ultralytics/examples/YOLOv8-LibTorch-CPP-Inference

YOLOv8 LibTorch Inference C++

This example demonstrates how to perform inference using Ultralytics YOLOv8 models in C++ with the LibTorch (PyTorch C++ API). This allows for deploying YOLOv8 models in C++ environments for efficient execution.

⚙️ Dependencies

Ensure you have the following dependencies installed before proceeding:

Dependency	Version	Resource
OpenCV	>=4.0.0	https://opencv.org/
C++ Standard	>=17	https://isocpp.org/
CMake	>=3.18	https://cmake.org/
Libtorch	>=1.12.1	https://pytorch.org/

You can download the required version of LibTorch from the official PyTorch website. Make sure to select the correct version corresponding to your system and CUDA version (if using GPU).

🚀 Usage

Follow these steps to run the C++ inference example:

1. Clone the Ultralytics Repository: Use Git to clone the repository containing the example code and necessary files.

   git clone https://github.com/ultralytics/ultralytics
   

2. Install Ultralytics: Navigate to the cloned directory and install the ultralytics package using pip. This step is necessary for exporting the model. Refer to the Ultralytics Quickstart Guide for detailed installation instructions.

   cd ultralytics
   pip install .
   

3. Navigate to the Example Directory: Change the directory to the C++ LibTorch inference example.

   cd examples/YOLOv8-LibTorch-CPP-Inference
   

4. Build the Project: Create a build directory, use CMake to configure the project, and then compile it using Make. You might need to specify the path to your LibTorch and OpenCV installations if they are not found automatically by CMake.

   mkdir build
   cd build
   cmake .. # Add -DCMAKE_PREFIX_PATH=/path/to/libtorch;/path/to/opencv if needed
   make
   

5. Run the Inference: Execute the compiled binary. The application will load the exported YOLOv8 model and perform inference on a sample image (zidane.jpg included in the root ultralytics directory) or video.

   ./yolov8_libtorch_inference
   

✨ Exporting Ultralytics YOLOv8

To use an Ultralytics YOLOv8 model with LibTorch, you first need to export it to the TorchScript format. TorchScript is a way to create serializable and optimizable models from PyTorch code.

Use the yolo Command Line Interface (CLI) provided by the ultralytics package to export the model. For example, to export the yolov8s.pt model with an input image size of 640x640:

yolo export model=yolov8s.pt imgsz=640 format=torchscript

This command will generate a yolov8s.torchscript file in the model's directory. This file contains the serialized model that can be loaded and executed by the C++ application using LibTorch. For more details on exporting models to various formats, see the Ultralytics Export documentation.

🤝 Contributing

Contributions to enhance this example or add new features are welcome! Please see the Ultralytics Contributing Guide for guidelines on how to contribute to the project. Thank you for helping make Ultralytics YOLO the best Vision AI tool!