Enhancing Visual Datasets for Model Training

Convirzon works with organizations across diverse fields—such as autonomous systems, geospatial intelligence, and medical AI—to refine their machine learning capabilities. Our teams help enrich, annotate, and label the data that feeds vision-driven AI models.

Having processed more than millions of images and video frames, Convirzon’s delivery unit handles challenging data sets and detailed taxonomies to support the development of computer vision systems.

Services offered

• Bounding boxes: We use rectangular boxes to mark objects in images, enabling models to learn object localization and detection.
• Polygon annotation: For objects with irregular shapes, our teams trace detailed vertices, enabling algorithms to recognize precise contours.
• Semantic segmentation: We partition image pixels into categories, so models learn to distinguish each part of a scene at pixel-level detail.
• Panoptic segmentation: A combined approach where our labelling covers both the identification of each instance and its belonging class in the image.
• LiDAR annotation: For multi-sensor and 3D collection systems (e.g., for autonomous vehicles), our teams annotate 360° imagery to build ground-truth datasets.

Industries served

Our annotation capabilities span across sectors:

• Autonomous vehicles and robotics — working with 2D/3D data annotation for Mobility systems.
• Medical-AI — annotation teams are trained both in the tooling and in the domain so they can work on specialized use-cases.
• Geospatial intelligence — through annotation of aerial / satellite/drone imagery for mapping, analytics, infrastructure and defence.
• Government & public sector — secure, monitored environments to process sensitive data under strict protocols.

Getting started

In a climate where annotated data is increasingly essential for vision-based AI deployment, Convirzon combines semi-automated and manual approaches in its annotation workflows to accelerate time-to-production.