Models

timm.create_model

( model_name: strpretrained: bool = Falsepretrained_cfg: typing.Union[str, typing.Dict[str, typing.Any], timm.models._pretrained.PretrainedCfg, NoneType] = Nonepretrained_cfg_overlay: typing.Union[typing.Dict[str, typing.Any], NoneType] = Nonecheckpoint_path: str = ''scriptable: typing.Optional[bool] = Noneexportable: typing.Optional[bool] = Noneno_jit: typing.Optional[bool] = None**kwargs )

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.

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:

Copied

>>> 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

timm.list_models

( filter: typing.Union[str, typing.List[str]] = ''module: str = ''pretrained: bool = Falseexclude_filters: typing.Union[str, typing.List[str]] = ''name_matches_cfg: bool = Falseinclude_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

PreviousREFERENCE NextData

Last updated 1 year ago