supervision Documentation

File: README.md

📑 Table of Contents

👋 Hello
💻 Install
🔥 Quickstart
🎬 Tutorials
💜 Built with Supervision
📚 Documentation
🏆 Contribution

👋 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.

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("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.

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

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("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.

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

👉 more dataset utils

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=...,
)

🎬 Tutorials

Want to learn how to use Supervision? Explore our how-to guides, end-to-end examples, cheatsheet, and cookbooks!

Dwell Time Analysis with Computer Vision | Real-Time Stream Processing
Created: 5 Apr 2024
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.

Speed Estimation & Vehicle Tracking | Computer Vision | Open Source
Created: 11 Jan 2024
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://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!

File: docs/index.md

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👋 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

You can install supervision in a
Python>=3.9 environment.

!!! example "Installation"

text

=== "pip (recommended)"
    [](https://badge.fury.io/py/supervision)
    [](https://pypistats.org/packages/supervision)
    [](https://github.com/roboflow/supervision/blob/main/LICENSE.md)
    [](https://badge.fury.io/py/supervision)

    ```bash
    pip install supervision
    ```

=== "poetry"
    [](https://badge.fury.io/py/supervision)
    [](https://pypistats.org/packages/supervision)
    [](https://github.com/roboflow/supervision/blob/main/LICENSE.md)
    [](https://badge.fury.io/py/supervision)

    ```bash
    poetry add supervision
    ```

=== "uv"
    [](https://badge.fury.io/py/supervision)
    [](https://pypistats.org/packages/supervision)
    [](https://github.com/roboflow/supervision/blob/main/LICENSE.md)
    [](https://badge.fury.io/py/supervision)

    ```bash
    uv pip install supervision
    ```

    For uv projects:

    ```bash
    uv add supervision
    ```

=== "rye"
    [](https://badge.fury.io/py/supervision)
    [](https://pypistats.org/packages/supervision)
    [](https://github.com/roboflow/supervision/blob/main/LICENSE.md)
    [](https://badge.fury.io/py/supervision)

    ```bash
    rye add supervision
    ```

!!! example "conda/mamba install"
=== "conda"

text

```bash
    conda install -c conda-forge supervision
    ```

=== "mamba"
    [](https://anaconda.org/conda-forge/supervision) [](https://anaconda.org/conda-forge/supervision) [](https://anaconda.org/conda-forge/supervision) [](https://anaconda.org/conda-forge/supervision)

    ```bash
    mamba install -c conda-forge supervision
    ```

!!! example "git clone (for development)"
=== "virtualenv"
```bash
# clone repository and navigate to root directory
git clone --depth 1 -b develop https://github.com/roboflow/supervision.git
cd supervision

text

# setup python environment and activate it
    python3 -m venv venv
    source venv/bin/activate
    pip install --upgrade pip

    # installation
    pip install -e "."
    ```

=== "uv"
    ```bash
    # clone repository and navigate to root directory
    git clone --depth 1 -b develop https://github.com/roboflow/supervision.git
    cd supervision

    # setup python environment and activate it
    uv venv
    source .venv/bin/activate

    # installation
    uv pip install -r pyproject.toml -e . --all-extras

    ```

🚀 Quickstart

Detect and Annotate

Annotate predictions from a range of object detection and segmentation models

:octicons-arrow-right-24: Tutorial
Track Objects

Discover how to enhance video analysis by implementing seamless object tracking

:octicons-arrow-right-24: Tutorial
Detect Small Objects

Learn how to detect small objects in images

:octicons-arrow-right-24: Tutorial
Count Objects Crossing Line

Explore methods to accurately count and analyze objects crossing a predefined line

:octicons-arrow-right-24: Notebook
Filter Objects in Zone

Master the techniques to selectively filter and focus on objects within a specific zone
Cheatsheet

Access a quick reference guide to the most common supervision functions

:octicons-arrow-right-24: Cheatsheet

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Explore Related Technologies & Topics

File: README.md

👋 Hello

💻 Install

🔥 Quickstart

Models

Annotators

Datasets

🎬 Tutorials

💜 Built with Supervision

📚 Documentation

🏆 Contribution

File: docs/index.md

👋 Hello

💻 Install

🚀 Quickstart