Annotation With Orchvate

Orchvate is focused at empowering their neurodivergent members to enhance their employability for digital jobs in the AI/ML Operations field, by enabling them in the in-demand job skill of Data Annotation.

We train our members in data annotation skills. We have partnered with a couple of the largest Data Annotation Service Providers. We deliver data annotation projects outsourced by enterprises, where we engage our resource pool to work either on-premises or remotely, providing them with employment opportunities in this space.

AI and ML: Reshaping the Present and Pioneering the Future

Artificial Intelligence (AI) and Machine Learning (ML) have gone beyond being just buzzwords and have revolutionized technology across industries.

It will be pioneering a future where intelligent systems will be central to progress. AI/ML will transform every aspect of our lives in the years to come.

AI is Cool and Smart. How is it Connected with ML?

AI is built via a process called machine learning. There are essentially 5 parts to any machine learning process:
Similar to what goes on in any unique culinary creation, are a combination of ingredients, appliances, recipes, and finally the culinary masterpiece, in AI and ML domain too, the different parts are Gather Data (Data Collection), Prepare training data (Data Annotation), feed it into an algorithm (ML Algorithm), validate the model (ML/AI Model), and use it to analyze, draw insights and make better informed decisions (AI App).

What Is Data Annotation?

Data annotation is the process of attributing, tagging, or labeling data to help machine learning algorithms understand and classify the information they process. Just the way we humans learn from experience, so also AI and ML Models learn from data.
Data annotation, or data labeling, is crucial as it helps train algorithms to recognize patterns and make accurate predictions.

A self-driving car's AI Model relies on data from computer vision, natural language processing (NLP), and sensors to make accurate driving decisions. To be able to differentiate between other vehicles, pedestrians, traffic signals or pavements, the data it receives must be labeled or annotated.

Types Of Data Annotation

Annotated data can take various forms, image, video, text and audio data allowing AI models to be deployed in various applications like automation, chatbots, and speech recognition resulting in optimal performance and reliable outcomes.

Why Is Data Annotation Crucial?

It's only through the process of data annotation that models differentiate between a road from a pavement, a car from a truck, a mudguard from a front panel. Without data annotation, every image would be the same for machines as they don't have any inherent knowledge about anything in the world.

Data Annotation is essential for training ML/AI models, enabling them to accurately comprehend data types, such as images, audio files, video footage, or text. When an ML model is under development, it is fed with billions of AI training data points that make it better at making decisions.

Our Exciting Journey Into The AI Space

Our journey has been very interesting. While we were partnering with companies on inclusive hiring programs we were flooded by resumes from across the country and when we met them on one on ones we realized corporates would not hire them as their differences were marked. Their way of communication was very different from what the majority of us are used to. And they too wanted to be employed.

We started to think what do we do for this group? We dug deep into the problem and researched and identified this space in AI: Data Annotation, where their strengths could be leveraged best. We started with image annotation for computer vision and the pilot batch did remarkably well.

Why Neurodivergents Are The Best For This Job

Neurodivergent individuals have extraordinary capabilities that significantly contribute to the success of image annotation tasks. Their hyperfocus and exceptional attention to detail and pattern recognition abilities allow them to excel in repetitive and meticulous activities which are fundamental to accurate annotation. By leveraging their strengths, we ensure unparalleled precision and consistency.

They are highly motivated and engaged. With time they only improve their accuracy and efficiency. Also many of them have obsessions in niche subjects like automobiles and environmental sustainability. When they are working on images aligned to their field of interest they contribute and thrive.

Image Data Annotation

Bounding Box

Bounding box is used in computer vision to annotate objects by drawing rectangular boxes around them. It provides a simple representation of object location and size.

It is widely used in applications like object detection, face recognition, and autonomous driving.

Key Point

Key Point labels specific points of interest within objects in images or videos. It offers accurate shape and feature representation.

It finds a variety of applications in facial recognition, pose estimation, and medical imaging.

Skeleton

Skeleton offers spatial relationships and labels joints in skeletal structures of humans or animals in images or videos.

Skeleton is valuable for body motion, and accurate tracking and analysis of movement, pose and gesture.

Polygon

Polygon defines object boundaries using an arbitrary number of points around objects in images or videos.

Polygons are used for precise shape representation, particularly for objects with irregular shapes.

Image Segmentation

Image segmentation divides images or videos into regions and annotations are done at pixel or region levels.

It enables accurate object representation. It includes Instance, Panoptic and Semantic Segmentation.

LiDAR

LiDAR (Light Detection and Ranging) labels objects in 3D point cloud from data generated by LIDAR sensors.

LiDAR is used for autonomous driving and environmental monitoring, such as mapping and tracking the growth of forests or tracking changes in the coastlines due to erosion.