Auto annotation new parameters for SAM models (#17288)

Co-authored-by: UltralyticsAssistant <web@ultralytics.com>

docs/en/models/sam-2.md

@@ -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.

docs/en/models/sam.md

@@ -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.

ultralytics/data/annotator.py

@@ -5,7 +5,9 @@ from pathlib import Path
 from ultralytics import SAM, YOLO
 
 
-def auto_annotate(data, det_model="yolo11x.pt", sam_model="sam_b.pt", device="", output_dir=None):
+def auto_annotate(
+    data, det_model="yolo11x.pt", sam_model="sam_b.pt", device="", conf=0.25, iou=0.45, imgsz=640, output_dir=None
+):
     """
     Automatically annotates images using a YOLO object detection model and a SAM segmentation model.
 
@@ -17,6 +19,9 @@ def auto_annotate(data, det_model="yolo11x.pt", sam_model="sam_b.pt", device="",
         det_model (str): Path or name of the pre-trained YOLO detection model.
         sam_model (str): Path or name of the pre-trained SAM segmentation model.
         device (str): Device to run the models on (e.g., 'cpu', 'cuda', '0').
+        conf (float): Confidence threshold for detection model; default is 0.25.
+        iou (float): IoU threshold for filtering overlapping boxes in detection results; default is 0.45.
+        imgsz (int): Input image resize dimension; default is 640.
         output_dir (str | None): Directory to save the annotated results. If None, a default directory is created.
 
     Examples:
@@ -36,7 +41,7 @@ def auto_annotate(data, det_model="yolo11x.pt", sam_model="sam_b.pt", device="",
         output_dir = data.parent / f"{data.stem}_auto_annotate_labels"
     Path(output_dir).mkdir(exist_ok=True, parents=True)
 
-    det_results = det_model(data, stream=True, device=device)
+    det_results = det_model(data, stream=True, device=device, conf=conf, iou=iou, imgsz=imgsz)
 
     for result in det_results:
         class_ids = result.boxes.cls.int().tolist()  # noqa