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This directory provides examples that infer_xxxxx.cc fast finishes the deployment of RKYOLO models on Rockchip board through 2-nd generation NPU
Two steps before deployment
- Software and hardware should meet the requirements.
- Download the precompiled deployment library or deploy FastDeploy repository from scratch according to your development environment.
Refer to RK2 generation NPU deployment repository compilation
wget https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg
cd build
cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-linux-x64-x.x.x
make -j8
# infer yolov5
wget https://bj.bcebos.com/paddlehub/fastdeploy/rknpu2/yolov5-s-relu.zip
unzip yolov5-s-relu.zip
./infer_rkyolov5 yolov5-s-relu/yolov5s_relu_tk2_RK3588_i8.rknn 000000014439.jpg
# infer yolov7
wget https://bj.bcebos.com/paddlehub/fastdeploy/rknpu2/yolov7-tiny.zip
unzip yolov7-tiny.zip
./infer_rkyolov7 yolov7-tiny/yolov7-tiny_tk2_RK3588_i8.rknn 000000014439.jpg
# infer yolox
wget https://bj.bcebos.com/paddlehub/fastdeploy/rknpu2/yolox-s.zip
unzip yolox-s.zip
./infer_rkyolox yolox-s/yoloxs_tk2_RK3588_i8.rknn 000000014439.jpgIf you use the YOLOv5 model you have trained, you may encounter the problem of 'segmentation fault' after running the demo of FastDeploy. It is likely that the number of labels is inconsistent. You can use the following solution:
model.GetPostprocessor().SetClassNum(3);