👋 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 Python>=3.8 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, or MMDetection.
import cv2
import supervision as sv
from ultralytics import YOLO
image = cv2.imread(...)
model = YOLO('yolov8s.pt')
result = model(image)[0]
detections = sv.Detections.from_ultralytics(result)
len(detections)
# 5
👉 more model connectors
-
inference
Running with Inference requires a Roboflow API KEY.
import cv2 import supervision as sv from inference import get_model image = cv2.imread(...) model = get_model(model_id="yolov8s-640", 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(...)
detections = sv.Detections(...)
bounding_box_annotator = sv.BoundingBoxAnnotator()
annotated_frame = bounding_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
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
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, 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 0.20.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| supervision-0.20.0.tar.gz | 94.4 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| supervision-0.20.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 205.4 kB
Release files / supervision-0.20.0.tar.gz
| Download URL | supervision-0.20.0.tar.gz |
|---|---|
| Size | 94.4 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
2e8ca93b5bf854e8f0e01813f8789dc76251267032f745de90654f2d900213c2
|
|
BLAKE2b-256 checksum How to use checksums |
5a6010cb5bed70a1cc54c2d4686fb3262ffa3c3611be1ef118f6780cb386bbb1
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/5.0.0 CPython/3.12.3
|
Release files / supervision-0.20.0-py3-none-any.whl
| Download URL | supervision-0.20.0-py3-none-any.whl |
|---|---|
| Size | 111.0 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
58a91375b65fe05222ce1dc10a2c06095a6700b87bad5d8b52c333b90b330dff
|
|
BLAKE2b-256 checksum How to use checksums |
501a971a415d67011854c2c59716c49b3de151d54b422c6dd0a6489e0152b42e
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/5.0.0 CPython/3.12.3
|
