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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 format. ONNX (Open Neural Network Exchange) is an open standard that allows models to be transferred between different frameworks and platforms, enabling better interoperability and performance optimization. This tool is particularly useful for users who want to deploy PaddleOCR models in environments that support ONNX, such as.TensorRT, Core ML, or Edge Inference Engines.

Features

• Model Conversion: Converts PaddleOCR models to ONNX format for cross-platform compatibility.
• Optimized Inference: Supports optimization of models for inference, ensuring faster and more efficient deployment.
• Framework Compatibility: Facilitates deployment across multiple ML frameworks and platforms.
• Command-Line Interface: Provides an easy-to-use command-line tool for model conversion.
• Cross-Platform Support: Enables deployment on diverse operating systems and hardware configurations.

How to use PaddleOCRModelConverter ?

  1. Install the Tool: Install the PaddleOCRModelConverter using the provided installation instructions.
  2. Prepare the Model: Ensure you have a trained PaddleOCR model ready for conversion.
  3. Run the Conversion Command: Use the command-line interface to convert the model to ONNX format. Example command:
    paddle_ocr_model_converter --input_model path/to/model --output_path path/to/output
    
  4. Verify the Conversion: Check the output directory for the converted ONNX model and verify its correctness using ONNX-compatible tools.

Frequently Asked Questions

What models are supported by PaddleOCRModelConverter?
PaddleOCRModelConverter supports all standard PaddleOCR models, including but not limited to CRNN, Transformer, and PFAN models.

Why should I convert my PaddleOCR model to ONNX?
Converting to ONNX enables deployment in ONNX-compatible frameworks and platforms, which can improve inference performance and provide better interoperability.

Are there any specific dependencies required for the conversion?
Yes, ensure you have the latest versions of PaddlePaddle and ONNX runtime installed in your environment for smooth conversion and inference.

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