0.14.0

[SkalskiP]

🚀 Added

• Support for SAHI inference technique with sv.InferenceSlicer. (#282)

>>> import cv2
>>> import supervision as sv
>>> import numpy as np
>>> from ultralytics import YOLO

>>> image = cv2.imread(SOURCE_IMAGE_PATH)
>>> model = YOLO(...)

>>> def callback(image_slice: np.ndarray) -> sv.Detections:
...     result = model(image_slice)[0]
...     return sv.Detections.from_ultralytics(result)

>>> slicer = sv.InferenceSlicer(callback = callback)

>>> detections = slicer(image)

inference-slicer.mov

• Detections.from_deepsparse to enable seamless integration with DeepSparse framework. (#297)

• sv.Classifications.from_ultralytics to enable seamless integration with Ultralytics framework. This will enable you to use supervision with all models that Ultralytics supports. (#281)

  Warning
  sv.Detections.from_yolov8 and sv.Classifications.from_yolov8 are now deprecated and will be removed with supervision-0.16.0 release.

• First supervision usage example script showing how to detect and track objects on video using YOLOv8 + Supervision. (#341)

detect-and-track-objects-on-video.mov

🌱 Changed

• sv.ClassificationDataset and sv.DetectionDataset now use image path (not image name) as dataset keys. (#296)

🛠️ FIxed

• Detections.from_roboflow to filter out polygons with less than 3 points. (#300)

🏆 Contributors

@hardikdava (Hardik Dava), @onuralpszr (Onuralp SEZER), @mayankagarwals (Mayank Agarwal), @rizavelioglu (Riza Velioglu), @arjun-234 (Arjun D.), @mwitiderrick (Derrick Mwiti), @ShubhamKanitkar32, @gasparitiago (Tiago De Gaspari), @capjamesg (James Gallagher), @SkalskiP (Piotr Skalski)

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This discussion was created from the release 0.14.0.