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Build a Segmentation Model with One Line of Code

Build and train a neural network model for image segmentation in the fastest way

Mattia Gatti
TDS Archive
6 min readMar 6, 2023

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MartinThoma, CC0, via Wikimedia Commons (edited)

Neural network models have proven to be highly effective in solving segmentation problems, achieving state-of-the-art accuracy. They have led to significant improvements in various applications, including medical image analysis, autonomous driving, robotics, satellite imagery, video surveillance, and much more. However, building these models usually takes a long time, but after reading this guide you will be able to build one with just a few lines of code.

Table of content

  1. Introduction
  2. Building blocks
  3. Build a model
  4. Train the model

Introduction

Segmentation is the task of dividing an image into multiple segments or regions based on certain characteristics or properties. A segmentation model takes an image as input and returns a segmentation mask:

(Left) An input image | (Right) Its segmentation mask. Both images by PyTorch.

Segmentation neural network models consist of two parts:

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TDS Archive
TDS Archive

Published in TDS Archive

An archive of data science, data analytics, data engineering, machine learning, and artificial intelligence writing from the former Towards Data Science Medium publication.

Mattia Gatti
Mattia Gatti

Written by Mattia Gatti

Currently doing research into AI Remote Sensing. Writing about Deep Learning and Geospatial Data Analysis.

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