Instructions to use LSummer/ViT_dog_food with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LSummer/ViT_dog_food with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="LSummer/ViT_dog_food") 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("LSummer/ViT_dog_food") model = AutoModelForImageClassification.from_pretrained("LSummer/ViT_dog_food", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from LSummer/ViT_dog_food: direct link, hf CLI and curl.
- Browser
- Download file 5.37 kB
-
https://huggingface.co/LSummer/ViT_dog_food/resolve/main/training_args.bin
- Command line
-
hf download hf://LSummer/ViT_dog_food/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/LSummer/ViT_dog_food/resolve/main/training_args.bin
5.37 kB
- Xet hash:
- 10ca6dcf3579a3034c63fa8de9e2a8903477be99e48306e6419015563613c7a6
- Size of remote file:
- 5.37 kB
- SHA256:
- bf2bd218c8f7c4ec1bd2b3c08aa5cab8640a3f1ac902f41070c73196e3fffc21
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