roboflow/supervision

Dernière activité source enregistrée: 13 octobre 2026 à 16:07

Catalogue SkillsMP mis à jour: 5 novembre 2026 à 19:57

skills collectés: 1

Étoiles GitHub: 51 501
Forks GitHub: 4 916

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<div align="center"> <p> <a align="center" href="https://supervision.roboflow.com" target="_blank"> <img width="100%" src="https://media.roboflow.com/open-source/supervision/rf-supervision-banner.png?updatedAt=1678995927529" > </a> </p> <br> [notebooks](https://github.com/roboflow/notebooks) | [inference](https://github.com/roboflow/inference) | [autodistill](https://github.com/autodistill/autodistill) | [maestro](https://github.com/roboflow/multimodal-maestro) <br> [![version](https://badge.fury.io/py/supervision.svg)](https://badge.fury.io/py/supervision) [![downloads](https://img.shields.io/pypi/dm/supervision)](https://pypistats.org/packages/supervision) [![license](https://img.shields.io/pypi/l/supervision)](LICENSE.md) [![python-version](https://img.shields.io/pypi/pyversions/supervision)](https://badge.fury.io/py/supervision) [![codecov](https://codecov.io/gh/roboflow/supervision/graph/badge.svg?token=HMNJ5FVZ36)](https://codecov.io/gh/roboflow/supervision) [![Snyk Security](https://img.shields.io/badge/Snyk-Security-4C4A73?logo=snyk)](https://security.snyk.io/package/pip/supervision) [![colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/roboflow/supervision/blob/main/demo.ipynb) [![gradio](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/Roboflow/Annotators) [![discord](https://img.shields.io/discord/1159501506232451173?logo=discord&label=discord&labelColor=fff&color=5865f2&link=https%3A%2F%2Fdiscord.gg%2FGbfgXGJ8Bk)](https://discord.gg/GbfgXGJ8Bk) <div align="center"> <a href="https://trendshift.io/repositories/124" target="_blank"><img src="https://trendshift.io/api/badge/repositories/124" alt="roboflow%2Fsupervision | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a> </div> </div> <details> <summary><strong>📑 Table of Contents</strong></summary> - [👋 Hello](#-hello) - [💻 Install](#-install) - [🔥 Quickstart](#-quickstart) - [Models](#models) - [Annotators](#annotators) - [Datasets](#datasets) - [🎬 Tutorials](#-tutorials) - [💜 Built with Supervision](#-built-with-supervision) - [📚 Documentation](#-documentation) - [🏆 Contribution](#-contribution) </details> ## 👋 Hello **We are your essential toolkit for computer vision.** From data loading to real-time zone counting, we provide the building blocks so you can focus on building applications around your models. 🤝 ## 💻 Install Pip install the supervision package in a [**Python>=3.10**](https://www.python.org/) environment. ```bash pip install supervision ``` Read more about conda, mamba, and installing from source in our [guide](https://supervision.roboflow.com/). ## 🔥 Quickstart ### Models Supervision was designed to be model agnostic. Just plug in any classification, detection, or segmentation model. For your convenience, we have created [connectors](https://supervision.roboflow.com/latest/detection/core/#detections) for the most popular libraries like Ultralytics, Transformers, MMDetection, or Inference. Other integrations, like `rfdetr`, already return `sv.Detections` directly. Install the optional dependencies for this example with `pip install pillow rfdetr`. ```python import supervision as sv from PIL import Image from rfdetr import RFDETRSmall image = Image.open("path/to/image.jpg") model = RFDETRSmall() detections = model.predict(image, threshold=0.5) len(detections) # 5 ``` <details> <summary>👉 more model connectors</summary> - inference Running with [Inference](https://github.com/roboflow/inference) requires a [Roboflow API KEY](https://docs.roboflow.com/api-reference/authentication#retrieve-an-api-key). ```python import supervision as sv from PIL import Image from inference import get_model image = Image.open("path/to/image.jpg") model = get_model(model_id="rfdetr-small", api_key="ROBOFLOW_API_KEY") result = model.infer(image)[0] detections = sv.Detections.from_inference(result) len(detections) # 5 ``` </details> ### Annotators Supervision offers a wide range of highly customizable [annotators](https://supervision.roboflow.com/latest/detection/annotators/), allowing you to compose the perfect visualization for your use case. ```python import cv2 import supervision as sv image = cv2.imread("path/to/image.jpg") # Assuming detections are obtained from a model detections = sv.Detections(...) box_annotator = sv.BoxAnnotator() annotated_frame = box_annotator.annotate(scene=image.copy(), detections=detections) ``` https://github.com/roboflow/supervision/assets/26109316/691e219c-0565-4403-9218-ab5644f39bce ### Datasets Supervision provides a set of [utils](https://supervision.roboflow.com/latest/datasets/core/) that allow you to load, split, merge, and save datasets in one of the supported formats. ```python import supervision as sv from roboflow import Roboflow project = Roboflow().workspace("WORKSPACE_ID").project("PROJECT_ID") dataset = project.version("PROJECT_VERSION").download("coco") ds = sv.DetectionDataset.from_coco( images_directory_path=f"{dataset.location}/train", annotations_path=f"{dataset.location}/train/_annotations.coco.json", ) path, image, annotation = ds[0] # loads image on demand for path, image, annotation in ds: # loads image on demand pass ``` <details> <summary>👉 more dataset utils</summary> - load ```python dataset = sv.DetectionDataset.from_yolo( images_directory_path=..., annotations_directory_path=..., data_yaml_path=..., ) dataset = sv.DetectionDataset.from_pascal_voc( images_directory_path=..., annotations_directory_path=..., ) dataset = sv.DetectionDataset.from_coco( images_directory_path=..., annotations_path=..., ) ``` - split ```python train_dataset, test_dataset = dataset.split(split_ratio=0.7) test_dataset, valid_dataset = test_dataset.split(split_ratio=0.5) len(train_dataset), len(test_dataset), len(valid_dataset) # (700, 150, 150) ``` - merge ```python ds_1 = sv.DetectionDataset(...) len(ds_1) # 100 ds_1.classes # ['dog', 'person'] ds_2 = sv.DetectionDataset(...) len(ds_2) # 200 ds_2.classes # ['cat'] ds_merged = sv.DetectionDataset.merge([ds_1, ds_2]) len(ds_merged) # 300 ds_merged.classes # ['cat', 'dog', 'person'] ``` - save ```python dataset.as_yolo( images_directory_path=..., annotations_directory_path=..., data_yaml_path=..., ) dataset.as_pascal_voc( images_directory_path=..., annotations_directory_path=..., ) dataset.as_coco( images_directory_path=..., annotations_path=..., ) ``` - convert ```python sv.DetectionDataset.from_yolo( images_directory_path=..., annotations_directory_path=..., data_yaml_path=..., ).as_pascal_voc( images_directory_path=..., annotations_directory_path=..., ) ``` </details> ## 🎬 Tutorials Want to learn how to use Supervision? Explore our [how-to guides](https://supervision.roboflow.com/develop/how_to/detect_and_annotate/), [end-to-end examples](./examples), [cheatsheet](https://roboflow.github.io/cheatsheet-supervision/), and [cookbooks](https://supervision.roboflow.com/develop/cookbooks/)! <br/> <p align="left"> <a href="https://youtu.be/hAWpsIuem10" title="Dwell Time Analysis with Computer Vision | Real-Time Stream Processing"><img src="https://github.com/user-attachments/assets/014cffc7-72b3-4c0a-bb89-6de265b2c06b" alt="Dwell Time Analysis with Computer Vision | Real-Time Stream Processing" width="300px" align="left" /></a> <a href="https://youtu.be/hAWpsIuem10" title="Dwell Time Analysis with Computer Vision | Real-Time Stream Processing"><strong>Dwell Time Analysis with Computer Vision | Real-Time Stream Processing</strong></a> <div><strong>Created: 5 Apr 2024</strong></div> <br/>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.</p> <br/> <p align="left"> <a href="https://youtu.be/uWP6UjDeZvY" title="Speed Estimation & Vehicle Tracking | Computer Vision | Open Source"><img src="https://github.com/user-attachments/assets/b16b8e21-dc6c-4a73-a678-2f7d5d374793" alt="Speed Estimation & Vehicle Tracking | Computer Vision | Open Source" width="300px" align="left" /></a> <a href="https://youtu.be/uWP6UjDeZvY" title="Speed Estimation & Vehicle Tracking | Computer Vision | Open Source"><strong>Speed Estimation & Vehicle Tracking | Computer Vision | Open Source</strong></a> <div><strong>Created: 11 Jan 2024</strong></div> <br/>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.</p> ## 💜 Built with Supervision Did you build something cool using supervision? [Let us know!](https://github.com/roboflow/supervision/discussions/categories/built-with-supervision) https://user-images.githubusercontent.com/26109316/207858600-ee862b22-0353-440b-ad85-caa0c4777904.mp4 https://github.com/roboflow/supervision/assets/26109316/c9436828-9fbf-4c25-ae8c-60e9c81b3900 https://github.com/roboflow/supervision/assets/26109316/3ac6982f-4943-4108-9b7f-51787ef1a69f ## 📚 Documentation Visit our [documentation](https://supervision.roboflow.com/) page to learn how supervision can help you build computer vision applications faster and more reliably. ## 🏆 Contribution We love your input! Please see our [contributing guide](.github/CONTRIBUTING.md) to get started. Thank you 🙏 to all our contributors! <p align="center"> <a href="https://github.com/roboflow/supervision/graphs/contributors"> <img src="https://contrib.rocks/image?repo=roboflow/supervision" /> </a> </p> <br> <div align="center"> <a href="https://youtube.com/roboflow"> <img src="https://media.roboflow.com/notebooks/template/icons/purple/youtube.png?ik-sdk-version=javascript-1.4.3&updatedAt=1672949634652" width="3%" /> </a> <img src="https://raw.githubusercontent.com/ultralytics/assets/main/social/logo-transparent.png" width="3%"/> <a href="https://roboflow.com"> <img src="https://media.roboflow.com/notebooks/template/icons/purple/roboflow-app.png?ik-sdk-version=javascript-1.4.3&updatedAt=1672949746649" width="3%" /> </a> <img src="https://raw.githubusercontent.com/ultralytics/assets/main/social/logo-transparent.png" width="3%"/> <a href="https://www.linkedin.com/company/roboflow-ai/"> <img src="https://media.roboflow.com/notebooks/template/icons/purple/linkedin.png?ik-sdk-version=javascript-1.4.3&updatedAt=1672949633691" width="3%" /> </a> <img src="https://raw.githubusercontent.com/ultralytics/assets/main/social/logo-transparent.png" width="3%"/> <a href="https://docs.roboflow.com"> <img src="https://media.roboflow.com/notebooks/template/icons/purple/knowledge.png?ik-sdk-version=javascript-1.4.3&updatedAt=1672949634511" width="3%" /> </a> <img src="https://raw.githubusercontent.com/ultralytics/assets/main/social/logo-transparent.png" width="3%"/> <a href="https://discuss.roboflow.com"> <img src="https://media.roboflow.com/notebooks/template/icons/purple/forum.png?ik-sdk-version=javascript-1.4.3&updatedAt=1672949633584" width="3%" /> </a> <img src="https://raw.githubusercontent.com/ultralytics/assets/main/social/logo-transparent.png" width="3%"/> <a href="https://blog.roboflow.com"> <img src="https://media.roboflow.com/notebooks/template/icons/purple/blog.png?ik-sdk-version=javascript-1.4.3&updatedAt=1672949633605" width="3%" /> </a> </div>

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2026-10-13

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