Image Classification
Transformers
PyTorch
Spanish
English
multilingual
layoutlmv3
feature-extraction
Instructions to use fedihch/InvoiceReceiptClassifier_LayoutLMv3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fedihch/InvoiceReceiptClassifier_LayoutLMv3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="fedihch/InvoiceReceiptClassifier_LayoutLMv3") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("fedihch/InvoiceReceiptClassifier_LayoutLMv3") model = AutoModel.from_pretrained("fedihch/InvoiceReceiptClassifier_LayoutLMv3") - Notebooks
- Google Colab
- Kaggle
| { | |
| "apply_ocr": true, | |
| "do_normalize": true, | |
| "do_resize": true, | |
| "feature_extractor_type": "LayoutLMv3FeatureExtractor", | |
| "image_mean": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "image_std": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "ocr_lang": null, | |
| "processor_class": "LayoutLMv3Processor", | |
| "resample": 2, | |
| "size": 224, | |
| "tesseract_config": "" | |
| } | |