Data Labeling Services

Fully managed data annotation services

Create high-quality training data sets and scale your AI-initiative with human powered annotations

Data labeling is one of the most tedious and time-consuming tasks in the process of setting up your Machine Learning or Deep Learning Projects. About 83% of your AI project time is spent in gathering, cleansing, and labeling data. This wrangle of data uses the crucial time of your data scientists, researchers, and data labelers which is expensive.

Automaton AI’s capable team of annotators can be your time-saving, cost-effective backend.

Data Collection

Ease of uploading your data in any format on Automaton AI’s platform helps begin the process smoothly.

Annotation workforce

Based on your requirements, we assign the experienced and talented annotation workforce for the project.

Quality Assurance

Multi-layer QA ensures the quality and accuracy of work before completing the full training datasets.

Delivery

Our platform can deliver your training data-set in any specified format so the data sets can easily be deployed on any platform.

Product

ADVIT

The most advanced AI-assisted data annotation platform to create and manage high-quality training data all in one place.

Semantic Segmentation

3D Point Cloud

Bounding Box

Text Annotation

Key point annotation

Polygon

Cuboid

Semantic segmentation, also known as pixel-level labeling that is mainly used for labeling the image with pixel-wise annotation. It is the process of distributing a digital image into multiple segments that changes the presentation of an image into a more expressive manner. Since, we are estimating every pixel in an image, this is referred as dense prediction. Typical applications of semantic segmentations are in autonomous vehicles and medical image diagnostics.

This type of annotation consists of a group of points that represents the object in 3-dimensional space. We offer and evaluates three interaction models and annotation interfaces for object labelling in 3D LiDAR scans.

Bounding Box is one of the most recognizable annotation methods where the object is outlined in a box as per deep learning model requirements. It is very useful in object classification in images/Videos which localizes them for computer vision.

This is used for the images that contain textual information. This kind of data is labelled along with the text embedded.

Key-point annotation is mostly used in terms of annotating unique points localize them on the objects. For example, a pose estimation models localizes and identifies certain face and body key-points such as wrist, hip, neck, etc.

In Polygon technique, data is labelled to detect the exact shape of the object by drawing pixel-perfect polygons. Polygon annotation trains the data for models used in a precise application and these annotations gives the model clear idea about the structure of the object.

A cuboid is a 3D bounding box that involves high quality marking and labelling. It calculates the depth or distance of various objects like gadgets, building, vehicles and also on humans. This technique is mainly used for Autonomous car data.

Outsource your data labeling to Automaton AI

Use our fully-managed annotation teams and accelerate your AI-projects