GpflowOpt
IMEC has developed GpflowOpt, a novel Python framework for Bayesian optimization, which can be viewed as a modern spin-off of the widely used SUMO-toolbox. GpflowOpt allows the user to speed up expensive simulations or the tuning of deep learning vision systems. In the latter case, it achieves this by replacing such as grid-search by optimized sequential parameter tuning, drastically reducing the training time of such systems while achieving better or equivalent performance. The software has been adopted to allow more accurate paper-cardboard separation by among others more efficient tuning of deep learning vision technology and the ability to process diverse compositions of paper-cardboard of varying quality by rapid reconfiguration.
Area of the technology
- Machine Learning
- Surrogate modelling
- Bayesian Optimization
- Hyperparameter tuning of machine learning systems
Targeted Industrial Sectors
- Engineering
- 3D printing
- Automotive
- Recycling industry
Technology Readiness level
TRL 6 - technology demonstrated in relevant environment
Contact Information
Dirk Deschrijver