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Auto annotation new parameters for SAM models (#17288)

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Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
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Muhammad Rizwan Munawar 2024-10-31 17:35:26 +05:00 • committed by GitHub
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@ -211,9 +211,12 @@ To auto-annotate your dataset with the Ultralytics framework, use the `auto_anno
| Argument | Type | Description | Default |
| ------------ | --------------------- | ------------------------------------------------------------------------------------------------------- | -------------- |
| `data` | `str` | Path to a folder containing images to be annotated. | |
| `det_model` | `str`, optional | Pre-trained YOLO detection model. Defaults to 'yolo11x.pt'. | `'yolov8x.pt'` |
| `det_model` | `str`, optional | Pre-trained YOLO detection model. Defaults to 'yolo11x.pt'. | `'yolo11x.pt'` |
| `sam_model` | `str`, optional | Pre-trained SAM segmentation model. Defaults to 'sam_b.pt'. | `'sam_b.pt'` |
| `device` | `str`, optional | Device to run the models on. Defaults to an empty string (CPU or GPU, if available). | |
| `conf` | `float`, optional | Confidence threshold for detection model; default is 0.25. | `0.25` |
| `iou` | `float`, optional | IoU threshold for filtering overlapping boxes in detection results; default is 0.45. | `0.45` |
| `imgsz` | `int`, optional | Input image resize dimension; default is 640. | `640` |
| `output_dir` | `str`, None, optional | Directory to save the annotated results. Defaults to a 'labels' folder in the same directory as 'data'. | `None` |
The `auto_annotate` function takes the path to your images, with optional arguments for specifying the pre-trained detection and SAM segmentation models, the device to run the models on, and the output directory for saving the annotated results.