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This paper presents a machine learning based approach for solving the business problem of identifying from pictures the products chosen by the Kaufland customers. These pictures are all taken from the same angle and typically show one or multiple products from the same category in a bag which makes the background and the bag recurrent elements.
Here we explored the method of transfer learning – using already trained and very deep NN like InceptionV3, InceptionResnetV2, VGG19, Resnet50 with combinations of retraining and no retraining of the existing layers. We solved this multiclassification problem of predicting the probabilities of each class of products by adding different final custom layers and we obtain the best result of 85% accuracy on a validation set of 20% (which was never seen by the training model).
This result was achieved with the model VGG19 which distinguished itself not only for providing the best categorical accuracy but also for training speed, execution speed once deployed and reduced resource consumption.
Fruit Ninjas: Kaufland Case Tech: Microsoft Azure: 2 vauchers: W78FLCKPCAY42VJ1N9 W6Q5SI6BPKR8HQ9KPK Business understanding Kaufland is amongst the biggest hypermarket chains in Central and East Europe. The Kaufland team is devoted to enhancing customers’ satisfaction with the products and services offered by its stores and keeping up with the competition. The aim of the current […]
Case Kaufland¶ On 22.01.2018 Amazon opened Amazon Go – their first ever physical store without cashiers and checkout lines – customers just grab the products from the shelves and go. AI algorithms detect what product you have grabbed. Kaufland offers the unique opportunity to work with their internal data on a similar problem – developing […]
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