Instructions to use matteopilotto/vit-base-patch16-224-in21k-snacks with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use matteopilotto/vit-base-patch16-224-in21k-snacks with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="matteopilotto/vit-base-patch16-224-in21k-snacks") 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("matteopilotto/vit-base-patch16-224-in21k-snacks") model = AutoModelForImageClassification.from_pretrained("matteopilotto/vit-base-patch16-224-in21k-snacks") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ca4149fe2adb433620221d64e2d2368933ed826dd5998ca56f43d58d1c67da10
- Size of remote file:
- 343 MB
- SHA256:
- 564b95d5067bf66e4f3d8dcebbbc0b106c4d2f6d9c20eb7e041da50947c0b8d6
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