Data Labelling

Dataset Labelling & Annotation

Machine learning in AI has revolutionized our approach to solving problems in computer vision and natural language processing powered by enormous amounts of data. The world of AI is constantly evolving, and recent advances in text-to-image models already transforming how artists and designers work, enabling them to experiment with their ideas and create stunning illustrations at lightning-fast speeds.

Training intelligent machines with algorithms require precise datasets that are processed using different annotation techniques and needs immense experience and expertise in various types of data annotation services viz: Collect, Curate, and Annotate data, train models and evaluate.

The role of product images and videos cannot be underestimated and the importance of investing significant resources in elaborate photoshoots. However, the expenses and time associated with live photoshoots, and the high costs and limitations of 3D rendering, pose challenges that restrict the extent to which these creative efforts can be scaled and personalized. Generative AI has emerged as a potential game-changer in this landscape. By streamlining and automating the content creation process, as well as unlocking new avenues for product visualization and hyper-personalization, generative AI has the power to revolutionize the process of improving machine learning models with high quality, diverse and large datasets.

Data labelling is one of machine learning’s most critical and overlooked activities. The success of any ML/NLP is dependent on the data and label quality.

What is Data labelling & Annotation ?

Data labelling is the activity of assigning context or meaning to data so that machine learning algorithms can learn from the labels to achieve the desired result. Machine learning has three broad categories: supervised, unsupervised, and reinforcement learning.

Supervised machine learning algorithms leverage large amounts of labelled data to “train” neural networks or models to recognize patterns in the data that are useful for a given application. For example, data labellers will label all cars in a given scene for an autonomous vehicle object recognition model. The machine learning model will then learn to identify patterns across the labelled dataset. These models then make predictions on never before seen data.

Unstructured data is data that is not structured via predefined schemas and includes things like images, videos, LiDAR, Radar, some text data, and audio data.

Image Datasets

Image data powers many applications, from face recognition to manufacturing defect detection to diagnostic imaging.

To create high-quality supervised learning models, you need a large volume of data with high-quality labels. Automated Data labelling for large datasets consisting of well-known objects, it is possible to automate or partially automate data labelling. Custom Machine Learning models trained to label specific data types will automatically apply labels to the dataset.

Building in-house tools is an option selected by some large organizations that want tighter control over their ML pipelines. You have direct control over which features to build, support your desired use cases, and address your specific challenges. However, this approach is costly, and these tools will need to be maintained and updated to keep up with the state-of-the-art.

Commercial Platforms

Commercial platforms offer high-quality tooling, dedicated support, and experienced labelling workforces to help you scale and can also provide guidance on best practices for labelling and machine learning. Supporting many customers improves the quality of the platforms for all customers, so you get access to state-of-the-art functionality that you may not see with in-house or open-source labelling platforms.

VisionBot Visual AI Studio with its experience in deep learning provides a platform for best-in-class labelling infrastructure to accelerate ML / NLP projects, with labelling tools to support any use case and orchestration to optimize the performance of your workforce. Easily annotate, monitor, and improve the quality of your data.

Types of Data Labelling?

1. Bounding Box

The most commonly used and simplest data label, bounding boxes are rectangular boxes that identify the position of an object in an image or video. This box defines the object’s X and Y coordinates.

Typical Bounding Box Applications:

By “bounding” an object with this type of label, machine learning models have a more precise feature set from which to extract specific object attributes to help them conserve computing resources and more accurately detect objects of a particular type.

Object detection is the process of categorizing objects along with their location in an image. These X and Y coordinates can then be output in a machine-readable format such as JSON.

Autonomous driving and robotics to detect objects such as cars, people, or houses
Identifying damage or defects in manufactured objects
Household object detection for augmented reality applications
Anomaly detection in medical diagnostic imaging

2. Classification

Object classification means applying a label to an entire image based on predefined categories, known as classes. labelling images as containing a particular class such as “Dog,” “Dress,” or “Car” helps train an ML model to accurately predict objects of the same class when run on new data.

Typical Classification Applications:

Activity Classification
Product Categorization
Image Sentiment Analysis
Cricket Bat vs. Baseball Bat

3. Cuboids

Cuboids are 3-dimensional labels that identify the width, height, and depth of an object, as well as the object’s location.

Data labellers draw a cuboid over the object of interest such as a building, car, or household object, which defines the object’s X, Y, and Z coordinates. These coordinates are then output in a machine-readable format such as JSON. Cuboids enable models to precisely understand an object’s position in 3D space, which is essential in applications such as autonomous driving, indoor robotics, or 3D room planners. Reducing these objects to geometric primitives also makes understanding an entire scene more manageable and efficient.

4. Polygon/Contour Annotation

Objects in an image are labelled by drawing an accurate contour around it. Used in creating datasets for training precise application models.

5. Key point Annotation

Objects in an image are labelled using points to determine the shape of it. Key point annotation are used to label facial/skeletal features, automotive parts etc.

Benefits of VisionBot Visual AI Studio Data Labelling

Augmenting Data

Augment ground-truth training data with infinite varieties of synthetic data and seamlessly integrate with existing ML data pipelines.

Boost Model Performance

Create synthetic datasets from scratch, or combine synthetic and real data to enhance performance on key edge cases.

Close the Reality Gap

Leverage Scale's unique human-in-the-loop synthetic data generation process to create realistic synthetic data.

Experience with Expertise

We have experience of 5 years in the Visual AI field. Over the course of time, we have annotated data of various use-cases.

Premium Quality Services

We ensure the best quality services through a quality assurance system . The three level mechanism consists of annotators, approvers, and QC managers.

Rapid Prototyping and Deployment

Reduce data collection, curation, and annotation wait times to enable faster Model testing and prototyping.