Fix _process_batch() docstrings (#14454)

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>

ultralytics/models/yolo/segment/val.py

@@ -164,14 +164,34 @@ class SegmentationValidator(DetectionValidator):
 
     def _process_batch(self, detections, gt_bboxes, gt_cls, pred_masks=None, gt_masks=None, overlap=False, masks=False):
         """
-        Return correct prediction matrix.
+        Compute correct prediction matrix for a batch based on bounding boxes and optional masks.
 
         Args:
-            detections (array[N, 6]), x1, y1, x2, y2, conf, class
-            labels (array[M, 5]), class, x1, y1, x2, y2
+            detections (torch.Tensor): Tensor of shape (N, 6) representing detected bounding boxes and
+                associated confidence scores and class indices. Each row is of the format [x1, y1, x2, y2, conf, class].
+            gt_bboxes (torch.Tensor): Tensor of shape (M, 4) representing ground truth bounding box coordinates.
+                Each row is of the format [x1, y1, x2, y2].
+            gt_cls (torch.Tensor): Tensor of shape (M,) representing ground truth class indices.
+            pred_masks (torch.Tensor | None): Tensor representing predicted masks, if available. The shape should
+                match the ground truth masks.
+            gt_masks (torch.Tensor | None): Tensor of shape (M, H, W) representing ground truth masks, if available.
+            overlap (bool): Flag indicating if overlapping masks should be considered.
+            masks (bool): Flag indicating if the batch contains mask data.
 
         Returns:
-            correct (array[N, 10]), for 10 IoU levels
+            (torch.Tensor): A correct prediction matrix of shape (N, 10), where 10 represents different IoU levels.
+
+        Note:
+            - If `masks` is True, the function computes IoU between predicted and ground truth masks.
+            - If `overlap` is True and `masks` is True, overlapping masks are taken into account when computing IoU.
+
+        Example:
+            ```python
+            detections = torch.tensor([[25, 30, 200, 300, 0.8, 1], [50, 60, 180, 290, 0.75, 0]])
+            gt_bboxes = torch.tensor([[24, 29, 199, 299], [55, 65, 185, 295]])
+            gt_cls = torch.tensor([1, 0])
+            correct_preds = validator._process_batch(detections, gt_bboxes, gt_cls)
+            ```
         """
         if masks:
             if overlap: