LibreRFDETRl-pose
EXTREMELY experimental RF-DETR-l pose checkpoint for LibreYOLO.
This is a COCO-17 human pose preview checkpoint for LibreYOLO's task="pose" RF-DETR path. It is useful for testing and bootstrapping, but it is not a final benchmark release.
Checkpoint
- File:
LibreRFDETRl-pose.pt - Family:
LibreRFDETR - Size:
l - Task:
pose - Classes: person only
- Keypoints: COCO-17,
(x, y, visibility) - Validation image size:
704 - Additional training epochs for this checkpoint:
0
Initialization Method
Native RF-DETR-l detection checkpoint plus shared tensors from the trained LibreRFDETRs-pose checkpoint. The extra final decoder layer was initialized from the trained small-pose final decoder layer.
This method keeps the size-specific detection backbone and resolution-dependent tensors, then transfers the pose-specialized shared tensors from the small pose checkpoint. The checkpoint should still be treated as experimental until a full per-size training run is published.
COCO Keypoint Validation
Validation was run on COCO person keypoints val2017 through LibreYOLO's pose validator.
| Metric | Value |
|---|---|
| keypoints mAP50-95 | 0.570094 |
| keypoints mAP50 | 0.852700 |
| keypoints mAP75 | 0.626387 |
| keypoints AR50-95 | 0.674890 |
The validation artifacts are included as validation_metrics.json. Initialization details are included as initialization_summary.json.
Usage
from libreyolo import LibreRFDETR
model = LibreRFDETR("LibreRFDETRl-pose.pt", task="pose")
results = model.predict("image.jpg", imgsz=704)
print(results[0].keypoints)
Autodownload in LibreYOLO emits an experimental warning for this checkpoint.
Caveats
- Experimental checkpoint, not a final benchmark release.
- No additional fine-tuning epochs were run for this per-size checkpoint after transfer initialization.
- Pose export/runtime backends may have separate support status from PyTorch inference.
- Metrics are from LibreYOLO PR development artifacts, not from an independent external benchmark suite.