add yolo v10 and modify pipeline

ultralytics/models/yolo/classify/predict.py

@@ -1,6 +1,8 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
 
+import cv2
 import torch
+from PIL import Image
 
 from ultralytics.engine.predictor import BasePredictor
 from ultralytics.engine.results import Results
@@ -26,13 +28,23 @@ class ClassificationPredictor(BasePredictor):
     """
 
     def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None):
+        """Initializes ClassificationPredictor setting the task to 'classify'."""
         super().__init__(cfg, overrides, _callbacks)
-        self.args.task = 'classify'
+        self.args.task = "classify"
+        self._legacy_transform_name = "ultralytics.yolo.data.augment.ToTensor"
 
     def preprocess(self, img):
         """Converts input image to model-compatible data type."""
         if not isinstance(img, torch.Tensor):
-            img = torch.stack([self.transforms(im) for im in img], dim=0)
+            is_legacy_transform = any(
+                self._legacy_transform_name in str(transform) for transform in self.transforms.transforms
+            )
+            if is_legacy_transform:  # to handle legacy transforms
+                img = torch.stack([self.transforms(im) for im in img], dim=0)
+            else:
+                img = torch.stack(
+                    [self.transforms(Image.fromarray(cv2.cvtColor(im, cv2.COLOR_BGR2RGB))) for im in img], dim=0
+                )
         img = (img if isinstance(img, torch.Tensor) else torch.from_numpy(img)).to(self.model.device)
         return img.half() if self.model.fp16 else img.float()  # uint8 to fp16/32