OneSoil

@dimadewinn Looks good! What do you mean by special knowledge from in-house team? What can your crowd annotate?

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6yr ago

Dima Dewinn

Norm Model B Desk

Maker

@ivan_uvarov many thanks! Handl crowd is good for general tasks. You can assign tasks to groups of workers each of which is focused on a specific area. But if labeling requires the knowledge of Chinese or medical education, you may need your in-house team.

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6yr ago

Shashkova Natalia

TripleTen

Maker

@dimadewinn @ivan_uvarov one soil one love

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6yr ago

Dima Dewinn

Norm Model B Desk

Maker

Greetings Hunters, Dima from Handl here. I’m thrilled to introduce you to Handl, a tool to label and manage data for machine learning. On Handl, you will get your high-accuracy datasets with ease. We employ 25k qualified crowdworkers, who have labeled more than 6 million images, texts and sounds for tech companies and startups so far. Handl crowdworkers work remotely mostly from developing countries and get paid up to 3$/ho for their effort. If your labeling requires some special skills, you can invite your in-house team to do the job. We have a complete set of tools to cover data annotation needs: classes, bounding boxes, polygons, text input, and text segmentation, all easy to use and neat. Select and combine them the way you like. Above that, you can manage, share and safely store your datasets from here. Unlike MTurk and similar microtasking services, Handl stands for machine learning data labeling only. This allows us to acquire, train and qualify our crowd to perform labeling at the highest accuracy level on the market. Our consensus algorithm ensures quality by assigning the same task to a number of crowdworkers, until the proper accuracy is reached. Try Handl here — https://handl.ai On the occasion of the launch, Hunters get free annotations of up to 1,000 images or texts with the “PRODUCTHUNT” code. Follow the link — https://handl.ai/form We are happy to get your feedback and answer any questions. Cheers.

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6yr ago