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Now I am solving the problem of 3-class classification (in the task you need to understand who is in the picture - a panda, a cat or a dog). The dataset consists of 3000 images. To solve the problem, I use a slightly modified VGG architecture:enter image description here

After 200 epochs I got the following quality:enter image description here

In the problem, it is required to rich >= 85% quality on validation set. To be honest, I have no any special thoughts yet. Can you please give advice on what to change / add / remove in the neural network architecture in order to achieve the desired result? I wan to note, that I am already using data augmentation.

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The problem is dealing with multi-class classification. So, in output layer try of using "SoftMax" as the Activation layer.

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Are the weights the same for all three classes for training? I have a standard vgg16 modified for 3 classes (cancer images),but the training data was mostly of one class. Until I evened out the weight values (augmentation didn't seem to help as much), I could never get past ~80% accuracy. After about 200 epochs, it kind of just caught on and started showing accuracy beyond 90%.

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