算法部署-使用NCNN在Raspberry-Pi4平台上部署YOLOv7目标检测算法-优质算法部署项目实战.zip

# YoloV7 Raspberry Pi 4  ------------ ## 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](https://qengineering.eu/install-raspberry-64-os.html) <br/> - The Tencent ncnn framework installed. [Install ncnn](https://qengineering.eu/install-ncnn-on-raspberry-pi-4.html) <br/> - OpenCV 64-bit installed. [Install OpenCV 4.5](https://qengineering.eu/install-opencv-4.5-on-raspberry-64-os.html) <br/> - Code::Blocks installed. (```$ sudo apt-get install codeblocks```) ------------ ## Installing the app. To extract and run the network in Code::Blocks <br/> $ mkdir *MyDir* <br/> $ cd *MyDir* <br/> $ 下载工程 Your *MyDir* folder must now look like this: <br/> parking.jpg <br/> busstop.jpg <br/> YoloV7.cpb <br/> yolo.cpp <br/> yolo.h <br/> yoloV7main.cpp <br/> yolov7-tiny.bin <br/> yolov7-tiny.param <br/> ------------ ## Running the app. To run the application load the project file YoloV7.cbp in Code::Blocks. More info or<br/> 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/> ------------ ### Dynamic sizes. YoloV7 can handle different input resolutions without changing the deep learning model.<br/> On line 28 of `yolov7main.cpp` you can change the `target_size` (default 640).<br/> 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.<br/><br/>。 ------------ [](https://www.paypal.com/cgi-bin/webscr?cmd=_s-xclick&hosted_button_id=CPZTM5BB3FCYL)