Image Classification
Transformers
PyTorch
TensorBoard
vit
Generated from Trainer
Eval Results (legacy)
Instructions to use acaballero76/vit-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use acaballero76/vit-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="acaballero76/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("acaballero76/vit-model") model = AutoModelForImageClassification.from_pretrained("acaballero76/vit-model") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 328040403ee15136f372d6e032e33abad9a5d467b3e31df8b3fca8e3d7c5cd96
- Size of remote file:
- 343 MB
- SHA256:
- b48d750810ec32fe255e1c9b02fb492128e8ecdbc04c138ca386790e117946c9
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