ARTIFICIAL INTELLIGENCE
human effort when it comes to annotating the data. Tis is achieved by implementing a data management engine, which includes data selection and curation, data augmentation, usually using machine learning techniques or synthetic data generation, and automatic labelling.
Model deployment and lifelong learning Deploying the computer vision model is the most challenging aspect from a software and hardware architectural perspective. Models should be able to run on low-resource platforms; working with highly pruned machine learning models that operate with low- bit accuracy is the selection of choice for this hardware. Te model should also
support functionalities that give it the ability to learn how to improve from data supplied for each unit, local area or solution- level using self-supervised learning. More specifically, the
data distribution might shift significantly during the product lifetime; it can present periodic patterns related to winter or summer conditions, for instance. Tat’s why the model should be able to adapt to changes in data distribution to maintain accuracy. Also, the model needs to accept updates and provide an interface for user experience.
EV Lib workflow Te Irida Labs EV Lib workflow gives a solid task definition by iterating in model deployment and development. During this iteration Irida Labs is continuously translating the user experience from prototype installations in order to improve the data specifications and the performance of the model. Te iteration stops when human-level performance is achieved, or when the customer specification and experience are met. Tis approach allows Irida
Labs to optimise the final solution in terms of model performance, hardware costs
www.imveurope.com | @imveurope
and achieve reduced time-to- market. At the same time, the approach provides a mechanism for support with updates and new features throughout the product life cycle. Summarising the benefits of the EV Lib workflow, the customer will be guided through defining the task, acquiring the
data and optimising the model, allowing both Irida Labs and the customer to focus on the main objective – delivering a real- world AI solution. Additionally, any changes made in the models over time are sent back to the client deployment environment by releasing new model versions. O
Irida Labs is a vision firm in Patras, Greece. This article is based on Vassilis Tsagaris’ presentation at Embedded Vision Europe in October.
Precision Perfect images at high speed
Precision at high speed.
Precision skydiving is a perfect match for extreme athletes – and precision inspections at high speed are a perfect match for the LXT cameras. Thanks to Sony® Pregius™ sensors and 10 GigE interface, you benefit from high resolution, excellent image quality, high bandwidth and cost-efficient integration.
Learn more at:
www.baumer.com/cameras/LXT
VT-40_Precision-LXT-cameras_140x195_IMVE_EN_191008.indd 1 08.10.19 13:50 FEBRUARY/MARCH 2020 IMAGING AND MACHINE VISION EUROPE 23
Page 1 |
Page 2 |
Page 3 |
Page 4 |
Page 5 |
Page 6 |
Page 7 |
Page 8 |
Page 9 |
Page 10 |
Page 11 |
Page 12 |
Page 13 |
Page 14 |
Page 15 |
Page 16 |
Page 17 |
Page 18 |
Page 19 |
Page 20 |
Page 21 |
Page 22 |
Page 23 |
Page 24 |
Page 25 |
Page 26 |
Page 27 |
Page 28 |
Page 29 |
Page 30 |
Page 31 |
Page 32 |
Page 33 |
Page 34 |
Page 35 |
Page 36 |
Page 37 |
Page 38 |
Page 39 |
Page 40
