diff --git a/hubconf.py b/hubconf.py
index ee5a7d87224d..7f897d15c314 100644
--- a/hubconf.py
+++ b/hubconf.py
@@ -18,7 +18,7 @@
 check_requirements(Path(__file__).parent / 'requirements.txt', exclude=('tensorboard', 'pycocotools', 'thop'))
 
 
-def create(name, pretrained, channels, classes, autoshape, verbose):
+def create(name, pretrained, channels=3, classes=80, autoshape=True, verbose=True):
     """Creates a specified YOLOv5 model
 
     Arguments:
@@ -33,7 +33,7 @@ def create(name, pretrained, channels, classes, autoshape, verbose):
         YOLOv5 pytorch model
     """
     set_logging(verbose=verbose)
-    fname = f'{name}.pt'  # checkpoint filename
+    fname = Path(name).with_suffix('.pt')  # checkpoint filename
     try:
         if pretrained and channels == 3 and classes == 80:
             model = attempt_load(fname, map_location=torch.device('cpu'))  # download/load FP32 model
@@ -60,30 +60,9 @@ def create(name, pretrained, channels, classes, autoshape, verbose):
         raise Exception(s) from e
 
 
-def custom(path_or_model='path/to/model.pt', autoshape=True, verbose=True):
-    """YOLOv5-custom model https://github.com/ultralytics/yolov5
-
-    Arguments (3 options):
-        path_or_model (str): 'path/to/model.pt'
-        path_or_model (dict): torch.load('path/to/model.pt')
-        path_or_model (nn.Module): torch.load('path/to/model.pt')['model']
-
-    Returns:
-        pytorch model
-    """
-    set_logging(verbose=verbose)
-
-    model = torch.load(path_or_model) if isinstance(path_or_model, str) else path_or_model  # load checkpoint
-    if isinstance(model, dict):
-        model = model['ema' if model.get('ema') else 'model']  # load model
-
-    hub_model = Model(model.yaml).to(next(model.parameters()).device)  # create
-    hub_model.load_state_dict(model.float().state_dict())  # load state_dict
-    hub_model.names = model.names  # class names
-    if autoshape:
-        hub_model = hub_model.autoshape()  # for file/URI/PIL/cv2/np inputs and NMS
-    device = select_device('0' if torch.cuda.is_available() else 'cpu')  # default to GPU if available
-    return hub_model.to(device)
+def custom(path='path/to/model.pt', autoshape=True, verbose=True):
+    # YOLOv5 custom or local model
+    return create(path, autoshape, verbose)
 
 
 def yolov5s(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True):
@@ -127,7 +106,8 @@ def yolov5x6(pretrained=True, channels=3, classes=80, autoshape=True, verbose=Tr
 
 
 if __name__ == '__main__':
-    model = create(name='yolov5s', pretrained=True, channels=3, classes=80, autoshape=True, verbose=True)  # pretrained
+    model = create(name='weights/yolov5s.pt', pretrained=True, channels=3, classes=80, autoshape=True,
+                   verbose=True)  # pretrained
     # model = custom(path_or_model='path/to/model.pt')  # custom
 
     # Verify inference