January 2023
Intermediate to advanced
288 pages
6h 6m
Chinese
Content preview from 移动端AI与ML应用开发：基于iOS和Android
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24
|
第
2
章
在这里，你可以看到模型确定它正在看香蕉的可能性为 98.82%，而它看的是 Granny
Smith 苹果或无花果的可能性较小。虽然很明显这是香蕉，但当这个应用程序查看图像
时，它从图像中提取特征，其中一些特征可能存在于苹果中
—
例如皮肤纹理或者颜色。
可以想象，如果你想训练一个模型，你需要大量的图像示例，这些示例需要根据它们的
类别进行标记。值得庆幸的是，有一些基本数据集限制了范围以使其易于学习，接下来
我们将考虑从头开始构建分类器。
2.1.1
你的第一个分类器：识别衣物
对于第一个示例，让我们考虑如何识别图像中的衣物。例如，考虑图 2-8 中的物品。
图
2-8
：服装示例
这里有许多不同的衣物，你可以识别它们。你了解衬衫、外套或连衣裙是什么。但是，
你如何向从未见过衣服的人解释这一点？一双鞋呢？这张图片中有两只鞋，但你会如何
向别人描述呢？这是我们在第 1 章中谈到的基于规则的编程可能失败的另一个领域。有
时用规则来描述某件事是不可行的。
当然，计算机视觉也不例外。但是，想想你是如何学会识别所有这些物品的
—
通过查
看大量不同的例子并获得如何使用它们的经验。我们可以用计算机做同样的事情吗？答
案是肯定的，但有局限性。让我们看第一个例子，如何教计算机识别衣物，使用一个名
为 Fashion MNIST 的著名数据集。
2.1.2
数据：
Fashion MNIST
学习和基准算法的基础数据集之一是来自 Yann LeCun、Corinna Cortes 和 Christopher
Burges
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