zhutony / yolov7-ncnn-raspberry-pi-4

YoloV7 for a bare Raspberry Pi using ncnn.

YoloV7 Raspberry Pi 4

YoloV7 with the ncnn framework.

Paper: https://arxiv.org/pdf/2207.02696.pdf

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

---

Benchmark.

Model	size	objects	mAP	Jetson Nano 1479 MHz	RPi 4 64-OS 1950 MHz
NanoDet	320x320	80	20.6	26.2 FPS	13.0 FPS
NanoDet Plus	416x416	80	30.4	18.5 FPS	5.0 FPS
YoloFastestV2	352x352	80	24.1	38.4 FPS	18.8 FPS
YoloV2	416x416	20	19.2	10.1 FPS	3.0 FPS
YoloV3	352x352 tiny	20	16.6	17.7 FPS	4.4 FPS
YoloV4	416x416 tiny	80	21.7	16.1 FPS	3.4 FPS
YoloV4	608x608 full	80	45.3	1.3 FPS	0.2 FPS
YoloV5	640x640 small	80	22.5	5.0 FPS	1.6 FPS
YoloV6	640x640 nano	80	35.0	10.5 FPS	2.7 FPS
YoloV7	412x412 tiny	80	38.7	18.0 FPS	4.43 FPS
YoloV7	640x640 tiny	80	38.7	8.5 FPS	2.1 FPS
YoloV8	640x640 nano	80	37.3	14.5 FPS	3.1 FPS
YoloV8	640x640 small	80	44.9	4.5 FPS	1.47 FPS
YoloX	416x416 nano	80	25.8	22.6 FPS	7.0 FPS
YoloX	416x416 tiny	80	32.8	11.35 FPS	2.8 FPS
YoloX	640x640 small	80	40.5	3.65 FPS	0.9 FPS

---

Dependencies.

To run the application, you have to:

• A raspberry Pi 4 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. ($ sudo apt-get install codeblocks)

---

Installing the app.

To extract and run the network in Code::Blocks
$ mkdir MyDir
$ cd MyDir
$ wget https://github.com/Qengineering/YoloV7-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
YoloV7.cpb
yolo.cpp
yolo.h
yoloV7main.cpp
yolov7-tiny.bin
yolov7-tiny.param

---

Running the app.

To run the application load the project file YoloV7.cbp in Code::Blocks. More info or
if you want to connect a camera to the app, follow the instructions at Hands-On.

---

Dynamic sizes.

YoloV7 can handle different input resolutions without changing the deep learning model.
On line 28 of yolov7main.cpp you can change the target_size (default 640).
Decreasing the size to say 412 will speed up the inference time. On the other hand, the resizing makes the image less detailed; the model will no longer detect all objects.

Many thanks to nihui and Xiang Shin Wuu

---