用于年龄估算的紧凑型软件阶段回归网络.zip

# SSR-Net **[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)** <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"/> <img src="https://github.com/shamangary/SSR-Net/blob/master/table1.png" height="240"/> ### Real-time webcam demo <img src="https://media.giphy.com/media/AhXTjtGO9tnsyi2k6Q/giphy.gif" height="240"/> <img src="https://github.com/shamangary/SSR-Net/blob/master/age_gender_demo.png" height="240"/> ## Paper ### PDF https://github.com/shamangary/SSR-Net/blob/master/ijcai18_ssrnet_pdfa_2b.pdf ### Paper authors **[Tsun-Yi Yang](http://shamangary.logdown.com/), [Yi-Husan Huang](https://github.com/b02901145), [Yen-Yu Lin](https://www.citi.sinica.edu.tw/pages/yylin/index_zh.html), [Pi-Cheng Hsiu](https://www.citi.sinica.edu.tw/pages/pchsiu/index_en.html), and [Yung-Yu Chuang](https://www.csie.ntu.edu.tw/~cyy/)** ## 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 + Ubuntu ## Dependencies + 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 ``` ## Codes There are three different section of this project. 1. Data pre-processing 2. Training and testing 3. Video demo section We will go through the details in the following sections. This repository is for IMDB, WIKI, and Morph2 datasets. ### 1. Data pre-processing + 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 ``` ### 2. Training and testing <img src="https://github.com/shamangary/SSR-Net/blob/master/merge_val_morph2.png" height="300"/> 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 ``` ### 3. Video demo section 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. ### Real-time webcam demo 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. ### 4. Extension ### Training the gender model 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. ## Third Party Implementation + MXNET: https://github.com/wayen820/gender_age_estimation_mxnet + Pytorch: https://github.com/oukohou/SSR_Net_Pytorch + Pytorch: https://github.com/CrazySummerday/SSR-Net