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Optimize Auto-Annotation with all args (#17315)

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Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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Muhammad Rizwan Munawar 2024-11-03 04:55:55 +05:00 • committed by GitHub
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2
docs/en/models/sam-2.md
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@ -262,6 +262,8 @@ To auto-annotate your dataset using SAM 2, follow this example:
| `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` |
| `max_det` | `int`, optional | Limits detections per image to control outputs in dense scenes. | `300` |
| `classes` | `list`, optional | Filters predictions to specified class IDs, returning only relevant detections. | `None` |
| `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.

2
docs/en/models/sam.md
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@ -217,6 +217,8 @@ To auto-annotate your dataset with the Ultralytics framework, use the `auto_anno
| `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` |
| `max_det` | `int`, optional | Limits detections per image to control outputs in dense scenes. | `300` |
| `classes` | `list`, optional | Filters predictions to specified class IDs, returning only relevant detections. | `None` |
| `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.