mmdetection-satellite-dinov2

mmdetection-satellite-dinov2

https://github.com/wri/mmdetection-satellite-dinov2

Science Score: 67.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • CITATION.cff file
    Found CITATION.cff file
  • codemeta.json file
    Found codemeta.json file
  • .zenodo.json file
    Found .zenodo.json file
  • DOI references
    Found 2 DOI reference(s) in README
  • Academic publication links
    Links to: arxiv.org
  • Academic email domains
  • Institutional organization owner
  • JOSS paper metadata
  • Scientific vocabulary similarity
    Low similarity (7.4%) to scientific vocabulary
Last synced: 6 months ago · JSON representation ·

Repository

mmdetection-satellite-dinov2

Basic Info
  • Host: GitHub
  • Owner: wri
  • License: apache-2.0
  • Language: Jupyter Notebook
  • Default Branch: main
  • Size: 19.3 MB
Statistics
  • Stars: 21
  • Watchers: 5
  • Forks: 1
  • Open Issues: 1
  • Releases: 0
Created almost 2 years ago · Last pushed over 1 year ago
Metadata Files
Readme License Citation

README.md

mmdetection-satellite-dinov2

mmdetection-satellite-dinov2

[Paper][ArxiV [same content]] [Blog] [BibTeX]

Overview

This repository contains the (WORK IN PROGRESS) integration of the DiNOV2 SSL layer from Meta and WRI with MMDetection. MMdet 3.X has been updated to include the SSL layer as a backbone. Example configuration for training a ViTDet with a Cascade RCNN head on a COCO-style dataset can be found in the configs-dino folder.

Getting started

An example notebook can be found here to train a DINO DETR with the DINOv2 ViT backbone.

Detrex

Improvements to the DINO DETR detection model have been made in the past two years. Most notable are Rank DETR and Stable DINO. These models are implemented in Detrex, which is built on top of Detectron2.

Configuration files to train Rank DETR with the DINOv2 Vit-L backbone can be found here

Configuration files to train Stable DINO with the DINOv2 Vit-L backbone can be found here

Owner

  • Name: World Resources Institute
  • Login: wri
  • Kind: organization
  • Email: datalab@wri.org
  • Location: Washington, DC

Citation (CITATION.cff)

cff-version: 1.2.0
message: "If you use this software, please cite it as below."
authors:
  - name: "MMDetection Contributors"
title: "OpenMMLab Detection Toolbox and Benchmark"
date-released: 2018-08-22
url: "https://github.com/open-mmlab/mmdetection"
license: Apache-2.0

GitHub Events

Total
  • Issues event: 1
  • Watch event: 12
  • Push event: 5
  • Fork event: 1
Last Year
  • Issues event: 1
  • Watch event: 12
  • Push event: 5
  • Fork event: 1

Dependencies

docker/Dockerfile docker
  • pytorch/pytorch ${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel build
docker/serve/Dockerfile docker
  • pytorch/pytorch ${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel build
docker/serve_cn/Dockerfile docker
  • pytorch/pytorch ${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel build
mmdet.egg-info/requires.txt pypi
  • asynctest *
  • cityscapesscripts *
  • codecov *
  • cython *
  • emoji *
  • fairscale *
  • flake8 *
  • imagecorruptions *
  • instaboostfast *
  • interrogate *
  • isort ==4.3.21
  • jsonlines *
  • kwarray *
  • matplotlib *
  • memory_profiler *
  • mmcv <2.2.0,>=2.0.0rc4
  • mmengine <1.0.0,>=0.7.1
  • mmpretrain *
  • mmtrack *
  • motmetrics *
  • nltk *
  • numpy <1.24.0
  • numpy *
  • onnx ==1.7.0
  • onnxruntime >=1.8.0
  • parameterized *
  • prettytable *
  • protobuf <=3.20.1
  • psutil *
  • pycocoevalcap *
  • pycocotools *
  • pytest *
  • scikit-learn *
  • scipy *
  • seaborn *
  • shapely *
  • six *
  • terminaltables *
  • tqdm *
  • transformers *
  • ubelt *
  • xdoctest >=0.10.0
  • yapf *
requirements/albu.txt pypi
  • albumentations >=0.3.2
requirements/build.txt pypi
  • cython *
  • numpy *
requirements/docs.txt pypi
  • docutils ==0.16.0
  • myst-parser *
  • sphinx ==4.0.2
  • sphinx-copybutton *
  • sphinx_markdown_tables *
  • sphinx_rtd_theme ==0.5.2
  • urllib3 <2.0.0
requirements/mminstall.txt pypi
  • mmcv >=2.0.0rc4,<2.2.0
  • mmengine >=0.7.1,<1.0.0
requirements/multimodal.txt pypi
  • fairscale *
  • jsonlines *
  • nltk *
  • pycocoevalcap *
  • transformers *
requirements/optional.txt pypi
  • cityscapesscripts *
  • emoji *
  • fairscale *
  • imagecorruptions *
  • scikit-learn *
requirements/readthedocs.txt pypi
  • mmcv >=2.0.0rc4,<2.2.0
  • mmengine >=0.7.1,<1.0.0
  • scipy *
  • torch *
  • torchvision *
  • urllib3 <2.0.0
requirements/runtime.txt pypi
  • matplotlib *
  • numpy *
  • pycocotools *
  • scipy *
  • shapely *
  • six *
  • terminaltables *
  • tqdm *
requirements/tests.txt pypi
  • asynctest * test
  • cityscapesscripts * test
  • codecov * test
  • flake8 * test
  • imagecorruptions * test
  • instaboostfast * test
  • interrogate * test
  • isort ==4.3.21 test
  • kwarray * test
  • memory_profiler * test
  • nltk * test
  • onnx ==1.7.0 test
  • onnxruntime >=1.8.0 test
  • parameterized * test
  • prettytable * test
  • protobuf <=3.20.1 test
  • psutil * test
  • pytest * test
  • transformers * test
  • ubelt * test
  • xdoctest >=0.10.0 test
  • yapf * test
requirements/tracking.txt pypi
  • mmpretrain *
  • motmetrics *
  • numpy <1.24.0
  • scikit-learn *
  • seaborn *
requirements.txt pypi
setup.py pypi