{"id":13692776,"url":"https://github.com/open-mmlab/mmsegmentation","last_synced_at":"2025-05-15T00:04:34.837Z","repository":{"id":37081591,"uuid":"272133018","full_name":"open-mmlab/mmsegmentation","owner":"open-mmlab","description":"OpenMMLab Semantic Segmentation Toolbox and Benchmark.","archived":false,"fork":false,"pushed_at":"2024-08-13T08:53:34.000Z","size":45272,"stargazers_count":8867,"open_issues_count":857,"forks_count":2704,"subscribers_count":52,"default_branch":"main","last_synced_at":"2025-05-07T23:28:43.711Z","etag":null,"topics":["deeplabv3","image-segmentation","medical-image-segmentation","pspnet","pytorch","realtime-segmentation","retinal-vessel-segmentation","semantic-segmentation","swin-transformer","transformer","vessel-segmentation"],"latest_commit_sha":null,"homepage":"https://mmsegmentation.readthedocs.io/en/main/","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":".github/CONTRIBUTING.md","funding":null,"license":"LICENSE","code_of_conduct":".github/CODE_OF_CONDUCT.md","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":"2020-06-14T04:32:33.000Z","updated_at":"2025-05-07T13:04:58.000Z","dependencies_parsed_at":"2023-09-26T12:21:40.446Z","dependency_job_id":"391e7a69-be2f-43e3-9362-619ae8552f8c","html_url":"https://github.com/open-mmlab/mmsegmentation","commit_stats":{"total_commits":911,"total_committers":177,"mean_commits":5.146892655367232,"dds":0.8441273326015368,"last_synced_commit":"b040e147adfa027bbc071b624bedf0ae84dfc922"},"previous_names":[],"tags_count":47,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/open-mmlab%2Fmmsegmentation","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/open-mmlab%2Fmmsegmentation/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/open-mmlab%2Fmmsegmentation/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/open-mmlab%2Fmmsegmentation/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/open-mmlab","download_url":"https://codeload.github.com/open-mmlab/mmsegmentation/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":254249199,"owners_count":22039029,"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":["deeplabv3","image-segmentation","medical-image-segmentation","pspnet","pytorch","realtime-segmentation","retinal-vessel-segmentation","semantic-segmentation","swin-transformer","transformer","vessel-segmentation"],"created_at":"2024-08-02T17:01:01.940Z","updated_at":"2025-05-15T00:04:29.825Z","avatar_url":"https://github.com/open-mmlab.png","language":"Python","funding_links":[],"categories":["Topics","🎨 Semantic Segmentation","Pytorch \u0026 related libraries｜Pytorch \u0026 相关库","Computer Vision","Pytorch \u0026 related libraries","Python","对象检测、分割","计算机视觉 (CV)","Frameworks for segmentation","Repos","Deep Learning","CV","Libraies","Semantic \u0026 Open-Vocabulary Perception"],"sub_categories":["Perception","📚 Comprehensive Repositories","CV｜计算机视觉:","General Purpose CV","CV:","网络服务_其他","General-purpose research / production frameworks","8. Semantic Segmentation","Detection \u0026 Segmentation Frameworks"],"readme":"\u003cdiv align=\"center\"\u003e\n  \u003cimg src=\"resources/mmseg-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 - Python Version](https://img.shields.io/pypi/pyversions/mmsegmentation)](https://pypi.org/project/mmsegmentation/)\n[![PyPI](https://img.shields.io/pypi/v/mmsegmentation)](https://pypi.org/project/mmsegmentation)\n[![docs](https://img.shields.io/badge/docs-latest-blue)](https://mmsegmentation.readthedocs.io/en/latest/)\n[![badge](https://github.com/open-mmlab/mmsegmentation/workflows/build/badge.svg)](https://github.com/open-mmlab/mmsegmentation/actions)\n[![codecov](https://codecov.io/gh/open-mmlab/mmsegmentation/branch/master/graph/badge.svg)](https://codecov.io/gh/open-mmlab/mmsegmentation)\n[![license](https://img.shields.io/github/license/open-mmlab/mmsegmentation.svg)](https://github.com/open-mmlab/mmsegmentation/blob/main/LICENSE)\n[![issue resolution](https://isitmaintained.com/badge/resolution/open-mmlab/mmsegmentation.svg)](https://github.com/open-mmlab/mmsegmentation/issues)\n[![open issues](https://isitmaintained.com/badge/open/open-mmlab/mmsegmentation.svg)](https://github.com/open-mmlab/mmsegmentation/issues)\n[![Open in OpenXLab](https://cdn-static.openxlab.org.cn/app-center/openxlab_demo.svg)](https://openxlab.org.cn/apps?search=mmseg)\n\nDocumentation: \u003chttps://mmsegmentation.readthedocs.io/en/latest/\u003e\n\nEnglish | [简体中文](README_zh-CN.md)\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\nMMSegmentation is an open source semantic segmentation toolbox based on PyTorch.\nIt is a part of the OpenMMLab project.\n\nThe [main](https://github.com/open-mmlab/mmsegmentation/tree/main) branch works with PyTorch 1.6+.\n\n### 🎉 Introducing MMSegmentation v1.0.0 🎉\n\nWe are thrilled to announce the official release of MMSegmentation's latest version! For this new release, the [main](https://github.com/open-mmlab/mmsegmentation/tree/main) branch serves as the primary branch, while the development branch is [dev-1.x](https://github.com/open-mmlab/mmsegmentation/tree/dev-1.x). The stable branch for the previous release remains as the [0.x](https://github.com/open-mmlab/mmsegmentation/tree/0.x) branch. Please note that the [master](https://github.com/open-mmlab/mmsegmentation/tree/master) branch will only be maintained for a limited time before being removed. We encourage you to be mindful of branch selection and updates during use. Thank you for your unwavering support and enthusiasm, and let's work together to make MMSegmentation even more robust and powerful! 💪\n\nMMSegmentation v1.x brings remarkable improvements over the 0.x release, offering a more flexible and feature-packed experience. To utilize the new features in v1.x, we kindly invite you to consult our detailed [📚 migration guide](https://mmsegmentation.readthedocs.io/en/latest/migration/interface.html), which will help you seamlessly transition your projects. Your support is invaluable, and we eagerly await your feedback!\n\n![demo image](resources/seg_demo.gif)\n\n### Major features\n\n- **Unified Benchmark**\n\n  We provide a unified benchmark toolbox for various semantic segmentation methods.\n\n- **Modular Design**\n\n  We decompose the semantic segmentation framework into different components and one can easily construct a customized semantic segmentation framework by combining different modules.\n\n- **Support of multiple methods out of box**\n\n  The toolbox directly supports popular and contemporary semantic segmentation frameworks, *e.g.* PSPNet, DeepLabV3, PSANet, DeepLabV3+, etc.\n\n- **High efficiency**\n\n  The training speed is faster than or comparable to other codebases.\n\n## What's New\n\nv1.2.0 was released on 10/12/2023, from 1.1.0 to 1.2.0, we have added or updated the following features:\n\n### Highlights\n\n- Support for the open-vocabulary semantic segmentation algorithm [SAN](configs/san/README.md)\n\n- Support monocular depth estimation task, please refer to [VPD](configs/vpd/README.md) and [Adabins](projects/Adabins/README.md) for more details.\n\n  ![depth estimation](https://github.com/open-mmlab/mmsegmentation/assets/15952744/07afd0e9-8ace-4a00-aa1e-5bf0ca92dcbc)\n\n- Add new projects: open-vocabulary semantic segmentation algorithm [CAT-Seg](projects/CAT-Seg/README.md), real-time semantic segmentation algofithm [PP-MobileSeg](projects/pp_mobileseg/README.md)\n\n## Installation\n\nPlease refer to [get_started.md](docs/en/get_started.md#installation) for installation and [dataset_prepare.md](docs/en/user_guides/2_dataset_prepare.md#prepare-datasets) for dataset preparation.\n\n## Get Started\n\nPlease see [Overview](docs/en/overview.md) for the general introduction of MMSegmentation.\n\nPlease see [user guides](https://mmsegmentation.readthedocs.io/en/latest/user_guides/index.html#) for the basic usage of MMSegmentation.\nThere are also [advanced tutorials](https://mmsegmentation.readthedocs.io/en/latest/advanced_guides/index.html) for in-depth understanding of mmseg design and implementation .\n\nA Colab tutorial is also provided. You may preview the notebook [here](demo/MMSegmentation_Tutorial.ipynb) or directly [run](https://colab.research.google.com/github/open-mmlab/mmsegmentation/blob/main/demo/MMSegmentation_Tutorial.ipynb) on Colab.\n\nTo migrate from MMSegmentation 0.x, please refer to [migration](docs/en/migration).\n\n## Tutorial\n\n\u003cdiv align=\"center\"\u003e\n  \u003cb\u003eMMSegmentation Tutorials\u003c/b\u003e\n\u003c/div\u003e\n\u003ctable align=\"center\"\u003e\n  \u003ctbody\u003e\n    \u003ctr align=\"center\" valign=\"center\"\u003e\n      \u003ctd\u003e\n        \u003cb\u003eGet Started\u003c/b\u003e\n      \u003c/td\u003e\n      \u003ctd\u003e\n        \u003cb\u003eMMSeg Basic Tutorial\u003c/b\u003e\n      \u003c/td\u003e\n      \u003ctd\u003e\n        \u003cb\u003eMMSeg Detail Tutorial\u003c/b\u003e\n      \u003c/td\u003e\n      \u003ctd\u003e\n        \u003cb\u003eMMSeg Development Tutorial\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=\"docs/en/overview.md\"\u003eMMSeg overview\u003c/a\u003e\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"docs/en/get_started.md\"\u003eMMSeg Installation\u003c/a\u003e\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"docs/en/notes/faq.md\"\u003eFAQ\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=\"docs/en/user_guides/1_config.md\"\u003eTutorial 1: Learn about Configs\u003c/a\u003e\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"docs/en/user_guides/2_dataset_prepare.md\"\u003eTutorial 2: Prepare datasets\u003c/a\u003e\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"docs/en/user_guides/3_inference.md\"\u003eTutorial 3: Inference with existing models\u003c/a\u003e\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"docs/en/user_guides/4_train_test.md\"\u003eTutorial 4: Train and test with existing models\u003c/a\u003e\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"docs/en/user_guides/5_deployment.md\"\u003eTutorial 5: Model deployment\u003c/a\u003e\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"docs/zh_cn/user_guides/deploy_jetson.md\"\u003eDeploy mmsegmentation on Jetson platform\u003c/a\u003e\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"docs/en/user_guides/useful_tools.md\"\u003eUseful Tools\u003c/a\u003e\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"docs/en/user_guides/visualization_feature_map.md\"\u003eFeature Map Visualization\u003c/a\u003e\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"docs/en/user_guides/visualization.md\"\u003eVisualization\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=\"docs/en/advanced_guides/datasets.md\"\u003eMMSeg Dataset\u003c/a\u003e\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"docs/en/advanced_guides/models.md\"\u003eMMSeg Models\u003c/a\u003e\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"docs/en/advanced_guides/structures.md\"\u003eMMSeg Dataset Structures\u003c/a\u003e\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"docs/en/advanced_guides/transforms.md\"\u003eMMSeg Data Transforms\u003c/a\u003e\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"docs/en/advanced_guides/data_flow.md\"\u003eMMSeg Dataflow\u003c/a\u003e\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"docs/en/advanced_guides/engine.md\"\u003eMMSeg Training Engine\u003c/a\u003e\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"docs/en/advanced_guides/evaluation.md\"\u003eMMSeg Evaluation\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=\"docs/en/advanced_guides/add_datasets.md\"\u003eAdd New Datasets\u003c/a\u003e\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"docs/en/advanced_guides/add_metrics.md\"\u003eAdd New Metrics\u003c/a\u003e\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"docs/en/advanced_guides/add_models.md\"\u003eAdd New Modules\u003c/a\u003e\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"docs/en/advanced_guides/add_transforms.md\"\u003eAdd New Data Transforms\u003c/a\u003e\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"docs/en/advanced_guides/customize_runtime.md\"\u003eCustomize Runtime Settings\u003c/a\u003e\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"docs/en/advanced_guides/training_tricks.md\"\u003eTraining Tricks\u003c/a\u003e\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\".github/CONTRIBUTING.md\"\u003eContribute code to MMSeg\u003c/a\u003e\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"docs/zh_cn/advanced_guides/contribute_dataset.md\"\u003eContribute a standard dataset in projects\u003c/a\u003e\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"docs/en/device/npu.md\"\u003eNPU (HUAWEI Ascend)\u003c/a\u003e\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"docs/en/migration/interface.md\"\u003e0.x → 1.x migration\u003c/a\u003e\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"docs/en/migration/package.md\"\u003e0.x → 1.x package\u003c/a\u003e\u003c/li\u003e\n        \u003c/ul\u003e\n      \u003c/td\u003e\n    \u003c/tr\u003e\n  \u003c/tbody\u003e\n\u003c/table\u003e\n\n## Benchmark and model zoo\n\nResults and models are available in the [model zoo](docs/en/model_zoo.md).\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=\"center\"\u003e\n      \u003ctd\u003e\n        \u003cb\u003eSupported backbones\u003c/b\u003e\n      \u003c/td\u003e\n      \u003ctd\u003e\n        \u003cb\u003eSupported methods\u003c/b\u003e\n      \u003c/td\u003e\n      \u003ctd\u003e\n        \u003cb\u003eSupported Head\u003c/b\u003e\n      \u003c/td\u003e\n      \u003ctd\u003e\n        \u003cb\u003eSupported datasets\u003c/b\u003e\n      \u003c/td\u003e\n      \u003ctd\u003e\n        \u003cb\u003eOther\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=\"mmseg/models/backbones/resnet.py\"\u003eResNet(CVPR'2016)\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"mmseg/models/backbones/resnext.py\"\u003eResNeXt (CVPR'2017)\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"configs/hrnet\"\u003eHRNet (CVPR'2019)\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"configs/resnest\"\u003eResNeSt (ArXiv'2020)\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"configs/mobilenet_v2\"\u003eMobileNetV2 (CVPR'2018)\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"configs/mobilenet_v3\"\u003eMobileNetV3 (ICCV'2019)\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"configs/vit\"\u003eVision Transformer (ICLR'2021)\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"configs/swin\"\u003eSwin Transformer (ICCV'2021)\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"configs/twins\"\u003eTwins (NeurIPS'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/convnext\"\u003eConvNeXt (CVPR'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/poolformer\"\u003ePoolFormer (CVPR'2022)\u003c/a\u003e\u003c/li\u003e\n        \u003cli\u003e\u003ca href=\"configs/segnext\"\u003eSegNeXt (NeurIPS'2022)\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/san/\"\u003eSAN (CVPR'2023)\u003c/a\u003e\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"configs/vpd\"\u003eVPD (ICCV'2023)\u003c/a\u003e\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"configs/ddrnet\"\u003eDDRNet (T-ITS'2022)\u003c/a\u003e\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"configs/pidnet\"\u003ePIDNet (ArXiv'2022)\u003c/a\u003e\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"configs/mask2former\"\u003eMask2Former (CVPR'2022)\u003c/a\u003e\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"configs/maskformer\"\u003eMaskFormer (NeurIPS'2021)\u003c/a\u003e\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"configs/knet\"\u003eK-Net (NeurIPS'2021)\u003c/a\u003e\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"configs/segformer\"\u003eSegFormer (NeurIPS'2021)\u003c/a\u003e\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"configs/segmenter\"\u003eSegmenter (ICCV'2021)\u003c/a\u003e\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"configs/dpt\"\u003eDPT (ArXiv'2021)\u003c/a\u003e\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"configs/setr\"\u003eSETR (CVPR'2021)\u003c/a\u003e\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"configs/stdc\"\u003eSTDC (CVPR'2021)\u003c/a\u003e\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"configs/bisenetv2\"\u003eBiSeNetV2 (IJCV'2021)\u003c/a\u003e\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"configs/cgnet\"\u003eCGNet (TIP'2020)\u003c/a\u003e\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"configs/point_rend\"\u003ePointRend (CVPR'2020)\u003c/a\u003e\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"configs/dnlnet\"\u003eDNLNet (ECCV'2020)\u003c/a\u003e\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"configs/ocrnet\"\u003eOCRNet (ECCV'2020)\u003c/a\u003e\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"configs/isanet\"\u003eISANet (ArXiv'2019/IJCV'2021)\u003c/a\u003e\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"configs/fastscnn\"\u003eFast-SCNN (ArXiv'2019)\u003c/a\u003e\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"configs/fastfcn\"\u003eFastFCN (ArXiv'2019)\u003c/a\u003e\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"configs/gcnet\"\u003eGCNet (ICCVW'2019/TPAMI'2020)\u003c/a\u003e\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"configs/ann\"\u003eANN (ICCV'2019)\u003c/a\u003e\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"configs/emanet\"\u003eEMANet (ICCV'2019)\u003c/a\u003e\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"configs/ccnet\"\u003eCCNet (ICCV'2019)\u003c/a\u003e\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"configs/dmnet\"\u003eDMNet (ICCV'2019)\u003c/a\u003e\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"configs/sem_fpn\"\u003eSemantic FPN (CVPR'2019)\u003c/a\u003e\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"configs/danet\"\u003eDANet (CVPR'2019)\u003c/a\u003e\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"configs/apcnet\"\u003eAPCNet (CVPR'2019)\u003c/a\u003e\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"configs/nonlocal_net\"\u003eNonLocal Net (CVPR'2018)\u003c/a\u003e\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"configs/encnet\"\u003eEncNet (CVPR'2018)\u003c/a\u003e\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"configs/deeplabv3plus\"\u003eDeepLabV3+ (CVPR'2018)\u003c/a\u003e\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"configs/upernet\"\u003eUPerNet (ECCV'2018)\u003c/a\u003e\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"configs/icnet\"\u003eICNet (ECCV'2018)\u003c/a\u003e\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"configs/psanet\"\u003ePSANet (ECCV'2018)\u003c/a\u003e\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"configs/bisenetv1\"\u003eBiSeNetV1 (ECCV'2018)\u003c/a\u003e\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"configs/deeplabv3\"\u003eDeepLabV3 (ArXiv'2017)\u003c/a\u003e\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"configs/pspnet\"\u003ePSPNet (CVPR'2017)\u003c/a\u003e\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"configs/erfnet\"\u003eERFNet (T-ITS'2017)\u003c/a\u003e\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"configs/unet\"\u003eUNet (MICCAI'2016/Nat. Methods'2019)\u003c/a\u003e\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"configs/fcn\"\u003eFCN (CVPR'2015/TPAMI'2017)\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=\"mmseg/models/decode_heads/ann_head.py\"\u003eANN_Head\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"mmseg/models/decode_heads/apc_head.py\"\u003eAPC_Head\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"mmseg/models/decode_heads/aspp_head.py\"\u003eASPP_Head\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"mmseg/models/decode_heads/cc_head.py\"\u003eCC_Head\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"mmseg/models/decode_heads/da_head.py\"\u003eDA_Head\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"mmseg/models/decode_heads/ddr_head.py\"\u003eDDR_Head\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"mmseg/models/decode_heads/dm_head.py\"\u003eDM_Head\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"mmseg/models/decode_heads/dnl_head.py\"\u003eDNL_Head\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"mmseg/models/decode_heads/dpt_head.py\"\u003eDPT_HEAD\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"mmseg/models/decode_heads/ema_head.py\"\u003eEMA_Head\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"mmseg/models/decode_heads/enc_head.py\"\u003eENC_Head\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"mmseg/models/decode_heads/fcn_head.py\"\u003eFCN_Head\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"mmseg/models/decode_heads/fpn_head.py\"\u003eFPN_Head\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"mmseg/models/decode_heads/gc_head.py\"\u003eGC_Head\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"mmseg/models/decode_heads/ham_head.py\"\u003eLightHam_Head\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"mmseg/models/decode_heads/isa_head.py\"\u003eISA_Head\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"mmseg/models/decode_heads/knet_head.py\"\u003eKnet_Head\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"mmseg/models/decode_heads/lraspp_head.py\"\u003eLRASPP_Head\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"mmseg/models/decode_heads/mask2former_head.py\"\u003emask2former_Head\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"mmseg/models/decode_heads/maskformer_head.py\"\u003emaskformer_Head\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"mmseg/models/decode_heads/nl_head.py\"\u003eNL_Head\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"mmseg/models/decode_heads/ocr_head.py\"\u003eOCR_Head\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"mmseg/models/decode_heads/pid_head.py\"\u003ePID_Head\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"mmseg/models/decode_heads/point_head.py\"\u003epoint_Head\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"mmseg/models/decode_heads/psa_head.py\"\u003ePSA_Head\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"mmseg/models/decode_heads/psp_head.py\"\u003ePSP_Head\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"mmseg/models/decode_heads/san_head.py\"\u003eSAN_Head\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"mmseg/models/decode_heads/segformer_head.py\"\u003esegformer_Head\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"mmseg/models/decode_heads/segmenter_mask_head.py\"\u003esegmenter_mask_Head\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"mmseg/models/decode_heads/sep_aspp_head.py\"\u003eSepASPP_Head\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"mmseg/models/decode_heads/sep_fcn_head.py\"\u003eSepFCN_Head\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"mmseg/models/decode_heads/setr_mla_head.py\"\u003eSETRMLAHead_Head\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"mmseg/models/decode_heads/setr_up_head.py\"\u003eSETRUP_Head\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"mmseg/models/decode_heads/stdc_head.py\"\u003eSTDC_Head\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"mmseg/models/decode_heads/uper_head.py\"\u003eUper_Head\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"mmseg/models/decode_heads/vpd_depth_head.py\"\u003eVPDDepth_Head\u003c/li\u003e\n        \u003c/ul\u003e\n      \u003c/td\u003e\n      \u003ctd\u003e\n        \u003cul\u003e\n          \u003cli\u003e\u003ca href=\"https://github.com/open-mmlab/mmsegmentation/blob/main/docs/en/user_guides/2_dataset_prepare.md#cityscapes\"\u003eCityscapes\u003c/a\u003e\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"https://github.com/open-mmlab/mmsegmentation/blob/main/docs/en/user_guides/2_dataset_prepare.md#pascal-voc\"\u003ePASCAL VOC\u003c/a\u003e\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"https://github.com/open-mmlab/mmsegmentation/blob/main/docs/en/user_guides/2_dataset_prepare.md#ade20k\"\u003eADE20K\u003c/a\u003e\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"https://github.com/open-mmlab/mmsegmentation/blob/main/docs/en/user_guides/2_dataset_prepare.md#pascal-context\"\u003ePascal Context\u003c/a\u003e\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"https://github.com/open-mmlab/mmsegmentation/blob/main/docs/en/user_guides/2_dataset_prepare.md#coco-stuff-10k\"\u003eCOCO-Stuff 10k\u003c/a\u003e\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"https://github.com/open-mmlab/mmsegmentation/blob/main/docs/en/user_guides/2_dataset_prepare.md#coco-stuff-164k\"\u003eCOCO-Stuff 164k\u003c/a\u003e\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"https://github.com/open-mmlab/mmsegmentation/blob/main/docs/en/user_guides/2_dataset_prepare.md#chase-db1\"\u003eCHASE_DB1\u003c/a\u003e\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"https://github.com/open-mmlab/mmsegmentation/blob/main/docs/en/user_guides/2_dataset_prepare.md#drive\"\u003eDRIVE\u003c/a\u003e\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"https://github.com/open-mmlab/mmsegmentation/blob/main/docs/en/user_guides/2_dataset_prepare.md#hrf\"\u003eHRF\u003c/a\u003e\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"https://github.com/open-mmlab/mmsegmentation/blob/main/docs/en/user_guides/2_dataset_prepare.md#stare\"\u003eSTARE\u003c/a\u003e\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"https://github.com/open-mmlab/mmsegmentation/blob/main/docs/en/user_guides/2_dataset_prepare.md#dark-zurich\"\u003eDark Zurich\u003c/a\u003e\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"https://github.com/open-mmlab/mmsegmentation/blob/main/docs/en/user_guides/2_dataset_prepare.md#nighttime-driving\"\u003eNighttime Driving\u003c/a\u003e\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"https://github.com/open-mmlab/mmsegmentation/blob/main/docs/en/user_guides/2_dataset_prepare.md#loveda\"\u003eLoveDA\u003c/a\u003e\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"https://github.com/open-mmlab/mmsegmentation/blob/main/docs/en/user_guides/2_dataset_prepare.md#isprs-potsdam\"\u003ePotsdam\u003c/a\u003e\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"https://github.com/open-mmlab/mmsegmentation/blob/main/docs/en/user_guides/2_dataset_prepare.md#isprs-vaihingen\"\u003eVaihingen\u003c/a\u003e\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"https://github.com/open-mmlab/mmsegmentation/blob/main/docs/en/user_guides/2_dataset_prepare.md#isaid\"\u003eiSAID\u003c/a\u003e\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"https://github.com/open-mmlab/mmsegmentation/blob/main/docs/en/user_guides/2_dataset_prepare.md#mapillary-vistas-datasets\"\u003eMapillary Vistas\u003c/a\u003e\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"https://github.com/open-mmlab/mmsegmentation/blob/main/docs/en/user_guides/2_dataset_prepare.md#levir-cd\"\u003eLEVIR-CD\u003c/a\u003e\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"https://github.com/open-mmlab/mmsegmentation/blob/main/docs/en/user_guides/2_dataset_prepare.md#bdd100K\"\u003eBDD100K\u003c/a\u003e\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"https://github.com/open-mmlab/mmsegmentation/blob/main/docs/en/user_guides/2_dataset_prepare.md#nyu\"\u003eNYU\u003c/a\u003e\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"https://github.com/open-mmlab/mmsegmentation/blob/main/docs/en/user_guides/2_dataset_prepare.md#hsi-drive-2.0\"\u003eHSIDrive20\u003c/a\u003e\u003c/li\u003e\n        \u003c/ul\u003e\n      \u003c/td\u003e\n      \u003ctd\u003e\n        \u003cul\u003e\n          \u003cli\u003e\u003cb\u003eSupported loss\u003c/b\u003e\u003c/li\u003e\n        \u003cul\u003e\n          \u003cli\u003e\u003ca href=\"mmseg/models/losses/boundary_loss.py\"\u003eboundary_loss\u003c/a\u003e\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"mmseg/models/losses/cross_entropy_loss.py\"\u003ecross_entropy_loss\u003c/a\u003e\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"mmseg/models/losses/dice_loss.py\"\u003edice_loss\u003c/a\u003e\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"mmseg/models/losses/focal_loss.py\"\u003efocal_loss\u003c/a\u003e\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"mmseg/models/losses/huasdorff_distance_loss.py\"\u003ehuasdorff_distance_loss\u003c/a\u003e\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"mmseg/models/losses/kldiv_loss.py\"\u003ekldiv_loss\u003c/a\u003e\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"mmseg/models/losses/lovasz_loss.py\"\u003elovasz_loss\u003c/a\u003e\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"mmseg/models/losses/ohem_cross_entropy_loss.py\"\u003eohem_cross_entropy_loss\u003c/a\u003e\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"mmseg/models/losses/silog_loss.py\"\u003esilog_loss\u003c/a\u003e\u003c/li\u003e\n          \u003cli\u003e\u003ca href=\"mmseg/models/losses/tversky_loss.py\"\u003etversky_loss\u003c/a\u003e\u003c/li\u003e\n        \u003c/ul\u003e\n        \u003c/ul\u003e\n      \u003c/td\u003e\n  \u003c/tbody\u003e\n\u003c/table\u003e\n\nPlease refer to [FAQ](docs/en/notes/faq.md) for frequently asked questions.\n\n## Projects\n\n[Here](projects/README.md) are some implementations of SOTA models and solutions built on MMSegmentation, which are supported and maintained by community users. These projects demonstrate the best practices based on MMSegmentation for research and product development. We welcome and appreciate all the contributions to OpenMMLab ecosystem.\n\n## Contributing\n\nWe appreciate all contributions to improve MMSegmentation. Please refer to [CONTRIBUTING.md](.github/CONTRIBUTING.md) for the contributing guideline.\n\n## Acknowledgement\n\nMMSegmentation is an open source project that welcome any contribution and feedback.\nWe wish that the toolbox and benchmark could serve the growing research\ncommunity by providing a flexible as well as standardized toolkit to reimplement existing methods\nand develop their own new semantic segmentation methods.\n\n## Citation\n\nIf you find this project useful in your research, please consider cite:\n\n```bibtex\n@misc{mmseg2020,\n    title={{MMSegmentation}: OpenMMLab Semantic Segmentation Toolbox and Benchmark},\n    author={MMSegmentation Contributors},\n    howpublished = {\\url{https://github.com/open-mmlab/mmsegmentation}},\n    year={2020}\n}\n```\n\n## License\n\nThis project is released under the [Apache 2.0 license](LICENSE).\n\n## OpenMMLab Family\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- [MMPreTrain](https://github.com/open-mmlab/mmpretrain): OpenMMLab pre-training 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- [MMDetection](https://github.com/open-mmlab/mmdetection): OpenMMLab detection toolbox and benchmark.\n- [MMYOLO](https://github.com/open-mmlab/mmyolo): OpenMMLab YOLO series 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- [MMTracking](https://github.com/open-mmlab/mmtracking): OpenMMLab video perception 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- [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- [MMFlow](https://github.com/open-mmlab/mmflow): OpenMMLab optical flow toolbox and benchmark.\n- [MMDeploy](https://github.com/open-mmlab/mmdeploy): OpenMMLab Model Deployment Framework.\n- [MMRazor](https://github.com/open-mmlab/mmrazor): OpenMMLab model compression toolbox and benchmark.\n- [MIM](https://github.com/open-mmlab/mim): MIM installs OpenMMLab packages.\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%2Fmmsegmentation","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fopen-mmlab%2Fmmsegmentation","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fopen-mmlab%2Fmmsegmentation/lists"}