FastDeploy/examples/vision/detection/paddledetection/jetson/cpp

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PaddleDetection C++ Deployment Example

This directory provides examples that infer_xxx.cc fast finishes the deployment of PaddleDetection models, including SOLOv2 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 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

wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg
# CPU inference
./infer_solov2_demo ./solov2_r50_fpn_1x_coco 000000014439.jpg 0
# GPU inference
./infer_ppyoloe_demo ./ppyoloe_crn_l_300e_coco 000000014439.jpg 1