Instructions to use Straueri/vit-base-oxford-iiit-pets with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Straueri/vit-base-oxford-iiit-pets with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="Straueri/vit-base-oxford-iiit-pets") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# pip install -U transformers accelerate # Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("Straueri/vit-base-oxford-iiit-pets") model = AutoModelForImageClassification.from_pretrained("Straueri/vit-base-oxford-iiit-pets", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Straueri/vit-base-oxford-iiit-pets: direct link, hf CLI and curl.
- Browser
- Download file 5.43 kB
-
https://huggingface.co/Straueri/vit-base-oxford-iiit-pets/resolve/main/training_args.bin
- Command line
-
hf download hf://Straueri/vit-base-oxford-iiit-pets/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Straueri/vit-base-oxford-iiit-pets/resolve/main/training_args.bin
5.43 kB
- Xet hash:
- d45f9a70b14b546fea34f0226c95407c91c1ffdaf84a62f95e3b0b2c8eec3ce3
- Size of remote file:
- 5.43 kB
- SHA256:
- 998754ae0cd5b6e1463cf5aa49fee17c9ff5008c6c791598b68f1a6fde42cf5e
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