docling-onnx-models 0.1.3

ONNX Runtime implementations for Docling AI models

Docling ONNX Models

ONNX Runtime implementations for Docling AI models, providing improved performance and cross-platform compatibility.

Overview

docling-onnx-models is a drop-in replacement for the docling-ibm-models package, offering the same APIs but powered by ONNX Runtime. This provides several advantages:

• Improved Performance: ONNX Runtime optimizations for better inference speed
• Cross-Platform: Consistent behavior across different operating systems
• Hardware Acceleration: Support for CPU, CUDA, and other execution providers
• Reduced Dependencies: Lighter weight than full PyTorch models

Installation

pip install docling-onnx-models

For GPU support:

pip install docling-onnx-models[gpu]

Supported Models

Layout Model

• Input: Document page images
• Output: Layout element detection (text, tables, figures, etc.)
• Compatible with: docling.models.layout_model.LayoutModel

Document Figure Classifier

• Input: Figure/image regions
• Output: Classification into 16 figure types (charts, logos, maps, etc.)
• Compatible with: docling.models.document_picture_classifier.DocumentPictureClassifier

Table Structure Predictor

• Input: Table region images and token data
• Output: Table structure with rows, columns, and cell relationships
• Compatible with: docling.models.table_structure_model.TableStructureModel

Usage

Basic Usage

The ONNX models are designed as drop-in replacements for the original models:

# Instead of:
# from docling_ibm_models.layoutmodel.layout_predictor import LayoutPredictor

# Use:
from docling_onnx_models.layoutmodel.layout_predictor import LayoutPredictor

# Same API
predictor = LayoutPredictor(
    artifact_path="/path/to/onnx/model/directory",
    device="cpu",
    num_threads=4
)

predictions = predictor.predict_batch(images)

With Docling

To use ONNX models with Docling, ensure your model directories contain ONNX files:

model_directory/
├── model.onnx              # ONNX model file
├── config.json             # Model configuration
├── preprocessor_config.json # Preprocessing config
└── ...                     # Other model files

The models will be automatically detected and used by Docling when available.

Custom Execution Providers

from docling_onnx_models.layoutmodel.layout_predictor import LayoutPredictor

# Use specific execution providers
predictor = LayoutPredictor(
    artifact_path="/path/to/model",
    device="cuda",
    providers=["CUDAExecutionProvider", "CPUExecutionProvider"]
)

Model Conversion

If you have PyTorch models that need to be converted to ONNX:

# Example for converting a layout model
import torch
from transformers import AutoModelForObjectDetection

model = AutoModelForObjectDetection.from_pretrained("path/to/pytorch/model")
model.eval()

# Dummy input (adjust dimensions as needed)
dummy_input = torch.randn(1, 3, 640, 640)

# Export to ONNX
torch.onnx.export(
    model,
    dummy_input,
    "model.onnx",
    export_params=True,
    opset_version=11,
    do_constant_folding=True,
    input_names=["input"],
    output_names=["output"],
    dynamic_axes={
        "input": {0: "batch_size"},
        "output": {0: "batch_size"}
    }
)

Performance Tips

1. Use appropriate execution providers based on your hardware
2. Set optimal thread counts for CPU inference
3. Batch processing when possible for better throughput
4. Model quantization for reduced memory usage (if supported)

API Compatibility

This package maintains full API compatibility with docling-ibm-models:

• All method signatures are identical
• Input/output formats are preserved
• Configuration files use the same structure
• Error handling behavior is consistent

Requirements

• Python 3.10+
• ONNX Runtime 1.15.0+
• NumPy 1.21.0+
• Pillow 8.3.0+
• OpenCV 4.5.0+

Contributing

Please see the main Docling repository for contribution guidelines.

License

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