LibreRFDETRn-pose
EXTREMELY experimental RF-DETR-n 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:
LibreRFDETRn-pose.pt - Family:
LibreRFDETR - Size:
n - Task:
pose - Classes: person only
- Keypoints: COCO-17,
(x, y, visibility) - Validation image size:
512 - Additional training epochs for this checkpoint:
0
Initialization Method
Native RF-DETR-n detection checkpoint plus shared tensors from the trained LibreRFDETRs-pose checkpoint. No decoder cloning was needed because nano has fewer decoder layers than small.
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 | 0.506590 |
| keypoints mAP50 | 0.806117 |
| keypoints mAP75 | 0.544956 |
| keypoints AR50-95 | 0.626338 |
The validation artifacts are included as validation_metrics.json. Initialization details are included as initialization_summary.json.
Usage
from libreyolo import LibreRFDETR
model = LibreRFDETR("LibreRFDETRn-pose.pt", task="pose")
results = model.predict("image.jpg", imgsz=512)
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.