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U-Net Image Segmentation with TensorFlow/Keras (Oxford-IIIT Pets)

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Contents hide
1 Here is the code for U-Net Image Segmentation :
2 (Part 1): Prepare the Oxford-IIIT Pet Dataset (images + trimap masks)
2.1 Introduction
2.2 Master Computer Vision
2.3 Detailed guide
2.4 Preparing Libraries and Dataset Parameters
2.5 Initializing Data Structures and Loading Training Images
2.6 Converting Training Data to NumPy Arrays and Analyzing Masks
2.7 Loading Test Data and Saving Processed Arrays
3 (Part 2): Build a Clean U-Net in Keras (encoder–decoder with skip connections)
3.1 Introduction
3.2 Detailed guide
3.3 Importing Required Libraries
3.4 Defining the Convolutional Block
3.5 Building the U-Net Architecture
3.6 Running the Model
4 (Part 3): Train the U-Net (loss, callbacks, mIoU, curves)
4.1 Introduction
4.2 Detailed guide
4.3 Loading Data and Preparing Masks
4.4 Splitting Data into Training and Validation Sets
4.5 Building and Training the U-Net Model
4.6 Visualizing Training Results
5 (Part 4): Inference & Evaluation on the Test Set (side-by-side visuals)
5.1 Introduction
5.2 Detailed guide
5.3 Loading the Trained U-Net Model and Test Data
5.4 Running Prediction on a Test Image
5.5 Visualizing the Predicted Mask
5.6 Extracting the Object from the Image
5.7 Connect :

Last Updated on 22/04/2026 by Eran Feit

This tutorial provides a step-by-step guide on how to implement and train a U-Net Image Segmentation TensorFlow .

The tutorial is divided into four parts: