CODEWITHSUNDEEP
computer-visiontensorflowdeep-learningcaffeimage-segmentation
user288609
user288609

Reputation: 13015

probability map for semantic segmantion

With respect to semantic segmentation, it seems to me that there are multiple ways for the final pixel-wise labeling, such as
softmax, sigmoid, logistic regression or other classical classification methods.

However, for softmax approach, we need to ensure the output map resulting from the network architecture has multiple channels. The number of channels matches the number of classes. For instance, if we are talking two-classes problem, masks and un-masks, then we will use two channels. Is this right?

Moreover, each channel in the output map can be treated as a probability map for a given class. Is this understanding right?

Upvotes: 1

Views: 1202

Answers (1)

Ash
Ash

Reputation: 4718

Yes to both questions. The goal of the softmax function is to transform the scores into probabilities so that you can maximize the probability of the true label.

Upvotes: 1

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