Fast.ai v3 2019课程中文版笔记

Part 1 (2019) UTC+8 China / SE Asia
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Lesson 3: Data blocks; Multi-label classification; Segmentation

第三课 data blocks, 多标签分类，图片像素隔离

Overview 综述

Lots to cover today! We start lesson 3 looking at an interesting dataset: Planet’s Understanding the Amazon from Space. In order to get this data in to the shape we need it for modeling, we’ll use one of fastai’s most powerful (and unique!) tools: the data block API. We’ll be coming back to this API many times over the coming lessons, and mastery of it will make you a real fastai superstar! Once you’ve finished this lesson, if you’re ready to learn more about the data block API, have a look at this great article: Finding Data Block Nirvana, by Wayde Gilliam.

本节课内容很多！一开始我们要看一个非常有趣的数据集：Planet’s Understanding the Amazon from Space. 为了让数据能“喂给”模型，我们需要用fastai强大且独特的data block API工具来处理数据。在后续的课时中，我们也会反复使用这个API，熟练掌握它能让你成为真正的fastai超级明星！当你完成本节课，如果你准备好学习更多data block API，可以看看这篇很棒的文章Finding Data Block Nirvana, 作者是 Wayde Gilliam.

One important feature of the Planet dataset is that it is a multi-label dataset. That is: each satellite image can contain multiple labels, whereas previous datasets we’ve looked at have had exactly one label per image. We’ll look at what changes we need to make to work with multi-label datasets.

planet数据集一个重要特征是多标签multi-label。也就是说：每张卫星图片可以包含多个标签/标注，而之前的数据集我们面对的是一张图对应一个标注。我们会学到需要做哪些调整来处理这个多标签问题。

Next, we will look at image segmentation, which is the process of labeling every pixel in an image with a category that shows what kind of object is portrayed by that pixel. We will use similar techniques to the earlier image classification models, with a few tweaks. fastai makes image segmentation modeling and interpretation just as easy as image classification, so there won’t be too many tweaks required.
接下来，我们将学习image segmentation 图片像素隔离，也就是对图片中每一个像素做类别标注，从而知道哪个像素对应哪个物体。我们会对前期所学的技巧做一些调整。fastai将图片像素隔离建模和解读做得跟图片分类一样简单，因此不会有太多需要调整的地方。

We will be using the popular Camvid dataset for this part of the lesson. In future lessons, we will come back to it and show a few extra tricks. Our final Camvid model will have dramatically lower error than an model we’ve been able to find in the academic literature!
我们将用著名的Camvid数据集来做图片像素隔离。后续课时中，还会回头学习更多技巧。我们最终Camvid模型对比所能找到的已发表的最优学术水平，将进一步大幅降低错误率。

What if your dependent variable is a continuous value, instead of a category? We answer that question next, looking at a keypoint dataset, and building a model that predicts face keypoints with high accuracy.
如果你的目标变量是连续的，而非类别，怎么办？我们将用下一个数据集keypoint来回答，我们将构建一个模型做高精度的脸部关键点预测。

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