It's all about the numbers

~0%
of images contain target
0%
Accuracy in findings
0M
Images per day
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Problem: Finding the Right Data Labeling Partner

This Agrotech company had millions of aerial images and needed to identify tiny pests in a huge field. Their internal Data Science teams found this extremely difficult since only 2% of their data had actual pests in the images. They were spending hundreds of hours reviewing data that ended up not having a single pest in the image. This was not an efficient way to utilize their Data Scientists’ time and resources and they knew they needed to make a change. 

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Solution: Tasq.ai Grid Technology for the Win - Cleaning Data at Rapid Speed

When this Agrotech company saw Tasq.ai’s process of deconstructing large datasets into millions of micro-tasks they knew this was their solution. Tasqers at the peak of the job were labeling millions of images a day. This increased their data labeling speed to be 30x faster but at a level of precise accuracy that they couldn’t achieve from other leading labeling solutions.  By breaking down the dataset into tiny parts of a much larger image, our Tasqers were able to identify if there was a pest present and if so what kind. This allowed our customer to be able to launch multiple models instead of one; identifying the specific type of pests and whether a pest was present. This also accelerated their roadmap by 9 months resulting in a more immediate and accurate way of identifying the problem and resolving it substantially faster. See below for the workflow that the Tasq.ai team implemented.

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The Workflow built by our Customer Success team was as follows:

  • Break down every image into a grid consisting of hundreds of smaller images focusing on a zoomed area at a time.
  •  Build a task for spotting, identifying and marking every object with multiple dynamic judgements for ensuring confidence scores.
  • Build a small initial ‘Gold Dataset’ that consists of all of the types of pests in order to train the Tasqers against and to check their level of accuracy over time. 
  • Enlarge the ‘Gold Dataset’ dynamically once starting to receive positive annotations and by that enable the growth in the number of Tasqers that can enter the job.
  • Gamify the tasks to create interest and avoid ‘boredom’.
  • Limiting each Tasqer only to a few minutes of work in order to avoid burnout and exhaustion.
  • Automatically labeling of bounding boxes around every marked object using ML for high confidence.
  • Human classification of every object per the catalogue, with multiple judgements to ensure high quality and confidence results.
  • Aggregation of all results and confidence scores per annotation to one single structured image.
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The Result

Millions of images were scanned through at ultra speed by hundreds of thousands of Tasqers to create a high quality dataset, improving the client’s model faster than expected. The client created a superior capability vs competitors, leading to immediate success.

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