{"id":13443956,"url":"https://github.com/conansherry/detectron2","last_synced_at":"2025-03-20T17:32:31.573Z","repository":{"id":41677215,"uuid":"219108333","full_name":"conansherry/detectron2","owner":"conansherry","description":"detectron2 windows build","archived":false,"fork":false,"pushed_at":"2021-06-18T20:55:31.000Z","size":1829,"stargazers_count":224,"open_issues_count":24,"forks_count":55,"subscribers_count":9,"default_branch":"master","last_synced_at":"2024-10-28T07:42:00.159Z","etag":null,"topics":["conda","detectron2","visual-studio","vs","win","windows"],"latest_commit_sha":null,"homepage":"","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/conansherry.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":null,"codeowners":null,"security":null,"support":null}},"created_at":"2019-11-02T05:29:17.000Z","updated_at":"2024-10-18T11:55:44.000Z","dependencies_parsed_at":"2022-07-09T08:45:05.824Z","dependency_job_id":null,"html_url":"https://github.com/conansherry/detectron2","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/conansherry%2Fdetectron2","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/conansherry%2Fdetectron2/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/conansherry%2Fdetectron2/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/conansherry%2Fdetectron2/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/conansherry","download_url":"https://codeload.github.com/conansherry/detectron2/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":244660715,"owners_count":20489384,"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":["conda","detectron2","visual-studio","vs","win","windows"],"created_at":"2024-07-31T03:02:14.945Z","updated_at":"2025-03-20T17:32:26.566Z","avatar_url":"https://github.com/conansherry.png","language":"Python","funding_links":[],"categories":["Python"],"sub_categories":[],"readme":"### Requirements\n- Python \u003e= 3.6(Conda)\n- PyTorch 1.3\n- [torchvision](https://github.com/pytorch/vision/) that matches the PyTorch installation.\n\tYou can install them together at [pytorch.org](https://pytorch.org) to make sure of this.\n- OpenCV, needed by demo and visualization\n- [fvcore](https://github.com/facebookresearch/fvcore/): `pip install git+https://github.com/facebookresearch/fvcore`\n- pycocotools: `pip install cython; pip install git+https://github.com/philferriere/cocoapi.git#subdirectory=PythonAPI`\n- VS2019(no test in older version)/CUDA10.1(no test in older version)\n\n### several files must be changed by manually.\n```\nfile1: \n  {your evn path}\\Lib\\site-packages\\torch\\include\\torch\\csrc\\jit\\argument_spec.h\n  example:\n  {C:\\Miniconda3\\envs\\py36}\\Lib\\site-packages\\torch\\include\\torch\\csrc\\jit\\argument_spec.h(190)\n    static constexpr size_t DEPTH_LIMIT = 128;\n      change to --\u003e\n    static const size_t DEPTH_LIMIT = 128;\nfile2: \n  {your evn path}\\Lib\\site-packages\\torch\\include\\pybind11\\cast.h\n  example:\n  {C:\\Miniconda3\\envs\\py36}\\Lib\\site-packages\\torch\\include\\pybind11\\cast.h(1449)\n    explicit operator type\u0026() { return *(this-\u003evalue); }\n      change to --\u003e\n    explicit operator type\u0026() { return *((type*)this-\u003evalue); }\n```\n\n### Build detectron2\n\nAfter having the above dependencies, run:\n```\nconda activate {your env}\n\n\"C:\\Program Files (x86)\\Microsoft Visual Studio\\2019\\Enterprise\\VC\\Auxiliary\\Build\\vcvars64.bat\"\n\ngit clone https://github.com/conansherry/detectron2\n\ncd detectron2\n\npython setup.py build develop\n```\nNote: you may need to rebuild detectron2 after reinstalling a different build of PyTorch.\n\n\u003cdiv align=\"center\"\u003e\n  \u003cimg src=\"docs/windows_build.png\"/\u003e\n\u003c/div\u003e\n\n\u003cimg src=\".github/Detectron2-Logo-Horz.svg\" width=\"300\" \u003e\n\nDetectron2 is Facebook AI Research's next generation software system\nthat implements state-of-the-art object detection algorithms.\nIt is a ground-up rewrite of the previous version,\n[Detectron](https://github.com/facebookresearch/Detectron/),\nand it originates from [maskrcnn-benchmark](https://github.com/facebookresearch/maskrcnn-benchmark/).\n\n\u003cdiv align=\"center\"\u003e\n  \u003cimg src=\"https://user-images.githubusercontent.com/1381301/66535560-d3422200-eace-11e9-9123-5535d469db19.png\"/\u003e\n\u003c/div\u003e\n\n### What's New\n* It is powered by the [PyTorch](https://pytorch.org) deep learning framework.\n* Includes more features such as panoptic segmentation, densepose, Cascade R-CNN, rotated bounding boxes, etc.\n* Can be used as a library to support [different projects](projects/) on top of it.\n  We'll open source more research projects in this way.\n* It [trains much faster](https://detectron2.readthedocs.io/notes/benchmarks.html).\n\nSee our [blog post](https://ai.facebook.com/blog/-detectron2-a-pytorch-based-modular-object-detection-library-/)\nto see more demos and learn about detectron2.\n\n## Installation\n\nSee [INSTALL.md](INSTALL.md).\n\n## Quick Start\n\nSee [GETTING_STARTED.md](GETTING_STARTED.md),\nor the [Colab Notebook](https://colab.research.google.com/drive/16jcaJoc6bCFAQ96jDe2HwtXj7BMD_-m5).\n\nLearn more at our [documentation](https://detectron2.readthedocs.org).\nAnd see [projects/](projects/) for some projects that are built on top of detectron2.\n\n## Model Zoo and Baselines\n\nWe provide a large set of baseline results and trained models available for download in the [Detectron2 Model Zoo](MODEL_ZOO.md).\n\n\n## License\n\nDetectron2 is released under the [Apache 2.0 license](LICENSE).\n\n## Citing Detectron\n\nIf you use Detectron2 in your research or wish to refer to the baseline results published in the [Model Zoo](MODEL_ZOO.md), please use the following BibTeX entry.\n\n```BibTeX\n@misc{wu2019detectron2,\n  author =       {Yuxin Wu and Alexander Kirillov and Francisco Massa and\n                  Wan-Yen Lo and Ross Girshick},\n  title =        {Detectron2},\n  howpublished = {\\url{https://github.com/facebookresearch/detectron2}},\n  year =         {2019}\n}\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fconansherry%2Fdetectron2","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fconansherry%2Fdetectron2","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fconansherry%2Fdetectron2/lists"}