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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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docs/en/models/sam-2.md
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@ -256,9 +256,12 @@ To auto-annotate your dataset using SAM 2, follow this example:
| 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 2 segmentation model. Defaults to 'sam2_b.pt'. | `'sam2_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` |
This function facilitates the rapid creation of high-quality segmentation datasets, ideal for researchers and developers aiming to accelerate their projects.