Instructions to use randomstate42/vit_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use randomstate42/vit_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="randomstate42/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("randomstate42/vit_model") model = AutoModelForImageClassification.from_pretrained("randomstate42/vit_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6f4693637698216772cb4af2d54badac7321a39e42c49126f4419f65bc3faba4
- Size of remote file:
- 4.03 kB
- SHA256:
- 8cf4e0e48148033df4c56248117bca2fba90be393622b76e58a123c5b2534ebf
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