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https://github.com/qengineering/yolov5-npu
YoloV5 NPU for the RK3566/68/88
https://github.com/qengineering/yolov5-npu
deep-learning npu orange-pi-5 radxa-zero-3-npu radxa-zero-3w rk3566 rk3568 rk3588 rk3588s rknpu rock-5 rock5c yolov5 yolov5s
Last synced: 5 days ago
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YoloV5 NPU for the RK3566/68/88
- Host: GitHub
- URL: https://github.com/qengineering/yolov5-npu
- Owner: Qengineering
- License: bsd-3-clause
- Created: 2023-08-29T08:51:54.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-06-19T13:19:17.000Z (6 months ago)
- Last Synced: 2024-12-10T14:52:02.143Z (12 days ago)
- Topics: deep-learning, npu, orange-pi-5, radxa-zero-3-npu, radxa-zero-3w, rk3566, rk3568, rk3588, rk3588s, rknpu, rock-5, rock5c, yolov5, yolov5s
- Language: C++
- Homepage: https://qengineering.eu/deep-learning-examples-on-raspberry-32-64-os.html
- Size: 104 MB
- Stars: 83
- Watchers: 3
- Forks: 12
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# YoloV5 NPU
## YoloV5 for RK3566/68/88 NPU (Rock 5, Orange Pi 5, Radxa Zero 3).
[](https://opensource.org/licenses/BSD-3-Clause)
Paper: https://towardsdatascience.com/yolo-v5-is-here-b668ce2a4908
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.
 [Rock 5 with **Ubuntu 22.04**, OpenCV, ncnn and **NPU**](https://github.com/Qengineering/Rock-5-Ubuntu-22-image)
 [Radxa Zero 3 with **Ubuntu 22.04**, OpenCV, ncnn and **NPU**](https://github.com/Qengineering/Radxa-Zero-3-NPU-Ubuntu22)
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
------------
## 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).
#### 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*
$ cd *MyDir*
$ git clone https://github.com/Qengineering/YoloV5-NPU.git
```
------------
## 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](https://qengineering.eu/deep-learning-examples-on-raspberry-32-64-os.html#HandsOn).
------------
[](https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&hosted_button_id=CPZTM5BB3FCYL)