supervision

👋 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 3.11>=Python>=3.8 environment.

pip install supervision[desktop]

Read more about desktop, headless, and local installation in our guide.

🔥 quickstart

detections processing

>>> import supervision as sv
>>> from ultralytics import YOLO

>>> model = YOLO('yolov8s.pt')
>>> result = model(IMAGE)[0]
>>> detections = sv.Detections.from_yolov8(result)

>>> len(detections)
5

👉 more detections utils

Easily switch inference pipeline between supported object detection/instance segmentation models

>>> import supervision as sv
>>> from segment_anything import sam_model_registry, SamAutomaticMaskGenerator

>>> sam = sam_model_registry[MODEL_TYPE](checkpoint=CHECKPOINT_PATH).to(device=DEVICE)
>>> mask_generator = SamAutomaticMaskGenerator(sam)
>>> sam_result = mask_generator.generate(IMAGE)
>>> detections = sv.Detections.from_sam(sam_result=sam_result)

Advanced filtering

>>> detections = detections[detections.class_id == 0]
>>> detections = detections[detections.confidence > 0.5]
>>> detections = detections[detections.area > 1000]

Image annotation

>>> import supervision as sv

>>> box_annotator = sv.BoxAnnotator()
>>> annotated_frame = box_annotator.annotate(
...     scene=IMAGE,
...     detections=detections
... )

datasets processing

>>> import supervision as sv

>>> dataset = sv.DetectionDataset.from_yolo(
...     images_directory_path='...',
...     annotations_directory_path='...',
...     data_yaml_path='...'
... )

>>> dataset.classes
['dog', 'person']

>>> len(dataset)
1000

👉 more dataset utils

Load object detection/instance segmentation datasets in one of the supported formats

>>> 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='...'
... )

Loop over dataset entries

>>> for name, image, labels in dataset:
...     print(labels.xyxy)

array([[404.      , 719.      , 538.      , 884.5     ],
       [155.      , 497.      , 404.      , 833.5     ],
       [ 20.154999, 347.825   , 416.125   , 915.895   ]], dtype=float32)

Split dataset for training, testing, and validation

>>> 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 multiple datasets

>>> 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 object detection/instance segmentation datasets in one of the supported formats

>>> 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 labels between supported formats

>>> sv.DetectionDataset.from_yolo(
...     images_directory_path='...',
...     annotations_directory_path='...',
...     data_yaml_path='...'
... ).as_pascal_voc(
...     images_directory_path='...',
...     annotations_directory_path='...'
... )

Load classification datasets in one of the supported formats

>>> cs = sv.ClassificationDataset.from_folder_structure(
...     root_directory_path='...'
... )

Save classification datasets in one of the supported formats

>>> cs.as_folder_structure(
...     root_directory_path='...'
... )

model evaluation

>>> import supervision as sv

>>> dataset = sv.DetectionDataset.from_yolo(...)

>>> def callback(image: np.ndarray) -> sv.Detections:
...     ...

>>> confusion_matrix = sv.ConfusionMatrix.benchmark(
...     dataset = dataset,
...     callback = callback
... )

>>> confusion_matrix.matrix
array([
    [0., 0., 0., 0.],
    [0., 1., 0., 1.],
    [0., 1., 1., 0.],
    [1., 1., 0., 0.]
])

👉 more metrics

Mean average precision (mAP) for object detection tasks.

>>> import supervision as sv

>>> dataset = sv.DetectionDataset.from_yolo(...)

>>> def callback(image: np.ndarray) -> sv.Detections:
...     ...

>>> mean_average_precision = sv.MeanAveragePrecision.benchmark(
...     dataset = dataset,
...     callback = callback
... )

>>> mean_average_precision.map50_95
0.433

🎬 tutorials

Accelerate Image Annotation with SAM and Grounding DINO

Created: 20 Apr 2023 | Updated: 20 Apr 2023

Discover how to speed up your image annotation process using Grounding DINO and Segment Anything Model (SAM). Learn how to convert object detection datasets into instance segmentation datasets, and see the potential of using these models to automatically annotate your datasets for real-time detectors like YOLOv8...

SAM - Segment Anything Model by Meta AI: Complete Guide

Created: 11 Apr 2023 | Updated: 11 Apr 2023

Discover the incredible potential of Meta AI's Segment Anything Model (SAM)! We dive into SAM, an efficient and promptable model for image segmentation, which has revolutionized computer vision tasks. With over 1 billion masks on 11M licensed and privacy-respecting images, SAM's zero-shot performance is often competitive with or even superior to prior fully supervised results...

📚 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!

Metadata

Release files for supervision 0.13.0

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for supervision 0.13.0
File	Size	Uploaded
supervision-0.13.0.tar.gz	50.2 kB	Aug 8, 2023	Details

Built distribution (wheel)

Table of built distributions (wheels) for supervision 0.13.0
File	Interpreter	ABI	Platform	Reset
supervision-0.13.0-py3-none-any.whl 59.3 kB Aug 8, 2023	Python 3	none	any	Details

Total release size: 109.5 kB

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Download URL	supervision-0.13.0.tar.gz
Size	50.2 kB
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Uploaded via	`twine/4.0.2 CPython/3.10.8`

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Size	59.3 kB
Tags	Python 3
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Upload date	Aug 8, 2023
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/4.0.2 CPython/3.10.8`