Instructions to use binjn/vit-finetuned2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use binjn/vit-finetuned2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="binjn/vit-finetuned2") 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-finetuned2") model = AutoModelForImageClassification.from_pretrained("binjn/vit-finetuned2") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e0ab52c9dfc225387273890c8abfc240cefba3c1389ea16eff87ca775daf4d21
- Size of remote file:
- 5.3 kB
- SHA256:
- 14a6dc83114659f3102265b5d2752f8ff487079ba8d7d2284ac1282f1caacff6
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