Images Annotation
Image annotation forms the foundation for training Computer Vision models, enabling Artificial Intelligence solutions to accurately interpret images.
With the increased availability of image data for companies pursuing AI, the number of projects relying on image annotation has grown exponentially. Creating a comprehensive and efficient image annotation process has become increasingly important for organizations working in this area of machine learning.
Detection, Localization e Classification
Low-quality image annotations will lead your solution to make mistakes, such as misclassifying a disease, failing to detect a safety device, misunderstanding a road sign, with unpredictable consequences.
In the field of Computer Vision, data annotation involves labeling images with tags or metadata to identify the characteristics of the data that you want the Artificial Intelligence model to learn to recognize.
In this manner, a solution or application can detect and identify people, objects, places, vehicles, equipment, plants, animals, even locating them within the image.
Types Of Image Annotations
01. Classification
This annotation only applies a label to the image; it is a more general labeling because it does not specify the location of the labeled elements within the image.
02. 2D or Bounding boxes
Labelers apply rectangles or squares to the desired objects by highlighting and locating them in the image. This process is crucial for training the artificial intelligence model.
03. 3D ou Cuboids
The labelers use cubes to mark, locate, and define the depth of the desired object for the machine learning process.
04. Polygons
Your sentence is clear and well-constructed. If you'd like a minor adjustment for smoother flow, you could consider:
Polygonal annotation is employed when objects are asymmetrical and don't easily fit into a rectangle or square. Labelers use complex polygons to mark and locate them.
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