RK3566,68,88 的 YoloV5 NPU.zip

# YoloV5 NPU  ## YoloV5 for RK3566/68/88 NPU (Rock 5, Orange Pi 5, Radxa Zero 3). <br/> [](https://opensource.org/licenses/BSD-3-Clause)<br/><br/> Paper: https://towardsdatascience.com/yolo-v5-is-here-b668ce2a4908<br/><br/> Special made for the NPU, see [Q-engineering deep learning examples](https://qengineering.eu/deep-learning-examples-on-raspberry-32-64-os.html) ------------ ## Model performance benchmark (FPS) All models, with C++ examples can be found on the SD images.<br><br>  [Rock 5 with **Ubuntu 22.04**, OpenCV, ncnn and **NPU**](https://github.com/Qengineering/Rock-5-Ubuntu-22-image)<br><br>  [Radxa Zero 3 with **Ubuntu 22.04**, OpenCV, ncnn and **NPU**](https://github.com/Qengineering/Radxa-Zero-3-NPU-Ubuntu22)<br><br> All models are quantized to **int8**, unless otherwise noted.<br> | 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<br> ² Input size 480x480<br> ³ Input size 48x320, FP16<br> * 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. - 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](https://qengineering.eu/install-opencv-on-raspberry-64-os.html).<br> #### 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 <br/> ``` $ mkdir *MyDir* <br/> $ cd *MyDir* <br/> $ git clone https://github.com/Qengineering/YoloV5-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.<br><br> More info or if you want to connect a camera to the app, follow the instructions at [Hands-On](https://qengineering.eu/deep-learning-examples-on-raspberry-32-64-os.html#HandsOn).<br/><br/>  ------------ [](https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&hosted_button_id=CPZTM5BB3FCYL)