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August 30, 2022

Top 10 bounding boxes annotation best practices

August 30, 2022

What is bounding box annotation

It is an imaginary rectangular box which is an object detection and localisation and computer vision. Bounding box annotations contain coordinates which bear information about an object's location in the image or video. It is most suitable for uniformly shaped objects and those which do not overlap.

Ensure Intersection Over Union 

Intersection Over Union is a technique in computer vision used to identify as well as locate objects in digital images or videos.  Bounding box annotations are used to achieve this purpose through localisation.  

Avoid excessive Overlap

When there is excessive overlap between the predicting box and the bounding box, the model is unable to identify the targeted object within a dataset. The less the overlap, the more accurate the bounding box becomes. 

Do not begin with diagonal images

In bounding box annotation, it is not advised to start with diagonally shaped items. This is because they naturally take up smaller spaces within the bounding box. The machine model may mistake the actual background as the targeted object, defeating the original purpose of the annotation. 

Pay attention to size

It is advisable to focus on smaller objects because it hardly creates room for errors. Larger images are less accurate when used in bounding box datasets.

Tag every object of interest in an image 

Every object of interest needs to be labelled as training models need to identify them all for accurate performance. It will be a catastrophic error to label an image and leave another. This is because models have been built in a way to know which pixel patterns are compliant with an object of interest. 

Paying attention to box size variation

Box size variations must be consistent. This is owing to the fact that large objects usually underperform in datasets, especially when the same type of object looks smaller. Training models with same size objects will also not allow it to function to perfection.

Ensure pixel-perfect tightness

To ensure that pixels are perfectly tight the bounding box edges must touch the most out of pixels of the labelled object. Where gaps are left, a model may be unable to give accurate predictions.

Tagging of occluded objects

Occluded objects are those which are not in full view because there is an obstruction blocking its visibility. That does not warrant it being left partially labelled or completely unlabelled. It should be annotated just like the fully visible images in the bounding box. 

Complete and all-round labelling of an object

An object of interest should be thoroughly and fully labelled as partial labelling leads to confusion on the part of the model what the full object truly consists of. 

Give specific Labelling Names

It is best to be very specific in annotation rather than being general in outlook. This is because in the event of an error, a very specific label can be adjusted or re-annotated. However, in the event that labelling was general, a need will arise to label the whole dataset again to gain the needed accuracy.

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