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 preprocessor_config.json from google/vit-base-patch16-224: direct link, hf CLI and curl.
- Browser
- Download file 160 Bytes
-
https://huggingface.co/google/vit-base-patch16-224/resolve/refs%2Fpr%2F25/preprocessor_config.json
- Command line
-
hf download hf://google/vit-base-patch16-224@refs/pr/25/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/google/vit-base-patch16-224/resolve/refs%2Fpr%2F25/preprocessor_config.json
160 Bytes
| { | |
| "do_normalize": true, | |
| "do_resize": true, | |
| "image_mean": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "image_std": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "size": 224 | |
| } | |