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Asked: 2026-06-07T23:07:08+00:00

I would like to implement a Picture Classification using Neural Network. I want to

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I would like to implement a Picture Classification using Neural Network. I want to know the way to select the Features from the Picture and the number of Hidden units or Layers to go with.

For now i have an idea of changing the size of image to some 50×50 or smaller so that the number of Features are less and that all inputs have constant size.The features would be RGB value of each of the pixels.Will it be fine or there is some other better way?

Also i decided to go with 1 Hidden Layer with half the number of units as in Inputs. I can change the number to get better results. Or would i require more layers ?

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  1. Editorial Team

    There are numerous image data sets that are successfully learned by neural networks, like

    Not that you need many training examples. Usually one hidden layer is sufficient. But it can be hard to determine the “right” number of neurons. Sometimes the number of hidden neurons should even be greater than the number of inputs. When you use 2 or more hidden layer you will usually need less hidden nodes and the training will be faster. But when you have to many hidden layers it can be difficult to train the weights in the first layer.

    A kind of neural network that is designed especially for images are convolutional neural networks. They usually work much better than multilayer perceptrons and are much faster.

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