With image recognition the only manual input to the process are the photos taken of the products themselves and of the store shelves. Deep Convolutional Neural Networks automatically recognize the products and their visibility to the customer, helping achieve an increased sales revenue. The decision maker can use the results to generate realograms.
A potential added benefit of the solution, if repeated periodically, is the improved shelf stock management. The neural network can learn when a product is in danger of falling out of stock and can raise the necessary alerts to commence a stock refill.
This business case demonstrates the product recognition capabilities of a machine learning model.
To use this workflow in KNIME, download it from the below URL and open it in KNIME:
Download WorkflowDeploy, schedule, execute, and monitor your KNIME workflows locally, in the cloud or on-premises – with our brand new NodePit Runner.
Try NodePit Runner!Do you have feedback, questions, comments about NodePit, want to support this platform, or want your own nodes or workflows listed here as well? Do you think, the search results could be improved or something is missing? Then please get in touch! Alternatively, you can send us an email to mail@nodepit.com, follow @NodePit on Twitter or botsin.space/@nodepit on Mastodon.
Please note that this is only about NodePit. We do not provide general support for KNIME — please use the KNIME forums instead.