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besit/yolo-onnx
yolo-onnx is a object detection model from besit. Use it when you need objects located in an image. The card lists the license as agpl-3.0.
This repository provides YOLO26 detection models exported to ONNX format, for use with ONNX Runtime or any ONNX-compatible inference engine.
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Updated Sep 28, 2026
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From the Hugging Face model README
This repository provides YOLO26 detection models exported to ONNX format, for use with ONNX Runtime or any ONNX-compatible inference engine.
These models are not my work. YOLO26 was developed by Ultralytics and is distributed under the AGPL-3.0 license. I am sharing these pre-exported ONNX versions as a convenience for those who prefer not to handle the export step themselves.
| Variant | File |
|---|---|
| Nano | yolo26n.onnx |
| Small | yolo26s.onnx |
| Medium | yolo26m.onnx |
| Large | yolo26l.onnx |
| XLarge | yolo26x.onnx |
All models were exported from the official Ultralytics .pt checkpoints using:
yolo export model=yolo26n.pt format=onnx opset=18
Input shape: [1, 3, 640, 640] — normalized RGB, values in [0, 1].
Output shape: [1, 300, 6] — 300 detections × (x1, y1, x2, y2, confidence, class_id), in xyxy format, NMS-free.
If you intend to use these models in a commercial product, you will need a commercial license from Ultralytics. See ultralytics.com/license.
Exporting YOLO26 to ONNX requires a working Python environment with Ultralytics installed. This repo saves that step. If you prefer to export yourself, follow the official Ultralytics export guide.
Converted using Ultralytics tools.