Object Detection
ultralytics
ONNX
tracking
instance-segmentation
image-classification
pose-estimation
obb
yolo
yolov8
Eval Results (legacy)
Instructions to use webnn/yolo12n with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ultralytics
How to use webnn/yolo12n with ultralytics:
from ultralytics import YOLOvv8 model = YOLOvv8.from_pretrained("webnn/yolo12n") source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - Notebooks
- Google Colab
- Kaggle
Ultralytics YOLO12 is a cutting-edge, state-of-the-art (SOTA) model that builds upon the success of previous YOLO versions and introduces new features and improvements to further boost performance and flexibility.
YOLO12 is a versatile model that supports a wide range of core computer vision tasks. It excels in object detection, instance segmentation, image classification, pose estimation, and oriented object detection.
This is an ONNX version of Ultralytics YOLO12 modified for the usage of Transformers.js (WebNN backend).
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Evaluation results
- mAP@0.5:0.95 on cocovalidation set self-reported54.700