Using a Deep CONVNET to Build a Model for Classifying Different Races such as Mongoloid, Negroid and Caucasian
This kernel uses a deep CONVNET that was trained on Google GPU to perform Race Classification on a zipped file containing faces of different races.
Each of the image are either labelled as:
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Caucasian: includes people of American and European descent, also known as whites
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Mongoloid: includes people of Asian descent, especially Eastern Asian
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Negroid: includes people of African descent or black Americans
The zip Dataset contains various images of faces of different races which was aggregated from https://www.shutterstock.com/
I'll use it to build an face image classifier using a tf.keras.Sequential.model and build a data(input data pipline) using tf.keras.preprocessing.image.ImageDataGenerator.
This project workflow includes:
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Loading the zipped dataset from my google drive
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Examining and understanding the dataset
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Building a Data Image input pipeline
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Building a Deep CONVNET Architecture
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Training a CNN model
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Testing the model
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Using the model for prediction on new data
All these will be done with tensorflow 2.x.
