Instructions to use SharpAI/yolo11n-coreml-int8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ultralytics
How to use SharpAI/yolo11n-coreml-int8 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-int8") source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - Notebooks
- Google Colab
- Kaggle
| title: yolo11n_coreml_int8_auto | |
| tags: | |
| - yolo | |
| - object-detection | |
| - computer-vision | |
| - mlpackage | |
| - aegis-ai | |
| library_name: ultralytics | |
| license: agpl-3.0 | |
| # yolo11n_coreml_int8_auto | |
| ## Accuracy Evaluation Results | |
| **Evaluation Dataset**: coco | |
| | Metric | Value | | |
| |--------|--------| | |
| | mAP@0.5 | 0.417 (41.7%) | | |
| | mAP@0.5:0.95 | 0.309 (30.9%) | | |
| | Precision | 0.364 (36.4%) | | |
| | Recall | 0.139 (13.9%) | | |
| | F1 Score | 0.201 (20.1%) | | |
| | Evaluation FPS | 109.8 | | |
| | Avg Inference Time | 9.10 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.* | |