Data labeling is very important as it is the base for any machine learning project. These labeled data act as data set which are fed in algorithms to train various machine learning models.
Machine learning models usually needs lots of data labeling for each projects which is called training data and these labeled data should be of very high and very precise quality for a Machine Learning models to work accurately in real world scenario.
These Labeled data help AI recognize various objects, shapes and patterns. for example how tree looks like, how a mango looks like.
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.
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.
Text Annotation is the practice and the result of adding a note or gloss to a text, which may include highlights or underlining, comments, footnotes, tags, and links.
Text annotations helps machines to recognize the crucial words in sentence making it more meaningful.
In simple words Labeled data is a group of samples for example images, that have been tagged with one or more labels and the process to tag these data is called Data Labeling. Data labeling service lets machines learn what humans see, hear, or think.
Labeled or human labeled data or ground truth dataset is designed for to train specific ML models with an end application in perspective.
Labeled data is the data you need to train your models. You might just need to collect more of it to sharpen your model accuracy. As you build a great model you need great training data at scale.
How to get data labeled
We at PBS data labeling services could be a good partner in your journey just after all we annotated millions of images a day for some of the world’s most innovative companies. Whether it’s bounding boxes, dots, semantic segmentation or any sorts of shape, we can help you collect high-quality training data with high precision and recall value.
