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SSR-Net_megaage-asian

[IJCAI18] SSR-Net: A Compact Soft Stagewise Regression Network for Age Estimation

• A real time age estimation model with 0.32MB
• This repository is for MegaAge-Asian datasets
• See also https://github.com/shamangary/SSR-Net for more datasets and video demo

Last update: 2018/08/06 (Adding MegaAge-Asian dataset.)

<img src="https://media.giphy.com/media/ygBDe4FIU4Cybbfh2N/giphy.gif" height="240"/> <img src="https://media.giphy.com/media/bZvHMOp2hBsusr96fa/giphy.gif" height="240"/>

<img src="https://github.com/shamangary/SSR-Net/blob/master/demo/TGOP_tvbs.png" height="240"/> <img src="https://github.com/shamangary/SSR-Net/blob/master/demo/the_flash_cast.png" height="240"/>

Paper

PDF

https://github.com/shamangary/SSR-Net/blob/master/ijcai18_ssrnet_pdfa_2b.pdf

Authors

Tsun-Yi Yang, Yi-Husan Huang, Yen-Yu Lin, Pi-Cheng Hsiu, and Yung-Yu Chuang

Abstract

This paper presents a novel CNN model called Soft Stagewise Regression Network (SSR-Net) for age estimation from a single image with a compact model size. Inspired by DEX, we address age estimation by performing multi-class classification and then turning classification results into regression by calculating the expected values. SSR-Net takes a coarse-to-fine strategy and performs multi-class classification with multiple stages. Each stage is only responsible for refining the decision of the previous stage. Thus, each stage performs a task with few classes and requires few neurons, greatly reducing the model size. For addressing the quantization issue introduced by grouping ages into classes, SSR-Net assigns a dynamic range to each age class by allowing it to be shifted and scaled according to the input face image. Both the multi-stage strategy and the dynamic range are incorporated into the formulation of soft stagewise regression. A novel network architecture is proposed for carrying out soft stagewise regression. The resultant SSR-Net model is very compact and takes only 0.32 MB. Despite of its compact size, SSR-Net’s performance approaches those of the state-of-the-art methods whose model sizes are more than 1500x larger.

Platform

• Keras
• Tensorflow
• GTX-1080Ti and GTX-1080
• Ubuntu

Codes

This repository is for MegaAge-Asian datasets. There are three different section of this project.

• Data pre-processing
• Training
• Testing

We will go through the details in the following sections.

Data pre-processing

• Download MegaAge-Asian dataset from http://mmlab.ie.cuhk.edu.hk/projects/MegaAge/

• Extract under this folder

• Run the following codes for dataset pre-processing

  or

• Download from https://drive.google.com/open?id=1CismL8x4gi3sAfTi3qpxedWSStTPsrcp

python TYY_Megaage_asian_create_db.py

Training

• For SSR-Net

bash run_ssrnet_megaage.sh

• For MobileNet

bash run_megaage_MobileNet.sh

• For DenseNet

bash run_megaage_DenseNet.sh

Testing

Create predicted results and calculate CA (cumulative accuracy)

• For SSR-Net, MobileNet and DenseNet

bash run_CA.sh