Deiteo AI - Data Labelling Company

Deiteo Image Annotation

For training a Machine Learning model, images from a dataset are annotated. Therefore, the features to be recognised by the AI are labelled using Image Annotation.

types of Image Annotation

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AI Image Annotation

Bounding Box

A bounding box is a hypothetical rectangle that acts as a point of reference for object detection and generates a collision box for that item. These rectangles are drawn by annotators over images, identifying the X and Y coordinates of each item of interest inside an image. This helps machine learning algorithms to locate the region of interest, identify collision pathways, and saves precious computational resources.

Line and Polygon

In order to precisely and accurately annotate an item, polygon annotation involves choosing a sequence of x, y coordinates along its edges. It has pixel-perfect accuracy, is very adjustable, and can handle a wide range of complicated forms. For object identification and recognition models, Polygon annotation is best suited because of its versatility and pixel-perfect labelling capacity.

AI Image Annotation

Semantic Segmentation

The method of semantic segmentation involves assigning a tag to each and every pixel in an entire picture. Semantic segmentation enables computer vision to locate the pictures with dense prediction by classifying the images with pixel-by-pixel annotation of objects. Semantic segmentation is primarily used to train perception models on items in real environments, seeing numerous objects of the same class as a single entity.

Key-point Annotation

Shape variants are defined and tiny items are found using keypoint annotation. Our skilled annotators provide high-accuracy annotation results to educate computers to detect tiny objects and their properties by precisely plotting a series of points.

AI Image Annotation
AI Image Annotation

Image Classification

Image classification is the process of classifying and identifying sets of pixels or vectors within an image in accordance with predetermined criteria. One or more spectral or textural properties may be used to create the classification law.

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