I am trying to perform segmentation of coils on PC motherboards:
I have around 150 training samples; some have more coils or bigger coils and some have less, but on every image there is at least one coil.
I tried to realize the segmentation with a VGG-net that got a fully convolutional network on top of it. Without the fully connected layers of the VGG. Before that, I tried something similar with a way smaller architecture with the same outcome.
What I realized in both cases is that the net pretty fast starts to predict that the whole images is background. I thought now that this is the case because the coils are only a very small part of the whole image and for the net it is easier to optimize in the direction of an all black image.
I use as criterion BCEWithLogitsLoss and as optimizer RMSprop. Bellow are my statistics with the vggnet+fcn. In the IoUs array is the first value for the coils and the second for the background.(Source code with the vgg+fcn part from for my second try). I used vgg11 and just the normal fcn.
Can't I realize a segmentation with that dataset? Is the dataset too small? What else could be a reasons for this results?