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basketball-yolox-m-800
ortizeg/basketball-yolox-m-800
basketball-yolox-m-800 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.
YOLOX-M anchor-free detector fine-tuned on the basketball dataset at an 800x800 input resolution. Reused from an earlier training run (not part of the @640 final-comparison retrain batch); its harness test-set score c…
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Updated Jul 30, 2026
Repo size
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At a glance
- Task
- Object Detection
- Library
- onnx
- License
- apache-2.0
- Access
- Public
- Created
- Jul 30, 2026
- Updated
- Jul 30, 2026
- SHA
- 1c2b1f99
Model card
From the Hugging Face model README
yolox-m-800
YOLOX-M anchor-free detector fine-tuned on the basketball dataset at an 800x800 input resolution. Reused from an earlier training run (not part of the @640 final-comparison retrain batch); its harness test-set score came from correcting the preprocessing (top-left pad-114 letterbox, BGR, no normalization) to match training, which alone moved its mAP@50:95 from 30.8 to 72.3.
Metrics
Measured on basketball-player-detection-3 (test split), via the object-detection-eval harness.
| Metric | 5-class | 10-class |
|---|---|---|
| mAP@50:95 | 0.672 | — |
| mAP@50 | 0.934 | — |
Preprocessing
- Resize:
letterbox(alignment:top_left) - Normalize:
none - Channel order:
BGR - Input shape:
(3, 800, 800)(float32)
Provenance
- Source repo: https://github.com/ortizeg/YOLOX
- Command:
see docs/provenance/training-runs.md#yolox-m-800
Usage
This 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("yolox-m-800")
weights_path = download_weights(card)
- Task
- Object Detection
- Library
- onnx
- License
- apache-2.0
- Created
- Jul 30, 2026
- Updated
- Jul 30, 2026