Image Augmentation arxiv link In our work, we propose AugNet, a new deep le

Ming 74 Dec 28, 2022
Technical experimentations to beat the stock market using deep learning :chart_with_upwards_trend:

DeepStock Technical experimentations to beat the stock market using deep learning. Experimentations Deep Learning Stock Prediction with Daily News Hea

Keon 449 Dec 29, 2022
Large Scale Fine-Grained Categorization and Domain-Specific Transfer Learning. CVPR 2018

Large Scale Fine-Grained Categorization and Domain-Specific Transfer Learning Tensorflow code and models for the paper: Large Scale Fine-Grained Categ

Yin Cui 187 Oct 01, 2022
Sparse Physics-based and Interpretable Neural Networks

Sparse Physics-based and Interpretable Neural Networks for PDEs This repository contains the code and manuscript for research done on Sparse Physics-b

28 Jan 03, 2023
From the basics to slightly more interesting applications of Tensorflow

TensorFlow Tutorials You can find python source code under the python directory, and associated notebooks under notebooks. Source code Description 1 b

Parag K Mital 5.6k Jan 09, 2023
Best practices for segmentation of the corporate network of any company

Best-practice-for-network-segmentation What is this? This project was created to publish the best practices for segmentation of the corporate network

2k Jan 07, 2023
A hifiasm fork for metagenome assembly using Hifi reads.

hifiasm_meta - de novo metagenome assembler, based on hifiasm, a haplotype-resolved de novo assembler for PacBio Hifi reads.

44 Jul 10, 2022
Speedy Implementation of Instance-based Learning (IBL) agents in Python

A Python library to create single or multi Instance-based Learning (IBL) agents that are built based on Instance Based Learning Theory (IBLT) 1 Instal

0 Nov 18, 2021
A PyTorch Implementation of PGL-SUM from "Combining Global and Local Attention with Positional Encoding for Video Summarization", Proc. IEEE ISM 2021

PGL-SUM: Combining Global and Local Attention with Positional Encoding for Video Summarization PyTorch Implementation of PGL-SUM From "PGL-SUM: Combin

Evlampios Apostolidis 35 Dec 22, 2022
nextPARS, a novel Illumina-based implementation of in-vitro parallel probing of RNA structures.

nextPARS, a novel Illumina-based implementation of in-vitro parallel probing of RNA structures. Here you will find the scripts necessary to produce th

Jesse Willis 0 Jan 20, 2022
[ICLR 2022] Contact Points Discovery for Soft-Body Manipulations with Differentiable Physics

CPDeform Code and data for paper Contact Points Discovery for Soft-Body Manipulations with Differentiable Physics at ICLR 2022 (Spotlight). @InProceed

(Lester) Sizhe Li 29 Nov 29, 2022
A Next Generation ConvNet by FaceBookResearch Implementation in PyTorch(Original) and TensorFlow.

ConvNeXt A Next Generation ConvNet by FaceBookResearch Implementation in PyTorch(Original) and TensorFlow. A FacebookResearch Implementation on A Conv

Raghvender 2 Feb 14, 2022
CVPR 2021 Challenge on Super-Resolution Space

Learning the Super-Resolution Space Challenge NTIRE 2021 at CVPR Learning the Super-Resolution Space challenge is held as a part of the 6th edition of

andreas 104 Oct 26, 2022
Geometric Deep Learning Extension Library for PyTorch

Documentation | Paper | Colab Notebooks | External Resources | OGB Examples PyTorch Geometric (PyG) is a geometric deep learning extension library for

Matthias Fey 16.5k Jan 08, 2023