Values and Benefits

Quantity
SCALE
SPEED

Tasqers Vs Competitors

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Type Progress Accuracy Objects Workers
Tasq.ai
156 198
Single annotator
6 1
Internal team
19 6
BPO
43 30

An Iterative Approach to Data Labeling

The Tasq labeling solution is designed from the ground up to scale, without compromising on quality. It’s been the focus of our innovation from day 1, enabling millions of Tasqers to work on your annotation job. We do it with a groundbreaking combination of micro-tasks, instant qualification and quality refinement.

Building an Image Grid

Micro-tasks are all about making complicated requirements into simple ones. We define a workflow for each labeling project. The workflow breaks down the work of image labeling down to simple, easy to understand tasks. This enables hundreds of Tasqers to work on an individual image.

tennis-Girl

Validating Data to Build Mathematical Confidence in Results

Once an object is annotated and labeled, Tasqers validate the existence of the object in an image or video. Tasqers responsible for validating results are only shown relevant portions of images and asked whether or not the image they are looking at contains the object.

The Tasqers’ multiple judgments are validated, weighted, rated and aggregated into a structured schema of actionable insights.

OUI JA YEP YES JA OUI OUI Да JA JA はい YA はい YES はい Да YA YEP Да YA YES
NO NEIN НЕТ

Creating a Predefined Schema

The workflow also acts as a schema of the resulting annotations, defining a clear structure and relationship between annotations that further improves quality.

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TENNIS PLAYER
TENNIS RACQUET
TENNIS BALL

Instant Qualification

Tasq.ai has the ability to train a Tasqer in minutes, to perform a micro-task. It relies on sophisticated algorithms to train, qualify, test and monitor digital workers to perform micro-tasks accurately and diligently.

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Wisdom of the Crowd

Having an unprecedented amount of workers applied to an image allows us to change the paradigm of labeling from ‘quality sampling’ to ‘quality refinement’. Instead of sampling worker data and projecting quality, we assign each and every micro-task to multiple workers and then collect additional judgments until the required quality metrics are achieved. This creates the first true ‘Wisdom of the Crowd’ solution when every individual label was executed by multiple Tasqers and represents a consensus of opinion.

Frequently asked questions

Do I need to manage a workforce?
Read more
Does Tasq.ai commit to Quality levels of the annotated datasets?
Read more
Can we review the quality of the annotations?
Read more
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