This is thinking that has to be done upstream of the annotation phase not to have bad surprises. To avoid this error, It is the job of the annotation project manager to spend time thinking about clear and exhaustive instructions. Instead of drawing a bounding box around a pedestrian, the annotator may draw it around multiple pedestrians. Kili Technology Image annotation tool in action Misinterpretation of instruction The following concrete examples will be illustrated with image annotations.Īn example is if we are to build a model using Kili Technology, and we are to draw a bounding box around every pedestrian in a traffic, the following can go wrong: What may go wrong while labeling data differs for different data types, knowing these pitfalls and making a conscious effort to avoid them can help you to produce high-quality data. If the project isn't understood by the annotator before embarking on it or there isn't enough supervision to spot mistakes during annotation, this can cost a lot of hours in re-annotating the data, hence the importance of this topic. It is important to put proper data labeling into consideration while labeling data, a mistake or misunderstanding in data annotation can lead to a re-annotate of all data, and given the stress involved in data annotation, I believe this isn't something you will love to go through.
If your data is properly labeled, it results in good quality output, while improperly labeled data results in bad output.
Things to annotate software#
Unlike traditional software development where your code quality determines the output of a program, in data analysis and artificial intelligence, the quality of the model depends on how you label the data.
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