Initially, we installed the system in two stores to determine the correct processor model, camera type, and camera positions.
Next, we integrated the development with the store's cash registers and trained the system to recognize fruits and vegetables.
Installed the system in seven stores to see if it could pay for itself and accurately identify products in high volumes.
As a result, for 200 commodity items, we received 160 photos each. The average accuracy of getting into a selection of three products was 94%.
The retail chain decided to install "Smart Scales" in 100 stores. The store's security service was also interested in the development, since the neural network helps to fight unscrupulous customers who take expensive weight goods, but choose a cheap analogue at the self-service checkout.
The compute server is in the shop. This is necessary to protect data and speed up the system. Only updates come from the external server.
The system does not need the Internet, it is enough to connect to the network once a day to get the update.