Data annotation is a critical part of many modern machine-learning processes. The job involves more than simply labeling datasets or images, though. Consequently, lots of data annotation projects run into problems. To avoid these problems, you need to know the signs that something is wrong so you can adjust.
Monotonic Consistency
A diverse dataset shouldn't deliver results that are monotonous. If you're creating a convolution network to generate images, for example, you might find that a specific name as a prompt produces images with very little difference. Presuming the inputs are diverse, the annotation could be the problem. For example, the data labeling platform might be flagging one type of portrait as a person and not identifying anything else like full-body poses or turned images. If you're confident in the diversity of the inputs, you may need to assess the diversity of the annotation.
Excessive Specificity of Prompts
Some systems also work well — but only if you use excessively specific prompts. These datasets may be diverse, but the labels might be limited. For example, a human-in-the-loop data annotation platform may have workers that are applying one label to each image and moving on. The effect is that the final product may do a good job, but only if you drill down to a specific label that matches the original annotations precisely.
Ideally, a data annotation platform should provide labeling that's fairly general. Also, the labels should be plentiful enough that a machine can detect differences. If you have a batch of photos with men in hats and another batch of the same men without hats, the labels should reflect the difference in dress. Unfortunately, some systems would just label everything "man" or "person" and move on.
Low-Quality Labels
The quality of the annotations also matters a lot. If the operator of a data annotation platform isn't focused enough on quality control, workers may figure that out. In order to complete tasks faster and get more paid work, they might start entering junk labels. This can crater the quality of the labels, and it'll likely ruin the quality of the analysis entirely.
Never depend solely on the data labeling platform to perform quality control. Pull random samples and get eyes on them.
Lack of Balance
Datasets should reflect the real-world picture of whatever you're researching. If you're labeling diverse texts, for example, you want to see the labels reflect that diversity. There may be issues with the annotation process if a diverse set of texts doesn't likewise yield a diverse set of labels.
To learn more, contact a professional data annotation platform provider such as Superb-Ai Inc and upgrade your system.
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