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Custom Dataset Creation with Tensorflow Framework and Image Processing for Google T-Rex

机译:使用Tensorflow框架自定义数据集创建和Google T-Rex的图像处理

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This paper describes a methodology of creating custom dataset and then using convolution neural network (CNN) model for performing image processing operations on Google’s t-rex game using tensorflow and keras framework. Machine learning professionals depend on online available datasets, specifically for computer vision based algorithms, since deep neural networks require specific input shape, size and channels before starting training process. Unfortunately, google’s t-rex game does not have any directly available dataset unlike various image processing datasets. Therefore in this paper step by step process is provided for creating, training, and testing customized dataset with help of CNN model coded in python programming language for both GPU and CPU architectures.
机译:本文介绍了一种创建自定义数据集,然后使用卷积神经网络(CNN)模型通过tensorflow和keras框架在Google的t-rex游戏上执行图像处理操作的方法。机器学习专业人员依赖于在线可用数据集,尤其是基于计算机视觉的算法,因为深度神经网络在开始训练过程之前需要特定的输入形状,大小和通道。不幸的是,与各种图像处理数据集不同,Google的霸王龙游戏没有任何直接可用的数据集。因此,在本文中,通过分步过程提供了用于创建,训练和测试自定义数据集的方法,并借助以Python编程语言编码的CNN模型对GPU和CPU架构进行了编码。

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