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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.

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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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