FastDeploy/examples/vision/detection/yolov8/cpp

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YOLOv8 C++ Deployment Example

This directory provides the example that infer.cc fast finishes the deployment of YOLOv8 on CPU/GPU and GPU through TensorRT.

Two steps before deployment

• 1. Software and hardware should meet the requirements. Please refer to FastDeploy Environment Requirements
• 2. Download the precompiled deployment library and samples code based on your development environment. Refer to FastDeploy Precompiled Library

Taking the CPU inference on Linux as an example, FastDeploy version 1.0.3 or above (x.x.x>=1.0.3) is required to support this model.

mkdir build
cd build
# Download the FastDeploy precompiled library. Users can choose your appropriate version in the `FastDeploy Precompiled Library` mentioned above 
wget https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-x64-x.x.x.tgz
tar xvf fastdeploy-linux-x64-x.x.x.tgz
cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-linux-x64-x.x.x
make -j

# 1. Download the official converted YOLOv8 ONNX model files and test images 
wget https://bj.bcebos.com/paddlehub/fastdeploy/yolov8s.onnx
wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg

# CPU inference
./infer_demo yolov8s.onnx 000000014439.jpg 0
# GPU inference
./infer_demo yolov8s.onnx 000000014439.jpg 1
# TensorRT inference on GPU 
./infer_demo yolov8s.onnx 000000014439.jpg 2

The visualized result is as follows

he above command works for Linux or MacOS. For SDK in Windows, refer to:

• How to use FastDeploy C++ SDK in Windows

If you use Huawei Ascend NPU deployment, refer to the following document to initialize the deployment environment:

• How to use Huawei Ascend NPU deployment

YOLOv8 C++ Interface

YOLOv8

fastdeploy::vision::detection::YOLOv8(
        const string& model_file,
        const string& params_file = "",
        const RuntimeOption& runtime_option = RuntimeOption(),
        const ModelFormat& model_format = ModelFormat::ONNX)

YOLOv8 model loading and initialization, among which model_file is the exported ONNX model format

Parameter

• model_file(str): Model file path
• params_file(str): Parameter file path. Merely passing an empty string when the model is in ONNX format
• runtime_option(RuntimeOption): Backend inference configuration. None by default, which is the default configuration
• model_format(ModelFormat): Model format. ONNX format by default

Predict function

YOLOv8::Predict(cv::Mat* im, DetectionResult* result)

Model prediction interface. Input images and output detection results

Parameter

• im: Input images in HWC or BGR format
• result: Detection results, including detection box and confidence of each box. Refer to Vision Model Prediction Results for DetectionResult.

Class Member Variable

Pre-processing Parameter

Users can modify the following preprocessing parameters based on actual needs to change the final inference and deployment results

• size(vector<int>): This parameter changes the resize used during preprocessing, containing two integer elements for [width, height] with default value [640, 640]
• padding_value(vector<float>): This parameter is used to change the padding value of images during resize, containing three floating-point elements that represent the value of three channels. Default value [114, 114, 114]
• is_no_pad(bool): Specify whether to resize the image through padding. is_no_pad=ture represents no paddling. Default is_no_pad=false
• is_mini_pad(bool): This parameter sets the width and height of the image after resize to the value nearest to the size member variable and to the point where the padded pixel size is divisible by the stride member variable. Default is_mini_pad=false
• stride(int): Used with the stris_mini_pad member variable. Default stride=32

• Model Description
• Python Deployment
• Vision Model Prediction Results
• How to switch the backend engine