add yolo v10 and modify pipeline

ultralytics/models/nas/val.py

@@ -5,20 +5,46 @@ import torch
 from ultralytics.models.yolo.detect import DetectionValidator
 from ultralytics.utils import ops
 
-__all__ = ['NASValidator']
+__all__ = ["NASValidator"]
 
 
 class NASValidator(DetectionValidator):
+    """
+    Ultralytics YOLO NAS Validator for object detection.
+
+    Extends `DetectionValidator` from the Ultralytics models package and is designed to post-process the raw predictions
+    generated by YOLO NAS models. It performs non-maximum suppression to remove overlapping and low-confidence boxes,
+    ultimately producing the final detections.
+
+    Attributes:
+        args (Namespace): Namespace containing various configurations for post-processing, such as confidence and IoU thresholds.
+        lb (torch.Tensor): Optional tensor for multilabel NMS.
+
+    Example:
+        ```python
+        from ultralytics import NAS
+
+        model = NAS('yolo_nas_s')
+        validator = model.validator
+        # Assumes that raw_preds are available
+        final_preds = validator.postprocess(raw_preds)
+        ```
+
+    Note:
+        This class is generally not instantiated directly but is used internally within the `NAS` class.
+    """
 
     def postprocess(self, preds_in):
         """Apply Non-maximum suppression to prediction outputs."""
         boxes = ops.xyxy2xywh(preds_in[0][0])
         preds = torch.cat((boxes, preds_in[0][1]), -1).permute(0, 2, 1)
-        return ops.non_max_suppression(preds,
-                                       self.args.conf,
-                                       self.args.iou,
-                                       labels=self.lb,
-                                       multi_label=False,
-                                       agnostic=self.args.single_cls,
-                                       max_det=self.args.max_det,
-                                       max_time_img=0.5)
+        return ops.non_max_suppression(
+            preds,
+            self.args.conf,
+            self.args.iou,
+            labels=self.lb,
+            multi_label=False,
+            agnostic=self.args.single_cls,
+            max_det=self.args.max_det,
+            max_time_img=0.5,
+        )