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55 lines
1.8 KiB
Plaintext
55 lines
1.8 KiB
Plaintext
# Sharing and Loading Models From the Hugging Face Hub
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The `timm` library has a built-in integration with the Hugging Face Hub, making it easy to share and load models from the 🤗 Hub.
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In this short guide, we'll see how to:
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1. Share a `timm` model on the Hub
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2. How to load that model back from the Hub
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## Authenticating
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First, you'll need to make sure you have the `huggingface_hub` package installed.
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```bash
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pip install huggingface_hub
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```
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Then, you'll need to authenticate yourself. You can do this by running the following command:
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```bash
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huggingface-cli login
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```
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Or, if you're using a notebook, you can use the `notebook_login` helper:
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```py
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>>> from huggingface_hub import notebook_login
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>>> notebook_login()
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```
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## Sharing a Model
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```py
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>>> import timm
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>>> model = timm.create_model('resnet18', pretrained=True, num_classes=4)
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```
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Here is where you would normally train or fine-tune the model. We'll skip that for the sake of this tutorial.
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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.
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```py
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>>> model_cfg = dict(labels=['a', 'b', 'c', 'd'])
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>>> timm.models.hub.push_to_hf_hub(model, 'resnet18-random', model_config=model_cfg)
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```
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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!
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## Loading a Model
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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`](https://huggingface.co/nateraw/resnet18-random), which is the model we just pushed to the Hub.
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```py
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>>> model_reloaded = timm.create_model('hf_hub:nateraw/resnet18-random', pretrained=True)
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```
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