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
- Aug26_11-54-12_ip-172-31-14-234
- Aug26_12-04-24_ip-172-31-14-234
- Aug26_12-05-14_ip-172-31-14-234
- Aug26_12-05-49_ip-172-31-14-234
- Aug26_12-06-57_ip-172-31-14-234
- Aug26_12-10-06_ip-172-31-14-234
- Aug26_12-57-08_ip-172-31-14-234
- Aug26_12-58-28_ip-172-31-14-234
- Aug26_13-38-52_ip-172-31-14-234
- Aug26_18-17-48_ip-172-31-14-234