Instructions to use venuv62/vit-base-patch16-224 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use venuv62/vit-base-patch16-224 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="venuv62/vit-base-patch16-224") 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("venuv62/vit-base-patch16-224") model = AutoModelForImageClassification.from_pretrained("venuv62/vit-base-patch16-224", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from venuv62/vit-base-patch16-224: direct link, hf CLI and curl.
- Browser
- Download file 3.45 kB
-
https://huggingface.co/venuv62/vit-base-patch16-224/resolve/main/training_args.bin
- Command line
-
hf download hf://venuv62/vit-base-patch16-224/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/venuv62/vit-base-patch16-224/resolve/main/training_args.bin
3.45 kB
- Xet hash:
- b6b729fae03c7884670ffcd9967ccbcc0332ca0b985294836965342e66b11e68
- Size of remote file:
- 3.45 kB
- SHA256:
- 9da88fc184499e7b01b3150eb6032740381c80e0311d71eaecb0bdf1f768aeea
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