add yolo v10 and modify pipeline

ultralytics/models/nas/predict.py

@@ -8,6 +8,29 @@ from ultralytics.utils import ops
 
 
 class NASPredictor(BasePredictor):
+    """
+    Ultralytics YOLO NAS Predictor for object detection.
+
+    This class extends the `BasePredictor` from Ultralytics engine and is responsible for post-processing the
+    raw predictions generated by the YOLO NAS models. It applies operations like non-maximum suppression and
+    scaling the bounding boxes to fit the original image dimensions.
+
+    Attributes:
+        args (Namespace): Namespace containing various configurations for post-processing.
+
+    Example:
+        ```python
+        from ultralytics import NAS
+
+        model = NAS('yolo_nas_s')
+        predictor = model.predictor
+        # Assumes that raw_preds, img, orig_imgs are available
+        results = predictor.postprocess(raw_preds, img, orig_imgs)
+        ```
+
+    Note:
+        Typically, this class is not instantiated directly. It is used internally within the `NAS` class.
+    """
 
     def postprocess(self, preds_in, img, orig_imgs):
         """Postprocess predictions and returns a list of Results objects."""
@@ -16,12 +39,14 @@ class NASPredictor(BasePredictor):
         boxes = ops.xyxy2xywh(preds_in[0][0])
         preds = torch.cat((boxes, preds_in[0][1]), -1).permute(0, 2, 1)
 
-        preds = ops.non_max_suppression(preds,
-                                        self.args.conf,
-                                        self.args.iou,
-                                        agnostic=self.args.agnostic_nms,
-                                        max_det=self.args.max_det,
-                                        classes=self.args.classes)
+        preds = ops.non_max_suppression(
+            preds,
+            self.args.conf,
+            self.args.iou,
+            agnostic=self.args.agnostic_nms,
+            max_det=self.args.max_det,
+            classes=self.args.classes,
+        )
 
         if not isinstance(orig_imgs, list):  # input images are a torch.Tensor, not a list
             orig_imgs = ops.convert_torch2numpy_batch(orig_imgs)