TripleNet

Efficient Convolutional Neural Networks on Raspberry Pi for Image Classification

Architecture

Layers

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

GitHub

• torchstat
• pytorch-cifar10
• HarDNet