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Data labeling is essential to the development process of machine learning models. It ensures data accuracy by providing properly annotated datasets.
The rapid advancement in AI technology has made it easier for businesses, developers, and researchers to advance in data labeling without incurring huge costs.
AI tools for data labeling offer a range of features from text and image annotation to audio and video labeling. This helps minimize effort and save time and costs resulting in an enhanced quality of your machine-learning projects.
Labelbox is an AI-based labeling service with free plans for small projects. They provide an intuitive interface that can support a wide variety of data types, including images, text, and videos.
Built-in collaboration tools enable teams to collaborate on labeling tasks. The platform also supports automation that reduces the time for manual annotation. Labelbox integrates well into machine learning frameworks, making it a good fit for organizations seeking to streamline AI workflows.
Sloth is an open-source, lightweight tool that is great at labeling images and videos. It allows for customizable configurations, thus the users can fine-tune the tool to fit their specific needs.
Sloth can be easily used for small and large-scale visual dataset creations. It can be executed quickly, and the simplicity of Sloth allows for quick annotation.
SuperAnnotate is a tool powered by AI that can annotate any type of data, be it images, videos, or text. It has free plans available for startups and students, giving full access to all the simple tools making it apt for small data labeling projects or beginners in the field.
The most significant benefit of SuperAnnotate is the user-friendly interface, along with its built-in automation features, which include AI model assistance. It accelerates the labeling process dramatically.
SuperAnnotate is a fantastic tool for businesses and researchers dealing with big datasets with very specific annotations.
CVAT is an open-source labeling tool commonly used for computer vision tasks. CVAT is a tool that Intel developed to help users easily annotate their images and video data.
As a free platform, it becomes an ideal choice for any size of project, and it demonstrates high flexibility when customizing the labeling workflow. On top of this, it supports integration with other machine-learning frameworks for quick iteration while training. It is a user-friendly tool offering a high degree of customization for complex projects.
LabelImg is an open-source graphical image annotation tool, developed in Python. It is free to use and is used to label object detection models. Therefore, it is one of the tools that data scientists and developers often go for when working on computer vision projects.
It is somewhat inferior compared to other advanced automation AI tools, but its simplicity and convenience make it a top choice for working on smaller projects.
AI-based data labeling tools have revolutionized healthcare, primarily in medical imaging. Tools such as Labelbox and CVAT are being used for labeling medical images, helping AI models detect cancerous conditions more accurately.
Some of these tools, like Supervisely and CVAT, have been applied in the automotive sector for annotation of datasets to be used in vehicle navigation. This means labeling, for instance, road signs, pedestrians, and other obstacles along the road. This has helped advance self-driving car technology further.
Data labeling emerges as an important process while developing high-performance machine learning models with the evolution of AI. Whether you are working with images, videos, or text, the above-mentioned AI tools handle annotation tasks well to help you train your models better and iterate faster.
The use of these tools provides businesses and developers with an opportunity to have more reliable data without any financial burdens such as expensive software. Thus, the use of free AI data labeling tools helps save time and money, especially helping smaller businesses and individual contributors.
