📑 Table of Contents
👋 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 environment.
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.
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
👉 more model connectors
-
inference
Running with Inference requires a Roboflow API KEY.
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
Annotators
Supervision offers a wide range of highly customizable annotators, allowing you to compose the perfect visualization for your use case.
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 that allow you to load, split, merge, and save datasets in one of the supported formats.
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
👉 more dataset utils
-
load
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
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
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
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
sv.DetectionDataset.from_yolo( images_directory_path=..., annotations_directory_path=..., data_yaml_path=..., ).as_pascal_voc( images_directory_path=..., annotations_directory_path=..., )
🎬 Tutorials
Want to learn how to use Supervision? Explore our how-to guides, end-to-end examples, cheatsheet, and cookbooks!
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.
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.
💜 Built with Supervision
Did you build something cool using supervision? Let us know!
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!
1 maintainer
Metadata
1 maintainer
Release files for supervision 0.31.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | |
|---|---|---|---|---|
| supervision-0.31.1.tar.gz | 426.1 kB | With provenance | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| supervision-0.31.1-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 895.2 kB
Release files / supervision-0.31.1.tar.gz
| Download URL | supervision-0.31.1.tar.gz |
|---|---|
| Size | 426.1 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
02bf7d4faf9cb8593ae159db6df01fb87e0b840e2912b0de109c58c1118ed97f
|
|
BLAKE2b-256 checksum How to use checksums |
e3713ab6c9d36ed74c1fd0e380e96f0b2ffa16b8550f4e5efd61256ee1c8c4a5
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/7.0.0 CPython/3.13.14
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Oct 20, 2026.
Transparency logRelease files / supervision-0.31.1-py3-none-any.whl
| Download URL | supervision-0.31.1-py3-none-any.whl |
|---|---|
| Size | 469.1 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
f9043e6423a87bb4630742b9281030817602ed08885fee38f8e2d17742509042
|
|
BLAKE2b-256 checksum How to use checksums |
d455dcbfcd1f2472480fcf776f9ef20f879a902d3b1e4c36f936e72f06b936b8
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/7.0.0 CPython/3.13.14
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Oct 20, 2026.
Transparency logSecurity for supervision 0.31.1
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
Full provenance
All 2 files in this release have been uploaded with provenance metadata.
Attestation for supervision-0.31.1.tar.gz
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Oct 20, 2026.
Transparency logA note on package security
PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.
