Instructions to use microsoft/beit-base-patch16-224 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use microsoft/beit-base-patch16-224 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="microsoft/beit-base-patch16-224") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("microsoft/beit-base-patch16-224") model = AutoModelForImageClassification.from_pretrained("microsoft/beit-base-patch16-224", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from microsoft/beit-base-patch16-224: direct link, hf CLI and curl.
- Browser
- Download file 346 MB
-
https://huggingface.co/microsoft/beit-base-patch16-224/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://microsoft/beit-base-patch16-224/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/microsoft/beit-base-patch16-224/resolve/main/flax_model.msgpack
346 MB
- Xet hash:
- 04041d41a24b44f9e50a4e8ccf3d2a5173999fde5a693967347af54e48184895
- Size of remote file:
- 346 MB
- SHA256:
- 7773d31f101f1e5247d1f8bd1a7a2d3fa2f4f80e1bfe2ec6c65e529db7e3bb5c
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