问题描述
我正在尝试通过tensorflow对象检测api训练自定义数据集.数据集包含numpy ndarray格式(uint8
)的40k训练图像和标签.训练数据集shape = 2([40000,23456])且标签shape = 1([0 ...,3]).我想为此数据集生成tfrecord.我该怎么办?
I'm trying to train a custom dataset through tensorflow object detection api. Dataset contains 40k training images and labels which are in numpy ndarray format (uint8
). training dataset shape=2 ([40000,23456]) and labels shape = 1 ([0..., 3]). I want to generate tfrecord for this dataset. how do I do that?
推荐答案
本教程将引导您完成从数据创建TFRecord的过程:
This tutorial will walk you through the process of creating TFRecords from your data:
但是,现在有了使用数据集输入管道进行预处理的更简便方法.我更喜欢将数据保留为最原始的格式,并建立预处理管道来处理它.这是您要阅读以了解有关数据集预处理管道的主要指南:
However there are easier ways of dealing with preprocessing now using the Dataset input pipeline. I prefer to keep my data in it's most original format and build a preprocessing pipeline to deal with it. Here's the primary guide you want to read to learn about the Dataset preprocessing pipeline:
https://www.tensorflow.org/programmers_guide/datasets
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