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I'm a newbie in neural networks. I'm trying to fit my neural network that has 3 different outputs:

  1. semantic segmentation,
  2. box mask and
  3. box coordinates.

When my model is training, the loss of semantic segmentation and box coordinates are decreasing, but the loss of the box mask is increasing too much.

My neural network is a CNN and it's based on Chargrid from here. The architecture is this:

enter image description here

For semantic segmentation outputs, it's expected to have 15 classes, for box mask it's expected to have 2 classes (foreground vs background) and for box coordinates it's expected to have 5 classes (1 for each corner of bounding box + 1 for None).

Loss details

Step 1

Here's the loss/accuracy for each of the three outputs at the end of the first step.

  1. Semantic segmentation (Output 1) - 0.0181/0.958
  2. Box mask (Output 2) - 13.88/0.946
  3. Box coordinates (Output 3) - 0.2174/0.0000000867

Last step

Here's the loss/accuracy at the last step.

  1. Semantic segmentation (Output 1) - 0.0157/0.963
  2. Box mask (Output 2) - 73.02/0.935
  3. Box coordinates (Output 3) - 0.06/0.82

Is that normal? How can I interpret these results?

I will leave below, the output of the model fit.

enter image description here enter image description here enter image description here

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  • 1
    $\begingroup$ Hi, you ought to be a bit more specific and provide some more details, otherwise nobody will be able to answer your question. Also, the images are pretty much unreadable, so a summary as text would be better. $\endgroup$ – Oliver Mason Feb 20 at 16:10
  • $\begingroup$ @OliverMason what do you mean about being more specific? $\endgroup$ – João Castilho Feb 20 at 16:23
  • $\begingroup$ Give us more detail about your data, architecture, etc $\endgroup$ – Oliver Mason Feb 20 at 16:25
  • $\begingroup$ @OliverMason is this better now? $\endgroup$ – João Castilho Feb 20 at 16:36

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