[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-1344":3},{"id":4,"name":5,"fullName":6,"owner":7,"repo":5,"description":8,"homepage":9,"htmlUrl":10,"language":11,"languages":10,"totalLinesOfCode":10,"stars":12,"forks":13,"watchers":14,"openIssues":15,"contributorsCount":16,"subscribersCount":16,"size":16,"stars1d":17,"stars7d":18,"stars30d":19,"stars90d":16,"forks30d":16,"starsTrendScore":20,"compositeScore":21,"rankGlobal":10,"rankLanguage":10,"license":22,"archived":23,"fork":23,"defaultBranch":24,"hasWiki":23,"hasPages":25,"topics":26,"createdAt":10,"pushedAt":10,"updatedAt":46,"readmeContent":47,"aiSummary":48,"trendingCount":16,"starSnapshotCount":16,"syncStatus":49,"lastSyncTime":50,"discoverSource":51},1344,"supervision","roboflow\u002Fsupervision","roboflow","We write your reusable computer vision tools. 💜","https:\u002F\u002Fsupervision.roboflow.com",null,"Python",43797,3891,238,48,0,339,3820,5319,2290,120,"MIT License",false,"develop",true,[27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45],"classification","coco","computer-vision","deep-learning","hacktoberfest","image-processing","instance-segmentation","low-code","machine-learning","metrics","object-detection","oriented-bounding-box","pascal-voc","python","pytorch","tensorflow","tracking","video-processing","yolo","2026-06-12 04:00:08","\u003Cdiv align=\"center\">\n  \u003Cp>\n    \u003Ca align=\"center\" href=\"\" target=\"https:\u002F\u002Fsupervision.roboflow.com\">\n      \u003Cimg\n        width=\"100%\"\n        src=\"https:\u002F\u002Fmedia.roboflow.com\u002Fopen-source\u002Fsupervision\u002Frf-supervision-banner.png?updatedAt=1678995927529\"\n      >\n    \u003C\u002Fa>\n  \u003C\u002Fp>\n\n\u003Cbr>\n\n[notebooks](https:\u002F\u002Fgithub.com\u002Froboflow\u002Fnotebooks) | [inference](https:\u002F\u002Fgithub.com\u002Froboflow\u002Finference) | [autodistill](https:\u002F\u002Fgithub.com\u002Fautodistill\u002Fautodistill) | [maestro](https:\u002F\u002Fgithub.com\u002Froboflow\u002Fmultimodal-maestro)\n\n\u003Cbr>\n\n[![version](https:\u002F\u002Fbadge.fury.io\u002Fpy\u002Fsupervision.svg)](https:\u002F\u002Fbadge.fury.io\u002Fpy\u002Fsupervision)\n[![downloads](https:\u002F\u002Fimg.shields.io\u002Fpypi\u002Fdm\u002Fsupervision)](https:\u002F\u002Fpypistats.org\u002Fpackages\u002Fsupervision)\n[![license](https:\u002F\u002Fimg.shields.io\u002Fpypi\u002Fl\u002Fsupervision)](LICENSE.md)\n[![python-version](https:\u002F\u002Fimg.shields.io\u002Fpypi\u002Fpyversions\u002Fsupervision)](https:\u002F\u002Fbadge.fury.io\u002Fpy\u002Fsupervision)\n[![codecov](https:\u002F\u002Fcodecov.io\u002Fgh\u002Froboflow\u002Fsupervision\u002Fgraph\u002Fbadge.svg?token=HMNJ5FVZ36)](https:\u002F\u002Fcodecov.io\u002Fgh\u002Froboflow\u002Fsupervision)\n\n[![snyk](https:\u002F\u002Fsnyk.io\u002Fadvisor\u002Fpython\u002Fsupervision\u002Fbadge.svg)](https:\u002F\u002Fsnyk.io\u002Fadvisor\u002Fpython\u002Fsupervision)\n[![colab](https:\u002F\u002Fcolab.research.google.com\u002Fassets\u002Fcolab-badge.svg)](https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Froboflow\u002Fsupervision\u002Fblob\u002Fmain\u002Fdemo.ipynb)\n[![gradio](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002F%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https:\u002F\u002Fhuggingface.co\u002Fspaces\u002FRoboflow\u002FAnnotators)\n[![discord](https:\u002F\u002Fimg.shields.io\u002Fdiscord\u002F1159501506232451173?logo=discord&label=discord&labelColor=fff&color=5865f2&link=https%3A%2F%2Fdiscord.gg%2FGbfgXGJ8Bk)](https:\u002F\u002Fdiscord.gg\u002FGbfgXGJ8Bk)\n\n\u003Cdiv align=\"center\">\n    \u003Ca href=\"https:\u002F\u002Ftrendshift.io\u002Frepositories\u002F124\"  target=\"_blank\">\u003Cimg src=\"https:\u002F\u002Ftrendshift.io\u002Fapi\u002Fbadge\u002Frepositories\u002F124\" alt=\"roboflow%2Fsupervision | Trendshift\" style=\"width: 250px; height: 55px;\" width=\"250\" height=\"55\"\u002F>\u003C\u002Fa>\n  \u003C\u002Fdiv>\n\n\u003C\u002Fdiv>\n\n## 👋 hello\n\n**We write your reusable computer vision tools.** Whether you need to load your dataset from your hard drive, draw detections on an image or video, or count how many detections are in a zone. You can count on us! 🤝\n\n## 💻 install\n\nPip install the supervision package in a\n[**Python>=3.9**](https:\u002F\u002Fwww.python.org\u002F) environment.\n\n```bash\npip install supervision\n```\n\nRead more about conda, mamba, and installing from source in our [guide](https:\u002F\u002Froboflow.github.io\u002Fsupervision\u002F).\n\n## 🔥 quickstart\n\n### models\n\nSupervision was designed to be model agnostic. Just plug in any classification, detection, or segmentation model. For your convenience, we have created [connectors](https:\u002F\u002Fsupervision.roboflow.com\u002Flatest\u002Fdetection\u002Fcore\u002F#detections) for the most popular libraries like Ultralytics, Transformers, MMDetection, or Inference. Other integrations, like `rfdetr`, already return `sv.Detections` directly.\n\nInstall the optional dependencies for this example with `pip install pillow rfdetr`.\n\n```python\nimport supervision as sv\nfrom PIL import Image\nfrom rfdetr import RFDETRSmall\n\nimage = Image.open(...)\nmodel = RFDETRSmall()\ndetections = model.predict(image, threshold=0.5)\n\nlen(detections)\n# 5\n```\n\n\u003Cdetails>\n\u003Csummary>👉 more model connectors\u003C\u002Fsummary>\n\n- inference\n\n    Running with [Inference](https:\u002F\u002Fgithub.com\u002Froboflow\u002Finference) requires a [Roboflow API KEY](https:\u002F\u002Fdocs.roboflow.com\u002Fapi-reference\u002Fauthentication#retrieve-an-api-key).\n\n    ```python\n    import supervision as sv\n    from PIL import Image\n    from inference import get_model\n\n    image = Image.open(...)\n    model = get_model(model_id=\"rfdetr-small\", api_key=\"ROBOFLOW_API_KEY\")\n    result = model.infer(image)[0]\n    detections = sv.Detections.from_inference(result)\n\n    len(detections)\n    # 5\n    ```\n\n\u003C\u002Fdetails>\n\n### annotators\n\nSupervision offers a wide range of highly customizable [annotators](https:\u002F\u002Fsupervision.roboflow.com\u002Flatest\u002Fdetection\u002Fannotators\u002F), allowing you to compose the perfect visualization for your use case.\n\n```python\nimport cv2\nimport supervision as sv\n\nimage = cv2.imread(...)\ndetections = sv.Detections(...)\n\nbox_annotator = sv.BoxAnnotator()\nannotated_frame = box_annotator.annotate(scene=image.copy(), detections=detections)\n```\n\nhttps:\u002F\u002Fgithub.com\u002Froboflow\u002Fsupervision\u002Fassets\u002F26109316\u002F691e219c-0565-4403-9218-ab5644f39bce\n\n### datasets\n\nSupervision provides a set of [utils](https:\u002F\u002Fsupervision.roboflow.com\u002Flatest\u002Fdatasets\u002Fcore\u002F) that allow you to load, split, merge, and save datasets in one of the supported formats.\n\n```python\nimport supervision as sv\nfrom roboflow import Roboflow\n\nproject = Roboflow().workspace(\"WORKSPACE_ID\").project(\"PROJECT_ID\")\ndataset = project.version(\"PROJECT_VERSION\").download(\"coco\")\n\nds = sv.DetectionDataset.from_coco(\n    images_directory_path=f\"{dataset.location}\u002Ftrain\",\n    annotations_path=f\"{dataset.location}\u002Ftrain\u002F_annotations.coco.json\",\n)\n\npath, image, annotation = ds[0]\n# loads image on demand\n\nfor path, image, annotation in ds:\n    # loads image on demand\n    pass\n```\n\n\u003Cdetails close>\n\u003Csummary>👉 more dataset utils\u003C\u002Fsummary>\n\n- load\n\n    ```python\n    dataset = sv.DetectionDataset.from_yolo(\n        images_directory_path=...,\n        annotations_directory_path=...,\n        data_yaml_path=...,\n    )\n\n    dataset = sv.DetectionDataset.from_pascal_voc(\n        images_directory_path=...,\n        annotations_directory_path=...,\n    )\n\n    dataset = sv.DetectionDataset.from_coco(\n        images_directory_path=...,\n        annotations_path=...,\n    )\n    ```\n\n- split\n\n    ```python\n    train_dataset, test_dataset = dataset.split(split_ratio=0.7)\n    test_dataset, valid_dataset = test_dataset.split(split_ratio=0.5)\n\n    len(train_dataset), len(test_dataset), len(valid_dataset)\n    # (700, 150, 150)\n    ```\n\n- merge\n\n    ```python\n    ds_1 = sv.DetectionDataset(...)\n    len(ds_1)\n    # 100\n    ds_1.classes\n    # ['dog', 'person']\n\n    ds_2 = sv.DetectionDataset(...)\n    len(ds_2)\n    # 200\n    ds_2.classes\n    # ['cat']\n\n    ds_merged = sv.DetectionDataset.merge([ds_1, ds_2])\n    len(ds_merged)\n    # 300\n    ds_merged.classes\n    # ['cat', 'dog', 'person']\n    ```\n\n- save\n\n    ```python\n    dataset.as_yolo(\n        images_directory_path=...,\n        annotations_directory_path=...,\n        data_yaml_path=...,\n    )\n\n    dataset.as_pascal_voc(\n        images_directory_path=...,\n        annotations_directory_path=...,\n    )\n\n    dataset.as_coco(\n        images_directory_path=...,\n        annotations_path=...,\n    )\n    ```\n\n- convert\n\n    ```python\n    sv.DetectionDataset.from_yolo(\n        images_directory_path=...,\n        annotations_directory_path=...,\n        data_yaml_path=...,\n    ).as_pascal_voc(\n        images_directory_path=...,\n        annotations_directory_path=...,\n    )\n    ```\n\n\u003C\u002Fdetails>\n\n## 🎬 tutorials\n\nWant to learn how to use Supervision? Explore our [how-to guides](https:\u002F\u002Fsupervision.roboflow.com\u002Fdevelop\u002Fhow_to\u002Fdetect_and_annotate\u002F), [end-to-end examples](.\u002Fexamples), [cheatsheet](https:\u002F\u002Froboflow.github.io\u002Fcheatsheet-supervision\u002F), and [cookbooks](https:\u002F\u002Fsupervision.roboflow.com\u002Fdevelop\u002Fcookbooks\u002F)!\n\n\u003Cbr\u002F>\n\n\u003Cp align=\"left\">\n\u003Ca href=\"https:\u002F\u002Fyoutu.be\u002FhAWpsIuem10\" title=\"Dwell Time Analysis with Computer Vision | Real-Time Stream Processing\">\u003Cimg src=\"https:\u002F\u002Fgithub.com\u002Fuser-attachments\u002Fassets\u002F014cffc7-72b3-4c0a-bb89-6de265b2c06b\" alt=\"Dwell Time Analysis with Computer Vision | Real-Time Stream Processing\" width=\"300px\" align=\"left\" \u002F>\u003C\u002Fa>\n\u003Ca href=\"https:\u002F\u002Fyoutu.be\u002FhAWpsIuem10\" title=\"Dwell Time Analysis with Computer Vision | Real-Time Stream Processing\">\u003Cstrong>Dwell Time Analysis with Computer Vision | Real-Time Stream Processing\u003C\u002Fstrong>\u003C\u002Fa>\n\u003Cdiv>\u003Cstrong>Created: 5 Apr 2024\u003C\u002Fstrong>\u003C\u002Fdiv>\n\u003Cbr\u002F>Learn how to use computer vision to analyze wait times and optimize processes. This tutorial covers object detection, tracking, and calculating time spent in designated zones. Use these techniques to improve customer experience in retail, traffic management, or other scenarios.\u003C\u002Fp>\n\n\u003Cbr\u002F>\n\n\u003Cp align=\"left\">\n\u003Ca href=\"https:\u002F\u002Fyoutu.be\u002FuWP6UjDeZvY\" title=\"Speed Estimation & Vehicle Tracking | Computer Vision | Open Source\">\u003Cimg src=\"https:\u002F\u002Fgithub.com\u002Fuser-attachments\u002Fassets\u002Fb16b8e21-dc6c-4a73-a678-2f7d5d374793\" alt=\"Speed Estimation & Vehicle Tracking | Computer Vision | Open Source\" width=\"300px\" align=\"left\" \u002F>\u003C\u002Fa>\n\u003Ca href=\"https:\u002F\u002Fyoutu.be\u002FuWP6UjDeZvY\" title=\"Speed Estimation & Vehicle Tracking | Computer Vision | Open Source\">\u003Cstrong>Speed Estimation & Vehicle Tracking | Computer Vision | Open Source\u003C\u002Fstrong>\u003C\u002Fa>\n\u003Cdiv>\u003Cstrong>Created: 11 Jan 2024\u003C\u002Fstrong>\u003C\u002Fdiv>\n\u003Cbr\u002F>Learn how to track and estimate the speed of vehicles using YOLO, ByteTrack, and Roboflow Inference. This comprehensive tutorial covers object detection, multi-object tracking, filtering detections, perspective transformation, speed estimation, visualization improvements, and more.\u003C\u002Fp>\n\n## 💜 built with supervision\n\nDid you build something cool using supervision? [Let us know!](https:\u002F\u002Fgithub.com\u002Froboflow\u002Fsupervision\u002Fdiscussions\u002Fcategories\u002Fbuilt-with-supervision)\n\nhttps:\u002F\u002Fuser-images.githubusercontent.com\u002F26109316\u002F207858600-ee862b22-0353-440b-ad85-caa0c4777904.mp4\n\nhttps:\u002F\u002Fgithub.com\u002Froboflow\u002Fsupervision\u002Fassets\u002F26109316\u002Fc9436828-9fbf-4c25-ae8c-60e9c81b3900\n\nhttps:\u002F\u002Fgithub.com\u002Froboflow\u002Fsupervision\u002Fassets\u002F26109316\u002F3ac6982f-4943-4108-9b7f-51787ef1a69f\n\n## 📚 documentation\n\nVisit our [documentation](https:\u002F\u002Froboflow.github.io\u002Fsupervision) page to learn how supervision can help you build computer vision applications faster and more reliably.\n\n## 🏆 contribution\n\nWe love your input! Please see our [contributing guide](.github\u002FCONTRIBUTING.md) to get started. Thank you 🙏 to all our contributors!\n\n\u003Cp align=\"center\">\n    \u003Ca href=\"https:\u002F\u002Fgithub.com\u002Froboflow\u002Fsupervision\u002Fgraphs\u002Fcontributors\">\n      \u003Cimg src=\"https:\u002F\u002Fcontrib.rocks\u002Fimage?repo=roboflow\u002Fsupervision\" \u002F>\n    \u003C\u002Fa>\n\u003C\u002Fp>\n\n\u003Cbr>\n\n\u003Cdiv align=\"center\">\n\n\u003Cdiv align=\"center\">\n      \u003Ca href=\"https:\u002F\u002Fyoutube.com\u002Froboflow\">\n          \u003Cimg\n            src=\"https:\u002F\u002Fmedia.roboflow.com\u002Fnotebooks\u002Ftemplate\u002Ficons\u002Fpurple\u002Fyoutube.png?ik-sdk-version=javascript-1.4.3&updatedAt=1672949634652\"\n            width=\"3%\"\n          \u002F>\n      \u003C\u002Fa>\n      \u003Cimg src=\"https:\u002F\u002Fraw.githubusercontent.com\u002Fultralytics\u002Fassets\u002Fmain\u002Fsocial\u002Flogo-transparent.png\" width=\"3%\"\u002F>\n      \u003Ca href=\"https:\u002F\u002Froboflow.com\">\n          \u003Cimg\n            src=\"https:\u002F\u002Fmedia.roboflow.com\u002Fnotebooks\u002Ftemplate\u002Ficons\u002Fpurple\u002Froboflow-app.png?ik-sdk-version=javascript-1.4.3&updatedAt=1672949746649\"\n            width=\"3%\"\n          \u002F>\n      \u003C\u002Fa>\n      \u003Cimg src=\"https:\u002F\u002Fraw.githubusercontent.com\u002Fultralytics\u002Fassets\u002Fmain\u002Fsocial\u002Flogo-transparent.png\" width=\"3%\"\u002F>\n      \u003Ca href=\"https:\u002F\u002Fwww.linkedin.com\u002Fcompany\u002Froboflow-ai\u002F\">\n          \u003Cimg\n            src=\"https:\u002F\u002Fmedia.roboflow.com\u002Fnotebooks\u002Ftemplate\u002Ficons\u002Fpurple\u002Flinkedin.png?ik-sdk-version=javascript-1.4.3&updatedAt=1672949633691\"\n            width=\"3%\"\n          \u002F>\n      \u003C\u002Fa>\n      \u003Cimg src=\"https:\u002F\u002Fraw.githubusercontent.com\u002Fultralytics\u002Fassets\u002Fmain\u002Fsocial\u002Flogo-transparent.png\" width=\"3%\"\u002F>\n      \u003Ca href=\"https:\u002F\u002Fdocs.roboflow.com\">\n          \u003Cimg\n            src=\"https:\u002F\u002Fmedia.roboflow.com\u002Fnotebooks\u002Ftemplate\u002Ficons\u002Fpurple\u002Fknowledge.png?ik-sdk-version=javascript-1.4.3&updatedAt=1672949634511\"\n            width=\"3%\"\n          \u002F>\n      \u003C\u002Fa>\n      \u003Cimg src=\"https:\u002F\u002Fraw.githubusercontent.com\u002Fultralytics\u002Fassets\u002Fmain\u002Fsocial\u002Flogo-transparent.png\" width=\"3%\"\u002F>\n      \u003Ca href=\"https:\u002F\u002Fdiscuss.roboflow.com\">\n          \u003Cimg\n            src=\"https:\u002F\u002Fmedia.roboflow.com\u002Fnotebooks\u002Ftemplate\u002Ficons\u002Fpurple\u002Fforum.png?ik-sdk-version=javascript-1.4.3&updatedAt=1672949633584\"\n            width=\"3%\"\n          \u002F>\n      \u003Cimg src=\"https:\u002F\u002Fraw.githubusercontent.com\u002Fultralytics\u002Fassets\u002Fmain\u002Fsocial\u002Flogo-transparent.png\" width=\"3%\"\u002F>\n      \u003Ca href=\"https:\u002F\u002Fblog.roboflow.com\">\n          \u003Cimg\n            src=\"https:\u002F\u002Fmedia.roboflow.com\u002Fnotebooks\u002Ftemplate\u002Ficons\u002Fpurple\u002Fblog.png?ik-sdk-version=javascript-1.4.3&updatedAt=1672949633605\"\n            width=\"3%\"\n          \u002F>\n      \u003C\u002Fa>\n      \u003C\u002Fa>\n  \u003C\u002Fdiv>\n\u003C\u002Fdiv>\n","roboflow\u002Fsupervision 是一个用于构建可复用计算机视觉工具的 Python 库。它支持多种核心功能，包括图像和视频中的目标检测、分类、实例分割等，并且可以与主流深度学习框架如 PyTorch 和 TensorFlow 无缝集成。此外，supervision 提供了丰富的数据集加载、可视化以及性能评估工具，使得开发者能够快速搭建并测试自己的计算机视觉应用。适合需要高效处理图像和视频数据、进行模型训练及部署的各种场景，无论是科研项目还是工业应用都能从中受益。",2,"2026-06-11 02:43:11","top_all"]