This tutorial provides a step-by-step guide on how to implement and train a U-Net model for polyp segmentation using TensorFlow/Keras.
The tutorial is divided into four parts:
Data Preprocessing and Preparation In this part, you load and preprocess the polyp dataset, including resizing images and masks, converting masks to binary format, and splitting the data into training, validation, and testing sets.
U-Net Model Architecture This part defines the U-Net model architecture using Keras. It includes building blocks for convolutional layers, constructing the encoder and decoder parts of the U-Net, and defining the final output layer.
Model Training Here, you load the preprocessed data and train the U-Net model. You compile the model, define training parameters like learning rate and batch size, and use callbacks for model checkpointing, learning rate reduction, and early stopping. The training history is also visualized.
Evaluation and Inference The final part demonstrates how to load the trained model, perform inference on test data, and visualize the predicted segmentation masks.
Check out our tutorial here : https://youtu.be/YmWHTuefiws&list=UULFTiWJJhaH6BviSWKLJUM9sg
Link for the code here : https://ko-fi.com/s/62988f9cc6
Link for my blog : https://eranfeit.net/blog/
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Before we continue , I actually recommend this book for deep learning based on Tensorflow and Keras : https://amzn.to/3STWZ2N
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Enjoy
Eran