Instructions to use microsoft/beit-base-patch16-224-pt22k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use microsoft/beit-base-patch16-224-pt22k with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="microsoft/beit-base-patch16-224-pt22k") 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, BeitForMaskedImageModeling processor = AutoImageProcessor.from_pretrained("microsoft/beit-base-patch16-224-pt22k") model = BeitForMaskedImageModeling.from_pretrained("microsoft/beit-base-patch16-224-pt22k", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from microsoft/beit-base-patch16-224-pt22k: direct link, hf CLI and curl.
- Browser
- Download file 368 MB
-
https://huggingface.co/microsoft/beit-base-patch16-224-pt22k/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://microsoft/beit-base-patch16-224-pt22k/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/microsoft/beit-base-patch16-224-pt22k/resolve/main/flax_model.msgpack
368 MB
- Xet hash:
- 4076a00460ed1ef961a34a4000debfdfc20b311ee2ab2bd35fbe2e5a2f0bc181
- Size of remote file:
- 368 MB
- SHA256:
- 992202b28ad4d6f77a846849e498a6a634af5d8e92619ad78afb358c8e8084d1
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.