Instructions to use uisikdag/vit-tiny-patch16-224-from-scratch-oxford-pets-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use uisikdag/vit-tiny-patch16-224-from-scratch-oxford-pets-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="uisikdag/vit-tiny-patch16-224-from-scratch-oxford-pets-classification") 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("uisikdag/vit-tiny-patch16-224-from-scratch-oxford-pets-classification") model = AutoModelForImageClassification.from_pretrained("uisikdag/vit-tiny-patch16-224-from-scratch-oxford-pets-classification", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f6fec57fbe5829ea420d8dabd2fc2381fad430d6fe9c65953a0ca4536b1c8f58
- Size of remote file:
- 5.11 kB
- SHA256:
- 246cbee002a5cb584758b2f365feedcf56296f84653f9f721a6f0896b9000741
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