title: Semantic segmentation without labels in a single class
body: <p>I am kind of new to semantic segmentation. I am trying to perform segmentation of images having defects.
  I have the defect images annotated using a annotation tool and I created the mask for each image. I wanted to predict If an image has defect and where exactly it is located. But my problem is my defects does not look same in all the images. Example: Defects on steel- Steel breakage, erroded surface etc. I am just trying to classify if the image has defect or not and where it is located. So is it wrong to train the neural network with these all types considered as defects even though not everything lookalike?</p>
  <p>I thought to do a binary segmentation of defect to no defect. If I am not correct how can I perform segmentation for defect and non defect images?</p>