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task="pose" path. It should be treated as experimental while the pose implementation and release process settle.LibreRFDETRs-pose.ptLibreRFDETRspose(x, y, visibility)768| Metric | Value |
|---|---|
| keypoints mAP50-95 | 0.604812 |
| keypoints mAP50 | 0.866018 |
| keypoints mAP75 | 0.669 from COCO summary |
| keypoints AR50-95 | 0.713 from COCO summary |
768x768, batch-10 refinement phase. The training history is staged continuation from an RF-DETR-small detection backbone, with 18 logged validation epochs across the 512, 640, and 768 refinement phases.
1from libreyolo import LibreRFDETR
2
3model = LibreRFDETR("LibreRFDETRs-pose.pt", task="pose")
4results = model.predict("image.jpg")