by Cole Murray
通过科尔·默里(Cole Murray)
In my last tutorial, you learned about how to combine a convolutional neural network and Long short-term memory (LTSM) to create captions given an image. In this tutorial, you’ll learn how to build and train a multi-task machine learning model to predict the age and gender of a subject in an image.
在我的上一教程中 ，您学习了如何结合卷积神经网络和长短期记忆(LTSM)来创建给定图像的字幕。 在本教程中，您将学习如何构建和训练多任务机器学习模型，以预测图像中对象的年龄和性别。
总览 (Overview)
- Introduction to age and gender model 年龄和性别模型介绍
- Building a Multi-task Tensorflow Estimator 构建多任务Tensorflow估算器
- Training 训练
先决条件 (Prerequisites)
- basic understanding of convolutional neural networks (CNN) 卷积神经网络(CNN)的基本理解
- basic understanding of TensorFlow 对TensorFlow的基本了解
- GPU (optional) GPU(可选)
年龄和性别模型介绍 (Introduction to Age and Gender Model)
In 2015, researchers from Computer Vision Lab, D-ITET, published a paper DEX and made public their IMDB-WIKI consisting of 500K+ face images with age and gender labels.
2015年，D-ITET计算机视觉实验室的研究人员发表了一篇论文DEX ，并公开了其IMDB-WIKI，该数据库由500K +带有年龄和性别标签的面部图像组成。
DEX outlines an neural network architecture involving a pretrained imagenet vgg16 model that estimates the apparent age in face images. DEX placed first in ChaLearn LAP 2015 — a competition that deals with recognizing people in an image — outperforming human reference.
DEX概述了一种神经网络架构，其中涉及一个预训练的imagenet vgg16模型，该模型可估计人脸图像中的表观年龄。 DEX在ChaLearn LAP 2015中名列第一，该竞赛旨在识别图像中的人物，其表现优于人类参考。
年龄作为分类问题
