The Future of Retail Shelf Auditing:
How Synthetic Computer Vision is Changing the Game
Attention Field Marketing Agencies, CPGs, and Sales Force Automation companies. Are you tired of relying on traditional image recognition methods for retail audits that are time-consuming, lack accuracy, and scalability? It's time to embrace the future of retail auditing that’s powered by Synthetic Computer Vision.
What you'll find inside:
Explore the obstacles associated with traditional image recognition and the necessity for more effective solutions.
Uncover the power of synthetic computer vision, how it works and how it's revolutionising the world of image recognition, providing unparalleled speed, accuracy, and cost-effectiveness in retail audits.
Discover the benefits of deploying synthetic computer vision in retail.
Learn about synthetic data and how it offers a simpler, more accurate and reliable approach for producing training data for image recognition.
Find out how to easily integrate synthetic computer vision into existing retail processes to optimise retail audits, ensure better inventory management, increase sales, and improve compliance.
Download the Future of Retail Shelf Auditing eBook now!
Synthetic Image Recognition
No manual annotations or real data
Synthetic image recognition eliminates the need for manual annotations and provides an always up-to-date catalogue so you never have to waste time waiting for data.
Quick onboarding and fast time to market
Using synthetic image recognition, you can onboard a new Consumer Packaged Goods customer in just one day and provide a time to market of one week for up to 1,000 SKUs.
Accurate detection and advanced analytics
Synthetic image recognition delivers an object detection accuracy rate of +95% from the outset and maintains this consistently. Specific categories
can achieve +98% accuracy.
Data diversity for maximum accuracy
Synthetic datasets are both large and accurate, with millions of images generated in a few hours. This diversity is crucial for accuracy and something that can only be achieved with synthetic data.
Expand catalogues coverage with ease
Synthetic image recognition makes it easy to scale the catalogue size across multiple retail locations without compromising on time to market or accuracy levels.
Dynamic and flexible catalogues
Synthetic image recognition's dynamic, cloud-based catalogue enables you to respond quickly to changing market needs and even upload SKUs before they hit the shelf.
A LOOK INSIDE
Revolutionising Retail: Uncover Synthetic Computer Vision
This groundbreaking eBook sheds light on the challenges posed by traditional image recognition models and the way in which new technologies are revolutionising the retail sector. Uncover Synthetic Computer Vision, the innovative technique which uses 3D digital models of real-world objects in simulated settings to produce synthetic data for teaching algorithms to achieve unparalleled accuracy in retail audits.
Discover how synthetic data is transforming the industry, offering a more affordable, faster, and more precise way to generate training data for image recognition. Field reps can complete shelf audits more efficiently, CPGs can access real-time, accurate data and insights, and FMAs can track KPIs and other product-related insights with ease.
Best of all, this Synthetic Computer Vision technology can be quickly integrated into your current system, with no need for intensive onboarding or laborious data labelling.
A LOOK INSIDE
Behind the scenes:
A look into Neurolabs
At Neurolabs, we are at the forefront of revolutionising in-store retail performance with our state-of-the-art image recognition technology, ZIA. Our cutting-edge technology empowers FMAs, SFAs, FMCGs and CPG brands to optimise store execution, enhance the customer experience, and boost revenue by building the most comprehensive 3D asset library for product recognition in the CPG industry.
Find out more about ZIA >
Sign up to our newsletter!
Your source for the latest news and insights in the world of Computer Vision, Synthetic Data and Image Recognition.