Instructions to use Kevin-M-Smith/vit_600_600_1600 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Kevin-M-Smith/vit_600_600_1600 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Kevin-M-Smith/vit_600_600_1600") 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("Kevin-M-Smith/vit_600_600_1600") model = AutoModelForImageClassification.from_pretrained("Kevin-M-Smith/vit_600_600_1600") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- afab4878463950cb18d73deadec0bb83dc2286e0ce688585d21ee1a690b6c453
- Size of remote file:
- 343 MB
- SHA256:
- 95dc83661755c4a5aba12da84f6b916c49b38cdcdfa17b1647aa69c6781c30be
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