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YoloV5-seg NPU

output image

YoloV5 segmentation for RK3566/68/88 NPU (Rock 5, Orange Pi 5, Radxa Zero 3).

License

Special made for the NPU, see Q-engineering deep learning examples


Model performance benchmark(FPS)

All models, with C++ examples can be found on the SD images.

output image Rock 5 with Ubuntu 22.04, OpenCV, ncnn and NPU

output image Radxa Zero 3 with Ubuntu 22.04, OpenCV, ncnn and NPU

All models are quantized to int8, unless otherwise noted.

demo model_name RK3588 RK3566/68
yolov5 yolov5s_relu 50.0 14.8
yolov5n 58.8 19.5
yolov5s 37.7 11.7
yolov5m 16.2 5.7
yolov6 yolov6n 63.0 18.0
yolov6s 29.5 8.1
yolov6m 15.4 4.5
yolov7 yolov7-tiny 53.4 16.1
yolov7 9.4 3.4
yolov8 yolov8n 53.1 18.2
yolov8s 28.5 8.9
yolov8m 12.1 4.4
yolox yolox_s 30.0 10.0
yolox_m 12.9 4.8
ppyoloe ppyoloe_s 28.8 9.2
ppyoloe_m 13.1 5.04
yolov5_seg yolov5n-seg 9.4 1.04
yolov5s-seg 7.8 0.87
yolov5m-seg 6.1 0.71
yolov8_seg yolov8n-seg 8.9 0.91
yolov8s-seg 7.3 0.87
yolov8m-seg 4.5 0.7
ppseg ppseg_lite_1024x512 27.5 2.4
RetinaFace RetinaFace_mobile3201 243.6 88.5
RetinaFace_resnet50_3201 43.4 11.8
PPOCR-Det ppocrv4_det2 31.5 15.1
PPOCR-Rec ppocrv4_rec3 35.7 17.3

1 Input size 320x320
2 Input size 480x480
3 Input size 48x320, FP16

  • Due to the pixel-wise filling and drawing, segmentation models are relatively slow

Dependencies.

To run the application, you have to:

  • OpenCV 64-bit installed.
  • Code::Blocks installed. ($ sudo apt-get install codeblocks)

Installing the dependencies.

Start with the usual

$ sudo apt-get update 
$ sudo apt-get upgrade
$ sudo apt-get install cmake wget curl

OpenCV

Follow the Raspberry Pi 4 guide.

RKNPU2

$ git clone https://github.com/airockchip/rknn-toolkit2.git

We only use a few files.

rknn-toolkit2-master
│      
└── rknpu2
    │      
    └── runtime
        │       
        └── Linux
            │      
            └── librknn_api
                ├── aarch64
                │   └── librknnrt.so
                └── include
                    ├── rknn_api.h
                    ├── rknn_custom_op.h
                    └── rknn_matmul_api.h

$ cd ~/rknn-toolkit2-master/rknpu2/runtime/Linux/librknn_api/aarch64
$ sudo cp ./librknnrt.so /usr/local/lib
$ cd ~/rknn-toolkit2-master/rknpu2/runtime/Linux/librknn_api/include
$ sudo cp ./rknn_* /usr/local/include

Save 2 GB of disk space by removing the toolkit. We do not need it anymore.

$ cd ~
$ sudo rm -rf ./rknn-toolkit2-master

Installing the app.

To extract and run the network in Code::Blocks

$ mkdir *MyDir* <br/>
$ cd *MyDir* <br/>
$ git clone https://github.com/Qengineering/YoloV5-seg-NPU.git <br/>

Running the app.

You can use Code::Blocks.

  • Load the project file *.cbp in Code::Blocks.
  • Select Release, not Debug.
  • Compile and run with F9.
  • You can alter command line arguments with Project -> Set programs arguments...

Or use Cmake.

$ cd *MyDir*
$ mkdir build
$ cd build
$ cmake ..
$ make -j4

Make sure you use the model fitting your system.

More info or if you want to connect a camera to the app, follow the instructions at Hands-On.

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