Model coverage cleanup (#4585)

ultralytics/nn/tasks.py

@@ -322,31 +322,10 @@ class PoseModel(DetectionModel):
 class ClassificationModel(BaseModel):
     """YOLOv8 classification model."""
 
-    def __init__(self,
-                 cfg='yolov8n-cls.yaml',
-                 model=None,
-                 ch=3,
-                 nc=None,
-                 cutoff=10,
-                 verbose=True):  # YAML, model, channels, number of classes, cutoff index, verbose flag
+    def __init__(self, cfg='yolov8n-cls.yaml', ch=3, nc=None, verbose=True):
+        """Init ClassificationModel with YAML, channels, number of classes, verbose flag."""
         super().__init__()
-        self._from_detection_model(model, nc, cutoff) if model is not None else self._from_yaml(cfg, ch, nc, verbose)
-
-    def _from_detection_model(self, model, nc=1000, cutoff=10):
-        """Create a YOLOv5 classification model from a YOLOv5 detection model."""
-        from ultralytics.nn.autobackend import AutoBackend
-        if isinstance(model, AutoBackend):
-            model = model.model  # unwrap DetectMultiBackend
-        model.model = model.model[:cutoff]  # backbone
-        m = model.model[-1]  # last layer
-        ch = m.conv.in_channels if hasattr(m, 'conv') else m.cv1.conv.in_channels  # ch into module
-        c = Classify(ch, nc)  # Classify()
-        c.i, c.f, c.type = m.i, m.f, 'models.common.Classify'  # index, from, type
-        model.model[-1] = c  # replace
-        self.model = model.model
-        self.stride = model.stride
-        self.save = []
-        self.nc = nc
+        self._from_yaml(cfg, ch, nc, verbose)
 
     def _from_yaml(self, cfg, ch, nc, verbose):
         """Set YOLOv8 model configurations and define the model architecture."""