Instructions to use jjmcarrascosa/vit_receipts_classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jjmcarrascosa/vit_receipts_classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="jjmcarrascosa/vit_receipts_classifier") 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("jjmcarrascosa/vit_receipts_classifier") model = AutoModelForImageClassification.from_pretrained("jjmcarrascosa/vit_receipts_classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 63a382c6781a80e92072cbda6c1aefb3003fcd9de0950b8233c8ea3ed9b8bc10
- Size of remote file:
- 3.38 kB
- SHA256:
- 39f257e82f28143d7ff7b116edd3fbbc7443cda81b679e766139180799cfb6e4
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