Instructions to use inference4j/yolo26n with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ultralytics
How to use inference4j/yolo26n with ultralytics:
# Couldn't find a valid YOLO version tag. # Replace XX with the correct version. from ultralytics import YOLOvXX model = YOLOvXX.from_pretrained("inference4j/yolo26n") source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - Notebooks
- Google Colab
- Kaggle
YOLO26n β ONNX
ONNX export of YOLO26n, the nano variant of Ultralytics YOLO26 object detection model. Features built-in NMS (no post-processing required). Trained on COCO with 80-class output.
Mirrored for use with inference4j, an inference-only AI library for Java.
Original Source
- Repository: onnx-community (Ultralytics YOLO26)
- License: agpl-3.0
Usage with inference4j
try (Yolo26 model = Yolo26.fromPretrained("models/yolo26n")) {
List<Detection> detections = model.detect(Path.of("street.jpg"));
detections.forEach(d -> System.out.printf("%s (%.0f%%) at [%.0f, %.0f, %.0f, %.0f]%n",
d.label(), d.confidence() * 100,
d.box().x1(), d.box().y1(), d.box().x2(), d.box().y2()));
}
Model Details
| Property | Value |
|---|---|
| Architecture | YOLO26 Nano (NMS-free single-shot detector) |
| Task | Object detection (COCO 80 classes) |
| Input | [1, 3, 640, 640] β RGB, normalized 0-1 |
| Output | NMS-free β outputs filtered detections directly |
| Post-processing | None required (built-in NMS) |
| Original framework | PyTorch (Ultralytics) |
License
This model is licensed under the AGPL-3.0 License. Original model by Ultralytics, exported by onnx-community.