Instructions to use pphildan/vit-base-patch16-224-v17_4e with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pphildan/vit-base-patch16-224-v17_4e with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="pphildan/vit-base-patch16-224-v17_4e") 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-v17_4e") model = AutoModelForImageClassification.from_pretrained("pphildan/vit-base-patch16-224-v17_4e", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from pphildan/vit-base-patch16-224-v17_4e: direct link, hf CLI and curl.
- Browser
- Download file 343 MB
-
https://huggingface.co/pphildan/vit-base-patch16-224-v17_4e/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://pphildan/vit-base-patch16-224-v17_4e/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/pphildan/vit-base-patch16-224-v17_4e/resolve/main/pytorch_model.bin
343 MB
- Xet hash:
- 8b833ec96f62e1231ee1b2d99d45eea87856df63123dd5a454fa548ea51e1c6d
- Size of remote file:
- 343 MB
- SHA256:
- 69843e1ac020b36769f6d31543a7e2b82232f710e29e3fcfa0e47c0bd870dba4
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