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* Using Rayon to accelarate YOLO post-processing * Refactor YOLO with outputs format * Optimize `conf * clss` for yolov5 v6 v7 * Add depth-anything-v2 * Update README.md * Update CHANGELOG.md
1.8 KiB
1.8 KiB
v0.0.5 - 2024-07-12
Changed
- Accelerated
YOLO's post-processing usingRayon. Now,YOLOv8-segtakes only around ~8ms (~20ms in the previous version), depending on your machine. Note that this repo's implementation ofYOLOv8-Segmentsaves not only the masks but also their contour points. The officialYOLOv8Python version only saves the masks, making it appear much faster. - Merged all
YOLOv8-relatedsolution models into YOLO examples. - Consolidated all
YOLO-seriesmodel examples into the YOLO example. - Refactored the
YOLOstruct to unify allYOLO versionsandYOLO tasks. It now supports user-defined YOLO models with differentPreds Tensor Formats. - Introduced a new
Nmstrait, combiningapply_bboxes_nms()andapply_mbrs_nms()intoapply_nms().
Added
- Added support for
YOLOv6andYOLOv7. - Updated documentation for
y.rs. - Updated documentation for
bbox.rs. - Updated the
README.md. - Added
with_yolo_preds()toOptions. - Added support for
Depth-Anything-v2. - Added
RTDETRto theYOLOVersionstruct.
Removed
- Merged the following models' examples into the YOLOv8 example:
yolov8-face,yolov8-falldown,yolov8-head,yolov8-trash,fastsam, andface-parsing. - Removed
anchors_first,conf_independent, and their related methods fromOptions.
v0.0.4 - 2024-06-30
Added
- Add X struct to handle input and preprocessing
- Add Ops struct to manage common operations
- Use SIMD (fast_image_resize) to accelerate model pre-processing and post-processing.YOLOv8-seg post-processing (~120ms => ~20ms), Depth-Anything post-processing (~23ms => ~2ms).
Deprecated
- Mark
Ops::descale_mask()as deprecated.