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

零售是我们成为“即插即用”计算机视觉平台的垫脚石，该平台由合成数据生成引擎驱动，可适应多个用例和行业。未来，我们将致力于成为世界领先的计算机视觉服务提供商，为消费品供应链中的许多应用提供服务，例如废物管理和回收中的产品分类、自动结账商店中的产品识别或仓库中的库存可视性。利用爱丁堡大学专家团队和韩国团队之间的协同作用，我们将成为创造计算机视觉模型的先驱，为它们的性能提供绝对的保证。我们的主要目标是为我们的平台实现跨领域的通用性，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各个领域的模型开发。在Neurolabs，我们致力于使计算机视觉领域民主化，确保每一个中小型企业都能利用这种变革性技术的力量。为了实现这一目标，我们正在开源整个项目中开发的一系列突破性算法，并将其纳入优化包中。这一战略举措旨在将这些算法的适用性扩展到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.