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 README.md from webnn/yolo11n: direct link, hf CLI and curl.
- Browser
- Download file 1.21 kB
-
https://huggingface.co/webnn/yolo11n/resolve/main/README.md
- Command line
-
hf download hf://webnn/yolo11n/README.md
-
curl -L -o README.md https://huggingface.co/webnn/yolo11n/resolve/main/README.md
1.21 kB
metadata
license: agpl-3.0
pipeline_tag: object-detection
tags:
- ultralytics
- tracking
- instance-segmentation
- image-classification
- pose-estimation
- obb
- object-detection
- yolo
- yolov8
library_name: ultralytics
model-index:
- name: ultralytics/yolo11
results:
- task:
type: object-detection
dataset:
type: merve/coco
name: coco
split: validation
metrics:
- type: precision
value: 54.7
name: mAP@0.5:0.95
Ultralytics YOLO11 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. YOLO11 is designed to be fast, accurate, and easy to use, making it an excellent choice for a wide range of object detection and tracking, instance segmentation, image classification and pose estimation tasks.
This is an ONNX version of https://huggingface.co/Ultralytics/YOLO11 modified for the usage of Transformers.js JavaScript library.