Sharing and Loading Models From the Model Database Hub

The timm library has a built-in integration with the Model Database Hub, making it easy to share and load models from the 🤗 Hub.

In this short guide, we’ll see how to:

1. Share a timm model on the Hub
2. How to load that model back from the Hub

Authenticating

First, you’ll need to make sure you have the huggingface_hub package installed.

pip install huggingface_hub

Then, you’ll need to authenticate yourself. You can do this by running the following command:

huggingface-cli login

Or, if you’re using a notebook, you can use the notebook_login helper:

>>> from huggingface_hub import notebook_login
>>> notebook_login()

Sharing a Model

>>> import timm
>>> model = timm.create_model('resnet18', pretrained=True, num_classes=4)

Here is where you would normally train or fine-tune the model. We’ll skip that for the sake of this tutorial.

Let’s pretend we’ve now fine-tuned the model. The next step would be to push it to the Hub! We can do this with the timm.models.hub.push_to_hf_hub function.

>>> model_cfg = dict(labels=['a', 'b', 'c', 'd'])
>>> timm.models.hub.push_to_hf_hub(model, 'resnet18-random', model_config=model_cfg)

Running the above would push the model to <your-username>/resnet18-random on the Hub. You can now share this model with your friends, or use it in your own code!

Loading a Model

Loading a model from the Hub is as simple as calling timm.create_model with the pretrained argument set to the name of the model you want to load. In this case, we’ll use nateraw/resnet18-random, which is the model we just pushed to the Hub.

>>> model_reloaded = timm.create_model('hf_hub:nateraw/resnet18-random', pretrained=True)