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:
# pip install -U transformers accelerate # 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
Commit History
-
Adding `safetensors` variant of this model
Verified signatureThis is an automated PR created with https://huggingface.co/spaces/safetensors/convert This new file is equivalent to `pytorch_model.bin` but safe in the sense that no arbitrary code can be put into it. These files also happen to load much faster than their pytorch counterpart: https://colab.research.google.com/github/huggingface/notebooks/blob/main/safetensors_doc/en/speed.ipynb The widgets on your model page will run using this model even if this is not merged making sure the file actually works. If you find any issues: please report here: https://huggingface.co/spaces/safetensors/convert/discussions Feel free to ignore this PR.0dfb9b7
-
Update README.md
f4bf8a5
-
Update README.md
87dd4fa -
add TF model
Joao Ganteb7984ad
-
Update README.md
621ecfe -
Fix code example
54a0cf5 -
Create README.md
07a35ff -
Remove feature_extractor_type
127ef37 -
Update preprocessor_config.json
e473027
-
First commit
4550604 -
initial commit
9c5ed9a