This is the blog post by Anolytics describing how data is labeled using the semantic segmentation for deep learning models. Semantic segmentation is the type of image annotation to annotate the objects in the single class for more accurate detection of objects in visual perception based AI models.
The process and techniques of semantic segmentation is discussed right here. Explained with examples and relevant image makes easier to understand what and how objects are detected through semantic segmentation image annotation for different types of AI models.
How to label images for semantic segmentation:
- Objects and Nested Classifications for Instance Segmentation
- Bordering in Semantic Segmentation
- Brightness and Contrasting of Objects
- Zooming/Panning and Exporting Images
The entire blog post contains the useful information about annotating the objects for different types of AI models. From bordering to brightness and contrasting or zooming and exporting the images, everything is explain here with semantic image annotation technique.
Anolytics provides all types of image annotation technique to annotate the images while ensuring the accuracy. It can annotate all types of objects with semantic segmentation for self-driving cars, autonomous vehicles, robots and autonomous flying objects like drones or other machines.