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I've trained a CNN model based on landscape and pool images. The main purpose is to make model classify if the image contains a pool or not. The accuracy on the test set was about 94% but when I used my own image to test, sometimes model did wrong predictions then I implemented GradCam and realized that the model looks only at the edges of the pool when I would classify the pool by the water. How can I fix my model so it could make better predictions and look not only at the edges?
I solved the problem, I added an additional conv layer with 64 feature maps and increased size of dense layer from 512 to 1024 also I trained the model with the increased number of epochs(from 50 to 150). The result is impressive! Everything as I wanted.
