{"id":13529109,"url":"https://github.com/facebookresearch/Detectron","last_synced_at":"2025-04-01T15:30:34.600Z","repository":{"id":37907866,"uuid":"105919803","full_name":"facebookresearch/Detectron","owner":"facebookresearch","description":"FAIR's research platform for object detection research, implementing popular algorithms like Mask R-CNN and RetinaNet.","archived":true,"fork":false,"pushed_at":"2023-11-20T09:13:34.000Z","size":4525,"stargazers_count":26154,"open_issues_count":332,"forks_count":5451,"subscribers_count":942,"default_branch":"main","last_synced_at":"2024-05-23T04:37:00.727Z","etag":null,"topics":[],"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/facebookresearch.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":"CONTRIBUTING.md","funding":null,"license":"LICENSE","code_of_conduct":"CODE_OF_CONDUCT.md","threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null}},"created_at":"2017-10-05T17:32:00.000Z","updated_at":"2024-05-22T19:21:17.000Z","dependencies_parsed_at":"2024-01-06T13:08:25.414Z","dependency_job_id":"caefe747-8385-4148-842c-6fbec6c80444","html_url":"https://github.com/facebookresearch/Detectron","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/facebookresearch%2FDetectron","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/facebookresearch%2FDetectron/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/facebookresearch%2FDetectron/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/facebookresearch%2FDetectron/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/facebookresearch","download_url":"https://codeload.github.com/facebookresearch/Detectron/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":222735280,"owners_count":17030816,"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":[],"created_at":"2024-08-01T07:00:33.304Z","updated_at":"2024-11-02T15:32:03.451Z","avatar_url":"https://github.com/facebookresearch.png","language":"Python","readme":"**Detectron is deprecated. Please see [detectron2](https://github.com/facebookresearch/detectron2), a ground-up rewrite of Detectron in PyTorch.**\n\n# Detectron\n\nDetectron is Facebook AI Research's software system that implements state-of-the-art object detection algorithms, including [Mask R-CNN](https://arxiv.org/abs/1703.06870). It is written in Python and powered by the [Caffe2](https://github.com/caffe2/caffe2) deep learning framework.\n\nAt FAIR, Detectron has enabled numerous research projects, including: [Feature Pyramid Networks for Object Detection](https://arxiv.org/abs/1612.03144), [Mask R-CNN](https://arxiv.org/abs/1703.06870), [Detecting and Recognizing Human-Object Interactions](https://arxiv.org/abs/1704.07333), [Focal Loss for Dense Object Detection](https://arxiv.org/abs/1708.02002), [Non-local Neural Networks](https://arxiv.org/abs/1711.07971), [Learning to Segment Every Thing](https://arxiv.org/abs/1711.10370), [Data Distillation: Towards Omni-Supervised Learning](https://arxiv.org/abs/1712.04440), [DensePose: Dense Human Pose Estimation In The Wild](https://arxiv.org/abs/1802.00434), and [Group Normalization](https://arxiv.org/abs/1803.08494).\n\n\u003cdiv align=\"center\"\u003e\n  \u003cimg src=\"demo/output/33823288584_1d21cf0a26_k_example_output.jpg\" width=\"700px\" /\u003e\n  \u003cp\u003eExample Mask R-CNN output.\u003c/p\u003e\n\u003c/div\u003e\n\n## Introduction\n\nThe goal of Detectron is to provide a high-quality, high-performance\ncodebase for object detection *research*. It is designed to be flexible in order\nto support rapid implementation and evaluation of novel research. Detectron\nincludes implementations of the following object detection algorithms:\n\n- [Mask R-CNN](https://arxiv.org/abs/1703.06870) -- *Marr Prize at ICCV 2017*\n- [RetinaNet](https://arxiv.org/abs/1708.02002) -- *Best Student Paper Award at ICCV 2017*\n- [Faster R-CNN](https://arxiv.org/abs/1506.01497)\n- [RPN](https://arxiv.org/abs/1506.01497)\n- [Fast R-CNN](https://arxiv.org/abs/1504.08083)\n- [R-FCN](https://arxiv.org/abs/1605.06409)\n\nusing the following backbone network architectures:\n\n- [ResNeXt{50,101,152}](https://arxiv.org/abs/1611.05431)\n- [ResNet{50,101,152}](https://arxiv.org/abs/1512.03385)\n- [Feature Pyramid Networks](https://arxiv.org/abs/1612.03144) (with ResNet/ResNeXt)\n- [VGG16](https://arxiv.org/abs/1409.1556)\n\nAdditional backbone architectures may be easily implemented. For more details about these models, please see [References](#references) below.\n\n## Update\n\n- 4/2018: Support Group Normalization - see [`GN/README.md`](./projects/GN/README.md)\n\n## License\n\nDetectron is released under the [Apache 2.0 license](https://github.com/facebookresearch/detectron/blob/master/LICENSE). See the [NOTICE](https://github.com/facebookresearch/detectron/blob/master/NOTICE) file for additional details.\n\n## Citing Detectron\n\nIf you use Detectron 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```\n@misc{Detectron2018,\n  author =       {Ross Girshick and Ilija Radosavovic and Georgia Gkioxari and\n                  Piotr Doll\\'{a}r and Kaiming He},\n  title =        {Detectron},\n  howpublished = {\\url{https://github.com/facebookresearch/detectron}},\n  year =         {2018}\n}\n```\n\n## Model Zoo and Baselines\n\nWe provide a large set of baseline results and trained models available for download in the [Detectron Model Zoo](MODEL_ZOO.md).\n\n## Installation\n\nPlease find installation instructions for Caffe2 and Detectron in [`INSTALL.md`](INSTALL.md).\n\n## Quick Start: Using Detectron\n\nAfter installation, please see [`GETTING_STARTED.md`](GETTING_STARTED.md) for brief tutorials covering inference and training with Detectron.\n\n## Getting Help\n\nTo start, please check the [troubleshooting](INSTALL.md#troubleshooting) section of our installation instructions as well as our [FAQ](FAQ.md). If you couldn't find help there, try searching our GitHub issues. We intend the issues page to be a forum in which the community collectively troubleshoots problems.\n\nIf bugs are found, **we appreciate pull requests** (including adding Q\u0026A's to `FAQ.md` and improving our installation instructions and troubleshooting documents). Please see [CONTRIBUTING.md](CONTRIBUTING.md) for more information about contributing to Detectron.\n\n## References\n\n- [Data Distillation: Towards Omni-Supervised Learning](https://arxiv.org/abs/1712.04440).\n  Ilija Radosavovic, Piotr Dollár, Ross Girshick, Georgia Gkioxari, and Kaiming He.\n  Tech report, arXiv, Dec. 2017.\n- [Learning to Segment Every Thing](https://arxiv.org/abs/1711.10370).\n  Ronghang Hu, Piotr Dollár, Kaiming He, Trevor Darrell, and Ross Girshick.\n  Tech report, arXiv, Nov. 2017.\n- [Non-Local Neural Networks](https://arxiv.org/abs/1711.07971).\n  Xiaolong Wang, Ross Girshick, Abhinav Gupta, and Kaiming He.\n  Tech report, arXiv, Nov. 2017.\n- [Mask R-CNN](https://arxiv.org/abs/1703.06870).\n  Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross Girshick.\n  IEEE International Conference on Computer Vision (ICCV), 2017.\n- [Focal Loss for Dense Object Detection](https://arxiv.org/abs/1708.02002).\n  Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dollár.\n  IEEE International Conference on Computer Vision (ICCV), 2017.\n- [Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour](https://arxiv.org/abs/1706.02677).\n  Priya Goyal, Piotr Dollár, Ross Girshick, Pieter Noordhuis, Lukasz Wesolowski, Aapo Kyrola, Andrew Tulloch, Yangqing Jia, and Kaiming He.\n  Tech report, arXiv, June 2017.\n- [Detecting and Recognizing Human-Object Interactions](https://arxiv.org/abs/1704.07333).\n  Georgia Gkioxari, Ross Girshick, Piotr Dollár, and Kaiming He.\n  Tech report, arXiv, Apr. 2017.\n- [Feature Pyramid Networks for Object Detection](https://arxiv.org/abs/1612.03144).\n  Tsung-Yi Lin, Piotr Dollár, Ross Girshick, Kaiming He, Bharath Hariharan, and Serge Belongie.\n  IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.\n- [Aggregated Residual Transformations for Deep Neural Networks](https://arxiv.org/abs/1611.05431).\n  Saining Xie, Ross Girshick, Piotr Dollár, Zhuowen Tu, and Kaiming He.\n  IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.\n- [R-FCN: Object Detection via Region-based Fully Convolutional Networks](http://arxiv.org/abs/1605.06409).\n  Jifeng Dai, Yi Li, Kaiming He, and Jian Sun.\n  Conference on Neural Information Processing Systems (NIPS), 2016.\n- [Deep Residual Learning for Image Recognition](http://arxiv.org/abs/1512.03385).\n  Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun.\n  IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.\n- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](http://arxiv.org/abs/1506.01497)\n  Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun.\n  Conference on Neural Information Processing Systems (NIPS), 2015.\n- [Fast R-CNN](http://arxiv.org/abs/1504.08083).\n  Ross Girshick.\n  IEEE International Conference on Computer Vision (ICCV), 2015.\n","funding_links":[],"categories":["Neural Networks (NN) and Deep Neural Networks (DNN)","Python","Object Recognition","Other","📦 Legacy \u0026 Inactive Projects","对象检测、分割","Appendix: Object Detection for Natural Scene"],"sub_categories":["NN/DNN Models","Object Detection","网络服务_其他","General-Purpose Machine Learning","Papers"],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffacebookresearch%2FDetectron","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ffacebookresearch%2FDetectron","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffacebookresearch%2FDetectron/lists"}