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 data labeling platform for streamlining the annotation process across various data types.
We will cover the following topics in this chapter:
- Labeling image, text, and audio data using Azure Machine Learning
- Labeling image, video, and text data using Label Studio
- Labeling image and video data using pyOpenAnnotate and CVAT
Join us as we navigate the intricacies of data labeling with Azure Machine Learning, empowering you to harness the full potential of annotated datasets and propel your machine learning endeavors to new heights.
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’s install this tool using the Python editor as follows:
%pip install pyOpenAnnotate
The dataset and code used in this chapter are available on GitHub:
- Dataset: https://github.com/PacktPublishing/Data-Labeling-in-Machine-Learning-with-Python/tree/main/datasets/Ch12
- Code: https://github.com/PacktPublishing/Data-Labeling-in-Machine-Learning-with-Python/tree/main/code/Ch12