Implementing ResUNet++ For Image Segmentation in Tensorflow Keras | Deep Learning In Computer Vision
📺 In this video, we'll learn how to implement ResUNet+, an architecture for medical image segmentation.
What is ResUNet++:
The ResUNet++ architecture is based on the Deep Residual U-Net (ResUNet), which uses the strength of deep residual learning and U-Net. The proposed ResUNet++ architecture takes advantage of the residual blocks, the squeeze and excitation block, ASPP, and the attention block.
Research Paper: https://arxiv.org/pdf/1911.07067.pdf
🔧 Code: https://github.com/nikhilroxtomar/Sem...
🕒 Timeline:
00:00 - Introduction
00:10 - What is ResUNet++
01:07 - Importing TensorFlow
01:20 - Squeeze and Excitation Network
06:20 - Stem block
09:24 - ResNet block
12:22 - ASPP block
13:56 - Building ResUNet++ (Encoder and Bridge)
16:03 - Attention block
18:18 - Building ResUNet++ (Decoder and Output)
20:56 - Ending
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