In this video, we are going to work on biomedical image segmentation task. For this we are going to use Unet, but this time we are going to replace the Unet encoder with a pre-trained model. This pre-trained model is going to be the MobileNetV2. We are going to integrate the pre-trained MobileNetV2 with the UNet.

In this video, you will learn:

1. What is UNet
2. What is MobileNetV2
3. Dataset
4. Advantages of using MobileNetV2 as an encoder.
5. Implementation in python 3.8 using TensorFlow.

What is semantic segmentation?
The goal of semantic image segmentation is to label each pixel of an image with a corresponding class. It is also called Dense prediction.

What is U-Net?
U-Net is a fully convolutional neural network that was developed by Olaf Ronneberger. It was especially developed for the purpose of biomedical image segmentation.

CODE: https://github.com/nikhilroxtomar/Unet-with-Pretrained-Encoder/blob/master/U-Net_with_Pretrained_MobileNetV2_as_Encoder.ipynb

#tensorflow #unet