Attention U-Net, ResUnet, & many more

It's been a while since the inception of U-Net, the network was initially designed to do medical image segmentation but since then it’s been used for all sorts of segmentation tasks. In my limited experience, I’ve always found that segmentation works better than object detection (given that you have the labeled data). The probable reason for segmentation being better than detection is that it is learning to identify each pixel as part of the object or not whereas detection tries to learn the four sets of coordinates surrounding the object (a much tougher and error-prone task to optimize). In this blog, we are going to take a look at all the different versions of U-Net that are highly efficient and take up the performance of the original U-Net by multiple notches.

Following are the different versions of U-Net:

• U-Net
• V-Net
• U-Net++
• R2U-Net
• Attention U-Net
• ResUnet
• U²-Net
• UNET3+
• TransUNET
• Swin-UNET

Let’s take a look into the all-new exciting world of U-Net. Feel free to skip the explanation of U-Net (most of you are already aware of that).

U-Net

To understand the architecture of U-Net let’s understand the given task first. Given an input image network should try to generate a segmentation output mask which means each…