Instructions to use SharpAI/yolo12n-coreml with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- ultralytics
How to use SharpAI/yolo12n-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/yolo12n-coreml") source = 'http://images.cocodataset.org/val2017/000000039769.jpg' model.predict(source=source, save=True) - Notebooks
- Google Colab
- Kaggle
| title: yolo12n_coreml_fp32_auto | |
| tags: | |
| - yolo | |
| - object-detection | |
| - computer-vision | |
| - mlpackage | |
| - aegis-ai | |
| library_name: ultralytics | |
| license: agpl-3.0 | |
| # yolo12n_coreml_fp32_auto | |
| ## Accuracy Evaluation Results | |
| **Evaluation Dataset**: coco | |
| | Metric | Value | | |
| |--------|--------| | |
| | mAP@0.5 | 0.431 (43.1%) | | |
| | mAP@0.5:0.95 | 0.322 (32.2%) | | |
| | Precision | 0.375 (37.5%) | | |
| | Recall | 0.137 (13.7%) | | |
| | F1 Score | 0.201 (20.1%) | | |
| | Evaluation FPS | 89.1 | | |
| | Avg Inference Time | 11.22 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.* | |