classification ultralytics

supervision/classification/core.py

@@ -5,6 +5,8 @@ from typing import Any, Optional, Tuple
 
 import numpy as np
 
+from supervision.utils.internal import deprecated
+
 
 def _validate_class_ids(class_id: Any, n: int) -> None:
     """
@@ -40,6 +42,9 @@ class Classifications:
         _validate_confidence(self.confidence, n)
 
     @classmethod
+    @deprecated(
+        "This method is deprecated and removed in 0.15.0 release. Use sv.Classifications.from_ultralytics() instead."
+    )
     def from_yolov8(cls, yolov8_results) -> Classifications:
         """
         Creates a Classifications instance from a [YOLOv8](https://github.com/ultralytics/ultralytics) inference result.
@@ -48,7 +53,7 @@ class Classifications:
             yolov8_results (ultralytics.yolo.engine.results.Results): The output Results instance from YOLOv8
 
         Returns:
-            Detections: A new Classifications object.
+            Classifications: A new Classifications object.
 
         Example:
             ```python
@@ -65,6 +70,40 @@ class Classifications:
         confidence = yolov8_results.probs.data.cpu().numpy()
         return cls(class_id=np.arange(confidence.shape[0]), confidence=confidence)
 
+    @classmethod
+    def from_ultralytics(cls, ultralytics_results) -> Classifications:
+        """
+        Creates a Classifications instance from a (https://github.com/ultralytics/ultralytics) inference result.
+
+        Args:
+            ultralytics_results (ultralytics.yolo.engine.results.Results): The output Results instance from ultralytics model
+
+        Returns:
+            Classifications: A new Classifications object.
+
+        Example:
+            ```python
+            >>> import cv2
+            >>> from ultralytics import YOLO, FastSAM, SAM, RTDETR
+            >>> import supervision as sv
+
+            >>> image = cv2.imread(SOURCE_IMAGE_PATH)
+            >>> model = YOLO('yolov8s.pt')
+            >>> model = SAM('sam_b.pt')
+            >>> model = SAM('mobile_sam.pt')
+            >>> model = FastSAM('FastSAM-s.pt')
+            >>> model = RTDETR('rtdetr-l.pt')
+
+            >>> result = model(image)[0]
+            >>> classifications = sv.Classifications.from_ultralytics(result)
+            ```
+        """
+        confidence = ultralytics_results.boxes.conf.cpu().numpy()
+        return cls(class_id=np.arange(confidence.shape[0]), confidence=confidence)
+
+
+
+
     def get_top_k(self, k: int) -> Tuple[np.ndarray, np.ndarray]:
         """
         Retrieve the top k class IDs and confidences, ordered in descending order by confidence.