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, | |
| "eval_f1": 0.9990009990009989, | |
| "eval_loss": 0.0017420644871890545, | |
| "eval_runtime": 30.428, | |
| "eval_samples_per_second": 37.794, | |
| "eval_steps_per_second": 4.732, | |
| "total_flos": 5.463185267828736e+17, | |
| "train_loss": 0.009828982074758727, | |
| "train_runtime": 378.8692, | |
| "train_samples_per_second": 18.608, | |
| "train_steps_per_second": 1.164 | |
| } |