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Model Benchmarking
PaddleOCRModelConverter

PaddleOCRModelConverter

Convert PaddleOCR models to ONNX format

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What is PaddleOCRModelConverter ?

PaddleOCRModelConverter is a tool designed to convert PaddleOCR models into the ONNX (Open Neural Network Exchange) format. This conversion enables models to be used across different frameworks and platforms, providing greater flexibility and compatibility for deployment in various environments.

Features

• Compatibility: Converts PaddleOCR models to ONNX format for broader compatibility.
• Flexibility: Supports deployment on multiple devices and frameworks.
• High Performance: Optimizes models for inference speed and efficiency.
• Easy Integration: Simplifies the process of using PaddleOCR models in different workflows.
• Model Support: Works with a wide range of PaddleOCR models for text recognition, detection, and other tasks.

How to use PaddleOCRModelConverter ?

  1. Install the Tool: Install PaddleOCR and the PaddleOCRModelConverter package using pip.
    pip install paddleocr paddleonnx
    
  2. Export the Model: Use the conversion script to export your PaddleOCR model to ONNX format.
    paddleonnx_model_exporter --model_dir <model_path> --output_dir <output_path>
    
  3. Optimize the Model: Optionally, use ONNX optimization tools to further optimize the converted model for inference.
  4. Deploy the Model: Use the ONNX model in your preferred framework or environment, such as TensorFlow, PyTorch, or Edge devices.

Frequently Asked Questions

What is ONNX and why is it useful?
ONNX is an open standard for representing machine learning models, enabling models to be transferred between different frameworks and hardware. It allows for better performance and compatibility across various platforms.

Can PaddleOCRModelConverter handle all PaddleOCR models?
PaddleOCRModelConverter supports a wide range of PaddleOCR models, but certain models with proprietary or unsupported operations may not be fully compatible. Check the official documentation for specific model support.

How do I optimize the converted ONNX model for inference?
You can use tools like ONNX Runtime or TensorRT to further optimize the ONNX model for inference. These tools provide options for quantization, pruning, and other optimizations to improve performance.

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