Ooredoo-Group/ooredoo-stamp-detection

Ooredoo Stamp Detection Model

A fine-tuned YOLOS model for detecting stamps in documents.

Model Description

This model is based on hustvl/yolos-small and fine-tuned for single-class stamp detection in documents. It can identify and localize stamps with high accuracy.

• Base Model: hustvl/yolos-small
• Task: Object Detection
• Classes: 1 (stamp)
• Input: RGB images
• Output: Bounding boxes + confidence scores

Author

• V. Hisamutdinovs

Usage

from transformers import AutoImageProcessor, AutoModelForObjectDetection
from PIL import Image
import torch

# Load model
processor = AutoImageProcessor.from_pretrained("Ooredoo-Group/ooredoo-stamp-detection")
model = AutoModelForObjectDetection.from_pretrained("Ooredoo-Group/ooredoo-stamp-detection")

# Inference
image = Image.open("document_with_stamp.jpg")
inputs = processor(images=image, return_tensors="pt")
outputs = model(**inputs)

# Post-process results
target_sizes = torch.tensor([image.size[::-1]])
results = processor.post_process_object_detection(outputs, target_sizes=target_sizes)[0]

# Display results
for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
    print(f"Detected stamp with confidence {score:.2f}")
    print(f"Bounding box: {box}")

Training Data

• Total Images: 350
• Total Annotations: 711 bounding boxes
• Format: COCO format
• Split: Train (80%) / Val (15%) / Test (5%)
• Sources: Kaggle stamp dataset + HuggingFace datasets

Performance

• Confidence: High confidence detections (0.99+)
• Accuracy: Excellent performance on document stamp detection
• Speed: Fast inference suitable for production use

Limitations

• Designed specifically for stamp detection
• Performance may vary on different document types
• Requires proper image preprocessing

Organization

Ooredoo-Group

License

Apache-2.0