Building a Web-Based Data Annotation Tool with Labelbox: A Step-by-Step Guide

Unlock the Power of Efficient Data Labeling for Your AI Projects

Photo by Ambitious Studio* - Rick Barrett on Unsplash

In the world of artificial intelligence (AI) and machine learning (ML), high-quality labeled data is the foundation upon which successful models are built. However, the process of manually annotating vast amounts of data can be time-consuming and error-prone.

This is where Labelbox comes in — a powerful web-based data annotation platform designed to streamline and simplify the labeling process.

In this article, we’ll walk through the steps to develop a web-based data annotation tool using Labelbox, complete with code examples to get you started.

Setting up the Project

Before we dive into the coding part, let’s ensure we have the necessary prerequisites in place:

1. Sign up for a Labelbox account: Head over to labelbox.com and create an account if you haven’t already.
2. Install the Labelbox Python SDK: You can install the SDK using pip: pip install labelbox
3. Create a new project: Log in to your Labelbox account, and create a new project where you’ll be uploading and annotating your data.