General refactoring and improvements (#373)

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

ultralytics/yolo/v8/classify/predict.py

@@ -58,10 +58,9 @@ class ClassificationPredictor(BasePredictor):
 
 @hydra.main(version_base=None, config_path=str(DEFAULT_CONFIG.parent), config_name=DEFAULT_CONFIG.name)
 def predict(cfg):
-    cfg.model = cfg.model or "squeezenet1_0"
+    cfg.model = cfg.model or "yolov8n-cls.pt"  # or "resnet18"
     cfg.imgsz = check_imgsz(cfg.imgsz, min_dim=2)  # check image size
     cfg.source = cfg.source if cfg.source is not None else ROOT / "assets"
-
     predictor = ClassificationPredictor(cfg)
     predictor.predict_cli()
 

ultralytics/yolo/v8/classify/train.py

@@ -136,7 +136,7 @@ class ClassificationTrainer(BaseTrainer):
 
 @hydra.main(version_base=None, config_path=str(DEFAULT_CONFIG.parent), config_name=DEFAULT_CONFIG.name)
 def train(cfg):
-    cfg.model = cfg.model or "yolov8n-cls.yaml"  # or "resnet18"
+    cfg.model = cfg.model or "yolov8n-cls.pt"  # or "resnet18"
     cfg.data = cfg.data or "mnist160"  # or yolo.ClassificationDataset("mnist")
     cfg.lr0 = 0.1
     cfg.weight_decay = 5e-5
@@ -151,10 +151,4 @@ def train(cfg):
 
 
 if __name__ == "__main__":
-    """
-    yolo task=classify mode=train model=yolov8n-cls.pt data=mnist160 epochs=10 imgsz=32
-    yolo task=classify mode=val model=runs/classify/train/weights/last.pt data=mnist160 imgsz=32
-    yolo task=classify mode=predict model=runs/classify/train/weights/last.pt imgsz=32 source=ultralytics/assets/bus.jpg
-    yolo mode=export model=runs/classify/train/weights/last.pt imgsz=32 format=torchscript
-    """
     train()

ultralytics/yolo/v8/classify/val.py

@@ -48,8 +48,8 @@ class ClassificationValidator(BaseValidator):
 
 @hydra.main(version_base=None, config_path=str(DEFAULT_CONFIG.parent), config_name=DEFAULT_CONFIG.name)
 def val(cfg):
+    cfg.model = cfg.model or "yolov8n-cls.pt"  # or "resnet18"
     cfg.data = cfg.data or "imagenette160"
-    cfg.model = cfg.model or "resnet18"
     validator = ClassificationValidator(args=cfg)
     validator(model=cfg.model)