Instructions to use Kaspar/vit-base-railspace with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Kaspar/vit-base-railspace with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Kaspar/vit-base-railspace") 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("Kaspar/vit-base-railspace") model = AutoModelForImageClassification.from_pretrained("Kaspar/vit-base-railspace") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- af3788b87842f44669b223b2f32439f05f2da7e0adda6d7aae17873861109995
- Size of remote file:
- 3.52 kB
- SHA256:
- 21312f30733caab70299264bb3b98de58467fd05290edc3114ae31b025ebcdf9
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