Appologizes for misuse of technical terms. I am working on a project of semantic segmentation via convolutional neural networks (CNNs) ; trying to implement an architecture of type Encoder-Decoder, therefore output is the same size as the input.
How do you design the labels ? What loss function should one apply ? Especially in the situation of heavy class inbalance (but the ratio between the classes is variable from image to image).
The problem deals with two classes (objects of interest and background). I am using Keras with tensorflow backend.
So far, I am going with designing expected outputs to be the same dimensions as the input images, applying pixel-wise labeling. Final layer of model has either softmax activation (for 2 classes), or sigmoid activation ( to express probability that the pixels belong to the objects class). I am having trouble with designing a suitable objective function for such a task, of type:
in agreement with Keras.
Please,try to be specific with the dimensions of tensors involved (input/output of the model). Any thoughts and suggestions are much appreciated. Thank you !