Feed your Machine-Learning Models with High Quality Data

Data Annotation is the way toward adding metadata to a dataset. This metadata normally appears as labels,including text, pictures, and video. Adding far reaching and reliable labels is a critical piece of building up a preparation dataset for AI.

For determining the location and boundaries of the object more precisely and accurately, Polygonal annotation is used. The main advantage of Polygonal annotation is, it neglects the unnecessary pixels around the objects that can confuse the classifier.

Semantic segmentation is method of image annotation that involves separating an image into different regions, assigning a label to every pixel in an image. This is basically used for classifying the objects needs high-accuracy.

The bounding box is one of the common annotation types in machine learning for computer vision. It is a versatile and simple method of annotation in which objects are enclosed and the computer vision is assisted to locate the object of interest.

Line Annotation is used to detect lanes for self-driving vehicles. This type of annotation ensures safe and trouble-free driving of vehicles by detecting the directions, divergence and contradict directions. Raw data of the street is adequately enough for the annotators to identify the lane marking by annotating lines on the object which ultimately helps your self-driving vehicle to identify such lanes on the street.

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Video annotation includes adding metadata to unlabeled video to prepare an AI calculation. This metadata, likewise alluded to as labels or marks, could be anything from a bounding box around a specific piece of the picture to full division.

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