FastDeploy/examples/vision/detection/fastestdet/cpp

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FastestDet C++ Deployment Example

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

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

Taking the CPU inference on Linux as an example, the compilation test can be completed by executing the following command in this directory.

mkdir build
cd build
wget https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-x64-1.0.3.tgz
tar xvf fastdeploy-linux-x64-1.0.3.tgz
cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-linux-x64-1.0.3
make -j

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


# CPU inference
./infer_demo FastestDet.onnx 000000014439.jpg 0
# GPU inference
./infer_demo FastestDet.onnx 000000014439.jpg 1
# TensorRT inference on GPU
./infer_demo FastestDet.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:

• How to use FastDeploy C++ SDK in Windows

FastestDet C++ Interface

FastestDet Class

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

FastestDet 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. Only 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

FastestDet::Predict(cv::Mat* im, DetectionResult* result,
                float conf_threshold = 0.65,
                float nms_iou_threshold = 0.45)

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 [352, 352]

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