Image recognition

Team Cherry. The Kaufland Case. Fast and Accurate Image Classification Architecture for Recognizing Produce in a Real-Life Groceries’ Setting

Posted vsenderov, valan, hvrigazov, cecopld, mitzev, tonypetrov, pepe Mentors: tony6 CommentsPosted in Image recognition, Learn, Team solutions

Our best model (derived from VGG) achieved 99.46% top3 accuracy (90.18% top1) with processing time during training of 0.006 s per image on a single GPU Titan X (200s / epoch with 37 000 images).

The teams vision is for the team members to see where they stand compared to others in terms of ideas and approaches to computer vision and to learn new ideas and approaches from the other team-mates and the mentors.

Therefore the team is pursuing a pure computer vision approach to solving the Kaufland and/or the ReceiptBank cases.

Fruit Ninjas: Kaufland Case

Posted zhiko, deqncho, desinik, zhiko, ivelinavasileva, sentient, stgram, tony, hl, rumen-mihaylov Mentors: hl, mvelic2 CommentsPosted in Image recognition, Learn, Team solutions

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 […]

Team GrBrGr, Case Kaufland

Posted madalina, stephan, pepe Mentors: hl5 CommentsPosted in Image recognition, Learn, Team solutions

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.