[Enhancement] Add YOLOv8 OBB Models Directly to Zoo and Document #4238
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WalkthroughThe update introduces direct support for Ultralytics YOLO oriented bounding box models in FiftyOne's Changes
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## develop #4238 +/- ##
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- Coverage 99.24% 28.22% -71.02%
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Files ignored due to path filters (1)
fiftyone/zoo/models/manifest-torch.jsonis excluded by!**/*.json
Files selected for processing (2)
- docs/source/integrations/ultralytics.rst (1 hunks)
- fiftyone/utils/ultralytics.py (2 hunks)
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docs/source/integrations/ultralytics.rst (1)
244-293: The new documentation section on oriented bounding boxes (OBB) is clear and informative. It correctly explains how to use Ultralytics YOLO OBB models with FiftyOne, including loading models from the FiftyOne Model Zoo and applying them to datasets. The note about the DOTA dataset and the specific use case for bird's eye view images is particularly helpful for users.fiftyone/utils/ultralytics.py (1)
437-443: > 📝 NOTEThis review was outside the diff hunks and was mapped to the diff hunk with the greatest overlap. Original lines [440-506]
The renaming of
FiftyOneYOLOOBBConfigtoFiftyOneYOLOOBBModelConfigis consistent and aligns with the naming conventions used for other model configurations in FiftyOne. This change improves clarity and makes the purpose of the class more explicit.
Recent community PR #4230 extended FiftyOne's Ultralytics integration to support oriented bounding box prediction models.
This PR adds those models directly to the FiftyOne Model Zoo, and documents these additions to the integration on the Ultralytics integration doc page.
(Please fill in changes proposed in this fix)
How is this patch tested? If it is not, please explain why.
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Release Notes
Is this a user-facing change that should be mentioned in the release notes?
notes for FiftyOne users.
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if this PR is part of a larger change.)
What areas of FiftyOne does this PR affect?
fiftyonePython library changesSummary by CodeRabbit
apply_model()function.FiftyOneYOLOOBBConfigtoFiftyOneYOLOOBBModelConfigto better reflect its purpose in configuring YOLO object detection models.