Instructions to use binjn/vit-finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use binjn/vit-finetuned with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="binjn/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("binjn/vit-finetuned") model = AutoModelForImageClassification.from_pretrained("binjn/vit-finetuned") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8c6baa756c0c5eedea1090de75855115b3d0eebbdd2c11dfc2ffac410011960e
- Size of remote file:
- 5.3 kB
- SHA256:
- c8776503c5f8752e0cf739985eaeb8632acd0d771d741f47dbe7cd79610d90a6
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