| ---
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| license: apache-2.0
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| library_name: libreyolo
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| tags:
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| - computer-vision
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| - pose-estimation
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| - keypoint-detection
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| - coco
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| - libreyolo
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| - rfdetr
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| datasets:
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| - detection-datasets/coco
|
| ---
|
|
|
| # LibreRFDETRm-pose
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|
|
| **EXTREMELY experimental** RF-DETR-m pose checkpoint for LibreYOLO.
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|
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| 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.
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|
|
| ## Checkpoint
|
|
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| - File: `LibreRFDETRm-pose.pt`
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| - Family: `LibreRFDETR`
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| - Size: `m`
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| - Task: `pose`
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| - Classes: person only
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| - Keypoints: COCO-17, `(x, y, visibility)`
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| - Validation image size: `576`
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| - Additional training epochs for this checkpoint: `0`
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|
|
| ## Initialization Method
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|
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| Native RF-DETR-m 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.
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|
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| 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.
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|
|
| ## COCO Keypoint Validation
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|
|
| Validation was run on COCO person keypoints val2017 through LibreYOLO's pose validator.
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|
|
| | Metric | Value |
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| | --- | ---: |
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| | keypoints mAP50-95 | `0.532909` |
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| | keypoints mAP50 | `0.837690` |
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| | keypoints mAP75 | `0.581342` |
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| | keypoints AR50-95 | `0.641814` |
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|
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| The validation artifacts are included as `validation_metrics.json`. Initialization details are included as `initialization_summary.json`.
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|
|
| ## Usage
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|
|
| ```python
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| from libreyolo import LibreRFDETR
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|
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| model = LibreRFDETR("LibreRFDETRm-pose.pt", task="pose")
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| results = model.predict("image.jpg", imgsz=576)
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| print(results[0].keypoints)
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| ```
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|
|
| Autodownload in LibreYOLO emits an experimental warning for this checkpoint.
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|
|
| ## Caveats
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|
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| - Experimental checkpoint, not a final benchmark release.
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| - No additional fine-tuning epochs were run for this per-size checkpoint after transfer initialization.
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| - Pose export/runtime backends may have separate support status from PyTorch inference.
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| - Metrics are from LibreYOLO PR development artifacts, not from an independent external benchmark suite.
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|
