Image Classification
Transformers
TensorBoard
Safetensors
vit
Generated from Trainer
Eval Results (legacy)
Instructions to use jefercania/vit_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jefercania/vit_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="jefercania/vit_model") 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("jefercania/vit_model") model = AutoModelForImageClassification.from_pretrained("jefercania/vit_model") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b63d3c54f17c2da89333968e9b016bbf6e22c667f51210a6f02648cdcc8f976f
- Size of remote file:
- 4.6 kB
- SHA256:
- 0fd61694d49f72c49f2b9e24d0d18b889e0e30a4e7b8dd1b2f3f6e8bbd4081a9
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