supervision

We write your reusable computer vision tools. 💜

49,343 stars Python Markdown Skills API Spec #classification#coco#computer-vision#deep-learning
AI Prompts & Specs

Repository: roboflow/supervision


Stars: 38110

CLAUDE.md

Claude Code Project Instructions

<!-- Imports AGENTS.md, which contains agent roles, behavioral rules, and coding constraints for this project. -->

@AGENTS.md


README.md

<div align="center">
<p>
<a align="center" href="" target="https://supervision.roboflow.com">
<img
width="100%"
src="https://media.roboflow.com/open-source/supervision/rf-supervision-banner.png?updatedAt=1678995927529"
>
</a>
</p>

<br>

notebooks | inference | autodistill | maestro

<br>

![version](https://badge.fury.io/py/supervision)
![downloads](https://pypistats.org/packages/supervision)
![license](LICENSE.md)
![python-version](https://badge.fury.io/py/supervision)
![codecov](https://codecov.io/gh/roboflow/supervision)

![snyk](https://snyk.io/advisor/python/supervision)
![colab](https://colab.research.google.com/github/roboflow/supervision/blob/main/demo.ipynb)
![gradio](https://huggingface.co/spaces/Roboflow/Annotators)
![discord](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>

👋 hello

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! 🤝

💻 install

Pip install the supervision package in a
Python>=3.9 environment.

bash
pip install supervision

Read more about conda, mamba, and installing from source in our guide.

🔥 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 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(...)
model = RFDETRSmall()
detections = model.predict(image, threshold=0.5)
len(detections)
5

<details>
<summary>👉 more model connectors</summary>

- inference

Running with Inference requires a Roboflow API KEY.

python
import supervision as sv
    from PIL import Image
    from inference import get_model
    image = Image.open(...)
    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, allowing you to compose the perfect visualization for your use case.

python
import cv2
import supervision as sv
image = cv2.imread(...)
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 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 close>
<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, end-to-end examples, cheatsheet, and 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/SkalskiP/SkalskiP/assets/26109316/a742823d-c158-407d-b30f-063a5d11b4e1" 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/SkalskiP/SkalskiP/assets/26109316/61a444c8-b135-48ce-b979-2a5ab47c5a91" 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://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 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 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">

<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%"
/>
<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>
</a>
</div>
</div>