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ortizeg/basketball-rtmdet-m-640
basketball-rtmdet-m-640 is a object detection model from ortizeg. Use it when you need objects located in an image. It is set up for onnx. The card lists the license as apache-2.0.
RTMDet-M (MMDetection) fine-tuned on the basketball dataset at 640x640, the mmdet-default-warmup ("recipe warmup") training. A short-warmup ablation variant exists (not registered here) and was found to be statistical…
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Updated Jul 30, 2026
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From the Hugging Face model README
RTMDet-M (MMDetection) fine-tuned on the basketball dataset at 640x640, the mmdet-default-warmup ("recipe warmup") training. A short-warmup ablation variant exists (not registered here) and was found to be statistically indistinguishable on the harness test slice.
Measured on basketball-player-detection-3 (test split), via the object-detection-eval harness.
| Metric | 5-class | 10-class |
|---|---|---|
| mAP@50:95 | 0.628 | 0.580 |
| mAP@50 | 0.878 | 0.779 |
letterbox (alignment: top_left)mean_stdBGR(3, 640, 640) (float32)docs/provenance/configs/rtmdet_m/rtmdet_basketball.pytorchrun --nproc_per_node=1 tools/train.py docs/provenance/configs/rtmdet_m/rtmdet_basketball.py --launcher pytorchThis ONNX file is one of the 7-model roster benchmarked in object-detection-eval. Load it through the registry for verified, hash-checked download and the exact preprocessing recipe above:
from object_detection_eval.registry import ModelRegistry, download_weights
registry = ModelRegistry.from_directory("registry")
card = registry.get("rtmdet-m-640")
weights_path = download_weights(card)