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Models

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 )

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:

>>> 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

< >

( 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 )

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)

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

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