<p>Classification generally refers to the problem of classifying an entire entity. For example, in ImageNet, each image is given a class, regardless of whether the relevant information is only a subset of the image.</p>
<p>Segmentation refers to the problem of dividing up and then classifying parts of an image. For example: imagine an image of a cow. Classification would be saying this image contains a cow. Segmentation would try and divide which pixels are cow, which are grass, etc.</p>
<p>Both classification and segmentation are often done with a variety of loss functions. The most popular loss for classification is probably cross entropy, which may be appropriate for some forms of segmentation as well.</p>

CC BY-SA 4.0
2023-01-13T05:29:30.263Z
I'd like to try GeneralizeDiceLoss optimized based on Dice_Loss, but I don't know if this loss function can be used for segmentation. In addition, I have seen the paper of GHMLoss based on FocalLoss optimization, which is used to solve the problem of classification. But I haven't done many semantic segmentation projects, so can these two loss functions be used to make semantic segmentation loss functions?
lllittleX
CC BY-SA 4.0
2023-01-13T04:46:11.017Z
2023-01-13T04:46:11.017Z
chessprogrammer
<p>What's the difference between classification and segmentation in deep learning?</p>
<p>In particular, can the classification loss function be used for segmentation problems?</p>

<deep-learning><comparison><classification><objective-functions><image-segmentation>
What's the difference between classification and segmentation in deep learning?
lllittleX
# What's the difference between classification and segmentation in deep learning?

(Asked by: username_0 on 2023-01-13)

What's the difference between classification and segmentation in deep learning?

In particular, can the classification loss function be used for segmentation problems?

**Tags:** deep-learning, comparison, classification, objective-functions, image-segmentation


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## 1 answers

### Answer by username_1 on 2023-01-13. Score: 1

Classification generally refers to the problem of classifying an entire entity. For example, in ImageNet, each image is given a class, regardless of whether the relevant information is only a subset of the image.

Segmentation refers to the problem of dividing up and then classifying parts of an image. For example: imagine an image of a cow. Classification would be saying this image contains a cow. Segmentation would try and divide which pixels are cow, which are grass, etc.

Both classification and segmentation are often done with a variety of loss functions. The most popular loss for classification is probably cross entropy, which may be appropriate for some forms of segmentation as well.

> **Comments:**
> **username_0**: I'd like to try GeneralizeDiceLoss optimized based on Dice_Loss, but I don't know if this loss function can be used for segmentation. In addition, I have seen the paper of GHMLoss based on FocalLoss optimization, which is used to solve the problem of classification. But I haven't done many semantic segmentation projects, so can these two loss functions be used to make semantic segmentation loss functions?  

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