Instructions to use facebook/vit-mae-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/vit-mae-base with Transformers:
# Load model directly from transformers import AutoImageProcessor, AutoModelForPreTraining processor = AutoImageProcessor.from_pretrained("facebook/vit-mae-base") model = AutoModelForPreTraining.from_pretrained("facebook/vit-mae-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from facebook/vit-mae-base: direct link, hf CLI and curl.
- Browser
- Download file 448 MB
-
https://huggingface.co/facebook/vit-mae-base/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://facebook/vit-mae-base/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/facebook/vit-mae-base/resolve/main/pytorch_model.bin
448 MB
- Xet hash:
- 29721fe1b6496dfd3c4b3e38c4f090d3596e288f334dcb5fd176cd9b247f0f5f
- Size of remote file:
- 448 MB
- SHA256:
- 6748bebd304601d36283b156ab4f9212258a8aed3745beb2e2b7bc55a1992d99
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