For each and every industry, there are hundreds of different projects, working on different kinds of objects, hence different quality requirements.
We can take the simple example with roads annotation and medical data annotation. For roads annotation, the work is quite straightforward, and you only need annotators who are capable of common knowledge to do the work. For this annotation project, the number of datasets that need annotating can add up to millions of videos or pictures, and the annotators have to keep the productivity high in an acceptable level quality.
On the other hand, medical data requires annotators who work in the medical field with particular knowledge. For the case of diabetic retinopathy, trained doctors are asked to grade the severity of diabetic retinopathy from photographs so that deep learning can be applied in this particular field.
Even for well-trained doctors, not all of their annotations agree with one another. To have a consistent outcome, one annotation team might have to annotate each file multiple times to eventually come to a correlation.
It is a matter of how complicated the given data is and how detailed the clients want the data output to be. Once these things are clarified, the team leader can work on the allocation of resources for the required outcomes. Metrics and the relevant Quality Assurance process will be defined after this.
Source: Lotus Quality Assurance
—
To learn more about Blueeye and AI data training platform, we would like to invite you to
Sign-up for Pro-plan to get FREE ANNOTATION hours: shorturl.at/dzEGY
Visit: https://blueeye.ai/landingBlueEye’s insight: https://lnkd.in/gi5r9uCPBlueEye’s case studies: https://lnkd.in/gTn9KYYv
