Instructions to use sasha/vit-finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sasha/vit-finetuned with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="sasha/vit-finetuned") 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("sasha/vit-finetuned") model = AutoModelForImageClassification.from_pretrained("sasha/vit-finetuned") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 957279fa52127eead2965d5f927cd39659db35bd64ed302417c788414d0bba28
- Size of remote file:
- 5.27 kB
- SHA256:
- 1f15ea3a3e495d67eab7ccffac84212c3e24c2d8654d6c3b39aec81b43306cf4
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