Instructions to use pphildan/vit-base-patch16-224-v21 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pphildan/vit-base-patch16-224-v21 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="pphildan/vit-base-patch16-224-v21") 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-v21") model = AutoModelForImageClassification.from_pretrained("pphildan/vit-base-patch16-224-v21", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from pphildan/vit-base-patch16-224-v21: direct link, hf CLI and curl.
- Browser
- Download file 3.96 kB
-
https://huggingface.co/pphildan/vit-base-patch16-224-v21/resolve/refs%2Fpr%2F1/training_args.bin
- Command line
-
hf download hf://pphildan/vit-base-patch16-224-v21@refs/pr/1/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/pphildan/vit-base-patch16-224-v21/resolve/refs%2Fpr%2F1/training_args.bin
3.96 kB
- Xet hash:
- 9f5b0a02470104c76be2fd31095cc637de79e46ca02eb4fe9defaa9066a31072
- Size of remote file:
- 3.96 kB
- SHA256:
- f750b7fd17714d9ffb61484b5306f4f750d83fd5169f7ce272efbfd3964202b1
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