Everything depends on the kind of use case you have. When you’re building your own labeled training data sets in large scale, it’s helpful to familiarize yourself with the right image annotation tool and its usage.
Here are ways to label data:
Bounding Box Annotation
As it sounds like, labeler is asked to draw a box over the objects of interest based on the requirements of the data scientist. Object classification and localization models can be trained using bounding boxes.
Polygonal Segmentation
The Polygonal segmentation masks are mainly used to annotate objects with irregular shapes. Unlike boxes, which can capture a lot of unnecessary objects around the target, leading to confuse training your computer vision models, polygons are more precise when it comes to localization.
Line Annotation
The Line Annotation(a.k.a Lane Annotation), as it sounds like its used to draw lanes to train vehicle perception models for lane detection. Unlike bounding box, it avoids a lot of white space and additional noises.
Landmark Annotation
The Dot annotation (a.k.a Landmark annotation) is used to detect shape variations and count minute objects.
3D Cuboids
The 3D cuboids are used to calculate the depth/distance of the vehicle and furnitures.
Semantic Segmentation
The Semantic Segmentation(or) Pixel-level labeling is used to label each and every pixel in the image. Unlike polygonal segmentation devised specifically to detect a defined object(s) of interest, full semantic segmentation provides a complete understanding of every pixel of the scene in the image.
If you have any of the above data labeling requirements, you can get on touch with PBS data labeling Services - A data labeling and image Annotation Company
www.pbsdatalabelingservices.co
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