đŦTechnical Deep Dive
Full Specifications [+]
Quick Commands
pip install supervision âī¸ Nexus Index V2.0
đŦ Index Insight
FNI V2.0 for supervision: Semantic (S:50), Authority (A:0), Popularity (P:78), Recency (R:100), Quality (Q:50).
Verification Authority
đ Specs
- Language
- Python
- License
- MIT
- Version
- 1.0.0
Usage documentation not yet indexed for this tool.
Technical Documentation
đ 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! đ¤
đģ install
Pip install the supervision package in a Python>=3.9 environment.
pip install supervisionRead more about conda, mamba, and installing from source in our guide.
đĨ 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.
import supervision as sv
from PIL import Image
from rfdetr import RFDETRSmall
image = Image.open(...)
model = RFDETRSmall()
detections = model.predict(image, threshold=0.5)
len(detections)
# 5đ more model connectors
inference
Running with Inference requires a Roboflow API KEY.
pythonimport supervision as sv from PIL import Image from inference import get_model image = Image.open(...) 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.
import cv2
import supervision as sv
image = cv2.imread(...)
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.
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
passđ more dataset utils
load
pythondataset = 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
pythontrain_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
pythonds_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
pythondataset.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
pythonsv.DetectionDataset.from_yolo( images_directory_path=..., annotations_directory_path=..., data_yaml_path=..., ).as_pascal_voc( images_directory_path=..., annotations_directory_path=..., )
đŦ 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
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
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.
đ 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
đ documentation
Visit our documentation page to learn how supervision can help you build computer vision applications faster and more reliably.
đ contribution
We love your input! Please see our contributing guide to get started. Thank you đ to all our contributors!
đ Quick Start
pip install supervisionAI Summary: Based on GitHub metadata. Not a recommendation.
đĄī¸ Tool Transparency Report
Technical metadata sourced from upstream repositories.
đ Identity & Source
- id
- gh-tool--roboflow--supervision
- slug
- roboflow--supervision
- source
- github
- author
- roboflow
- license
- MIT
- tags
- classification, coco, computer-vision, deep-learning, hacktoberfest, image-processing, instance-segmentation, low-code, machine-learning, metrics, object-detection, oriented-bounding-box, pascal-voc, python, pytorch, tensorflow, tracking, video-processing, yolo
âī¸ Technical Specs
- architecture
- null
- params billions
- null
- context length
- null
- pipeline tag
- object-detection
đ Engagement & Metrics
- downloads
- 0
- stars
- 37,468
- forks
- 0
- github stars
- 37,468
Data indexed from public sources. Updated daily.