@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.
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.
Hi Dmitry, please provide more usecases on how your product can be used by different companies with different goals. I don't understand, if your product can be helpful for me.
@anna_gotta we can't share details about our customers, but summarily more than 6 million annotations have been already done on Handl for companies as Nvidia, Nestle, Cherry Home, etc. Whatever they do with it)
Thanks @roman_tezikov. Unlike Amazon’s 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. And we have a different internal workflow for data annotation — crowdworkers don't choose what tasks to perform. They just work properly and get paid based on the time spent and their accuracy reached.
@ravil_zaripov our pricing is much more accessible and its structure is different - we charge by hours instead of labeled data points. We go through all the husle with setting up the task and coming up with proper instructions in multiple languages for labelers by ourselves, and can provide assistance on machine learning-related projects beyond data annotation.
Looks really really interesting! Questions:
1) What is the maximum and the minimum data volume for labeling?
2) Can we buy ready "cats" (for example) vertical datasets? Is it going to be a marketplace for different verticals?
3) What other datasets verticals do you have?
@goldenalf13 thanks Andrew! We can deal with any data volume and we have a flexile pricing for that. For now, we work with client's data only. We do not trade in any way.
Thanks @mikhail_larionov! Pricing for our tools starts from $399/mo. Crowd workforce is charged separately, pricing depends on complexity of your annotation tasks. Learn more here — https://handl.ai/#howitworks
@_klen thanks! We guarantee data security as we never show the whole data to a single worker. For example, when labeling IDs, we divide them into several fields so that one worker does not see the whole document. Later on, we put the fields together inside the client's contour.
@anya_pozniak yes, we do. Usually we cut them in frames and label the resulted images. If you need to classify actions on videos, you can upload them in video clips.
@alex_pustov We work closely with machine learning teams from technology companies and startups helping them to deal with data preparation of any kind.
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