Instructions to use SharpAI/yolo11n-coreml with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ultralytics
How to use SharpAI/yolo11n-coreml with ultralytics:
# Couldn't find a valid YOLO version tag. # Replace XX with the correct version. from ultralytics import YOLOvXX model = YOLOvXX.from_pretrained("SharpAI/yolo11n-coreml") source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - Notebooks
- Google Colab
- Kaggle
File size: 686 Bytes
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title: yolo11n_coreml_fp32_auto
tags:
- yolo
- object-detection
- computer-vision
- mlpackage
- aegis-ai
library_name: ultralytics
license: agpl-3.0
---
# yolo11n_coreml_fp32_auto
## Accuracy Evaluation Results
**Evaluation Dataset**: coco
| Metric | Value |
|--------|--------|
| mAP@0.5 | 0.417 (41.7%) |
| mAP@0.5:0.95 | 0.310 (31.0%) |
| Precision | 0.363 (36.3%) |
| Recall | 0.140 (14.0%) |
| F1 Score | 0.202 (20.2%) |
| Evaluation FPS | 62.5 |
| Avg Inference Time | 16.01 ms |
*These metrics were computed using the Aegis AI evaluation framework on the coco dataset.*
---
*This model was automatically converted and uploaded by the Aegis AI Model Conversion Tool.*
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