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)
 

ultralytics/yolo/v8/detect/train.py

@@ -197,7 +197,7 @@ class Loss:
 
 @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.yaml"
+    cfg.model = cfg.model or "yolov8n.pt"
     cfg.data = cfg.data or "coco128.yaml"  # or yolo.ClassificationDataset("mnist")
     cfg.device = cfg.device if cfg.device is not None else ''
     # trainer = DetectionTrainer(cfg)
@@ -208,11 +208,4 @@ def train(cfg):
 
 
 if __name__ == "__main__":
-    """
-    CLI usage:
-    python ultralytics/yolo/v8/detect/train.py model=yolov8n.yaml data=coco128 epochs=100 imgsz=640
-
-    TODO:
-    yolo task=detect mode=train model=yolov8n.yaml data=coco128.yaml epochs=100
-    """
     train()

ultralytics/yolo/v8/detect/val.py

@@ -234,6 +234,7 @@ class DetectionValidator(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.pt"
     cfg.data = cfg.data or "coco128.yaml"
     validator = DetectionValidator(args=cfg)
     validator(model=cfg.model)

ultralytics/yolo/v8/segment/train.py

@@ -143,7 +143,7 @@ class SegLoss(Loss):
 
 @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-seg.yaml"
+    cfg.model = cfg.model or "yolov8n-seg.pt"
     cfg.data = cfg.data or "coco128-seg.yaml"  # or yolo.ClassificationDataset("mnist")
     cfg.device = cfg.device if cfg.device is not None else ''
     # trainer = SegmentationTrainer(cfg)
@@ -154,11 +154,4 @@ def train(cfg):
 
 
 if __name__ == "__main__":
-    """
-    CLI usage:
-    python ultralytics/yolo/v8/segment/train.py model=yolov8n-seg.yaml data=coco128-segments epochs=100 imgsz=640
-
-    TODO:
-    Direct cli support, i.e, yolov8 classify_train args.epochs 10
-    """
     train()

ultralytics/yolo/v8/segment/val.py

@@ -114,8 +114,9 @@ class SegmentationValidator(DetectionValidator):
                                                     masks=True)
                 if self.args.plots:
                     self.confusion_matrix.process_batch(predn, labelsn)
-            self.stats.append((correct_masks, correct_bboxes, pred[:, 4], pred[:,
-                                                                               5], cls.squeeze(-1)))  # conf, pcls, tcls
+
+            # Append correct_masks, correct_boxes, pconf, pcls, tcls
+            self.stats.append((correct_masks, correct_bboxes, pred[:, 4], pred[:, 5], cls.squeeze(-1)))
 
             pred_masks = torch.as_tensor(pred_masks, dtype=torch.uint8)
             if self.args.plots and self.batch_i < 3: