304 lines
11 KiB
Markdown
304 lines
11 KiB
Markdown
<div align="center">
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<p>
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<a align="center" href="" target="_blank">
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<img
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width="100%"
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src="https://media.roboflow.com/open-source/supervision/rf-supervision-banner.png?updatedAt=1678995927529"
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>
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</a>
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</p>
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<br>
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[notebooks](https://github.com/roboflow/notebooks) | [inference](https://github.com/roboflow/inference) | [autodistill](https://github.com/autodistill/autodistill) | [collect](https://github.com/roboflow/roboflow-collect)
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<br>
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[](https://badge.fury.io/py/supervision)
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[](https://pypistats.org/packages/supervision)
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[](https://github.com/roboflow/supervision/blob/main/LICENSE.md)
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[](https://badge.fury.io/py/supervision)
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[](https://colab.research.google.com/github/roboflow/supervision/blob/main/demo.ipynb)
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[](https://huggingface.co/spaces/Roboflow/Annotators)
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[](https://discord.gg/GbfgXGJ8Bk)
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[](https://squidfunk.github.io/mkdocs-material/)
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</div>
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## 👋 hello
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**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! 🤝
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## 💻 install
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Pip install the supervision package in a
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[**Python>=3.8**](https://www.python.org/) environment.
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```bash
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pip install supervision
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```
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Read more about desktop, headless, and local installation in our [guide](https://roboflow.github.io/supervision/).
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## 🔥 quickstart
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### models
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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, or MMDetection.
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```python
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import cv2
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import supervision as sv
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from ultralytics import YOLO
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image = cv2.imread(...)
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model = YOLO('yolov8s.pt')
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result = model(image)[0]
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detections = sv.Detections.from_ultralytics(result)
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len(detections)
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# 5
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```
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<details>
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<summary>👉 more model connectors</summary>
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- inference
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Running with [Inference](https://github.com/roboflow/inference) requires a [Roboflow API KEY](https://docs.roboflow.com/api-reference/authentication#retrieve-an-api-key).
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```python
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import cv2
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import supervision as sv
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from inference.models.utils import get_roboflow_model
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image = cv2.imread(...)
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model = get_roboflow_model(model_id="yolov8s-640", api_key=<ROBOFLOW API KEY>)
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result = model.infer(image)[0]
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detections = sv.Detections.from_inference(result)
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len(detections)
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# 5
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```
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</details>
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### annotators
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Supervision offers a wide range of highly customizable [annotators](https://supervision.roboflow.com/latest/annotators/), allowing you to compose the perfect visualization for your use case.
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```python
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import cv2
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import supervision as sv
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image = cv2.imread(...)
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detections = sv.Detections(...)
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bounding_box_annotator = sv.BoundingBoxAnnotator()
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annotated_frame = bounding_box_annotator.annotate(
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scene=image.copy(),
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detections=detections
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)
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```
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https://github.com/roboflow/supervision/assets/26109316/691e219c-0565-4403-9218-ab5644f39bce
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### datasets
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Supervision provides a set of [utils](https://supervision.roboflow.com/latest/datasets/) that allow you to load, split, merge, and save datasets in one of the supported formats.
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```python
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import supervision as sv
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dataset = sv.DetectionDataset.from_yolo(
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images_directory_path=...,
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annotations_directory_path=...,
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data_yaml_path=...
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)
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dataset.classes
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['dog', 'person']
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len(dataset)
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# 1000
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```
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<details close>
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<summary>👉 more dataset utils</summary>
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- load
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```python
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dataset = sv.DetectionDataset.from_yolo(
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images_directory_path=...,
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annotations_directory_path=...,
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data_yaml_path=...
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)
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dataset = sv.DetectionDataset.from_pascal_voc(
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images_directory_path=...,
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annotations_directory_path=...
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)
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dataset = sv.DetectionDataset.from_coco(
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images_directory_path=...,
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annotations_path=...
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)
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```
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- split
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```python
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train_dataset, test_dataset = dataset.split(split_ratio=0.7)
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test_dataset, valid_dataset = test_dataset.split(split_ratio=0.5)
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len(train_dataset), len(test_dataset), len(valid_dataset)
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# (700, 150, 150)
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```
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- merge
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```python
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ds_1 = sv.DetectionDataset(...)
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len(ds_1)
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# 100
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ds_1.classes
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# ['dog', 'person']
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ds_2 = sv.DetectionDataset(...)
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len(ds_2)
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# 200
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ds_2.classes
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# ['cat']
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ds_merged = sv.DetectionDataset.merge([ds_1, ds_2])
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len(ds_merged)
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# 300
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ds_merged.classes
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# ['cat', 'dog', 'person']
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```
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- save
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```python
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dataset.as_yolo(
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images_directory_path=...,
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annotations_directory_path=...,
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data_yaml_path=...
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)
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dataset.as_pascal_voc(
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images_directory_path=...,
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annotations_directory_path=...
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)
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dataset.as_coco(
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images_directory_path=...,
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annotations_path=...
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)
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```
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- convert
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```python
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sv.DetectionDataset.from_yolo(
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images_directory_path=...,
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annotations_directory_path=...,
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data_yaml_path=...
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).as_pascal_voc(
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images_directory_path=...,
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annotations_directory_path=...
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)
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```
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</details>
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## 🎬 tutorials
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<p align="left">
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<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>
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<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>
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<div><strong>Created: 11 Jan 2024</strong> | <strong>Updated: 11 Jan 2024</strong></div>
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<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>
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<br/>
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<p align="left">
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<a href="https://youtu.be/4Q3ut7vqD5o" title="Traffic Analysis with YOLOv8 and ByteTrack - Vehicle Detection and Tracking"><img src="https://github.com/roboflow/supervision/assets/26109316/54afdf1c-218c-4451-8f12-627fb85f1682" alt="Traffic Analysis with YOLOv8 and ByteTrack - Vehicle Detection and Tracking" width="300px" align="left" /></a>
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<a href="https://youtu.be/4Q3ut7vqD5o" title="Traffic Analysis with YOLOv8 and ByteTrack - Vehicle Detection and Tracking"><strong>Traffic Analysis with YOLOv8 and ByteTrack - Vehicle Detection and Tracking</strong></a>
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<div><strong>Created: 6 Sep 2023</strong> | <strong>Updated: 6 Sep 2023</strong></div>
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<br/> In this video, we explore real-time traffic analysis using YOLOv8 and ByteTrack to detect and track vehicles on aerial images. Harnessing the power of Python and Supervision, we delve deep into assigning cars to specific entry zones and understanding their direction of movement. By visualizing their paths, we gain insights into traffic flow across bustling roundabouts... </p>
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## 💜 built with supervision
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Did you build something cool using supervision? [Let us know!](https://github.com/roboflow/supervision/discussions/categories/built-with-supervision)
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https://user-images.githubusercontent.com/26109316/207858600-ee862b22-0353-440b-ad85-caa0c4777904.mp4
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https://github.com/roboflow/supervision/assets/26109316/c9436828-9fbf-4c25-ae8c-60e9c81b3900
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https://github.com/roboflow/supervision/assets/26109316/3ac6982f-4943-4108-9b7f-51787ef1a69f
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## 📚 documentation
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Visit our [documentation](https://roboflow.github.io/supervision) page to learn how supervision can help you build computer vision applications faster and more reliably.
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## 🏆 contribution
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We love your input! Please see our [contributing guide](https://github.com/roboflow/supervision/blob/main/CONTRIBUTING.md) to get started. Thank you 🙏 to all our contributors!
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<p align="center">
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<a href="https://github.com/roboflow/supervision/graphs/contributors">
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<img src="https://contrib.rocks/image?repo=roboflow/supervision" />
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</a>
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</p>
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<br>
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<div align="center">
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<div align="center">
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<a href="https://youtube.com/roboflow">
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<img
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src="https://media.roboflow.com/notebooks/template/icons/purple/youtube.png?ik-sdk-version=javascript-1.4.3&updatedAt=1672949634652"
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width="3%"
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/>
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</a>
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<img src="https://raw.githubusercontent.com/ultralytics/assets/main/social/logo-transparent.png" width="3%"/>
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<a href="https://roboflow.com">
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<img
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src="https://media.roboflow.com/notebooks/template/icons/purple/roboflow-app.png?ik-sdk-version=javascript-1.4.3&updatedAt=1672949746649"
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width="3%"
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/>
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</a>
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<img src="https://raw.githubusercontent.com/ultralytics/assets/main/social/logo-transparent.png" width="3%"/>
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<a href="https://www.linkedin.com/company/roboflow-ai/">
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<img
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src="https://media.roboflow.com/notebooks/template/icons/purple/linkedin.png?ik-sdk-version=javascript-1.4.3&updatedAt=1672949633691"
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width="3%"
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/>
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</a>
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<img src="https://raw.githubusercontent.com/ultralytics/assets/main/social/logo-transparent.png" width="3%"/>
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<a href="https://docs.roboflow.com">
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<img
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src="https://media.roboflow.com/notebooks/template/icons/purple/knowledge.png?ik-sdk-version=javascript-1.4.3&updatedAt=1672949634511"
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width="3%"
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/>
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</a>
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<img src="https://raw.githubusercontent.com/ultralytics/assets/main/social/logo-transparent.png" width="3%"/>
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<a href="https://disuss.roboflow.com">
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<img
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src="https://media.roboflow.com/notebooks/template/icons/purple/forum.png?ik-sdk-version=javascript-1.4.3&updatedAt=1672949633584"
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width="3%"
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/>
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<img src="https://raw.githubusercontent.com/ultralytics/assets/main/social/logo-transparent.png" width="3%"/>
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<a href="https://blog.roboflow.com">
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<img
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src="https://media.roboflow.com/notebooks/template/icons/purple/blog.png?ik-sdk-version=javascript-1.4.3&updatedAt=1672949633605"
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width="3%"
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/>
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</a>
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</a>
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</div>
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</div>
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