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")# pip install -U transformers accelerate # 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 pytorch_model.bin from microsoft/beit-base-patch16-224: direct link, hf CLI and curl.
- Browser
- Download file 350 MB
-
https://huggingface.co/microsoft/beit-base-patch16-224/resolve/refs%2Fpr%2F4/pytorch_model.bin
- Command line
-
hf download hf://microsoft/beit-base-patch16-224@refs/pr/4/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/microsoft/beit-base-patch16-224/resolve/refs%2Fpr%2F4/pytorch_model.bin
350 MB
- Xet hash:
- 2c5ad2d7a1be1c9b39f0c17c3cfc92ad979934045045c492f3304a60e6c04335
- Size of remote file:
- 350 MB
- SHA256:
- 5574866866a425d7fc2b21e400f064d67d3650529ef9b9ab6b1ff3f9b6c9cbb6
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