A truly impressive YOLO family member. As long as the images are not too large and/or the objects are too small, very high frame rates are achieved with more than acceptable accuracy. Thanks dog-qiuqiu for all the hard work.
Special adapt for a bare Raspberry Pi 4, see Q-engineering deep learning examples
Numbers in FPS and reflect only the inference timing. Grabbing frames, post-processing and drawing are not taken into account.
| Model | size | mAP | Jetson Nano | RPi 4 1950 | RPi 5 2900 | Rock 5 | RK35881 NPU |
RK3566/682 NPU |
Nano TensorRT |
Orin TensorRT |
|---|---|---|---|---|---|---|---|---|---|---|
| NanoDet | 320x320 | 20.6 | 26.2 | 13.0 | 43.2 | 36.0 | ||||
| NanoDet Plus | 416x416 | 30.4 | 18.5 | 5.0 | 30.0 | 24.9 | ||||
| PP-PicoDet | 320x320 | 27.0 | 24.0 | 7.5 | 53.7 | 46.7 | ||||
| YoloFastestV2 | 352x352 | 24.1 | 38.4 | 18.8 | 78.5 | 65.4 | ||||
| YoloV2 20 | 416x416 | 19.2 | 10.1 | 3.0 | 24.0 | 20.0 | ||||
| YoloV3 20 | 352x352 tiny | 16.6 | 17.7 | 4.4 | 18.1 | 15.0 | ||||
| YoloV4 | 416x416 tiny | 21.7 | 16.1 | 3.4 | 17.5 | 22.4 | ||||
| YoloV4 | 608x608 full | 45.3 | 1.3 | 0.2 | 1.82 | 1.5 | ||||
| YoloV5 | 640x640 nano | 22.5 | 5.0 | 1.6 | 13.6 | 12.5 | 58.8 | 14.8 | 19.0 | 100 |
| YoloV5 | 640x640 small | 22.5 | 5.0 | 1.6 | 6.3 | 12.5 | 37.7 | 11.7 | 9.25 | 100 |
| YoloV6 | 640x640 nano | 35.0 | 10.5 | 2.7 | 15.8 | 20.8 | 63.0 | 18.0 | ||
| YoloV7 | 640x640 tiny | 38.7 | 8.5 | 2.1 | 14.4 | 17.9 | 53.4 | 16.1 | 15.0 | |
| YoloV8 | 640x640 nano | 37.3 | 14.5 | 3.1 | 20.0 | 16.3 | 53.1 | 18.2 | ||
| YoloV8 | 640x640 small | 44.9 | 4.5 | 1.47 | 11.0 | 9.2 | 28.5 | 8.9 | ||
| YoloV9 | 640x640 comp | 53.0 | 1.2 | 0.28 | 1.5 | 1.2 | ||||
| YoloX | 416x416 nano | 25.8 | 22.6 | 7.0 | 38.6 | 28.5 | ||||
| YoloX | 416x416 tiny | 32.8 | 11.35 | 2.8 | 17.2 | 18.1 | ||||
| YoloX | 640x640 small | 40.5 | 3.65 | 0.9 | 4.5 | 7.5 | 30.0 | 10.0 |
1 The Rock 5 and Orange Pi5 have the RK3588 on board.
2 The Rock 3, Radxa Zero 3 and Orange Pi3B have the RK3566 on board.
20 Recognize 20 objects (VOC) instead of 80 (COCO)
To run the application, you have to:
- A Raspberry Pi 4 with a 64-bit operating system. It can be the Raspberry 64-bit OS, or Ubuntu 18.04 / 20.04. Install 64-bit OS
- The Tencent ncnn framework installed. Install ncnn
- OpenCV 64-bit installed. Install OpenCV 4.5
- Code::Blocks installed (Optional). (
$ sudo apt-get install codeblocks)
To extract and run the network in Code::Blocks
$ mkdir MyDir
$ cd MyDir
$ wget https://github.com/Qengineering/YoloFastestV2-ncnn-Raspberry-Pi-4/archive/refs/heads/main.zip
$ unzip -j master.zip
Remove master.zip, LICENSE and README.md as they are no longer needed.
$ rm master.zip
$ rm LICENSE
$ rm README.md
Your MyDir folder must now look like this:
James.mp4
parking.jpg
parking_tiny.jpg
YoloFastestV2.cpb
mainFV2.cpp
yolo-fastestv2.cpp
yolo-fastestv2.h
yolo-fastestv2-opt.bin
yolo-fastestv2-opt.param
To run the application load the project file YoloFastestV2.cbp in Code::Blocks.
Or use Cmake.
$ cd *MyDir*
$ mkdir build
$ cd build
$ cmake ..
$ make -j4
More info or if you want to connect a camera to the app, follow the instructions at Hands-On.
Many thanks to dog-qiuqiu
