basketball-yolox-m-640

ortizeg/basketball-yolox-m-640

basketball-yolox-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.

Downloads · 30 days

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Public

Updated Aug 23, 2026

Repo size

101 MB

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Public

Hugging Face

Repo makeup

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.onnx101 MB · 100%

At a glance

Task: Object Detection
Library: onnx
License: apache-2.0
Access: Public
Created: Aug 23, 2026
Updated: Aug 23, 2026
SHA: ad60ffd8

Model card

From the Hugging Face model README

yolox-m-640

YOLOX-M anchor-free detector fine-tuned on the basketball dataset at a 640x640 input resolution. This is the checkpoint every published YOLOX-M number in the final @640 comparison is produced from, and the fastest model in that roster on a dedicated T4 (5.68 ms fp16 to-boxes), while carrying a permissive Apache-2.0 licence.

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	0.580
mAP@50	0.934	0.789

Preprocessing

Resize: letterbox (alignment: top_left)
Normalize: none
Channel order: BGR
Input shape: (3, 640, 640) (float32)

Provenance

Source repo: https://github.com/ortizeg/YOLOX
Command: see docs/provenance/training-runs.md#yolox-m-640

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-640")
weights_path = download_weights(card)

Task: Object Detection
Library: onnx
License: apache-2.0
Created: Aug 23, 2026
Updated: Aug 23, 2026