RuiyangJu / TripleNet

TripleNet Image Classification on CIFAR-10 by raspberrypi

Efficient Convolutional Neural Networks on Raspberry Pi for Image Classification

Efficient Convolutional Neural Networks on Raspberry Pi for Image Classification

Abstract

TripleNet is adopted from the concept of block connections in ThreshNet, it compresses and accelerates the network model, reduces the amount of parameters of the network, and shortens the inference time of each image while ensuring the accuracy. TripleNet and other state-of-the-art (SOTA) neural networks perform image classification experiments with the CIFAR-10 and SVHN datasets on Raspberry Pi. The experimental results show that, compared with MobileNet, ThreshNet, EfficientNet, and HarDNet, the inference time of TripleNet per image is shortened by 16%, 17%, 24%, and 30%, respectively.

Citation

If you find TripleNet useful in your research, please consider citing:

@article{ju2023efficient,
  title={Efficient convolutional neural networks on Raspberry Pi for image classification},
  author={Ju, Rui-Yang and Lin, Ting-Yu and Jian, Jia-Hao and Chiang, Jen-Shiun},
  journal={Journal of Real-Time Image Processing},
  volume={20},
  number={2},
  pages={1--9},
  year={2023},
  publisher={Springer}
}

Contents

1. Introduction
2. Usage
3. Config
4. Model
5. Results
6. Requirements
7. References

Usage

python3 main.py

optional arguments:

--lr                default=1e-3    learning rate
--epoch             default=200     number of epochs tp train for
--trainBatchSize    default=64     training batch size
--testBatchSize     default=64     test batch size

pre-training:

return TripleNet(pretrained=True, weight_path='your pre-trained model address')

Config

Optimizer

Adam Optimizer

Learning Rate

1e-3 for [1,74] epochs
5e-4 for [75,149] epochs
2.5e-4 for [150,200) epochs

Model

Model	Layer	Channel	Growth Rate
TripleNet-S	6, 16, 16, 16, 2	128, 192, 256, 320, 720	32, 16, 20, 40, 160
TripleNet-B	6, 16, 16, 16, 3	128, 192, 256, 320, 1080	32, 16, 20, 40, 160

Results

Name	Raspberry Pi 4 Time (ms)	C10 Error (%)	FLOPs (G)	MAdd (G)	Memory (MB)	#Params (M)
TripleNet-S	40.6	13.05	4.17	8.32	90.25	9.67
ShuffleNet	44.1	13.35	2.22	4.31	617.00	1.01
ThreshNet-28	45.3	14.75	2.28	4.55	83.26	10.18
TripleNet-B	65.1	12.97	4.29	8.57	91.33	12.63
MobileNetV2	67.4	14.06	2.42	4.75	384.78	2.37
MobileNet	76.8	16.12	2.34	4.63	230.84	3.32
ThreshNet-95	77.9	13.31	4.07	8.12	132.34	16.19
EfficientNet-B0	85.4	13.40	1.51	2.99	203.74	3.60
HarDNet-85	92.5	13.89	9.10	18.18	74.65	36.67

* Raspberry Pi Time is the inference time per image on Raspberry Pi 4

Requirements

Raspberry Pi 4 Model B 4GB

• python3 - 3.9.2
• torch - 1.11.0
• torchvision - 0.12
• numpy - 1.22.3

References

• torchstat
• pytorch-cifar10
• HarDNet