Have you ever pondered how much data is generated per day in the data-driven society we live in? Tech experts have been wondering, too. The stats demonstrate that this number equals 2.5 quintillion of bytes of data. This is exactly how much data we produce every single day.

However, this is the number we get at the current pace of development and increased AI deployment in business. The situation is changing, though, with more and more companies adopting the latest digital trends and technologies, like AI, big data, IoT, digital twins, etc. The same is true for artificial intelligence, whose impact on society grows by leaps and bounds.

That said, each of these sophisticated solutions is powered by data, which is naturally raw and unstructured. So, for this data to be truly useful, it needs to be properly annotated. Data labeling is “the key ally in supporting AI initiatives” for major industries today, as stated by Label Your Data. It requires a competent fusion of human labor and technology. By technology, we mean specialized tooling that brings a pinch of automation to the tedious process of annotating data.

Let’s see what the main tool options are available for the major leaders in the industry or any AI enthusiast wishing to scale their projects!

Annotated Data as the Chief Asset of the Big Industries

95% of machine learning algorithms that are in active use across the industry are supervised. This means that roughly the same number of AI initiatives rely on annotated data. However, industrial data is a complex matter.

The majority of industrial activities may be significantly improved, and industries can prosper fast, by knowing how to handle the industrial data produced by systems, sensors, and assets. For this, data annotators must thoroughly analyze and annotate the data used to run the ML models for various industrial purposes.

Data Labeling Tool Options to Scale Your AI Project

As we’ve uncovered the main points of the data annotation role for major industries today, it’s time to reveal what labeling tools they use to stay ahead of the digital curve (or which annotators use to help them do so):

Image Annotation

One of the leading image annotation tools is an automated annotation platform for AI-assisted image annotation, video annotation, and dataset management

• CVAT:Short for computer vision annotation tool, it’s an interactive tool for machine vision annotation of videos and images.
• LabelMe: A free, online annotation software for computer vision tasks.
• VoTT: An open-source visual object tagging tool for image and video data.
• LabelImg:An image annotation tool to label objects in the images in bounding boxes.
• Labelbox: One of the leading annotation tools used to create computer vision applications.
• ImgLab:A web-based data annotation tool to label objects in photos that can be used to train the Dlib or other object detectors.
• YOLO Mark: A graphical user interface used for image annotation with bounding boxes for training neural networks (YOLO v3 and v2).
• ImageTagger: A collaborative, open-source online platform for image annotation.
• DeepLabel: A cross-platform image labeling tool for machine learning tasks.
• MedTagger: A collaborative platform for labeling medical datasets using crowdsourcing.
• Anno-Mage: A semi-automated tool for image annotation, which includes an object detection model, RetinaNet, containing up to 80 classes.
• LOST: An adaptable system for semi-automated image annotation.
• Annotorious: A JavaScript library for image annotation.
• CATMAID: Another collaborative data labeling tool used to handle large volumes of images.
• Pixel Annotation Tool: A software for fast manual data labeling of images in directories.
• OpenLabeling: A tool for annotating images and videos for computer vision applications.

Video Annotation

• VATIC: An online solution for video labeling tool, which aids computer vision researchers and outsources to Amazon’s Mechanical Turk.
• UltimateLabeling: A flexible graphical user interface for video annotation in Python that has an integrated SOTA detector and tracker.

Text Annotation

• LightTag: A data labeling platform specialized in NLP tasks and is suitable for in-house activities.
• doccano: Simple, open-source software that is fully configurable using the web user interface.
• YEDDA: A methodical approach to text span annotation, encompassing both administrator review and user collaboration.
• Tagtog: A text annotation tool for both automatic and manual annotation with pre-trained NER models.

Audio Annotation

• EchoML:An audio annotation solution for playing, visualizing, and labeling audio files.
• Praat:A tool that allows you to do phonetics by computer.
• Aubio:A labeling tool developed for retrieving annotations from audio signals.
• Audio Annotator:A JavaScript interface for labeling audio data.
• js:A web-based UI component for dealing with audio waveforms and audio waveform visualization (created by BBC UK).

LiDAR & 3D Annotation

• Semantic Segmentation Editor:A web-based data annotation tool for camera and LIDAR data and is used for creating ML training datasets (2D and 3D).
• webKnossos: An open-source, web-based solution for visualizing, labeling, and exchanging big 3D image datasets.
• KNOSSOS: A 3D annotation software for visualizing and labeling 3D image data, which was designed for the quick reconstruction of neural connection and morphology.

Time Series

• Curve:An open-source labeling tool option to deal with anomalies in time-series data.
• Time Series Annotator:A data annotation tool for implementing classification tasks for time series.
• TagAnomaly:A tool for anomaly detection analysis and annotation, which is particularly helpful for multiple time series.
• WDK: Short for Wearables Development Toolkit, it’s a collection of tools that make it easier to create wearable device apps for activity detection.

Multi-Domain Tools

• Label Studio:A flexible data annotation tool that can handle many data formats.
• Dataturks:A platform that enables end-to-end tagging of data objects, such as text, photos, and video, for machine learning applications.

Industrial Applications of Labeled Data

A good example of the industrial application of data annotation is an industrial robot (excuse the tautology). High-quality labeled data is the main driving force behind this technology, which can perform object recognition and tracking, navigation, crack detection, and robot arm guidance.

Predictive maintenance and quality control are other two examples of how labeled data can be of great value in the manufacturing and production stages. Predictive capabilities of AI help foresee any potential issues and technical malfunctions, while quality control is enabled through computer vision systems that are based on annotated data.

The most common industries to use data annotation services for their model training are the medical sector, automotive industry, agriculture, retail, and social media. The data annotation types they mostly use are object detection and tracking, instance segmentation, and semantic segmentation. Most of them are covered and professionally handled here at https://labelyourdata.com/services.

Summary

In AI, data annotation is invariably the key to success. This is because labeled data provides machine learning models with a structured and organized source to learn from and be trained on. As a result, today’s major industries are benefiting from the vast number of solutions that AI offers them.

The bulk of contemporary technologies provide users with data annotation tools that can be web-based, downloaded, or used online. Either way, for each type of data and labeling method, there is a wide range of tool options available. With that being said, ensure you make a sensible pick.