title: neural network converges too fast and predicts blank results
body: <p>I am using a UNet model to train a segmentation algorithm with roughly 1,000 grayscale medical images and 1,000 corresponding masks where the section of interest in the medical image is white pixel and the background is black. </p>
  
  <p>I am using dice loss and a similar dice score as an accuracy metric to account for the fact that my white pixels are generally less in number than the black background pixels. But I am still having a few problems when training</p>
  
  <p>1) <strong>The loss converges too fast.</strong> If I have my SGD optimizer's learning rate at 0.01 for example, at around 2 epochs the loss (training and validation) will drop to 0.00009 and the accuracy shoots up and settles at 100% in proportion. Testing on an unseen set gives blank images.  </p>
  
  <p><strong>Assumption - Overfitting:</strong>
  I assumed this was due to overfitting, so I augmented the dataset as much as possible with rigid transformations - flipping and rotating, but still no help.
  Also if I test the model against the same data I used to train it, it still predicts blank images. So does this mean it isn't a case of overfitting?</p>
  
  <p>2)<strong>Model doesn't look like it's even training.</strong> I was able to check the model before it reduced all the test data to blackness, but even then the results would look like blurry versions of the original without segmenting the features highlighted by my training mask</p>
  
  <p>3) <strong>The loss vs epochs and accuracy vs epochs output charts are very smooth:</strong> They present none of the oscillating behaviour that I expect to see when doing semantic segmentation. According to <a href="https://stackoverflow.com/questions/47649786/what-fast-loss-convergence-indicates-on-a-cnn">this</a> related post a smooth chart usually occurs when there is only one class. I however assumed that my model would see the training masks (white pixels vs black pixels) and see that as a two class problem. Am I wrong in this assumption? </p>
  
  <p>4) According to <a href="https://stackoverflow.com/questions/50181116/unet-segmentation-model-predicts-blank-image">this</a> post Dice is good for an unbalanced training set. I have also tried to get precision/recall/F1 results as they suggest, but was unable to do it and assuming it might be related to my 3rd issue where the model sees my segmentation task as a single class problem. </p>
  
  <p><strong>TLDR:</strong> How can I fix the black output results I am getting? Can you please help me clarify if my learning model is actually seeing my white and black pixels in each mask as two separate classes and if not what is it actually doing?</p>