Instructions to use pphildan/vit-base-patch16-224-v22 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pphildan/vit-base-patch16-224-v22 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="pphildan/vit-base-patch16-224-v22") 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("pphildan/vit-base-patch16-224-v22") model = AutoModelForImageClassification.from_pretrained("pphildan/vit-base-patch16-224-v22", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from pphildan/vit-base-patch16-224-v22: direct link, hf CLI and curl.
- Browser
- Download file 343 MB
-
https://huggingface.co/pphildan/vit-base-patch16-224-v22/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://pphildan/vit-base-patch16-224-v22/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/pphildan/vit-base-patch16-224-v22/resolve/main/pytorch_model.bin
343 MB
- Xet hash:
- 7401c6771fe9fe84779f3b5dc468ee860f753240fc0d70425e0a7ca0adb3cdd2
- Size of remote file:
- 343 MB
- SHA256:
- e4961c02b4c7c918fae4e24197556d79d102d0993ffd90e71fa1d15a528ed3e5
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