`ultralytics 8.0.159` add Classify training `resume` feature (#4482)

ultralytics/__init__.py

@@ -1,6 +1,6 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
 
-__version__ = '8.0.158'
+__version__ = '8.0.159'
 
 from ultralytics.hub import start
 from ultralytics.models import RTDETR, SAM, YOLO

ultralytics/cfg/__init__.py

@@ -419,7 +419,7 @@ def entrypoint(debug=''):
         overrides['source'] = DEFAULT_CFG.source or ASSETS
         LOGGER.warning(f"WARNING ⚠️ 'source' is missing. Using default 'source={overrides['source']}'.")
     elif mode in ('train', 'val'):
-        if 'data' not in overrides:
+        if 'data' not in overrides and 'resume' not in overrides:
             overrides['data'] = TASK2DATA.get(task or DEFAULT_CFG.task, DEFAULT_CFG.data)
             LOGGER.warning(f"WARNING ⚠️ 'data' is missing. Using default 'data={overrides['data']}'.")
     elif mode == 'export':

ultralytics/models/yolo/classify/train.py

@@ -62,10 +62,10 @@ class ClassificationTrainer(BaseTrainer):
         if isinstance(self.model, torch.nn.Module):  # if model is loaded beforehand. No setup needed
             return
 
-        model = str(self.model)
+        model, ckpt = str(self.model), None
         # Load a YOLO model locally, from torchvision, or from Ultralytics assets
         if model.endswith('.pt'):
-            self.model, _ = attempt_load_one_weight(model, device='cpu')
+            self.model, ckpt = attempt_load_one_weight(model, device='cpu')
             for p in self.model.parameters():
                 p.requires_grad = True  # for training
         elif model.split('.')[-1] in ('yaml', 'yml'):
@@ -76,7 +76,7 @@ class ClassificationTrainer(BaseTrainer):
             FileNotFoundError(f'ERROR: model={model} not found locally or online. Please check model name.')
         ClassificationModel.reshape_outputs(self.model, self.data['nc'])
 
-        return  # do not return ckpt. Classification doesn't support resume
+        return ckpt
 
     def build_dataset(self, img_path, mode='train', batch=None):
         return ClassificationDataset(root=img_path, args=self.args, augment=mode == 'train')
@@ -122,10 +122,6 @@ class ClassificationTrainer(BaseTrainer):
         loss_items = [round(float(loss_items), 5)]
         return dict(zip(keys, loss_items))
 
-    def resume_training(self, ckpt):
-        """Resumes training from a given checkpoint."""
-        pass
-
     def plot_metrics(self):
         """Plots metrics from a CSV file."""
         plot_results(file=self.csv, classify=True, on_plot=self.on_plot)  # save results.png