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ortizeg/basketball-rt-detrv2-m-640
basketball-rt-detrv2-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.
RT-DETRv2-M (ResNet-34-vd backbone, r34vd) fine-tuned on the basketball dataset at 640x640. Reuses the DEIM/D-FINE deploy ONNX format and identical preprocessing (square resize, RGB, /255, antialias). The weakest mode…
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Updated Jul 30, 2026
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From the Hugging Face model README
RT-DETRv2-M (ResNet-34-vd backbone, r34vd) fine-tuned on the basketball dataset at 640x640. Reuses the DEIM/D-FINE deploy ONNX format and identical preprocessing (square resize, RGB, /255, antialias). The weakest model in the 7-model roster 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 | 0.581 | 0.501 |
| mAP@50 | 0.862 | 0.708 |
square (alignment: none)div255RGB(3, 640, 640) (float32)docs/provenance/configs/rtdetrv2_m/rtdetrv2_basketball_m.ymlpython tools/train.py -c docs/provenance/configs/rtdetrv2_m/rtdetrv2_basketball_m.yml -t rtdetrv2_r34vd_120e_coco_ema.pth --use-amp --seed 0This 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("rt-detrv2-m-640")
weights_path = download_weights(card)