Image Classification
Transformers
PyTorch
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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
InvoiceReceiptClassifier_LayoutLMv3 is a fine-tuned LayoutLMv3 model that classifies a document to an invoice or receipt.
Quick start: using the raw model
from transformers import (
AutoModelForSequenceClassification,
AutoProcessor,
)
from PIL import Image
from urllib.request import urlopen
model = AutoModelForSequenceClassification.from_pretrained("fedihch/InvoiceReceiptClassifier_LayoutLMv3")
processor = AutoProcessor.from_pretrained("fedihch/InvoiceReceiptClassifier_LayoutLMv3")
input_img_url = "https://upload.wikimedia.org/wikipedia/commons/thumb/0/0b/ReceiptSwiss.jpg/1024px-ReceiptSwiss.jpg"
with urlopen(input_img_url) as testImage:
input_img = Image.open(testImage).convert("RGB")
encoded_inputs = processor(input_img, padding="max_length", return_tensors="pt")
outputs = model(**encoded_inputs)
logits = outputs.logits
predicted_class_idx = logits.argmax(-1).item()
id2label = {0: "invoice", 1: "receipt"}
print(id2label[predicted_class_idx])
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