Getting Started

Welcome

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.

Install

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

Conda Environment

All development and testing has been done in Conda Python 3 environments on Linux x86-64 systems, specifically Python 3.6.x, 3.7.x., 3.8.x., 3.9

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 1.4, 1.5.x, 1.6, 1.7.x, and 1.8 have been tested with 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=11.1 -c pytorch -c conda-forge
conda install pyyaml

Load a Pretrained Model

Pretrained models can be loaded using timm.create_model

import timm

m = timm.create_model('mobilenetv3_large_100', pretrained=True)
m.eval()

List Models with Pretrained Weights

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',
...
]

List Model Architectures by Wildcard

import timm
from pprint import pprint
model_names = timm.list_models('*resne*t*')
pprint(model_names)
>>> ['cspresnet50',
 'cspresnet50d',
 'cspresnet50w',
 'cspresnext50',
...
]