Services
Synthetic Data Generation
We create synthetic data in customized volumes and with unlimited variations across different contexts. Our data is designed to optimize model training, providing the necessary diversity to enhance accuracy and generalization in AI/ML solutions
End-to-End Development
We offer end-to-end model development, covering all stages from training to deployment. We also provide a script for fine-tuning with real data, ensuring that your models are refined for optimal performance in real-world scenarios
Consulting
With our extensive expertise in computer vision, we effectively enhance processes and products using both synthetic data and other innovative methods. We provide comprehensive support throughout all stages of developing your AI/ML solutions
Image Classification
We offer the generation of synthetic images organized into directories for each desired class. The images are created with various variations within each category, ensuring a comprehensive dataset for training image classification models efficiently and accurately.
Object Detection
We generate synthetic images with all objects of interest annotated precisely, supporting multiple annotation formats such as YOLO, VOC, and COCO. These images are organized and configured to facilitate the training of object detection models in various scenarios and environments.
Instance Segmentation
We produce synthetic images with detailed annotations for each instance, providing binary masks for each object of interest. The annotations follow the COCO format, with precise polygons ensuring accurate and organized segmentation of images, ready for instance segmentation applications.
Visual Odometry
We create synthetic images annotated for visual odometry, either with semantic segmentation or instance segmentation, according to the KITTI dataset standards. These images complement real datasets, allowing for optimization and improvement of visual odometry performance in specific scenarios.
Anomaly Detection
We offer synthetic image generation with precise annotations in various formats (YOLO, VOC, COCO), where anomalies are inserted procedurally according to defined rules. These anomalies are generated randomly to enrich datasets and improve the detection of unusual patterns or defects.
Hyperspectral Images
We develop synthetic multi- or hyperspectral images, organized with multiple variations. The images can have the required number of spectral channels, starting from a few real examples, creating a robust dataset for hyperspectral analysis.