Instructions to use DevShubham/vit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DevShubham/vit with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="DevShubham/vit") 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("DevShubham/vit") model = AutoModelForImageClassification.from_pretrained("DevShubham/vit") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- acd25e985a428b7205e75338df53e559aa557060fa87688328318f8b58fb2459
- Size of remote file:
- 346 MB
- SHA256:
- 5f17067668129d23b52524f90a805e7d9914c276d90a59a13ebe81a09e40ceca
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