TestINT
Intelligent Image Synthesis for Robust AI Models
Turn limited image datasets into robust, tested AI models.
Computer vision models are only as reliable as the data they're validated
against. Collecting real-world images for every weather condition,
adversarial scenario, or edge case is slow, costly, and often impossible for
autonomous vehicle and aerial imaging teams.
TestINT expands image datasets with synthetic scenarios — adversarial
attacks, weather changes, day-to-night transformations and basic
corruptions — then tests your deep learning-based image classification
models against them, surfacing performance and robustness issues before
deployment.
- Generate synthetic test images covering adversarial attacks, weather
conditions, day-to-night transitions and basic corruptions.
- Test your deep learning-based image classification models on newly
generated datasets.
- Evaluate model performance and robustness with a range of test adequacy
metrics.
- Analyze results through detailed reports with tables and bar charts.
- Built for autonomous vehicle perception and aerial/aviation imaging use
cases.