<p>If you are asking for arbitrary ML task dealing with 1000+ classes the most straigtforward thing that comes to mind is the <code>ImageNet</code> - <a href="https://en.wikipedia.org/wiki/ImageNet#cite_note-nytimes_2012-2" rel="nofollow noreferrer">https://en.wikipedia.org/wiki/ImageNet#cite_note-nytimes_2012-2</a>. It has more the 20k categories in the present time.</p>
<p>In order to perform such classification task you need</p>
<ul>
<li>A large enough dataset, such that each class occurs frequently enough or efficient data augmentation</li>
<li>A powerful model, such that can adapt to variety of different problems</li>
</ul>
<p>The first neural network to tackle this problem was <code>AlexNet</code> and since then plethora of architectures : <code>VGG's</code>, <code>ResNet's</code>, <code>EfficientNet</code> got better and better quality.</p>
<p>They have a lot of filters that react on different patterns.</p>

CC BY-SA 4.0
2021-02-03T15:24:59.107Z
2021-02-03T15:24:59.107Z
spiridon_the_sun_rotator
<p>I am trying to find research paper with theory(preferably implementation) that is about classifying 1000 (or more) classes. I have heard of an implementation, that initially clustering needs to be done then classification with something like softmax. Does anyone know of any research paper that implements 1000+ class classification.</p>

<machine-learning><classification><text-classification>
How to go about classifying 1000 classes?
Naveen Reddy Marthala
# How to go about classifying 1000 classes?

(Asked by: username_0 on 2021-02-03)

I am trying to find research paper with theory(preferably implementation) that is about classifying 1000 (or more) classes. I have heard of an implementation, that initially clustering needs to be done then classification with something like softmax. Does anyone know of any research paper that implements 1000+ class classification.

**Tags:** machine-learning, classification, text-classification


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## 1 answers

### Answer by username_1 on 2021-02-03. Score: 4

If you are asking for arbitrary ML task dealing with 1000+ classes the most straigtforward thing that comes to mind is the `ImageNet` - <https://en.wikipedia.org/wiki/ImageNet#cite_note-nytimes_2012-2>. It has more the 20k categories in the present time.

In order to perform such classification task you need

* A large enough dataset, such that each class occurs frequently enough or efficient data augmentation
* A powerful model, such that can adapt to variety of different problems

The first neural network to tackle this problem was `AlexNet` and since then plethora of architectures : `VGG's`, `ResNet's`, `EfficientNet` got better and better quality.

They have a lot of filters that react on different patterns.


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