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YOLOv5 C++ Deployment Example

This directory provides examples that infer.cc fast finishes the deployment of YOLOv5 on CPU/GPU and GPU accelerated by TensorRT. Before deployment, two steps require confirmation

Taking the CPU inference on Linux as an example, the compilation test can be completed by executing the following command in this directory. FastDeploy version 0.7.0 or above (x.x.x>=0.7.0) 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
# Download the official converted yolov5 Paddle model files and test images
wget https://bj.bcebos.com/paddlehub/fastdeploy/yolov5s_infer.tar
tar -xvf yolov5s_infer.tar
wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg


# CPU inference
./infer_paddle_demo yolov5s_infer 000000014439.jpg 0
# GPU inference
./infer_paddle_demo yolov5s_infer 000000014439.jpg 1
# TensorRT inference on GPU
./infer_paddle_demo yolov5s_infer 000000014439.jpg 2
# KunlunXin XPU inference
./infer_paddle_demo yolov5s_infer 000000014439.jpg 3
# Huawei Ascend Inference
./infer_paddle_demo yolov5s_infer 000000014439.jpg 4

The above steps apply to the inference of Paddle models. If you want to conduct the inference of ONNX models, follow these steps:

# 1. Download the official converted yolov5 ONNX model files and test images
wget https://bj.bcebos.com/paddlehub/fastdeploy/yolov5s.onnx
wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg

# CPU inference
./infer_demo yolov5s.onnx 000000014439.jpg 0
# GPU inference
./infer_demo yolov5s.onnx 000000014439.jpg 1
# TensorRT inference on GPU
./infer_demo yolov5s.onnx 000000014439.jpg 2

The visualized result after running is as follows

The above command works for Linux or MacOS. For SDK use-pattern in Windows, refer to:

YOLOv5 C++ Interface

YOLOv5 Class

fastdeploy::vision::detection::YOLOv5(
        const string& model_file,
        const string& params_file = "",
        const RuntimeOption& runtime_option = RuntimeOption(),
        const ModelFormat& model_format = ModelFormat::ONNX)

YOLOv5 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

YOLOv5::Predict(cv::Mat* im, DetectionResult* result,
                float conf_threshold = 0.25,
                float nms_iou_threshold = 0.5)

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
  • conf_threshold: Filtering threshold of detection box confidence
  • nms_iou_threshold: iou threshold during NMS processing

Class Member Variable

Pre-processing Parameter

Users can modify the following pre-processing parameters to their needs, which affects the final inference and deployment results

  • size(vector<int>): This parameter changes the size of 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