Instructions to use microsoft/beit-large-patch16-224 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use microsoft/beit-large-patch16-224 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="microsoft/beit-large-patch16-224") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("microsoft/beit-large-patch16-224") model = AutoModelForImageClassification.from_pretrained("microsoft/beit-large-patch16-224", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from microsoft/beit-large-patch16-224: direct link, hf CLI and curl.
- Browser
- Download file 1.23 GB
-
https://huggingface.co/microsoft/beit-large-patch16-224/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://microsoft/beit-large-patch16-224/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/microsoft/beit-large-patch16-224/resolve/main/pytorch_model.bin
1.23 GB
- Xet hash:
- 712aefb2d82548263aa9572c77fe4baebb1a13b4e9a126d222e3ebf6264e6f0b
- Size of remote file:
- 1.23 GB
- SHA256:
- d92061f7389194d1bc7e332e6b7e8312970240209ea5c6ff37862171cbda2f19
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