Science Score: 54.0%

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
  • Academic publication links
    Links to: arxiv.org
  • Academic email domains
  • Institutional organization owner
  • JOSS paper metadata
  • Scientific vocabulary similarity
    Low similarity (12.7%) to scientific vocabulary
Last synced: 6 months ago · JSON representation ·

Repository

Basic Info
  • Host: GitHub
  • Owner: bear-coder-9527
  • License: apache-2.0
  • Language: Python
  • Default Branch: main
  • Size: 5.8 MB
Statistics
  • Stars: 1
  • Watchers: 1
  • Forks: 0
  • Open Issues: 0
  • Releases: 0
Created over 1 year ago · Last pushed about 1 year ago
Metadata Files
Readme Contributing License Citation

README.md

Introduction

This project is for personal learning and use, based on MMPreTrain. It aims to track and reproduce the latest backbone models since MMPreTrain hasn’t released any new models for over a year.MMPreTrain is an open source pre-training toolbox based on PyTorch. It is a part of the OpenMMLab project.

The main branch works with PyTorch 1.8+.

What's new

🌟 (2024.12.5) Support StartNet Rewrite the Stars. - More details StartNet.

🌟 Support CAS-ViT. - More details CAS-ViT.

Installation

Below are quick steps for installation:

shell conda create -n BackboneHub python=3.8 pytorch==1.10.1 torchvision==0.11.2 cudatoolkit=11.3 -c pytorch -y conda activate BackboneHub pip install openmim git clone https://github.com/bear-coder-9527/BackboneHub.git cd BackboneHub mim install -e .

Please refer to installation documentation for more detailed installation and dataset preparation.

For multi-modality models support, please install the extra dependencies by:

shell mim install -e ".[multimodal]"

User Guides

The basic usage is same as MMPreTrain for new users:

For more information, please refer to our documentation.

Model zoo

Results and models are available in the model zoo.

Overview
Supported Backbones Self-supervised Learning Multi-Modality Algorithms Others
Image Retrieval Task: Training&Test Tips:

Acknowledgement

This project is based on MMPreTrain. Thanks for their public repository and excellent contributions!

Owner

  • Login: bear-coder-9527
  • Kind: user

Citation (CITATION.cff)

cff-version: 1.2.0
message: "If you use this software, please cite it as below."
title: "OpenMMLab's Pre-training Toolbox and Benchmark"
authors:
  - name: "MMPreTrain Contributors"
version: 0.15.0
date-released: 2023-04-06
repository-code: "https://github.com/open-mmlab/mmpretrain"
license: Apache-2.0

GitHub Events

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Dependencies

tests/data/meta.yml cpan
docker/Dockerfile docker
  • pytorch/pytorch ${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel build
docker/serve/Dockerfile docker
  • pytorch/torchserve latest-gpu build
projects/internimage_classification/ops_dcnv3/setup.py pypi
requirements/docs.txt pypi
  • docutils ==0.18.1
  • modelindex *
  • myst-parser *
  • pytorch_sphinx_theme *
  • sphinx ==6.1.3
  • sphinx-copybutton *
  • sphinx-notfound-page *
  • sphinx-tabs *
  • sphinxcontrib-jquery *
  • tabulate *
requirements/mminstall.txt pypi
  • mmcv >=2.0.0,<2.4.0
  • mmengine >=0.8.3,<1.0.0
requirements/multimodal.txt pypi
  • pycocotools *
  • transformers >=4.28.0
requirements/optional.txt pypi
  • albumentations >=0.3.2
  • grad-cam >=1.3.7,<1.5.0
  • requests *
  • scikit-learn *
requirements/readthedocs.txt pypi
  • mmcv-lite >=2.0.0rc4
  • mmengine *
  • pycocotools *
  • torch *
  • torchvision *
  • transformers *
requirements/runtime.txt pypi
  • einops *
  • importlib-metadata *
  • mat4py *
  • matplotlib *
  • modelindex *
  • numpy *
  • rich *
requirements/tests.txt pypi
  • coverage * test
  • interrogate * test
  • pytest * test
requirements.txt pypi
setup.py pypi