Fix model re-fuse() in inference loops (#466)

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

ultralytics/yolo/v8/classify/predict.py

@@ -5,7 +5,7 @@ import torch
 
 from ultralytics.yolo.engine.predictor import BasePredictor
 from ultralytics.yolo.engine.results import Results
-from ultralytics.yolo.utils import DEFAULT_CONFIG, ROOT
+from ultralytics.yolo.utils import DEFAULT_CONFIG, ROOT, is_git_directory
 from ultralytics.yolo.utils.plotting import Annotator
 
 
@@ -67,7 +67,8 @@ 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 "yolov8n-cls.pt"  # or "resnet18"
-    cfg.source = cfg.source if cfg.source is not None else ROOT / "assets"
+    cfg.source = cfg.source if cfg.source is not None else ROOT / "assets" if is_git_directory() \
+        else "https://ultralytics.com/images/bus.jpg"
     predictor = ClassificationPredictor(cfg)
     predictor.predict_cli()
 

ultralytics/yolo/v8/classify/train.py

@@ -140,10 +140,13 @@ class ClassificationTrainer(BaseTrainer):
 def train(cfg):
     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
-    cfg.label_smoothing = 0.1
-    cfg.warmup_epochs = 0.0
+
+    # Reproduce ImageNet results
+    # cfg.lr0 = 0.1
+    # cfg.weight_decay = 5e-5
+    # cfg.label_smoothing = 0.1
+    # cfg.warmup_epochs = 0.0
+
     cfg.device = cfg.device if cfg.device is not None else ''
     # trainer = ClassificationTrainer(cfg)
     # trainer.train()