Introduction: I am currently working on a computer vision problem, I have satellite images and I have to detect a particular archeological structure (Tell). I have access to the previously made researches and I have access to a Manet model trained on 5 thousand images of mesopotamic area. My project objective is to fine-tune this model in order to detect the same archeological structure in different geographical areas which have different characteristics, the problem is that I have something like 200 images of the "new" area which is why I am trying to fine-tune the old model I have access to. The problem in few words is: which layers do you usually add on top of a base model for finetuning? All the tutorial I have found online are for classification problems, however, the output of my model is a mask in (N, H, W) -> P format where N is the batch size, and H and W are respectively height and width whereas p is the probability that the particular pixel belongs to the "Tell" or not (binary semantic segmentation).

Thats what I am now thinking to do, is it sound correct to you?

  1. freeze old model weights
  2. add GlobalAveragePooling2D layer
  3. add Dropout(0.2) layer
  4. add Dense(512, 512) (imgs size)
  5. fine tuning
  6. unfreeze old model weights
  7. finetune the entire model with very low lr

While I know the whole workflow is correct, the thing that worries me the most is the process of editing the model (points 2-3-4), does it make sense?

That is the model summary with batch size 8 and image channels = 6:

model summary



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