FastDeploy/examples/vision/classification/yolov5cls/cpp

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YOLOv5Cls C++ Deployment Example

This directory provides examples that infer.cc fast finishes the deployment of YOLOv5Cls models 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 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 model file and test images 
wget https://bj.bcebos.com/paddlehub/fastdeploy/yolov5n-cls.onnx
wget https://gitee.com/paddlepaddle/PaddleClas/raw/release/2.4/deploy/images/ImageNet/ILSVRC2012_val_00000010.jpeg


# CPU inference
./infer_demo yolov5n-cls.onnx 000000014439.jpg 0
# GPU inference
./infer_demo yolov5n-cls.onnx 000000014439.jpg 1
# TensorRT Inference on GPU
./infer_demo yolov5n-cls.onnx 000000014439.jpg 2

The result returned after running is as follows

ClassifyResult(
label_ids: 265,
scores: 0.196327,
)

The above command works for Linux or MacOS. Refer to:

• How to use FastDeploy C++ SDK in Windows for SDK use-pattern in Windows.

YOLOv5Cls C++ Interface

YOLOv5Cls Class

fastdeploy::vision::classification::YOLOv5Cls(
        const string& model_file,
        const string& params_file = "",
        const RuntimeOption& runtime_option = RuntimeOption(),
        const ModelFormat& model_format = ModelFormat::ONNX)

YOLOv5Cls 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. (use the default configuration)
• model_format(ModelFormat): Model format. ONNX format by default

Predict Function

YOLOv5Cls::Predict(cv::Mat* im, int topk = 1)

Model prediction interface. Input images and output classification topk results directly.

Parameter

• input_image(np.ndarray): Input data in HWC or BGR format
• topk(int): Return the topk classification results with the highest prediction probability. Default 1

Return

Return fastdeploy.vision.ClassifyResult structure. Refer to Vision Model Prediction Results for the description of the structure.

Other Documents

• YOLOv5Cls Model Description
• YOLOv5Cls Python Deployment
• Model Prediction Results
• How to switch the model inference backend engine