Qengineering / YoloV10-ncnn-Raspberry-Pi-4

YoloV10 for a bare Raspberry Pi 4 or 5

YoloV10 Raspberry Pi 4 or 5

YoloV10 with the ncnn framework.

Paper: https://arxiv.org/pdf/2405.14458

Special made for a bare Raspberry Pi 4, see Q-engineering deep learning examples

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Benchmark.

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
YoloV10	640x640 nano	38.0		1.66
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)

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Opset.

YoloV10 use layer operations yet unknown to ncnn. To let YoloV10 run on an ncnn framework, zhouweigogogo has modified the onnx file by replacing these layers. At the same time, he expanded the original output to three. Some features, like variable input size, aren't supported now. Until the ncnn framework fully supports onnx opset 13, we face these limitations.

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Dependencies.

To run the application, you have to:

• A Raspberry Pi 4 or 5 with a 32 or 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)

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Installing the app.

To extract and run the network in Code::Blocks
$ mkdir MyDir
$ cd MyDir
$ wget https://github.com/Qengineering/YoloV10-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:
parking.jpg
busstop.jpg
YoloV10.cpb
yoloV10.cpp
yolov10n.ncnn.bin
yolov10n.ncnn.param

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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

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

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Thanks.

A more than special thanks to THU-MIG, who adapted the YoloV10 to onnx models.
And zhouweigogogo, who adapted the onnx model to the ncnn framework.

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