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
Ctrl+K
- Aug25_20-58-28_ip-172-31-14-234
- Aug25_21-02-48_ip-172-31-14-234
- Aug25_21-03-10_ip-172-31-14-234
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- Aug25_23-02-31_ip-172-31-14-234
- Aug25_23-03-04_ip-172-31-14-234
- Aug25_23-06-01_ip-172-31-14-234
- Aug25_23-13-09_ip-172-31-14-234
- Aug26_11-03-01_ip-172-31-14-234