You're reading from Data Labeling in Machine Learning with Python

Product typeBook

Published inJan 2024

PublisherPackt

ISBN-139781804610541

Edition1st Edition

Concepts

Machine Learning

Author (1)

Vijaya Kumar Suda

Hands-On Exploring Data Labeling Tools

In the dynamic landscape of machine learning and artificial intelligence, effective data annotation plays a pivotal role in enhancing model performance and fostering accurate predictions. As we delve into the intricacies of image, text, video, and audio annotation, we find ourselves immersed in the realm of the Azure Machine Learning service and its robust data labeling capabilities. This chapter serves as a comprehensive guide to leveraging Azure Machine Learning data labeling tools to create precise and meaningful annotations.

We will also look at another open source data labeling tool, Label Studio, for annotating image, video, and text data. Label Studio empowers data scientists, developers, and domain experts to collaboratively annotate various data types such as images, video, and text.

We also see how to annotate data using pyOpenAnnotate, and finally, we will explore Computer Vision Annotation Tool (CVAT), an open source, collaborative...

Technical requirements

Let’s understand the prerequisites needed for each tool we’ll discuss for you to follow along in this chapter.

Azure Machine Learning data labeling

Azure Machine Learning provides labeling tools to rapidly prepare data for machine learning projects. Let’s create an Azure subscription and Azure Machine Learning workspace as follows:

Azure subscription: You can create a free Azure subscription at https://azure.microsoft.com/en-us/free.
Azure Machine Learning workspace: Once your Azure subscription is ready, you can create an Azure Machine Learning workspace in that subscription.

Label Studio

Install the label-studio Python library using your Python editor:

%pip install label-studio

Then, start the Label Studio development server using the following shell command:

!label-studio start

pyOpenAnnotate

pyOpenAnnotate is a simple tool that helps to label and annotate images and videos using OpenCV.

Let...

Data labeling using Azure Machine Learning

With an increasing demand for sophisticated models capable of understanding diverse data types, the importance of accurate annotations cannot be overstated. Azure Machine Learning offers a powerful solution, providing a data labeling interface designed to streamline the annotation process for images, text, and audio. Azure Machine Learning’s data labeling capability facilitates the process of creating, managing, and monitoring data labeling projects and enables seamless collaboration among data scientists, domain experts, and annotators.

Let’s look at the benefits of data labeling with Azure Machine Learning.

Benefits of data labeling with Azure Machine Learning

Data labeling is used to train machine learning models and helps to improve the accuracy of these models. Azure Machine Learning data labeling tools can be used to create image, text, and audio labeling projects.

Azure Machine Learning data labeling tools...

Exploring Label Studio

Label Studio (https://labelstud.io/) is an open source data labeling and annotation platform designed to streamline the process of labeling diverse data types, including images, text, and audio. With a user-friendly interface, Label Studio empowers machine learning practitioners and data scientists to efficiently label and annotate datasets for training and evaluating models. Its versatility, collaborative features, and support for multiple labeling tasks make it a valuable tool in the development of robust and accurate machine learning models.

In this section, we are going to label four types of data: image, video, text, and audio.

Labeling the image data

Let us label the image data using Label Studio.

Once you have installed the Label Studio tool using the pip command as given in the Technical requirements section, start Label Studio, go to the browser, and type in the following URL: http://localhost:8080/. As we have deployed Label Studio using...

pyOpenAnnotate

pyOpenAnnotate is an open source Python-based annotation tool that automates the image annotation pipeline using OpenCV. It is particularly well-suited for annotating simple datasets, such as images with plain backgrounds or infrared images. pyOpenAnnotate is a single-class automated annotation tool that can help you label and annotate images and videos using computer vision techniques. It is built by harnessing the power of OpenCV. You can check out the Python library documentation to understand how pyOpenAnnotate has been designed: https://pypi.org/project/pyOpenAnnotate/.

You can load your images in a directory and then run the following command to start labeling the bounding boxes for your images:

!annotate --img /path/to/directory/Images

The following image is available in the book’s GitHub path for this chapter.

You can replace the directory path with your own dataset path. This will prompt the tool to label the objects in your image and you can...

Computer Vision Annotation Tool

CVAT is a free, open source tool that is widely used in various industries for annotating images to facilitate the training of machine learning models. This tool is designed to handle a large volume of images for labeling. Setting up and using CVAT for annotating images involves several steps. The following is a guide that covers the process.

Step 1 – Install Docker

CVAT is containerized using Docker, so you’ll need to have Docker installed on your machine. Follow the installation instructions for your operating system on the official Docker website: https://docs.docker.com/get-docker/.

Step 2 – Install Docker Compose

CVAT has multiple components, including a web server, a database, and a worker for background tasks. Docker Compose allows you to define and manage the dependencies between these components in a single configuration file (docker-compose.yml).

Docker Compose simplifies the management of multi-container...

Comparison of data labeling tools

Here is a table depicting the comparison of the tools on various features:

Advanced methods in data labeling

Active learning and semi-automated learning are popular machine learning techniques that help overcome the challenge of data labeling. Both involve presenting uncertain or challenging labels to human annotators for feedback; the key difference lies in the overall strategy and decision-making process. Let’s break down the distinction.

Active learning

Active learning is a machine learning paradigm in which a model is trained on a subset of the data, and then the model actively selects the most informative examples for labeling to improve its performance. The following list discusses various features of this method:

Workflow: The initial model is trained on a small labeled dataset. The model identifies instances where it is uncertain or likely to make errors. These uncertain or challenging instances are presented to human annotators for labeling. The model is updated with the new labeled data, and the process iterates.
Benefits...

Summary

In this chapter, we have learned how to use Azure Machine Learning to label image, video, and audio data. We also learned about the open source annotation tool Label Studio for image, video, and text annotation. Finally, we learned about pyOpenAnnotate and CVAT for labeling image and video data. Now, you can try using these open source tools to prepare the labeled data for machine learning model training.

As we reach the final pages of this book, I extend my heartfelt congratulations to you on completing this insightful journey into the world of data labeling for image, text, audio, and video data. Your dedication and curiosity have paved the way for a deeper understanding of cutting-edge technologies. May the knowledge gained here continue to inspire your future endeavors. Thank you for being a part of this enriching experience!

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Author (1)

Vijaya Kumar Suda

Vijaya Kumar Suda is a seasoned data and AI professional boasting over two decades of expertise collaborating with global clients. Having resided and worked in diverse locations such as Switzerland, Belgium, Mexico, Bahrain, India, Canada, and the USA, Vijaya has successfully assisted customers spanning various industries. Currently serving as a senior data and AI consultant at Microsoft, he is instrumental in guiding industry partners through their digital transformation endeavors using cutting-edge cloud technologies and AI capabilities. His proficiency encompasses architecture, data engineering, machine learning, generative AI, and cloud solutions.
Read more about Vijaya Kumar Suda

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Tool

Pros

Cons

Cost

Labeling Features Support

Scalability

Azure Machine Learning labeling

Rapid data preparation for machine learning projects.

Assisted machine learning.

Limited to Microsoft ecosystem. Limited support for custom labeling interfaces.

Azure services may have associated costs depending on the usage

Images, text documents, and audio

Ability to scale labeling tasks with the power of Azure cloud services

Label Studio

Open source and multi-type data labeling tool

Limited...