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How to systematically choose the architecture of a neural network (NN) for a concrete task?

For example, I am solving classification task with 3 classes (NN should recognize pandas, dogs and cats). What type of architecture I should choose? Is there a general rule of thumb?

So far, I have simply picked an architecture, which has 90% accuracy on CIFAR-10 dataset, and been trying to apply it to my task. Results are not very good (on 30 epoch, I got only about 40% accuracy on train set and 57% on val set).

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  • $\begingroup$ model architecture is also a hyperparameter (like learning rate) that you need to adjust yourself. $\endgroup$ Jun 4 at 8:41
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There's no thumb rule for designing a NN architecture. Every architecture is designed to perform well on specific problems, so you need to choose one for yourself, which best suites your problem.

You may consider these points while implementing a NN architecture for your problem,

  • Check if other researchers have worked on similar problems. If yes, review their approach and the NN architecture they designed or used ( with some modifications ).

  • For image classification, pretrained models like VGG, Inception, ResNet, MobileNets etc. are available which are trained on huge datasets like ImageNet. These models are used a "backbone" model for other problems, for better feature extraction and also to reduce the no. of trainable parameters. Consider using these models as a whole or use some layers only.

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