Instructions to use google/vit-base-patch16-224 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/vit-base-patch16-224 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="google/vit-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("google/vit-base-patch16-224") model = AutoModelForImageClassification.from_pretrained("google/vit-base-patch16-224", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from google/vit-base-patch16-224: direct link, hf CLI and curl.
- Browser
- Download file 346 MB
-
https://huggingface.co/google/vit-base-patch16-224/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://google/vit-base-patch16-224/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/google/vit-base-patch16-224/resolve/main/flax_model.msgpack
346 MB
- Xet hash:
- a05aa68c898ee18e9b3189f8aabde8206a0f319f708ceed0f252144b5331d53c
- Size of remote file:
- 346 MB
- SHA256:
- e0c809a1fe1d79c51c4da8a9ebd6c0923bac317ca469a33c6620977054149471
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