Semantic Segmentation is understanding an image at the pixel level and is used in computer-vision based applications that require high accuracy. This classification is when there are more than two categories in which the images can be classified.

This means an image belongs to more than one category and involves the prediction of multiple labels simultaneously associated with a single instance.

Data Labeler offers formats that will help you train your models on complex and highly structured image classifications, with multiple to endless probabilities.

We offer supervised learning solutions where an instance may be associated with multiple labels. The traditional method involved only the task of single-label classification.

Semantic Segmentation can be very useful in the pharmaceutical industry- information gathered about reactions to medicines says a lot about its side effects.

Also, used in case of autonomous vehicles and retail applications, where vision accuracy is of utmost importance.

We have highly specialized annotators, with expertise across industries, languages, and locales.

Our image classification workflow ensures that accuracy is met, and that we eliminate any human error or bias.

We train your data in a structured format and this helps you train your models on complex image classification.

Semantic segmentation is more time consuming and difficult as there are multiple instances which need to be simultaneously associated with many labels- we offer highly efficient and quick solutions to help model your needs and requirements.