深度学习图像分类问题的目标是什么 [英] what should be the target in this deep learning image classification problem
问题描述
我正在喀拉拉邦使用CNN进行图像分类项目。我有大约900个人的900张照片的数据集。每个人都有他不同年龄的多张照片。
我的目标是预测输入的任何人的照片的正确ID。
这是数据的概览。
I am doing a image classification project using CNN in keras. I have a dataset of about 900 photos of about 70 people .Each person has multiple photos of his different age. My goal is to predict the correct ID of the person if any one of his photo is in the input. Here is the glimpse of the data.
我的问题是：
-
我的目标列应该是什么？目标是年龄还是 ID？ 2-
是否需要对目标列进行热编码？例如，如果我使用
ID作为目标，那么我是否必须对ID列进行一次热编码？
What should be my target column ?Is Target 'AGE' or 'ID'? 2-Do I need to do hot-encoding of the target column? For example if I used ID as my target,then do I have to do one-hot-encoding of ID column?
如果我使用了以ID为目标，然后进行一次热编码后，
是否意味着我将有70个课程？
If I used ID as my target,then after one-hot-encoding, does it mean,I will be having 70 classes?
推荐答案
- 您将使用不同的年龄图像来识别同一个人。例如，在数据集中，您有100张不同的可汗图像，并训练了一个模型。现在，您提供可汗的第101张图片，模型将对其进行检测。因此，您的目标列应为
ID。 - 是的，有70个类，您会得到一个<$ c $的热编码矢量c> 900x70
- 它应该是softmax层，因为Sigmoid层用于二进制类或多标签问题。由于必须彼此检测70个不同的人，因此需要一个softmax类。
- 我不这样认为，这样您的模型将无法分辨出这是哪个人的图像（作为测试提供的图像）
- You are going to identify the same person using different age images. For example, in the dataset, you have 100 different images of khan and you trained a model. Now you provide the 101st image of khan, the model will detect it. So your target column should be
ID. - yes, there are 70 classes and you get one hot encoded vector of
900x70 - It should be a softmax layer because the sigmoid layer is used for binary class or multilabel problem. As you have to detect 70 different people from each other, you need a softmax class.
- I don't think so, in this way your model would not be capable of telling which person image is this (the one provided as a test)
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