💗 .NET Wrapper for PaddleInference C API, support Windows(x64) 💻, NVIDIA Cuda 11.8+ based GPU 🎮 and Linux(Ubuntu-22.04 x64) 🐧, currently contained following main components:

• PaddleOCR 📖 support 14 OCR languages model download on-demand, allow rotated text angle detection, 180 degree text detection, also support table recognition 📊.
• PaddleDetection 🎯 support PPYolo detection model and PicoDet model 🏹.
• RotationDetection 🔄 use Baidu's official text_image_orientation_infer model to detect text picture's rotation angle(0, 90, 180, 270).
• PaddleNLP ChineseSegmenter 📚 support PaddleNLP Lac Chinese segmenter model, supports tagging/customized words.
• Paddle2Onnx 🔄 Allow user export ONNX model using C#.

Please checkout this page 📄.

NuGet Package 💼	Version 📌	Description 📚
Sdcb.PaddleInference		Paddle Inference C API .NET binding ⚙️

Package	Version 📌	Description
Sdcb.PaddleInference.runtime.win64.mkl		Recommended for most users (CPU, MKL)
Sdcb.PaddleInference.runtime.win64.openblas		CPU, OpenBLAS
Sdcb.PaddleInference.runtime.win64.openblas-noavx		CPU, no AVX, for old CPUs
Sdcb.PaddleInference.runtime.win64.cu118_cudnn89_sm61		CUDA 11.8, GTX 10 Series
Sdcb.PaddleInference.runtime.win64.cu118_cudnn89_sm75		CUDA 11.8, RTX 20/GTX 16xx Series
Sdcb.PaddleInference.runtime.win64.cu118_cudnn89_sm86		CUDA 11.8, RTX 30 Series
Sdcb.PaddleInference.runtime.win64.cu118_cudnn89_sm89		CUDA 11.8, RTX 40 Series
Sdcb.PaddleInference.runtime.win64.cu126_cudnn95_sm61		CUDA 12.6, GTX 10 Series
Sdcb.PaddleInference.runtime.win64.cu126_cudnn95_sm75		CUDA 12.6, RTX 20/GTX 16xx Series
Sdcb.PaddleInference.runtime.win64.cu126_cudnn95_sm86		CUDA 12.6, RTX 30 Series
Sdcb.PaddleInference.runtime.win64.cu126_cudnn95_sm89		CUDA 12.6, RTX 40 Series
Sdcb.PaddleInference.runtime.win64.cu129_cudnn910_sm61		CUDA 12.9, GTX 10 Series
Sdcb.PaddleInference.runtime.win64.cu129_cudnn910_sm75		CUDA 12.9, RTX 20/GTX 16xx Series
Sdcb.PaddleInference.runtime.win64.cu129_cudnn910_sm86		CUDA 12.9, RTX 30 Series
Sdcb.PaddleInference.runtime.win64.cu129_cudnn910_sm89		CUDA 12.9, RTX 40 Series
Sdcb.PaddleInference.runtime.win64.cu129_cudnn910_sm120		CUDA 12.9, RTX 50 Series
Sdcb.PaddleInference.runtime.linux-x64.openblas		Linux x64, OpenBLAS
Sdcb.PaddleInference.runtime.linux-x64.mkl		Linux x64, MKL
Sdcb.PaddleInference.runtime.linux-x64		Linux x64, MKL+OpenVINO
Sdcb.PaddleInference.runtime.linux-arm64		Linux ARM64
Sdcb.PaddleInference.runtime.osx-x64		macOS x64, include ONNXRuntime
Sdcb.PaddleInference.runtime.osx-arm64		macOS ARM64

Package Selection Guide:

• We recommend Sdcb.PaddleInference.runtime.win64.mkl for most users. It offers the best balance between performance and package size. Please note that this package does not support GPU acceleration, making it suitable for most general scenarios.
• openblas-noavx is tailored for older CPUs that do not support the AVX2 instruction set.
• The remaining packages cover various CUDA combinations (GPU acceleration), supporting three CUDA versions:
  • CUDA 11.8: Supports 10–40 series NVIDIA GPUs
  • CUDA 12.6: Supports 10–40 series NVIDIA GPUs
  • CUDA 12.9: Supports 10–50 series NVIDIA GPUs

Important:
Not all GPU packages are suitable for every card. Please refer to the following GPU-to-sm suffix mapping:

Any other packages that starts with Sdcb.PaddleInference.runtime might deprecated.

All packages were compiled manually by me, with some code patches from here: https://github.com/sdcb/PaddleSharp/blob/master/build/capi.patch

• Mkldnn - PaddleDevice.Mkldnn()

  Based on Mkldnn, generally fast

• Openblas - PaddleDevice.Openblas()

  Based on openblas, slower, but dependencies file smaller and consume lesser memory

• Onnx - PaddleDevice.Onnx()

  Based on onnxruntime, is also pretty fast and consume less memory

• Gpu - PaddleDevice.Gpu()

  Much faster but relies on NVIDIA GPU and CUDA

  If you wants to use GPU, you should refer to FAQ How to enable GPU? section, CUDA/cuDNN/TensorRT need to be installed manually.

1. Please ensure the latest Visual C++ Redistributable was installed in Windows (typically it should automatically installed if you have Visual Studio installed) 🛠️ Otherwise, it will fail with the following error (Windows only):

   DllNotFoundException: Unable to load DLL 'paddle_inference_c' or one of its dependencies (0x8007007E)
   

   If it's Unable to load DLL OpenCvSharpExtern.dll or one of its dependencies, then most likely the Media Foundation is not installed in the Windows Server 2012 R2 machine:

2. Many old CPUs do not support AVX instructions, please ensure your CPU supports AVX, or download the x64-noavx-openblas DLLs and disable Mkldnn: PaddleDevice.Openblas() 🚀

3. If you're using Win7-x64, and your CPU does support AVX2, then you might also need to extract the following 3 DLLs into C:\Windows\System32 folder to make it run: 💾

   • api-ms-win-core-libraryloader-l1-2-0.dll
   • api-ms-win-core-processtopology-obsolete-l1-1-0.dll
   • API-MS-Win-Eventing-Provider-L1-1-0.dll

   You can download these 3 DLLs here: win7-x64-onnxruntime-missing-dlls.zip ⬇️

Enable GPU support can significantly improve the throughput and lower the CPU usage. 🚀

Steps to use GPU in Windows:

1. (for Windows) Install the package: Sdcb.PaddleInference.runtime.win64.cu120* instead of Sdcb.PaddleInference.runtime.win64.mkl, do not install both. 📦
2. Install CUDA from NVIDIA, and configure environment variables to PATH or LD_LIBRARY_PATH (Linux) 🔧
3. Install cuDNN from NVIDIA, and configure environment variables to PATH or LD_LIBRARY_PATH (Linux) 🛠️
4. Install TensorRT from NVIDIA, and configure environment variables to PATH or LD_LIBRARY_PATH (Linux) ⚙️

You can refer to this blog page for GPU in Windows: 关于PaddleSharp GPU使用 常见问题记录 📝

If you're using Linux, you need to compile your own OpenCvSharp4 environment following the docker build scripts and the CUDA/cuDNN/TensorRT configuration tasks. 🐧

After these steps are completed, you can try specifying PaddleDevice.Gpu() in the paddle device configuration parameter, then enjoy the performance boost! 🎉

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