Adaptive-diverse-capsule-network

This implement is an improved version of real-valued capsule network from our paper《Cv-CapsNet:complex-valued capsule network》 URL:https://ieeexplore.ieee.org/document/8744220， In this implement, a multi-scale feature fusion mechanism based on attention is proposed, named adaptive-diverse-model, which eliminates the manual setting of capsule size in coding stage, we also use the bottleneck from the MobilenetV3 to improve it(adaptive-diverse-model+). results of ablation research can be seen in Results.

Usage

Step 1. Clone this repository to local.

git clone https://github.com/Johnnan002/Adaptive-diverse-capsule-network
cd Adaptive-diverse-capsule-network

Step 2. Train the Adaptive-diverse-capsule-network model on CIFAR10

Training with default settings:

$ python Adaptive-diverse-capsule-network.py

More detailed usage run for help:

$ python Adaptive-diverse-capsule-network.py -h

Step 3. Test a pre-trained Adaptive-diverse-capsule-network model

Suppose you have trained a model using the above command, then the trained model will be saved to result/trained_model.h5. Now just launch the following command to get test results.

$ python Adaptive-diverse-capsule-network.py -t -w result/trained_model.h5

It will output the testing accuracy . The testing data is same as the validation data. It will be easy to test on new data, just change the code as you want

Results

Validation accuracy > 88.5% after 25 epochs on CIFAR10.
About 600 seconds per epoch on a single tesla k80 GPU card
 ________________________________________________________________________________________
|        Models         |     Parameters      |  Accuracy(25epoch) |       upgrade       |
|———————————————————————|—————————————————————|————————————————————|—————————————————————|
|     original model    |       7.99M         |       71.56%       |        —— ——        |
|———————————————————————|—————————————————————|————————————————————|—————————————————————|
|     diverse-model     |       5.3M          |       86.7%        |      ↑ 15.14%       |
|———————————————————————|—————————————————————|————————————————————|—————————————————————|
| adaptive-diverse-model|       5.3M          |       87.8%        |      ↑ 16.24%       |
|———————————————————————|—————————————————————|————————————————————|—————————————————————|
|adaptive-diverse-model+|       5.3M          |       88.5%        |      ↑ 16.94%       |   
|_______________________|_____________________|____________________|_____________________|

If you use the code in your research or wish to refer to the baseline results published in the Model , please use the following BibTeX entry.

@ARTICLE{8744220, 
author={X. {Cheng} and J. {He} and J. {He} and H. {Xu}}, 
journal={IEEE Access}, 
title={Cv-CapsNet: Complex-Valued Capsule Network}, 
year={2019}, 
volume={7}, 
number={}, 
pages={85492-85499},  
doi={10.1109/ACCESS.2019.2924548}, 
ISSN={2169-3536}, 
month={},}