Data labeling plays a crucial role in the development of Artificial Intelligence (AI) and Machine Learning (ML). Any ML model requires a structured set of data in order to get trained. But creating datasets that are precise is no cakewalk as they need a lot of time and effort. Let’s say an ML model is given an image and asked to identify whether or not the image contains a person. An ML model will not detect this unless it is trained with accurately labeled datasets.
So what does data labeling mean? Data labeling refers to identifying raw data and then adding tags to that data to make predictions of future occurrences. The raw data can be in the form of text, images, audio, video, etc. To label data and automate the process, organizations rely on data labeling tools. These days, we have access to a handful of labeling tools, which make labeling easy and give desired results. These tools come with template solutions that can satiate your generic demands. But, to meet your client-specific requirements, you need to have configurable data labeling platforms that will prove to be effective in all cases.
Data labeling platforms can be classified into closed source and open source. In this blog, we will talk about open source platforms as they can be easily modified or customized. The first and foremost reason to use an open-source data labeling platform is that it lets you easily and quickly customize your existing data labeling solutions, so you don’t have to start from scratch. Open-source data labeling platforms enable IT firms to implement the new customizable code into the company’s existing model and find feasible solutions.
List of open-source data labeling tools
CVAT is a powerful online tool for computer vision that lets you annotate videos and images. It is accessible on Google Chrome and can be used to perform tasks such as image segmentation, image classification, and object identification. In addition to this, CVAT also supports automation.
Sloth is another open-source data labeling tool that is used to annotate images and videos. It allows you to use default templates or customize configurations to create unique workflows. This tool is easy to use and enables you to handle the entire process right from installation to labeling and creating visualization items.
Label Studio, an open-source data labeling tool, provides data labeling for data types such as images, text, video, audio, time series, etc. This web application platform can be accessed on any browser and has a simple UI that lets you export to multiple formats. The datasets produced by this tool are accurate and can easily be incorporated into ML applications. Label Studio can be embedded into your personal applications with ease.
With a simple and straightforward UI, the Dataturk tool simplifies the labeling process and lets you create datasets in a short span of time. It provides services for labeling data such as video, text, and images. The process is pretty simple. You need to create a project depending on the requirement and then upload the data in any preferable format. When it is done, you can bring the workforce and start tagging. Dataturk is accessible to all since it is an open-source tool.
VoTT or Visual Object Tagging Tool is developed to label image and video data and uses TypeScript. This open-source web application streamlines any end-to-end machine learning pipeline. It acts as an extensible model for importing data from local or cloud storage providers and exporting it to cloud storage providers.
Data labeling or annotation plays an integral part in machine learning and artificial intelligence. The tools mentioned above have simplified the task of labeling data and saved a lot of time and manual effort. Furthermore, it gives precisely labeled datasets that are free from errors. These advanced and smart tools have made the work of annotation much simple and easy. Firms are now looking for experts like Springbord who can help further simplify the process. Connect with us today and gain the extra edge!