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YoloV10 NPU

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YoloV10 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 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
yolov10 yolov10n 35.1 12.5
yolov8s 23.4 7.3
yolov8m 9.7 3.4
yolov8x 5.1 1.8
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

Opset.

YoloV10 use layer operations yet unknown to Rock NPU toolset.
To let YoloV10 run on an NPU framework, kaylorchen has modified the onnx file by replacing these layers. At the same time, he expanded the original output to three. Until the NPU framework fully supports onnx opset 13, we face these limitations.

Dependencies.

To run the application, you have to:

  • OpenCV 64-bit installed.
  • Optional: Code::Blocks. ($ 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/YoloV10-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.

Thanks.

A more than special thanks to kaylorchen, who adapted the onnx model to the NPU framework.

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