Welcome to the timm
documentation, a lean set of docs that covers the basics of timm
.
For a more comprehensive set of docs (currently under development), please visit timmdocs by Aman Arora.
The library can be installed with pip:
pip install timm
I update the PyPi (pip) packages when I’m confident there are no significant model regressions from previous releases. If you want to pip install the bleeding edge from GitHub, use:
pip install git+https://github.com/rwightman/pytorch-image-models.git
All development and testing has been done in Conda Python 3 environments on Linux x86-64 systems, specifically 3.7, 3.8, 3.9, 3.10
Little to no care has been taken to be Python 2.x friendly and will not support it. If you run into any challenges running on Windows, or other OS, I’m definitely open to looking into those issues so long as it’s in a reproducible (read Conda) environment.
PyTorch versions 1.9, 1.10, 1.11 have been tested with the latest versions of this code.
I’ve tried to keep the dependencies minimal, the setup is as per the PyTorch default install instructions for Conda:
conda create -n torch-env conda activate torch-env conda install pytorch torchvision cudatoolkit=11.3 -c pytorch conda install pyyaml
Pretrained models can be loaded using timm.create_model
>>> import timm
>>> m = timm.create_model('mobilenetv3_large_100', pretrained=True)
>>> m.eval()
>>> import timm
>>> from pprint import pprint
>>> model_names = timm.list_models(pretrained=True)
>>> pprint(model_names)
[
'adv_inception_v3',
'cspdarknet53',
'cspresnext50',
'densenet121',
'densenet161',
'densenet169',
'densenet201',
'densenetblur121d',
'dla34',
'dla46_c',
]
>>> import timm
>>> from pprint import pprint
>>> model_names = timm.list_models('*resne*t*')
>>> pprint(model_names)
[
'cspresnet50',
'cspresnet50d',
'cspresnet50w',
'cspresnext50',
...
]