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<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://roboflow.github.io/supervision/).

## 🔥 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://roboflow.github.io/supervision) 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>

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