timm/pr_2749/en/reference/models.md

# Models[[timm.create_model]]

#### timm.create_model[[timm.create_model]]

```python
timm.create_model(model_name: str, pretrained: bool = False, pretrained_cfg: typing.Union[str, typing.Dict[str, typing.Any], timm.models._pretrained.PretrainedCfg, NoneType] = None, pretrained_cfg_overlay: typing.Optional[typing.Dict[str, typing.Any]] = None, checkpoint_path: typing.Union[str, pathlib.Path, NoneType] = None, cache_dir: typing.Union[str, pathlib.Path, NoneType] = None, scriptable: typing.Optional[bool] = None, exportable: typing.Optional[bool] = None, no_jit: typing.Optional[bool] = None, **kwargs: typing.Any)
```

[Source](https://github.com/huggingface/pytorch-image-models/blob/vr_2749/timm/models/_factory.py#L81)

**Parameters:**

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.

cache_dir : Override model cache dir for Hugging Face Hub and Torch checkpoints.

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).

Create a model.

Lookup model's entrypoint function and pass relevant args to create a new model.

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.

Keyword Args:
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

>>> # Create a Dinov2 small model with pretrained weights and save weights in a custom directory.
>>> model = create_model('vit_small_patch14_dinov2.lvd142m', pretrained=True, cache_dir="/data/my-models")
>>> # Data will be stored at */data/my-models/models--timm--vit_small_patch14_dinov2.lvd142m/*
```

#### timm.list_models[[timm.list_models]]

```python
timm.list_models(filter: typing.Union[str, typing.List[str]] = '', module: typing.Union[str, typing.List[str]] = '', pretrained: bool = False, exclude_filters: typing.Union[str, typing.List[str]] = '', name_matches_cfg: bool = False, include_tags: typing.Optional[bool] = None)
```

[Source](https://github.com/huggingface/pytorch-image-models/blob/vr_2749/timm/models/_registry.py#L185)

**Parameters:**

filter - Wildcard filter string that works with fnmatch --

module - Limit model selection to a specific submodule (ie 'vision_transformer') --

pretrained - Include only models with valid pretrained weights if True --

exclude_filters - Wildcard filters to exclude models after including them with filter --

name_matches_cfg - Include only models w/ model_name matching default_cfg name (excludes some aliases) --

include_tags - Include pretrained tags in model names (model.tag). If None, defaults : set to True when pretrained=True else False (default: None)

**Returns:**

models - The sorted list of models

Return list of available model names, sorted alphabetically

Example:
model_list('gluon_resnet*') -- returns all models starting with 'gluon_resnet'
model_list('*resnext*, 'resnet') -- returns all models with 'resnext' in 'resnet' module