AI innovation in the supply chain of consumer packaged-goods for recognising objects in retail execution, supply chain management and smart factories: using novel diffusion-based optimisation algorithms and diffusion-based generative models

零售业是我们成为“即插即用”计算机视觉平台的垫脚石，这个平台由合成数据生成引擎推动，可以适应多种用例和行业。在未来，我们努力成为世界领先的计算机视觉即服务提供商，为消费品供应链中存在的许多用例提供服务，即废物管理和回收中的产品分类，自主收银店的产品识别，或仓库中的库存可见性。利用爱丁堡大学的专家团队和韩国团队的协同作用，我们将成为创建计算机视觉模型的先驱，为其性能提供绝对保证。我们的主要目标是实现我们平台的跨域通用性，使我们能够通过利用我们在合成数据生成方面的专业知识来简化跨不同领域的模型开发。这种方法将导致需要更少的模型来高效地为多个客户服务。在NeuroLabs，我们致力于使计算机视觉领域民主化，确保每一家中小型企业都能利用这一变革性技术的力量。为了实现这一点，我们开源了整个项目开发的突破性算法的精选，将它们整合到优化包中。这一战略举措旨在将这些算法的适用性扩大到NeuroLabs专有模型之外，促进它们在更广泛的应用中的采用。

Retail is a stepping stone on our path to becoming _a "plug-and-play" Computer Vision platform propelled by a synthetic data generation engine_ that can be adapted to multiple use cases & industries. In the future, we strive to become _a world-leading provider of computer vision as a service_ to the many use cases that exist across the supply chain of consumer product goods, i.e product sorting in waste management and recycling, product recognition in autonomous checkout stores, or inventory visibility in warehouses.Using the synergy between a team of experts at the University of Edinburgh and the South Korean team, we will be the pioneers in the creation of computer vision models that offer absolute guarantees for their performance. Our primary objective is to attain cross-domain generalisability for our platform, enabling us to streamline model development across various domains by harnessing our expertise in synthetic data generation. This approach will result in fewer models required to serve multiple clients efficiently.At Neurolabs, we are committed to democratising the field of computer vision, making sure that every small and medium-sized enterprise can harness the power of this transformative technology. To achieve this, we are open sourcing a selection of the groundbreaking algorithms developed throughout this project, incorporating them into optimisation packages. This strategic move aims to expand the applicability of these algorithms beyond Neurolabs' proprietary models, fostering their adoption across a wider range of applications.