AINavi is a deep learning visual inspection software, specifically designed for manufacturing industries. It enables efficient identification of manufacturing defects, significantly reducing program development time. With an initiative interface, users can accurately train, verify, and deploy AI models swiftly and maintain operations independently. AINavi aims to resolve long-term production and manufacturing bottlenecks, aiding in digital transformation and production line management.
- Project-based Management - AINavi adopts a project-based approach instead of the traditional model-oriented management. This allows for flexible utilization of assets, relieves memory burden and enables swift selection of necessary images for different levels of model training.
- Flexible Model Iteration - The software framework allows users to easily trace back to any previous training step, expand the training set or adjust training parameters to better fulfill production line requirements.
- Open Production System – In addition to multiple optimized algorithms for industrial inspection, it offers third-party model import functionality, reducing the resources and time required for development and maintenance.
- Customizable Inferential Thresholds – Users can set inferential thresholds based on actual detection needs to ensure quick results under different scenarios and testing standards.
- In-built Annotation Tools - It understands the time and effort required for annotating data in AI flaw detection. It offers several annotation tools and a handy annotation assistant (Quick Mark tool) to save 80% of annotation time.
AINavi is a deep learning visual inspection software, specifically designed for manufacturing industries. It enables efficient identification of manufacturing defects, significantly reducing program development time. With an initiative interface, users can accurately train, verify, and deploy AI models swiftly and maintain operations independently. AINavi aims to resolve long-term production and manufacturing bottlenecks, aiding in digital transformation and production line management.
- Project-based Management - AINavi adopts a project-based approach instead of the traditional model-oriented management. This allows for flexible utilization of assets, relieves memory burden and enables swift selection of necessary images for different levels of model training.
- Flexible Model Iteration - The software framework allows users to easily trace back to any previous training step, expand the training set or adjust training parameters to better fulfill production line requirements.
- Open Production System – In addition to multiple optimized algorithms for industrial inspection, it offers third-party model import functionality, reducing the resources and time required for development and maintenance.
- Customizable Inferential Thresholds – Users can set inferential thresholds based on actual detection needs to ensure quick results under different scenarios and testing standards.
- In-built Annotation Tools - It understands the time and effort required for annotating data in AI flaw detection. It offers several annotation tools and a handy annotation assistant (Quick Mark tool) to save 80% of annotation time.