pip install timmOr 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) 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 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] 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 The fastai library has support for fine-tuning models from timm:
from fastai.vision.all import * path = untar_data(URLs.PETS)/'images' dls = ImageDataLoaders.from_name_func( path, get_image_files(path), valid_pct=0.2, label_func=lambda x: x[0].isupper(), item_tfms=Resize(224)) # if a string is passed into the model argument, it will now use timm (if it is installed) learn = vision_learner(dls, 'vit_tiny_patch16_224', metrics=error_rate) learn.fine_tune(1)