Instructions to use JCRios/ces-ViTModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JCRios/ces-ViTModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="JCRios/ces-ViTModel") 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("JCRios/ces-ViTModel") model = AutoModelForImageClassification.from_pretrained("JCRios/ces-ViTModel", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 12893ce3174427546b92fdda244dc698353cacd751b2ea80f0895d240e1e765f
- Size of remote file:
- 343 MB
- SHA256:
- 4067c1ff0f171a745e61e5ee8a9cbb1376ca2da0eb8ad3d5c453a31514648617
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