Image by Sahand Babali

Solutions for Computer Vision.

Logistics

Some of the biggest companies working in the fulfillment and logistics space have rolled out computer vision solutions to streamline their operations over the past few years, but you don’t have to be Amazon to realize efficiency gains from AI automation. It’s never been easier to find the tools or teams that can help you optimize your operations—but whether you're monitoring warehouse operations or accelerating picking—training an effective model requires vast amounts of high quality training data. Zumo Labs can help you generate synthetic training data that is tailored to your use case, saving you time and money.

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  • Smart Warehouse

There’s more AI in supply chain management than ever before, and it all starts in the warehouse. Deep learning unlocks real time inventory management, risk assessment, and spatial insights that were impossible to measure previously. Meanwhile, synthetic data gives that computer vision model superpowers—full ground truth means occlusion is never an issue, and objects can be located in space with full 6 dimensional pose.

  • Accelerated Picking 

 

Warehouse robots are more advanced than ever, many including (or easily retrofit with) vision systems that can automate and accelerate bin picking and even perform tasks like item recognition. But any vision system needs to be trained, and that model must be kept up to date. Synthetic data allows you to get started quickly and rapidly address edge cases that may show up in picks.

  • Data Scarcity

 

If you’re opening up a new warehouse or rolling out new robots, you may be facing a cold start problem where you have little to no training data for the machine learning model you’d like to implement. Synthetic training data that is tailored to your use case can be rapidly generated and used to supplement available data or even trained on exclusively.

  • Privacy Compliant

 

If you’re monitoring any human activity—employee zone detection, security, or otherwise—your models will need to be trained on human data. Rather than training your model on sourced images of real humans, which in addition to the cost can expose you to data privacy liabilities, consider using synthetic training data. It’s both GDPR and CCPA compliant by default.

 

 

Take complete control of your machine learning workflow and accelerate model development with synthetic training data.