https://github.com/qengineering/squeezenet-ncnn

SqueezeNet for ncnn framework
https://github.com/qengineering/squeezenet-ncnn

cpp deep-neural-networks ncnn ncnn-framework ncnn-squeezenet raspberry raspberry-pi raspberry-pi-3 raspberry-pi-4 squeezenet

Last synced: 7 days ago
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SqueezeNet for ncnn framework

• Host: GitHub
• URL: https://github.com/qengineering/squeezenet-ncnn
• Owner: Qengineering
• License: bsd-3-clause
• Created: 2019-09-03T12:05:05.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2021-12-27T11:02:26.000Z (about 3 years ago)
• Last Synced: 2024-11-27T11:36:20.531Z (2 months ago)
• Topics: cpp, deep-neural-networks, ncnn, ncnn-framework, ncnn-squeezenet, raspberry, raspberry-pi, raspberry-pi-3, raspberry-pi-4, squeezenet
• Language: C++
• Homepage: https://qengineering.eu/opencv-c-examples-on-raspberry-pi.html
• Size: 4.36 MB
• Stars: 2
• Watchers: 3
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# SqueezeNet for the ncnn framework

![output image]( https://qengineering.eu/images/SqueezeNet_Hippo.jpg )


## SqueezeNet with the ncnn framework. 


[![License](https://img.shields.io/badge/License-BSD%203--Clause-blue.svg)](https://opensource.org/licenses/BSD-3-Clause)



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



Special made for a bare Raspberry Pi 4 see [Q-engineering deep learning examples](https://qengineering.eu/deep-learning-examples-on-raspberry-32-64-os.html)

------------

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](https://qengineering.eu/install-raspberry-64-os.html) 


- The Tencent ncnn framework installed. [Install ncnn](https://qengineering.eu/install-ncnn-on-raspberry-pi-4.html) 


- OpenCV 64 bit installed. [Install OpenCV 4.5](https://qengineering.eu/install-opencv-4.5-on-raspberry-64-os.html) 


- 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](https://qengineering.eu/deep-learning-examples-on-raspberry-32-64-os.html#HandsOn).



------------

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