SqueezeNet for the ncnn framework

SqueezeNet with the ncnn framework.

Paper: https://arxiv.org/pdf/1602.07360.pdf

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

Training set: ImageNet 2012
Size: 227x227
Prediction time: 85 mSec (RPi 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
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/SqueezeNet-ncnn/archive/refs/heads/master.zip
$ unzip -j master.zip
Remove master.zip and README.md as they are no longer needed.
$ rm master.zip
$ rm README.md

Your MyDir folder must now look like this:
cat.jpg
hippo.jpg
shufflenet.bin
shufflenet.param
ShuffleNet.cpb
shufflenetv2.cpp

Running the app.

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