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asmud/
ds4sd-DocumentClassifier-onnx

onnxruntime

onnx

document-classification

ds4sd/document-corpus

2408.09869

mit

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DocumentClassifier ONNX

Optimized ONNX implementation of DS4SD DocumentClassifier for high-performance document type classification.

License: MIT

ONNX

Python 3.8+

🎯 Overview

DocumentClassifier is a deep learning model designed for automatic document type classification. This ONNX version provides optimized inference for production environments with enhanced performance through JPQD (Joint Pruning, Quantization, and Distillation) optimization.

Key Features

High Accuracy: Reliable document type classification across multiple categories
Fast Inference: ~28ms per document on CPU (35+ FPS)
Production Ready: ONNX format for cross-platform deployment
Memory Efficient: Optimized model size with JPQD compression
Easy Integration: Simple Python API with comprehensive examples

🚀 Quick Start

Installation

pip install onnxruntime opencv-python pillow numpy

Basic Usage

python

from example import DocumentClassifierONNX
import cv2

# Initialize model
classifier = DocumentClassifierONNX("DocumentClassifier.onnx")

# Classify document from image file
result = classifier.classify("document.jpg")
print(f"Document type: {result['predicted_category']}")
print(f"Confidence: {result['confidence']:.3f}")

# Get top predictions
for pred in result['top_predictions']:
    print(f"{pred['category']}: {pred['confidence']:.3f}")

Command Line Interface

bash

# Classify a document image
python example.py --image document.jpg

# Run performance benchmark  
python example.py --benchmark --iterations 100

# Demo with dummy data
python example.py

📊 Model Specifications

Specification	Value
Input Shape	`[1, 3, 224, 224]`
Input Type	`float32`
Output Shape	`[1, 1280, 7, 7]`
Output Type	`float32`
Model Size	~8.2MB
Parameters	~2.1M
Framework	ONNX Runtime

🏷️ Supported Document Categories

The model can classify documents into the following categories:

Article - News articles, blog posts, web content
Form - Application forms, surveys, questionnaires
Letter - Business letters, correspondence
Memo - Internal memos, notices
News - Newspaper articles, press releases
Presentation - Slides, presentation materials
Resume - CVs, resumes, professional profiles
Scientific - Research papers, academic documents
Specification - Technical specs, manuals
Table - Data tables, spreadsheet content
Other - Miscellaneous document types

⚡ Performance Benchmarks

Inference Speed (CPU)

Mean: 28.1ms ± 0.5ms
Throughput: ~35.6 FPS
Hardware: Modern CPU (single thread)
Batch Size: 1

Memory Usage

Model Loading: ~50MB RAM
Inference: ~100MB RAM
Peak Usage: ~150MB RAM

🔧 Advanced Usage

Batch Processing

python

import numpy as np
from example import DocumentClassifierONNX

classifier = DocumentClassifierONNX()

# Process multiple images
image_paths = ["doc1.jpg", "doc2.pdf", "doc3.png"]
results = []

for path in image_paths:
    result = classifier.classify(path)
    results.append({
        'file': path,
        'category': result['predicted_category'],
        'confidence': result['confidence']
    })

# Display results
for r in results:
    print(f"{r['file']}: {r['category']} ({r['confidence']:.3f})")

Custom Preprocessing

python

import cv2
import numpy as np

# Load and preprocess image manually
image = cv2.imread("document.jpg")
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

# Resize to model input size
resized = cv2.resize(image, (224, 224))
normalized = resized.astype(np.float32) / 255.0

# Convert to CHW format and add batch dimension
chw = np.transpose(normalized, (2, 0, 1))
batched = np.expand_dims(chw, axis=0)

# Run inference
classifier = DocumentClassifierONNX()
logits = classifier.predict(batched)
result = classifier.decode_output(logits)

🛠️ Integration Examples

Flask Web Service

python

from flask import Flask, request, jsonify
from example import DocumentClassifierONNX

app = Flask(__name__)
classifier = DocumentClassifierONNX()

@app.route('/classify', methods=['POST'])
def classify_document():
    file = request.files['document']
    
    # Save and process file
    file.save('temp_document.jpg')
    result = classifier.classify('temp_document.jpg')
    
    return jsonify({
        'category': result['predicted_category'],
        'confidence': float(result['confidence']),
        'top_predictions': result['top_predictions']
    })

if __name__ == '__main__':
    app.run(host='0.0.0.0', port=5000)

Batch Processing Script

python

import os
import glob
from example import DocumentClassifierONNX

def classify_directory(input_dir, output_file):
    classifier = DocumentClassifierONNX()
    
    # Find all image files
    extensions = ['*.jpg', '*.jpeg', '*.png', '*.pdf']
    files = []
    for ext in extensions:
        files.extend(glob.glob(os.path.join(input_dir, ext)))
    
    results = []
    for file_path in files:
        try:
            result = classifier.classify(file_path)
            results.append({
                'file': os.path.basename(file_path),
                'category': result['predicted_category'],
                'confidence': result['confidence']
            })
            print(f"✓ {file_path}: {result['predicted_category']}")
        except Exception as e:
            print(f"✗ {file_path}: Error - {e}")
    
    # Save results
    import json
    with open(output_file, 'w') as f:
        json.dump(results, f, indent=2)

# Usage
classify_directory("./documents", "classification_results.json")

📋 Requirements

System Requirements

Python: 3.8 or higher
RAM: Minimum 2GB available
CPU: x86_64 architecture recommended
OS: Windows, Linux, macOS

Dependencies

onnxruntime>=1.15.0
opencv-python>=4.5.0
numpy>=1.21.0
Pillow>=8.0.0

🔍 Troubleshooting

Common Issues

Model Loading Error

python

# Ensure model file exists
import os
if not os.path.exists("DocumentClassifier.onnx"):
    print("Model file not found!")

Memory Issues

python

# For low-memory systems, process images individually
# and clear variables after use
import gc
result = classifier.classify(image)
del image  # Free memory
gc.collect()

Image Format Issues

python

# Convert any image format to RGB
from PIL import Image
img = Image.open("document.pdf").convert("RGB")
result = classifier.classify(np.array(img))

📖 Technical Details

Architecture

Base Model: Deep Convolutional Neural Network
Input Processing: Standard ImageNet preprocessing
Feature Extraction: CNN backbone with global pooling
Classification Head: Dense layers with softmax activation
Optimization: JPQD quantization for size and speed

Preprocessing Pipeline

Image Loading: PIL/OpenCV image loading
Resizing: Bilinear interpolation to 224×224
Normalization: [0, 255] → [0, 1] range
Format Conversion: HWC → CHW (channels first)
Batch Addition: Single image → batch dimension

Output Processing

Feature Extraction: CNN backbone outputs [1, 1280, 7, 7]
Global Pooling: Spatial averaging to [1, 1280]
Classification: Map features to category probabilities
Top-K Selection: Return most likely categories

📚 Citation

If you use this model in your research, please cite:

bibtex

@article{docling2024,
  title={Docling Technical Report},
  author={DS4SD Team},
  journal={arXiv preprint arXiv:2408.09869},
  year={2024}
}

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request. For major changes, please open an issue first to discuss what you would like to change.

🆘 Support

Issues: GitHub Issues
Documentation: This README and inline code comments
Examples: See example.py for comprehensive usage examples

📈 Changelog

v1.0.0

Initial ONNX model release
JPQD optimization applied
Complete Python API
CLI interface
Comprehensive documentation
Performance benchmarks

Made with ❤️ by the DS4SD Community

asmud/ds4sd-DocumentClassifier-onnx

DocumentClassifier ONNX

🎯 Overview

Key Features

🚀 Quick Start

Installation

Basic Usage

Command Line Interface

📊 Model Specifications

🏷️ Supported Document Categories

⚡ Performance Benchmarks

Inference Speed (CPU)

Memory Usage

🔧 Advanced Usage

Batch Processing

Custom Preprocessing

🛠️ Integration Examples

Flask Web Service

Batch Processing Script

📋 Requirements

System Requirements

Dependencies

🔍 Troubleshooting

Common Issues

📖 Technical Details

Architecture

Preprocessing Pipeline

Output Processing

📚 Citation

📄 License

🤝 Contributing

🆘 Support

📈 Changelog

v1.0.0

asmud/
ds4sd-DocumentClassifier-onnx