LabelStudio
Introduction
As artificial intelligence (AI) and machine learning (ML) become an increasingly prevalent part of our daily lives, opportunities have arisen to streamline some tasks by leveraging the technology. However, the efficacy of trained machine learning models hinges on the quality and size of the dataset used. A small dataset may not generalize well or suffer from missing edge cases when it’s applied to test data that it hasn’t seen before. While data augmentation can be used to increase a dataset’s size there’s no substitute for a larger, more varied dataset. A well-labeled, varied dataset captures expertise on a subject, embedding subject matter expert’s knowledge into the dataset and leads to significantly better models after training.
Creating a dataset for training can be a time-consuming and costly process. Subject matter experts time for data labeling is a valuable resource and streamlining the data labeling process is paramount to keeping costs down. For large datasets, it is essential that the labelling environment supports team collaboration, since in larger projects multiple users will be annotating the dataset and reviewing each other’s work.
Effective data labeling requires an application that can securely store easily visualizable data and is straightforward for users to interact with. The tools for data labeling must be robust, catering to varied data types—text, images, and time series data each have unique labeling requirements. The solution must provide user-friendly tools for any data type.
LabelStudio as a Data Labelling Application
While many data labelling solutions exist, for this resource page we’ll focus on LabelStudio. LabelStudio is an open-source data labeling tool, that is highly customizable and capable of working with various types of data such as images, videos, time series data, audio and text. It can be easily deployed in a cloud or on-premises server solution allowing for flexibility in data storage options and facilitating team collaboration. Data can be imported into LabelStudio either directly from a user or using cloud storage buckets, allowing for secure storage of large datasets. Similarly, annotations can also be exported to cloud storage buckets allowing for rapid development in the cloud for training on labelled data.
Teams can easily collaborate on labelling data, with multiple users able to work on the same project and access annotations created by others. This collaborative approach allows multiple users to annotate the same data, compare results, and determine the most accurate annotation. This also allows for a subject matter expert to review and approve annotations created by others, to enhance the quality of the annotations, while reducing the burden on them.
One of the most exciting advancements in data labelling is the integration of machine learning models to speed up the process. Tools like the Segment Anything Model (SAM) for image segmentation can greatly reduce labelling time by giving an estimate of the segmented object that a user only needs to go and correct, instead of requiring them to create an annotation from scratch. Below, you can see a sample video of comparing image labelling manually (left) compared to using SAM (right), demonstrating the speed and accuracy of SAM for image labelling tasks.
