Fast GPU Segment Anything Pipeline with ONNXRuntime in C++ 🦾

Benefits ✨

• General Purpose implementation (in the future abstraction for different NN plague and play pipelines will be implemented)
• Friendly for deployment in the industrial sector.
• Faster than OpenCV's DNN inference on both CPU and GPU.
• Supports FP32 and FP16 CUDA acceleration.

Dependencies ⚙️

Dependency	Version
Onnxruntime(linux,windows,macos)	>=1.14.1
OpenCV	>=4.0.0
C++ Standard	>=17
Cmake	>=3.5
Cuda (Optional)	>=12.8
cuDNN (Cuda required)	=9

Note: The dependency on C++17 is due to the usage of the C++17 filesystem feature.

Note (2): Due to ONNX Runtime, we need to use CUDA 12(.8) and cuDNN 9. Keep in mind that this requirement might change in the future.

Build 🛠️

0. You can just use run install console ./install.sh. For manual installation.

1. Clone the repository to your local machine.

2. Navigate to the root directory of the repository.

3. Create a build directory and navigate to it SAM or YOLO:

   mkdir build && cd build

4. Run CMake to generate the build files:

   cmake ..

   Notice:

   If you encounter an error indicating that the ONNXRUNTIME_ROOT variable is not set correctly, you can resolve this by building the project using the appropriate command tailored to your system.

   # compiled in a win32 system
   cmake -D WIN32=TRUE ..
   # compiled in a linux system
   cmake -D LINUX=TRUE ..
   # compiled in an apple system
   cmake -D APPLE=TRUE ..

5. Build the project:

   make

6. The built executable should now be located in the build directory.

Usage of e.g. YOLO 🚀

./Yolov8OnnxRuntimeCPPInference

Notice: Make sure you have an image to on the build/image folder.