ThreshNet

ThreshNet: An Efficient DenseNet Using Threshold Mechanism to Reduce Connections

Architecture

Mechanism

Citation

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

@article{ju2022threshnet,
  title={ThreshNet: An Efficient DenseNet using Threshold Mechanism to Reduce Connections},
  author={Ju, Rui-Yang and Lin, Ting-Yu and Jian, Jia-Hao and Chiang, Jen-Shiun and Yang, Wei-Bin},
  journal={IEEE Access},
  volume={10},
  pages={82834--82843},
  year={2022},
  publisher={IEEE}
}

Contents

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

Usage

python3 main.py

optional arguments:

--lr                default=1e-3    learning rate
--epoch             default=200     number of epochs tp train for
--trainBatchSize    default=100     training batch size
--testBatchSize     default=100     test batch size

Results

Name	GPU Time (ms)	C10 Error (%)	FLOPs (G)	MAdd (G)	Memory (MB)	#Params (M)
ThreshNet28	0.35	14.75	2.28	4.55	83.26	10.18
SqueezeNet	0.36	14.25	2.69	5.32	211.42	0.78
MobileNet	0.38	16.12	2.34	4.63	230.84	3.32
ThreshNet79	0.42	13.66	3.46	6.90	109.68	14.31
HarDNet68	0.44	14.66	4.26	8.51	49.28	17.57
MobileNetV2	0.46	14.06	2.42	4.75	384.78	2.37
ThreshNet95	0.46	13.31	4.07	8.12	132.34	16.19
HarDNet85	0.50	13.89	9.10	18.18	74.65	36.67

* GPU Time is the inference time per image on NVIDIA RTX 3050

Requirements

• Python 3.6+
• Pytorch 0.4.0+
• Pandas 0.23.4+
• NumPy 1.14.3+

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
  

References

GitHub

• torchstat
• pytorch-cifar10
• HarDNet