Instructions to use VectorZhao/vit-base-oxford-iiit-pets with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use VectorZhao/vit-base-oxford-iiit-pets with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="VectorZhao/vit-base-oxford-iiit-pets") 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("VectorZhao/vit-base-oxford-iiit-pets") model = AutoModelForImageClassification.from_pretrained("VectorZhao/vit-base-oxford-iiit-pets") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0d33a5f3fd195ddc1fe932b7ba747928579df37a6fc2e6709a49535fd886b3fc
- Size of remote file:
- 5.11 kB
- SHA256:
- fd33855ecb62796c4bc4cccb4615b62fa03121950f04661af2b9171679ade3fd
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