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
| { | |
| "epoch": 1.0, | |
| "total_flos": 2.2123963034966016e+18, | |
| "train_loss": 0.019373888820827176, | |
| "train_runtime": 828.3192, | |
| "train_samples_per_second": 34.467, | |
| "train_steps_per_second": 2.155 | |
| } |