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:
- deaca31867d8750126f567ffdadf109e82b75810d2c92d526d6ffc90dc75a398
- Size of remote file:
- 344 MB
- SHA256:
- 558dbd865db0b510e9371f83a25e36e6d9cfe492a73e12fca0b0b47266dbe99e
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