{"id":13445550,"url":"https://github.com/open-mmlab/mmpretrain","last_synced_at":"2025-12-24T21:17:26.763Z","repository":{"id":37082780,"uuid":"278415292","full_name":"open-mmlab/mmpretrain","owner":"open-mmlab","description":"OpenMMLab Pre-training Toolbox and Benchmark","archived":false,"fork":false,"pushed_at":"2024-11-01T06:27:36.000Z","size":14127,"stargazers_count":3634,"open_issues_count":269,"forks_count":1081,"subscribers_count":29,"default_branch":"main","last_synced_at":"2025-04-20T03:32:56.421Z","etag":null,"topics":["beit","clip","constrastive-learning","convnext","deep-learning","image-classification","mae","masked-image-modeling","mobilenet","moco","multimodal","pretrained-models","pytorch","resnet","self-supervised-learning","swin-transformer","vision-transformer"],"latest_commit_sha":null,"homepage":"https://mmpretrain.readthedocs.io/en/latest/","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"apache-2.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/open-mmlab.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":"CONTRIBUTING.md","funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":"CITATION.cff","codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2020-07-09T16:25:04.000Z","updated_at":"2025-04-19T13:53:26.000Z","dependencies_parsed_at":"2024-01-11T02:51:17.159Z","dependency_job_id":"33ab828a-7983-4759-a6ba-1dabe4c439e9","html_url":"https://github.com/open-mmlab/mmpretrain","commit_stats":{"total_commits":915,"total_committers":141,"mean_commits":6.48936170212766,"dds":0.6841530054644809,"last_synced_commit":"17a886cb5825cd8c26df4e65f7112d404b99fe12"},"previous_names":["open-mmlab/mmclassification"],"tags_count":41,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/open-mmlab%2Fmmpretrain","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/open-mmlab%2Fmmpretrain/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/open-mmlab%2Fmmpretrain/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/open-mmlab%2Fmmpretrain/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/open-mmlab","download_url":"https://codeload.github.com/open-mmlab/mmpretrain/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":250007259,"owners_count":21359746,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["beit","clip","constrastive-learning","convnext","deep-learning","image-classification","mae","masked-image-modeling","mobilenet","moco","multimodal","pretrained-models","pytorch","resnet","self-supervised-learning","swin-transformer","vision-transformer"],"created_at":"2024-07-31T05:00:35.864Z","updated_at":"2025-12-24T21:17:21.742Z","avatar_url":"https://github.com/open-mmlab.png","language":"Python","funding_links":[],"categories":["Related Project","Acknowledgement","Classification"],"sub_categories":["Project of Self-supervised Learning","Development"],"readme":"\u003cdiv align=\"center\"\u003e\n\n\u003cimg src=\"resources/mmpt-logo.png\" width=\"600\"/\u003e\n  \u003cdiv\u003e\u0026nbsp;\u003c/div\u003e\n  \u003cdiv align=\"center\"\u003e\n    \u003cb\u003e\u003cfont size=\"5\"\u003eOpenMMLab website\u003c/font\u003e\u003c/b\u003e\n    \u003csup\u003e\n      \u003ca href=\"https://openmmlab.com\"\u003e\n        \u003ci\u003e\u003cfont size=\"4\"\u003eHOT\u003c/font\u003e\u003c/i\u003e\n      \u003c/a\u003e\n    \u003c/sup\u003e\n    \u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\n    \u003cb\u003e\u003cfont size=\"5\"\u003eOpenMMLab platform\u003c/font\u003e\u003c/b\u003e\n    \u003csup\u003e\n      \u003ca href=\"https://platform.openmmlab.com\"\u003e\n        \u003ci\u003e\u003cfont size=\"4\"\u003eTRY IT OUT\u003c/font\u003e\u003c/i\u003e\n      \u003c/a\u003e\n    \u003c/sup\u003e\n  \u003c/div\u003e\n  \u003cdiv\u003e\u0026nbsp;\u003c/div\u003e\n\n[![PyPI](https://img.shields.io/pypi/v/mmpretrain)](https://pypi.org/project/mmpretrain)\n[![Docs](https://img.shields.io/badge/docs-latest-blue)](https://mmpretrain.readthedocs.io/en/latest/)\n[![Build Status](https://github.com/open-mmlab/mmpretrain/workflows/build/badge.svg)](https://github.com/open-mmlab/mmpretrain/actions)\n[![codecov](https://codecov.io/gh/open-mmlab/mmpretrain/branch/main/graph/badge.svg)](https://codecov.io/gh/open-mmlab/mmpretrain)\n[![license](https://img.shields.io/github/license/open-mmlab/mmpretrain.svg)](https://github.com/open-mmlab/mmpretrain/blob/main/LICENSE)\n[![open issues](https://isitmaintained.com/badge/open/open-mmlab/mmpretrain.svg)](https://github.com/open-mmlab/mmpretrain/issues)\n[![issue resolution](https://isitmaintained.com/badge/resolution/open-mmlab/mmpretrain.svg)](https://github.com/open-mmlab/mmpretrain/issues)\n\n[📘 Documentation](https://mmpretrain.readthedocs.io/en/latest/) |\n[🛠️ Installation](https://mmpretrain.readthedocs.io/en/latest/get_started.html#installation) |\n[👀 Model Zoo](https://mmpretrain.readthedocs.io/en/latest/modelzoo_statistics.html) |\n[🆕 Update News](https://mmpretrain.readthedocs.io/en/latest/notes/changelog.html) |\n[🤔 Reporting Issues](https://github.com/open-mmlab/mmpretrain/issues/new/choose)\n\n\u003cimg src=\"https://user-images.githubusercontent.com/36138628/230307505-4727ad0a-7d71-4069-939d-b499c7e272b7.png\" width=\"400\"/\u003e\n\nEnglish | [简体中文](/README_zh-CN.md)\n\n\u003c/div\u003e\n\n\u003c/div\u003e\n\n\u003cdiv align=\"center\"\u003e\n  \u003ca href=\"https://openmmlab.medium.com/\" style=\"text-decoration:none;\"\u003e\n    \u003cimg src=\"https://user-images.githubusercontent.com/25839884/219255827-67c1a27f-f8c5-46a9-811d-5e57448c61d1.png\" width=\"3%\" alt=\"\" /\u003e\u003c/a\u003e\n  \u003cimg src=\"https://user-images.githubusercontent.com/25839884/218346358-56cc8e2f-a2b8-487f-9088-32480cceabcf.png\" width=\"3%\" alt=\"\" /\u003e\n  \u003ca href=\"https://discord.gg/raweFPmdzG\" style=\"text-decoration:none;\"\u003e\n    \u003cimg src=\"https://user-images.githubusercontent.com/25839884/218347213-c080267f-cbb6-443e-8532-8e1ed9a58ea9.png\" width=\"3%\" alt=\"\" /\u003e\u003c/a\u003e\n  \u003cimg src=\"https://user-images.githubusercontent.com/25839884/218346358-56cc8e2f-a2b8-487f-9088-32480cceabcf.png\" width=\"3%\" alt=\"\" /\u003e\n  \u003ca href=\"https://twitter.com/OpenMMLab\" style=\"text-decoration:none;\"\u003e\n    \u003cimg src=\"https://user-images.githubusercontent.com/25839884/218346637-d30c8a0f-3eba-4699-8131-512fb06d46db.png\" width=\"3%\" alt=\"\" /\u003e\u003c/a\u003e\n  \u003cimg src=\"https://user-images.githubusercontent.com/25839884/218346358-56cc8e2f-a2b8-487f-9088-32480cceabcf.png\" width=\"3%\" alt=\"\" /\u003e\n  \u003ca href=\"https://www.youtube.com/openmmlab\" style=\"text-decoration:none;\"\u003e\n    \u003cimg src=\"https://user-images.githubusercontent.com/25839884/218346691-ceb2116a-465a-40af-8424-9f30d2348ca9.png\" width=\"3%\" alt=\"\" /\u003e\u003c/a\u003e\n  \u003cimg src=\"https://user-images.githubusercontent.com/25839884/218346358-56cc8e2f-a2b8-487f-9088-32480cceabcf.png\" width=\"3%\" alt=\"\" /\u003e\n  \u003ca href=\"https://space.bilibili.com/1293512903\" style=\"text-decoration:none;\"\u003e\n    \u003cimg src=\"https://user-images.githubusercontent.com/25839884/219026751-d7d14cce-a7c9-4e82-9942-8375fca65b99.png\" width=\"3%\" alt=\"\" /\u003e\u003c/a\u003e\n  \u003cimg src=\"https://user-images.githubusercontent.com/25839884/218346358-56cc8e2f-a2b8-487f-9088-32480cceabcf.png\" width=\"3%\" alt=\"\" /\u003e\n  \u003ca href=\"https://www.zhihu.com/people/openmmlab\" style=\"text-decoration:none;\"\u003e\n    \u003cimg src=\"https://user-images.githubusercontent.com/25839884/219026120-ba71e48b-6e94-4bd4-b4e9-b7d175b5e362.png\" width=\"3%\" alt=\"\" /\u003e\u003c/a\u003e\n\u003c/div\u003e\n\n## Introduction\n\nMMPreTrain is an open source pre-training toolbox based on PyTorch. It is a part of the [OpenMMLab](https://openmmlab.com/) project.\n\nThe `main` branch works with **PyTorch 1.8+**.\n\n### Major features\n\n- Various backbones and pretrained models\n- Rich training strategies (supervised learning, self-supervised learning, multi-modality learning etc.)\n- Bag of training tricks\n- Large-scale training configs\n- High efficiency and extensibility\n- Powerful toolkits for model analysis and experiments\n- Various out-of-box inference tasks.\n  - Image Classification\n  - Image Caption\n  - Visual Question Answering\n  - Visual Grounding\n  - Retrieval (Image-To-Image, Text-To-Image, Image-To-Text)\n\nhttps://github.com/open-mmlab/mmpretrain/assets/26739999/e4dcd3a2-f895-4d1b-a351-fbc74a04e904\n\n## What's new\n\n🌟 v1.2.0 was released in 04/01/2023\n\n- Support LLaVA 1.5.\n- Implement of RAM with a gradio interface.\n\n🌟 v1.1.0 was released in 12/10/2023\n\n- Support Mini-GPT4 training and provide a Chinese model (based on Baichuan-7B)\n- Support zero-shot classification based on CLIP.\n\n🌟 v1.0.0 was released in 04/07/2023\n\n- Support inference of more **multi-modal** algorithms, such as [**LLaVA**](./configs/llava/), [**MiniGPT-4**](./configs/minigpt4), [**Otter**](./configs/otter/), etc.\n- Support around **10 multi-modal** datasets!\n- Add [**iTPN**](./configs/itpn/), [**SparK**](./configs/spark/) self-supervised learning algorithms.\n- Provide examples of [New Config](./mmpretrain/configs/) and [DeepSpeed/FSDP with FlexibleRunner](./configs/mae/benchmarks/). Here are the documentation links of [New Config](https://mmengine.readthedocs.io/en/latest/advanced_tutorials/config.html#a-pure-python-style-configuration-file-beta) and [DeepSpeed/FSDP with FlexibleRunner](https://mmengine.readthedocs.io/en/latest/api/generated/mmengine.runner.FlexibleRunner.html#mmengine.runner.FlexibleRunner).\n\n🌟 Upgrade from MMClassification to MMPreTrain\n\n- Integrated Self-supervised learning algorithms from **MMSelfSup**, such as **MAE**, **BEiT**, etc.\n- Support **RIFormer**, a simple but effective vision backbone by removing token mixer.\n- Refactor dataset pipeline visualization.\n- Support **LeViT**, **XCiT**, **ViG**, **ConvNeXt-V2**, **EVA**, **RevViT**, **EfficientnetV2**, **CLIP**, **TinyViT** and **MixMIM** backbones.\n\nThis release introduced a brand new and flexible training \u0026 test engine, but it's still in progress. Welcome\nto try according to [the documentation](https://mmpretrain.readthedocs.io/en/latest/).\n\nAnd there are some BC-breaking changes. Please check [the migration tutorial](https://mmpretrain.readthedocs.io/en/latest/migration.html).\n\nPlease refer to [changelog](https://mmpretrain.readthedocs.io/en/latest/notes/changelog.html) for more details and other release history.\n\n## Installation\n\nBelow are quick steps for installation:\n\n```shell\nconda create -n open-mmlab python=3.8 pytorch==1.10.1 torchvision==0.11.2 cudatoolkit=11.3 -c pytorch -y\nconda activate open-mmlab\npip install openmim\ngit clone https://github.com/open-mmlab/mmpretrain.git\ncd mmpretrain\nmim install -e .\n```\n\nPlease refer to [installation documentation](https://mmpretrain.readthedocs.io/en/latest/get_started.html) for more detailed installation and dataset preparation.\n\nFor multi-modality models support, please install the extra dependencies by:\n\n```shell\nmim install -e \".[multimodal]\"\n```\n\n## User Guides\n\nWe provided a series of tutorials about the basic usage of MMPreTrain for new users:\n\n- [Learn about Configs](https://mmpretrain.readthedocs.io/en/latest/user_guides/config.html)\n- [Prepare Dataset](https://mmpretrain.readthedocs.io/en/latest/user_guides/dataset_prepare.html)\n- [Inference with existing models](https://mmpretrain.readthedocs.io/en/latest/user_guides/inference.html)\n- [Train](https://mmpretrain.readthedocs.io/en/latest/user_guides/train.html)\n- [Test](https://mmpretrain.readthedocs.io/en/latest/user_guides/test.html)\n- [Downstream tasks](https://mmpretrain.readthedocs.io/en/latest/user_guides/downstream.html)\n\nFor more information, please refer to [our documentation](https://mmpretrain.readthedocs.io/en/latest/).\n\n## Model zoo\n\nResults and models are available in the [model zoo](https://mmpretrain.readthedocs.io/en/latest/modelzoo_statistics.html).\n\n\u003cdiv align=\"center\"\u003e\n  \u003cb\u003eOverview\u003c/b\u003e\n\u003c/div\u003e\n\u003ctable align=\"center\"\u003e\n  \u003ctbody\u003e\n    \u003ctr align=\"center\" valign=\"bottom\"\u003e\n      \u003ctd\u003e\n        \u003cb\u003eSupported Backbones\u003c/b\u003e\n      \u003c/td\u003e\n      \u003ctd\u003e\n        \u003cb\u003eSelf-supervised Learning\u003c/b\u003e\n      \u003c/td\u003e\n      \u003ctd\u003e\n        \u003cb\u003eMulti-Modality Algorithms\u003c/b\u003e\n      \u003c/td\u003e\n      \u003ctd\u003e\n        \u003cb\u003eOthers\u003c/b\u003e\n      \u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr valign=\"top\"\u003e\n      \u003ctd\u003e\n        \u003cul\u003e\n        \u003cli\u003e\u003ca href=\"configs/vgg\"\u003eVGG\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"configs/resnet\"\u003eResNet\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"configs/resnext\"\u003eResNeXt\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"configs/seresnet\"\u003eSE-ResNet\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"configs/seresnet\"\u003eSE-ResNeXt\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"configs/regnet\"\u003eRegNet\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"configs/shufflenet_v1\"\u003eShuffleNet V1\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"configs/shufflenet_v2\"\u003eShuffleNet V2\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"configs/mobilenet_v2\"\u003eMobileNet V2\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"configs/mobilenet_v3\"\u003eMobileNet V3\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"configs/swin_transformer\"\u003eSwin-Transformer\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"configs/swin_transformer_v2\"\u003eSwin-Transformer V2\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"configs/repvgg\"\u003eRepVGG\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"configs/vision_transformer\"\u003eVision-Transformer\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"configs/tnt\"\u003eTransformer-in-Transformer\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"configs/res2net\"\u003eRes2Net\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"configs/mlp_mixer\"\u003eMLP-Mixer\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"configs/deit\"\u003eDeiT\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"configs/deit3\"\u003eDeiT-3\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"configs/conformer\"\u003eConformer\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"configs/t2t_vit\"\u003eT2T-ViT\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"configs/twins\"\u003eTwins\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"configs/efficientnet\"\u003eEfficientNet\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"configs/edgenext\"\u003eEdgeNeXt\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"configs/convnext\"\u003eConvNeXt\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"configs/hrnet\"\u003eHRNet\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"configs/van\"\u003eVAN\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"configs/convmixer\"\u003eConvMixer\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"configs/cspnet\"\u003eCSPNet\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"configs/poolformer\"\u003ePoolFormer\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"configs/inception_v3\"\u003eInception V3\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"configs/mobileone\"\u003eMobileOne\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"configs/efficientformer\"\u003eEfficientFormer\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"configs/mvit\"\u003eMViT\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"configs/hornet\"\u003eHorNet\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"configs/mobilevit\"\u003eMobileViT\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"configs/davit\"\u003eDaViT\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"configs/replknet\"\u003eRepLKNet\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"configs/beit\"\u003eBEiT\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"configs/mixmim\"\u003eMixMIM\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"configs/efficientnet_v2\"\u003eEfficientNet V2\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"configs/revvit\"\u003eRevViT\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"configs/convnext_v2\"\u003eConvNeXt V2\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"configs/vig\"\u003eViG\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"configs/xcit\"\u003eXCiT\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"configs/levit\"\u003eLeViT\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"configs/riformer\"\u003eRIFormer\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"configs/glip\"\u003eGLIP\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"configs/sam\"\u003eViT SAM\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"configs/eva02\"\u003eEVA02\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"configs/dinov2\"\u003eDINO V2\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"configs/hivit\"\u003eHiViT\u003c/a\u003e\u003c/li\u003e\n        \u003c/ul\u003e\n      \u003c/td\u003e\n      \u003ctd\u003e\n        \u003cul\u003e\n        \u003cli\u003e\u003ca href=\"configs/mocov2\"\u003eMoCo V1 (CVPR'2020)\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"configs/simclr\"\u003eSimCLR (ICML'2020)\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"configs/mocov2\"\u003eMoCo V2 (arXiv'2020)\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"configs/byol\"\u003eBYOL (NeurIPS'2020)\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"configs/swav\"\u003eSwAV (NeurIPS'2020)\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"configs/densecl\"\u003eDenseCL (CVPR'2021)\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"configs/simsiam\"\u003eSimSiam (CVPR'2021)\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"configs/barlowtwins\"\u003eBarlow Twins (ICML'2021)\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"configs/mocov3\"\u003eMoCo V3 (ICCV'2021)\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"configs/beit\"\u003eBEiT (ICLR'2022)\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"configs/mae\"\u003eMAE (CVPR'2022)\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"configs/simmim\"\u003eSimMIM (CVPR'2022)\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"configs/maskfeat\"\u003eMaskFeat (CVPR'2022)\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"configs/cae\"\u003eCAE (arXiv'2022)\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"configs/milan\"\u003eMILAN (arXiv'2022)\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"configs/beitv2\"\u003eBEiT V2 (arXiv'2022)\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"configs/eva\"\u003eEVA (CVPR'2023)\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"configs/mixmim\"\u003eMixMIM (arXiv'2022)\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"configs/itpn\"\u003eiTPN (CVPR'2023)\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"configs/spark\"\u003eSparK (ICLR'2023)\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"configs/mff\"\u003eMFF (ICCV'2023)\u003c/a\u003e\u003c/li\u003e\n        \u003c/ul\u003e\n      \u003c/td\u003e\n      \u003ctd\u003e\n        \u003cul\u003e\n        \u003cli\u003e\u003ca href=\"configs/blip\"\u003eBLIP (arxiv'2022)\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"configs/blip2\"\u003eBLIP-2 (arxiv'2023)\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"configs/ofa\"\u003eOFA (CoRR'2022)\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"configs/flamingo\"\u003eFlamingo (NeurIPS'2022)\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"configs/chinese_clip\"\u003eChinese CLIP (arxiv'2022)\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"configs/minigpt4\"\u003eMiniGPT-4 (arxiv'2023)\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"configs/llava\"\u003eLLaVA (arxiv'2023)\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"configs/otter\"\u003eOtter (arxiv'2023)\u003c/a\u003e\u003c/li\u003e\n        \u003c/ul\u003e\n      \u003c/td\u003e\n      \u003ctd\u003e\n      Image Retrieval Task:\n        \u003cul\u003e\n        \u003cli\u003e\u003ca href=\"configs/arcface\"\u003eArcFace (CVPR'2019)\u003c/a\u003e\u003c/li\u003e\n        \u003c/ul\u003e\n      Training\u0026Test Tips:\n        \u003cul\u003e\n        \u003cli\u003e\u003ca href=\"https://arxiv.org/abs/1909.13719\"\u003eRandAug\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"https://arxiv.org/abs/1805.09501\"\u003eAutoAug\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"mmpretrain/datasets/samplers/repeat_aug.py\"\u003eRepeatAugSampler\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"mmpretrain/models/tta/score_tta.py\"\u003eTTA\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e...\u003c/li\u003e\n        \u003c/ul\u003e\n      \u003c/td\u003e\n  \u003c/tbody\u003e\n\u003c/table\u003e\n\n## Contributing\n\nWe appreciate all contributions to improve MMPreTrain.\nPlease refer to [CONTRUBUTING](https://mmpretrain.readthedocs.io/en/latest/notes/contribution_guide.html) for the contributing guideline.\n\n## Acknowledgement\n\nMMPreTrain is an open source project that is contributed by researchers and engineers from various colleges and companies. We appreciate all the contributors who implement their methods or add new features, as well as users who give valuable feedbacks.\nWe wish that the toolbox and benchmark could serve the growing research community by providing a flexible toolkit to reimplement existing methods and supporting their own academic research.\n\n## Citation\n\nIf you find this project useful in your research, please consider cite:\n\n```BibTeX\n@misc{2023mmpretrain,\n    title={OpenMMLab's Pre-training Toolbox and Benchmark},\n    author={MMPreTrain Contributors},\n    howpublished = {\\url{https://github.com/open-mmlab/mmpretrain}},\n    year={2023}\n}\n```\n\n## License\n\nThis project is released under the [Apache 2.0 license](LICENSE).\n\n## Projects in OpenMMLab\n\n- [MMEngine](https://github.com/open-mmlab/mmengine): OpenMMLab foundational library for training deep learning models.\n- [MMCV](https://github.com/open-mmlab/mmcv): OpenMMLab foundational library for computer vision.\n- [MIM](https://github.com/open-mmlab/mim): MIM installs OpenMMLab packages.\n- [MMEval](https://github.com/open-mmlab/mmeval): A unified evaluation library for multiple machine learning libraries.\n- [MMPreTrain](https://github.com/open-mmlab/mmpretrain): OpenMMLab pre-training toolbox and benchmark.\n- [MMDetection](https://github.com/open-mmlab/mmdetection): OpenMMLab detection toolbox and benchmark.\n- [MMDetection3D](https://github.com/open-mmlab/mmdetection3d): OpenMMLab's next-generation platform for general 3D object detection.\n- [MMRotate](https://github.com/open-mmlab/mmrotate): OpenMMLab rotated object detection toolbox and benchmark.\n- [MMYOLO](https://github.com/open-mmlab/mmyolo): OpenMMLab YOLO series toolbox and benchmark.\n- [MMSegmentation](https://github.com/open-mmlab/mmsegmentation): OpenMMLab semantic segmentation toolbox and benchmark.\n- [MMOCR](https://github.com/open-mmlab/mmocr): OpenMMLab text detection, recognition, and understanding toolbox.\n- [MMPose](https://github.com/open-mmlab/mmpose): OpenMMLab pose estimation toolbox and benchmark.\n- [MMHuman3D](https://github.com/open-mmlab/mmhuman3d): OpenMMLab 3D human parametric model toolbox and benchmark.\n- [MMSelfSup](https://github.com/open-mmlab/mmselfsup): OpenMMLab self-supervised learning toolbox and benchmark.\n- [MMRazor](https://github.com/open-mmlab/mmrazor): OpenMMLab model compression toolbox and benchmark.\n- [MMFewShot](https://github.com/open-mmlab/mmfewshot): OpenMMLab fewshot learning toolbox and benchmark.\n- [MMAction2](https://github.com/open-mmlab/mmaction2): OpenMMLab's next-generation action understanding toolbox and benchmark.\n- [MMTracking](https://github.com/open-mmlab/mmtracking): OpenMMLab video perception toolbox and benchmark.\n- [MMFlow](https://github.com/open-mmlab/mmflow): OpenMMLab optical flow toolbox and benchmark.\n- [MMagic](https://github.com/open-mmlab/mmagic): Open**MM**Lab **A**dvanced, **G**enerative and **I**ntelligent **C**reation toolbox.\n- [MMGeneration](https://github.com/open-mmlab/mmgeneration): OpenMMLab image and video generative models toolbox.\n- [MMDeploy](https://github.com/open-mmlab/mmdeploy): OpenMMLab model deployment framework.\n- [Playground](https://github.com/open-mmlab/playground): A central hub for gathering and showcasing amazing projects built upon OpenMMLab.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fopen-mmlab%2Fmmpretrain","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fopen-mmlab%2Fmmpretrain","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fopen-mmlab%2Fmmpretrain/lists"}