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Keymakr helps your computer vision systems learn about the real world by using semantic segmentation to label data down to a pixel-perfect level of detail.

Image and video semantic segmentation

Semantic segmentation annotation helps train computer vision based AI models by assigning each pixel in an image to a specific class of object. Instance segmentation annotation adds further detail to training imagery by separately labeling objects belonging to the same class.

Keymakr provides semantic and instance segmentation annotation to suit the needs of any machine learning project. Our experienced, in-house teams of annotators are able to accurately apply segmentation techniques to images and videos at an affordable price.

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Professional Data Annotation
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Data Annotation for COMPUTER VISION AI

Computer vision is the process of computers gaining high-level understanding of digital images and video.

Semantic segmentation annotation is one way in which machine learning models are able to gain this understanding. To create semantic segmentation imagery data annotators use annotation tools to divide images into sets of pixels that correspond to real-world categories.

By using polygon labeling techniques annotators can trace the outline of each relevant class. For example an image of road traffic would be separated, by pixel, into the classes: road, pedestrian, sidewalk, sky, etc.

This training data allows AI models to precisely locate objects in an image and distinguish between classes of objects. This process can be applied to individual images or the multiple frames of video data. Semantic segmentation has applications for a number of industries.


Data Annotation for FASHION

In order for AI models to distinguish between types of clothing items they need to be trained with semantically segmented image data.

Using annotation tools we can assign a colour to the pixels which make up each item of clothing in an image. This colour corresponds to a class of clothing item: shirts, shoes, dresses.These annotated training images allow for the creation of virtual wardrobes that allow customers to try on clothing through their computer screens.



Safety is paramount when it comes to autonomous vehicle AI. Self-driving cars need to be able to navigate complex environments and avoid dangerous situations.

To help them achieve this Keymakr provides annotated video training data to developers. In these videos, semantic segmentation divides the road from the sidewalk, and vehicles from pedestrians; thereby ensuring safety for all road users.


Instance segmentation is a subset of image annotation that adds additional levels of labeling detail. Instead of dividing each pixel into defined classes, instance segmentation identifies each instance of each object appearing in a given image.


The careful management of waste and the recycling process is vital for protecting humans and the environment. Instance segmentation of training images teaches waste management AI to sort waste into real world categories, whilst also recognising the quantity of each individual waste type.

Many waste items can be harmful to humans and should not be handled. AI models can quickly identify and isolate dangerous materials, and prevent harm to human operators.