Default classify training to `pretrained=True` (#3239)

ultralytics/yolo/cfg/default.yaml

@@ -19,7 +19,7 @@ workers: 8  # (int) number of worker threads for data loading (per RANK if DDP)
 project:  # (str, optional) project name
 name:  # (str, optional) experiment name, results saved to 'project/name' directory
 exist_ok: False  # (bool) whether to overwrite existing experiment
-pretrained: False  # (bool) whether to use a pretrained model
+pretrained: True  # (bool) whether to use a pretrained model
 optimizer: auto  # (str) optimizer to use, choices=[SGD, Adam, Adamax, AdamW, NAdam, RAdam, RMSProp, auto]
 verbose: True  # (bool) whether to print verbose output
 seed: 0  # (int) random seed for reproducibility

ultralytics/yolo/v8/classify/train.py

@@ -33,9 +33,8 @@ class ClassificationTrainer(BaseTrainer):
         if weights:
             model.load(weights)
 
-        pretrained = self.args.pretrained
         for m in model.modules():
-            if not pretrained and hasattr(m, 'reset_parameters'):
+            if not self.args.pretrained and hasattr(m, 'reset_parameters'):
                 m.reset_parameters()
             if isinstance(m, torch.nn.Dropout) and self.args.dropout:
                 m.p = self.args.dropout  # set dropout
@@ -61,8 +60,7 @@ class ClassificationTrainer(BaseTrainer):
         elif model.endswith('.yaml'):
             self.model = self.get_model(cfg=model)
         elif model in torchvision.models.__dict__:
-            pretrained = True
+            self.model = torchvision.models.__dict__[model](weights='IMAGENET1K_V1' if self.args.pretrained else None)
-            self.model = torchvision.models.__dict__[model](weights='IMAGENET1K_V1' if pretrained else None)
         else:
             FileNotFoundError(f'ERROR: model={model} not found locally or online. Please check model name.')
         ClassificationModel.reshape_outputs(self.model, self.data['nc'])