This tutorial provides a step-by-step guide on how to implement and train a U-Net model for animals segmentation using TensorFlow/Keras.

The tutorial is divided into four parts:

Part 1: Data Preprocessing and Preparation In this part, you load and preprocess the persons dataset, including resizing images and masks, converting masks to binary format, and splitting the data into training, validation, and testing sets.

Part 2: 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.

Part 3: 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.

Part 4: Model Evaluation and Inference The final part demonstrates how to load the trained model, perform inference on test data, and visualize the predicted segmentation masks.

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Enjoy

Eran

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