Explore and run machine learning code with Kaggle Notebooks | Using data from No attached data sources

This notebook consists of an implementation of U-Net using the following resources: Algorithm: Ronneberger et al., U-Net Convolutional Networks for Biomedical Image Segmentation

Follow the step-by-step instructions and code cells in the notebook to explore the U-Net implementation, train the model, evaluate results, and visualize predictions.

Mar 21, 2025 · In this report, I’ll present a UNet-based segmentation model trained on the Oxford-IIIT Pet Dataset from Kaggle . The model aims to accurately segment pets from images using a deep learning …

Explore and run machine learning code with Kaggle Notebooks | Using data from No attached data sources

The dataset includes images of 37 different pet breeds, and the goal is to segment each image into background, pet, and outline classes. 🎯 Goal: To demonstrate how to preprocess data, build a...

Nov 11, 2024 · By the end of this guide, you will have a solid understanding of how to preprocess image data, build a U-Net model from scratch, and train it to perform pixel-wise segmentation on your own …

If the issue persists, it's likely a problem on our side. at https://www.kaggle.com/static/assets/app.js?v=12847825b71da2f46a3a:2:3233614. at …

Nov 30, 2025 · Build and train a U-Net for multi-class pet segmentation in TensorFlow/Keras using Oxford-IIIT data for U-Net image segmentation TensorFlow.

This implementation uses the U-Net architecture introduced in the paper U-Net: Convolutional Networks for Biomedical Image Segmentation and leverages the Oxford-IIIT Pet Dataset for segmentation tasks.