74% of Vision AI PoCs do not scale*
* A recent research from McKinsey & Company revealed that 74% of respondents failed to scale Vision AI; deployments were stuck to prototype phase. A full software & orchestration infrastructure is necessary to deploy either 2 or 2000 devices.
A lot of open-source resources are publicly available, making tech components development seem easier in-house (e.g. Machine Learning software). However, cross-domain coordination is the key to success – and this is frequently discovered too late.
Camera systems can be more than just “eyes”; they can have intelligence. Storing huge numbers of images or video is no longer a necessity – all that makes sense is the AI-extracted information.
Each business case is unique in various ways, from cost limitations, accuracy requirements, to specific visual, product & environmental conditions, or integration with existing tech stack & processes.
Start-ups do good data ML but are not strong in computer vision, while embedded edge HW expertise is scarce. Most importantly, writing SW is far away from successful Product Development.
Irida Labs introduces PerCV.ai, a complete software and services platform that helps overcome the cross-industry problem of scaling Vision AI. Providing an end-to-end Vision AI infrastructure that brings together all the necessary building blocks: Vision Sensors & Edge Devices, Digital Vision Twin, Edge Hardware, Vision AI Software, Data Engine, APIs and AI Analytics.
Through strong partnerships with world-class leaders and Fortune 500 companies, such as HikVision, Intel, Renesas Electronics, Asus IoT, Qualcomm, Adlink, Irida Labs has built an ecosystem capable of holistically supporting even the most challenging computer vision applications.
In this blog post, we explore the importance of training data for computer vision applications and how synthetic data and photorealism can improve data quality. We also discuss the role of generative models in data synthesis and highlight examples of [...]
