Object Detection
ultralytics
ONNX
tracking
instance-segmentation
image-classification
pose-estimation
obb
yolo
yolov8
Eval Results (legacy)
Instructions to use webnn/yolo11n with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ultralytics
How to use webnn/yolo11n with ultralytics:
from huggingface_hub import hf_hub_download from ultralytics import YOLO # pick the weights file from this repo's "Files and versions" tab weights = hf_hub_download("webnn/yolo11n", "<weights>.pt") model = YOLO(weights) source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from webnn/yolo11n: direct link, hf CLI and curl.
- Browser
- Download file 269 Bytes
-
https://huggingface.co/webnn/yolo11n/resolve/main/preprocessor_config.json
- Command line
-
hf download hf://webnn/yolo11n/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/webnn/yolo11n/resolve/main/preprocessor_config.json
269 Bytes
| { | |
| "do_normalize": false, | |
| "do_pad": false, | |
| "do_rescale": true, | |
| "do_resize": true, | |
| "feature_extractor_type": "ImageFeatureExtractor", | |
| "resample": 2, | |
| "rescale_factor": 0.00392156862745098, | |
| "size": { | |
| "shortest_edge": 224 | |
| }, | |
| "size_divisibility": 32 | |
| } |