Optimising In-Store Retail Execution with Synthetic Computer Vision
Akcelita is a U.S.-based technology consultancy that specialises in the use of next generation technology to solve real world problems. Specifically, they are focused on solutions for Fast Moving Consumer Goods (FMCG) clients that help increase revenue and improve customer experience.
The Problem
Akcelita needed an image recognition software that could monitor very large numbers of retail products for the purposes of creating an Out-of-Shelf and Planogram Compliance solution for their clients.
They had experimented with training computer vision models themselves using a traditional approach to the problem, collecting hundreds of real images of each product and processing them for the purpose of image recognition training i.e. training an algorithm to detect Consumer Packaged Goods (CPG) products in images based off of the images they had collected and labelled of those products.
They found that this process was both extremely time consuming and lacking in quality. Collecting and classifying all the real images that they would need meant time became their biggest pain point. It was also considerably difficult to ensure that the collected images were of a high enough quality to make for an effective product detector. Garbage in means garbage out when it comes to image recognition.
What they needed were robust computer vision models that could detect any CPG products they needed them to detect. They also needed to be able to create and update these models with ease and at speed so as to maintain flexibility with their clients and save time. Spending days collecting and classifying real images for each use case was, therefore, out of the question.
A Solution in Sight
On their search for a relief from their computer vision challenges they discovered a novel approach to the problem with Neurolabs.
Neurolabs uses Synthetic Data to train computer vision models to detect CPG products in the real world. This saves teams the hassle of collecting and classifying countless real data as well as the laborious process of training a computer vision model with that data to detect a product on a supermarket shelf.
From the get-go they were impressed with the Neurolabs team, their speed and responsive, and how easy they were to work with. The fact that Neurolabs had a seamless pipeline in place already to solve the exact problem that Akcelita was trying to solve gave them great faith that they were on to a winner.
They established a Proof-of-Concept project to test how effectively Neurolabs could help them with their workflow. The scope included 35 supermarket products from the stores they were monitoring and the products were spread across many different images.
Akcelita’s pipeline included:
Collecting high quality images from the store using 3D depth cameras
Pre-processing that compared the image with the results from Neurolabs
A post-processing step that confirmed compliance and checked for outliers in the detection results
The finished solution would automatically detect Out-Of-Shelf products as well as any Planogram Compliance issues on shelves.
Problem Solved
Getting instant access to the images they needed from Neurolabs along with the detection results was a smooth and seamless process from start to finish.
Creating image recognition models quickly was paramount for Akcelita so that they could test the solution and iterate it quickly if necessary. The time saving that Neurolabs provided here was by far the biggest benefit. Synthetic Data really makes the process a lot quicker and removes the manual object classification process. For them, Neurolabs Synthetic approach to computer vision is the biggest time saver.
All synthetic data and model training was easily managed via the Neurolabs ZIA platform and the detection data made available via API. Overall Akcelita had an excellent experience implementing Neurolabs ZIA and moreover they improved the image recognition capabilities that they can now offer its clients, meaning more business and happier customers as a result.
Synthetic Future for Retail Execution
Using Synthetic Data, Neurolabs ZIA enables you to build a solution that excels at streamlining in-store retail execution where conventional solutions are limited in many ways:
Adaptability: The virtual nature of Synthetic Data makes it easy to transfer datasets and models between domains and CV use cases.
Speed: A real-world deployment can be implemented in less than one week, saving you a ton of time and radically cutting costs.
Scale: Easy access to image recognition datasets for over 100,000 SKUs through Neurolabs’ ZIA product.
Quality: Achieve 96% accuracy for SKU-level product recognition from day 1.
For Consumer Packaged Goods (CPG) brands, Synthetic Data enables the automation of visual-based processes such as in-store retail execution in real-world retail environments using virtual versions of Fast Moving Consumer Goods (FMCG).
The most innovative retail solution providers are already experiencing the benefits of using Synthetic Data by deploying Synthetic Computer Vision software like ZIA Neurolabs to automate retail operations.
At Neurolabs, we are revolutionising in-store retail performance with our advanced image recognition technology, ZIA. Our cutting-edge technology enables retailers, field marketing agencies and CPG brands to optimise store execution, enhance the customer experience, and boost revenue as we build the most comprehensive 3D asset library for product recognition in the CPG industry.
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