Annotation Labs

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Image Polygonal Annotation Services for training data

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Polygon Annotation for Irregular Objects

What is polygonal annotation tool?

Polygon Annotation produces superior annotation for more complex and irregular objects and images such as trees and asymmetrical objects and shapes. With clear edge-to-edge labeling, polygon annotation significantly increases accuracy of labelling data, crucial for the Computer Vision Models.

What are the uses of polygon annotation tool?

Image Polygonal Annotation for Irregular Objects
Polygon Annotation with Semantic Segmentation for Superior Image Labeling

Polygon annotation of irregular shapes and objects

Polygon Annotation with Semantic Segmentation for Superior Image Labeling

Polygon annotation with semantic segmentation & instance segmentation

Polygon annotation for higher accuracy than bounding boxes

Bounding boxes are simple and easy annotation solutions, but they lack precision, especially for long, irregular objects or diagonal objects. 

In some cases, using a bounding box may cause the AI & ML Model to ignore essential details. Polygon annotation is much more complex but offers a high level of precision, accuracy and object detection so the AI Algorithm knows the exact boundaries of the objects.

Polygon Annotation gets superior results over Bounding Box

Polygonal annotations are used across Industries

We carefully analyze your requirements and provide high quality data solutions to enhance your AI Models

Our beliefs behind each annotations

Core Value: Data Security & Privacy

Data Security & Privacy

Core Value: Fast Delivery & High Accuracy

Fast Delivery with High Accuracy

Core Value: Low Cost Pricing

Cost Effective Pricing

Core Value: Scalable Solutions by Expert

Scalable Solution by Experts

How to label images for object detection?

The process begins by preparing your dataset. The next step is to specify the labels for each class of objects that you need to detect. After that, the next step is to label the objects of interest using any annotation techniques depending on the use case and then label the objects using the earlier defined class labels. Continue the process until all objects of interest have been properly labeled and assigned class names.

How does image recognition work?

Image recognition works by using computer neural networks. The neural networks are trained to use different layers of templates to identify simple objects such as curves, dots, and lines. In an image, the neural networks check the lines, dots, and curves and use the information to determine if these features form a more complicated object, such as a human face, building, etc.

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