[IJCAI18] SSR-Net: A Compact Soft Stagewise Regression Network for Age Estimation
- A real-time age estimation model with 0.32MB.
- Gender regression is also added!
- Megaage-Asian is provided in https://github.com/b02901145/SSR-Net_megaage-asian
- Coreml model (0.17MB) is provided in https://github.com/shamangary/Keras-to-coreml-multiple-inputs-example
Code Author: Tsun-Yi Yang
Last update: 2019/09/19 (Renew the morph2 dataset link)
https://github.com/shamangary/SSR-Net/blob/master/ijcai18_ssrnet_pdfa_2b.pdf
Tsun-Yi Yang, Yi-Husan Huang, Yen-Yu Lin, Pi-Cheng Hsiu, and Yung-Yu Chuang
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.
- Keras
- Tensorflow
- GTX-1080Ti
- Ubuntu
- A guide for most dependencies. (in Chinese) http://shamangary.logdown.com/posts/3009851
- Anaconda
- OpenCV
- dlib
- MTCNN for demo
pip install mtcnn
- MobileNet (already in the codes) https://github.com/fchollet/keras/blob/master/keras/applications/mobilenet.py
- DenseNet (already in the codes) https://github.com/titu1994/DenseNet
- Face alignment (already in the codes) https://github.com/xyfeng/average_portrait
- Others
conda install -c conda-forge moviepy
conda install -c cogsci pygame
conda install -c conda-forge requests
conda install -c conda-forge pytables
There are three different section of this project.
- Data pre-processing
- Training and testing
- Video demo section We will go through the details in the following sections.
This repository is for IMDB, WIKI, and Morph2 datasets.
- Download IMDB-WIKI dataset (face only) from https://data.vision.ee.ethz.ch/cvl/rrothe/imdb-wiki/.
- Morph2 dataset requires application form https://www.faceaginggroup.com/?page_id=1414 https://ebill.uncw.edu/C20231_ustores/web/classic/product_detail.jsp?PRODUCTID=8
- Unzip them under './data'
- Run the following codes for dataset pre-processing.
cd ./data
python TYY_IMDBWIKI_create_db.py --db imdb --output imdb.npz
python TYY_IMDBWIKI_create_db.py --db wiki --output wiki.npz
python TYY_MORPH_create_db.py --output morph_db_align.npz
The experiments are done by randomly choosing 80% of the dataset as training and 20% of the dataset as validation (or testing). The details of the setting in each dataset is in the paper.
For MobileNet and DenseNet:
cd ./training_and_testing
sh run_all.sh
For SSR-Net:
cd ./training_and_testing
sh run_ssrnet.sh
- Note that we provide several different hyper-parameters combination in this code. If you only want a single hyper-parameter set, please alter the command inside "run_ssrnet.sh".
Plot the results: For example, after the training of IMDB dataset, you want to plot the curve and the results. Copy "plot.sh", "ssrnet_plot.sh", and "plot_reg.py" into "./imdb_models". The following command should plot the results of the training process.
sh plot.sh
sh ssrnet_plot.sh
Pure CPU demo command:
cd ./demo
KERAS_BACKEND=tensorflow CUDA_VISIBLE_DEVICES='' python TYY_demo_mtcnn.py TGOP.mp4
# Or you can use
KERAS_BACKEND=tensorflow CUDA_VISIBLE_DEVICES='' python TYY_demo_mtcnn.py TGOP.mp4 '3'
-
Note: You may choose different pre-trained models. However, the morph2 dataset is under a well controlled environment and it is much more smaller than IMDB and WIKI, the pre-trained models from morph2 may perform ly on the in-the-wild images. Therefore, IMDB or WIKI pre-trained models are recommended for in-the-wild images or video demo.
-
We use dlib detection and face alignment in the previous experimental section since the face data is well organized. However, dlib cannot provide satisfactory face detection for in-the-wild video. Therefore we use mtcnn as the detection process in the demo section.
Considering the face detection process (MTCNN or Dlib) is not fast enough for real-time demo. We show a real-time webcam version by using lbp face detector.
cd ./demo
KERAS_BACKEND=tensorflow CUDA_VISIBLE_DEVICES='' python TYY_demo_ssrnet_lbp_webcam.py
- Note that the covered region of face detection is different when you use MTCNN, Dlib, or LBP. You should choose similar size between the inference and the training.
- Also, the pre-trained models are mainly for the evaluation of the datasets. They are not really for the real-world images. You should always retrain the model by your own dataset. In webcam demo, we found that morph2 pre-trained model actually perform better than wiki pre-trained model. The discussion will be included in our future work.
- If you are Asian, you might want to use the megaage_asian pre-trained model.
- The Morph2 pre-trained model is good for webcam but the gender model is overfitted and not practical.
We can reformulate binary classification problem into regression problem, and SSR-Net can be used to predict the confidence. For example, we provide gender regression and demo in the code for the extension.
Training the gender network:
cd ./training_and_testing
sh run_ssrnet_gender.sh
Note that the score can be between [0,1] and the 'V' inside SSR-Net can be changed into 1 for general propose regression.
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