timmis a deep-learning library created by Ross Wightman and is a collection of SOTA computer vision models, layers, utilities, optimizers, schedulers, data-loaders, augmentations and also training/validating scripts with ability to reproduce ImageNet training results.
pip install timm
Or for an editable install,
git clone https://github.com/rwightman/pytorch-image-models
cd pytorch-image-models && pip install -e .
import timm
import torch
model = timm.create_model('resnet34')
x = torch.randn(1, 3, 224, 224)
model(x).shape
It is that simple to create a model using timm. The create_model function is a factory method that can be used to create over 300 models that are part of the timm library.
To create a pretrained model, simply pass in pretrained=True.
pretrained_resnet_34 = timm.create_model('resnet34', pretrained=True)
Downloading: "https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resnet34-43635321.pth" to /Users/amanarora/.cache/torch/hub/checkpoints/resnet34-43635321.pth
To create a model with a custom number of classes, simply pass in num_classes=<number_of_classes>.
import timm
import torch
model = timm.create_model('resnet34', num_classes=10)
x = torch.randn(1, 3, 224, 224)
model(x).shape
torch.Size([1, 10])
timm.list_models() returns a complete list of available models in timm. To have a look at a complete list of pretrained models, pass in pretrained=True in list_models.
avail_pretrained_models = timm.list_models(pretrained=True)
len(avail_pretrained_models), avail_pretrained_models[:5]
(271, ['adv_inception_v3', 'cspdarknet53', 'cspresnet50', 'cspresnext50', 'densenet121'])
There are a total of 271 models with pretrained weights currently available in timm!
It is also possible to search for model architectures using Wildcard as below:
all_densenet_models = timm.list_models('*densenet*')
all_densenet_models
['densenet121', 'densenet121d', 'densenet161', 'densenet169', 'densenet201', 'densenet264', 'densenet264d_iabn', 'densenetblur121d', 'tv_densenet121']