Update Docstring for create_model

timm/models/_factory.py

@@ -12,7 +12,7 @@
 __all__ = ['parse_model_name', 'safe_model_name', 'create_model']
 
 
-def parse_model_name(model_name):
+def parse_model_name(model_name: str):
     if model_name.startswith('hf_hub'):
         # NOTE for backwards compat, deprecate hf_hub use
         model_name = model_name.replace('hf_hub', 'hf-hub')
@@ -26,7 +26,7 @@ def parse_model_name(model_name):
         return 'timm', model_name
 
 
-def safe_model_name(model_name, remove_source=True):
+def safe_model_name(model_name: str, remove_source: bool = True):
     # return a filename / path safe model name
     def make_safe(name):
         return ''.join(c if c.isalnum() else '_' for c in name).rstrip('_')
@@ -46,27 +46,48 @@ def create_model(
         no_jit: Optional[bool] = None,
         **kwargs,
 ):
-    """Create a model
+    """Create a model.
 
     Lookup model's entrypoint function and pass relevant args to create a new model.
 
-    **kwargs will be passed through entrypoint fn to timm.models.build_model_with_cfg()
-    and then the model class __init__(). kwargs values set to None are pruned before passing.
+    <Tip>
+        **kwargs will be passed through entrypoint fn to ``timm.models.build_model_with_cfg()``
+        and then the model class __init__(). kwargs values set to None are pruned before passing.
+    </Tip>
 
     Args:
-        model_name (str): name of model to instantiate
-        pretrained (bool): load pretrained ImageNet-1k weights if true
-        pretrained_cfg (Union[str, dict, PretrainedCfg]): pass in external pretrained_cfg for model
-        pretrained_cfg_overlay (dict): replace key-values in base pretrained_cfg with these
-        checkpoint_path (str): path of checkpoint to load _after_ the model is initialized
-        scriptable (bool): set layer config so that model is jit scriptable (not working for all models yet)
-        exportable (bool): set layer config so that model is traceable / ONNX exportable (not fully impl/obeyed yet)
-        no_jit (bool): set layer config so that model doesn't utilize jit scripted layers (so far activations only)
+        model_name: Name of model to instantiate.
+        pretrained: If set to `True`, load pretrained ImageNet-1k weights.
+        pretrained_cfg: Pass in an external pretrained_cfg for model.
+        pretrained_cfg_overlay: Replace key-values in base pretrained_cfg with these.
+        checkpoint_path: Path of checkpoint to load _after_ the model is initialized.
+        scriptable: Set layer config so that model is jit scriptable (not working for all models yet).
+        exportable: Set layer config so that model is traceable / ONNX exportable (not fully impl/obeyed yet).
+        no_jit: Set layer config so that model doesn't utilize jit scripted layers (so far activations only).
 
     Keyword Args:
-        drop_rate (float): dropout rate for training (default: 0.0)
-        global_pool (str): global pool type (default: 'avg')
-        **: other kwargs are consumed by builder or model __init__()
+        drop_rate (float): Classifier dropout rate for training.
+        drop_path_rate (float): Stochastic depth drop rate for training.
+        global_pool (str): Classifier global pooling type.
+
+    Example:
+
+    ```py
+    >>> from timm import create_model
+
+    >>> # Create a MobileNetV3-Large model with no pretrained weights.
+    >>> model = create_model('mobilenetv3_large_100')
+
+    >>> # Create a MobileNetV3-Large model with pretrained weights.
+    >>> model = create_model('mobilenetv3_large_100', pretrained=True)
+    >>> model.num_classes
+    1000
+
+    >>> # Create a MobileNetV3-Large model with pretrained weights and a new head with 10 classes.
+    >>> model = create_model('mobilenetv3_large_100', pretrained=True, num_classes=10)
+    >>> model.num_classes
+    10
+    ```
     """
     # Parameters that aren't supported by all models or are intended to only override model defaults if set
     # should default to None in command line args/cfg. Remove them if they are present and not set so that