yinguobing / Face Marks
Licence: mit
Detect facial landmarks with TensorFlow and CoreML on iPhone.
Stars: ✭ 69
Programming Languages
swift
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Face Marks
An iOS demo APP shows how to detect facial landmarks with CoreML and TensorFlow model.
For more details: https://yinguobing.com/facial-landmark-localization-by-deep-learning-port-to-ios-with-coreml/
Background
This repo is a part of my deep learning series posts. For all the posts please refer to the following links.
第一篇：基于深度学习的人脸特征点检测 - 背景
为什么我决定采用深度学习实现面部特征点检测。阅读全文
第二篇：基于深度学习的人脸特征点检测 - 数据与方法
解决问题所需的数据来源与对应的方法。阅读全文
第三篇：基于深度学习的人脸特征点检测 - 数据集整理
从互联网获取的数据大多数情况下不是开箱即用的，这意味着我们需要对数据进行初步的整理，例如统计数据量、去除不需要的文件、必要的格式转换等。阅读全文
第四篇：基于深度学习的人脸特征点检测 - 数据预处理
如何使用Python从22万张图片中提取检测人脸特征点的可用样本。阅读全文
第五篇：基于深度学习的人脸特征点检测 - 生成TFRecord文件
将面部区域的图片与特征点位置一起打包成TensorFlow可用的TFRecord文件。阅读全文
第六篇：基于深度学习的人脸特征点检测 - 网络模型构建
如何使用TensorFlow构建一个属于你自己的神经网络模型。阅读全文
第七篇：基于深度学习的人脸特征点检测-模型导出与应用
使用Estimator API时，导出适用于推演的网络模型的正确方法。阅读全文
第八篇：基于深度学习的人脸特征点检测-移植到iPhone
如何通过CoreML在iPhone应用中使用TensorFlow模型。阅读全文
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