Instructions to use TheCluster/YOLOv11-CoreML with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ultralytics
How to use TheCluster/YOLOv11-CoreML 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("TheCluster/YOLOv11-CoreML", "<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 TheCluster/YOLOv11-CoreML: direct link, hf CLI and curl.
- Browser
- Download file 462 Bytes
-
https://huggingface.co/TheCluster/YOLOv11-CoreML/resolve/main/README.md
- Command line
-
hf download hf://TheCluster/YOLOv11-CoreML/README.md
-
curl -L -o README.md https://huggingface.co/TheCluster/YOLOv11-CoreML/resolve/main/README.md
462 Bytes
metadata
license: agpl-3.0
pipeline_tag: object-detection
tags:
- yolo
- yolov11
- apple
- coreml
- ane
- tracking
- ultralytics
YOLOv11 CoreML
YOLOv11 models converted to CoreML format.
Using:
- pytorch 2.5.0
- coremltools 8.0
- ultralytics 8.3.16
- macOS Sequoia 15.1
Tested on:
- M1
- M1 Ultra
- M4 (iPad Pro 2024)
- A15 (iPhone 14)
Models can be run using Apple Neural Engine
Model sources: https://github.com/ultralytics/ultralytics