changed save path to not override previous training

run_pgt_train.py

@@ -3,6 +3,7 @@ from ultralytics import YOLOv10, YOLO
 import os
 from ultralytics.models.yolo.segment import PGTSegmentationTrainer
 import argparse
+from datetime import datetime
 
 
 def main(args):
@@ -15,7 +16,7 @@ def main(args):
     # wget https://github.com/THU-MIG/yolov10/releases/download/v1.1/yolov10{n/s/m/b/l/x}.pt
     model = YOLOv10('yolov10n.pt', task='segment')
 
-    args = dict(model='yolov10n.pt', data='coco128-seg.yaml', 
+    args = dict(model='yolov10n.pt', data=args.data_yaml, 
                 epochs=args.epochs, batch=args.batch_size,
                 # cfg = 'pgt_train.yaml', # This can be edited for full control of the training process
                 )
@@ -25,8 +26,16 @@ def main(args):
                 # args = dict(pgt_coeff=0.1), # Should add later to config
                 )
 
-    # Save the trained model
-    model.save('yolov10_coco_trained.pt')
+    # Create a directory to save model weights if it doesn't exist
+    model_weights_dir = 'model_weights'
+    if not os.path.exists(model_weights_dir):
+        os.makedirs(model_weights_dir)
+
+    # Save the trained model with a unique name based on the current date and time
+    current_time = datetime.now().strftime('%Y%m%d_%H%M%S')
+    data_yaml_base = os.path.splitext(os.path.basename(args.data_yaml))[0]
+    model_save_path = os.path.join(model_weights_dir, f'yolov10_{data_yaml_base}_trained_{current_time}.pt')
+    model.save(model_save_path)  
 
     # Evaluate the model on the validation set
     results = model.val(data='coco.yaml')
@@ -39,6 +48,7 @@ if __name__ == "__main__":
     parser.add_argument('--device', type=str, default='0', help='CUDA device number')
     parser.add_argument('--batch_size', type=int, default=64, help='Batch size for training')
     parser.add_argument('--epochs', type=int, default=100, help='Number of epochs for training')
+    parser.add_argument('--data_yaml', type=str, required=True, default='coco.yaml', help='Path to the data YAML file')
     args = parser.parse_args()
 
     # Set CUDA device (only needed for multi-gpu machines)