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

Posted 6 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.

The SAP Case using KNIME and Multiple Linear Regression Method

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SAP Case Team Mentors:  Agamemnon Baltagiannis SAP Case Team: Abderrahim Khalifa                                                                | Morocco Andrei Deusteanu ( | Romania Julian Borisov (        […]

Using Convolutional Neural Networks for Real-Time Product Recognition in Smart Scales – Imagga’s Solution to the Kaufland Case

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Many big retailers offer in store a rapidly growing variety of fresh produce from local and global sources, such as fruit and vegetables that need to be weighed quickly and effortlessly to mark their quantity and respective price. Smart scales that use image recognition to optimise user experience and allow additional features, such as e.g. cooking recipes can provide a new solution to this problems. The solution we provide to the Kaufland case includes training a Convolutional Neural Network (CNN) with GoogLeNet architecture on the original Kaufland data set and fine-tuning it with a Custom Training Set we have created, achieving the following results (Kaufland Case Model #13): training accuracy: Top-1: 91%, Top-5: 100%; validation accuracy: Top-1: 86.1% , Top-5: 99%, and TEST dataset accuracy of: Top-1: 86.1%, Top-5: 99.2%. We have also created another model (Kaufland Case Model #14) by combining similar categories, achieving: training accuracy: Top-1: 96%, Top-5: 100%; validation accuracy: Top-1: 92.5%, Top-5: 100%, and TEST dataset accuracy: Top-1: 91.3%, Top-5: 100%. All trainings were done on our NVIDIA DGX Station training machine using BVLC Caffe and the NVIDIA DIGITS framework. In our article we show visualisations of our numerous trainings, provide an online demo with the best classifiers, which can be further tested. During the final DSS Datathon event we plan to show a live food recognition demo with one of our best models running on a mobile phone . Demo URL:

Team Chinchillas, Telenor SNA case

Posted 1 CommentPosted in SNA, Team solutions

Business Understanding Telenor wants to identify Social Network leaders from a list of A and B nodes, their connection counts and connection strengths. A second goal is to characterize a node’s possibility of turning “bad” from its relationship to a list of truly bad nodes. Data Understanding We have 118 690 unique nodes with 1 […]

Fruit Ninjas: Kaufland Case

Posted 2 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 5 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.

Datathon 2018- Receipt Bank Solution

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By Asma Afzal ( Mentor: Marin Delchev Platform: Python Jupyter notebook,  Scikit-learn Business Understanding Receipt bank manages bookkeeping for businesses. It makes use of powerful machine learning algorithms to extract useful information from receipts and invoices of many different formats. Data Understanding A large set of client data is a collection of receipts in a single portable […]

404 Telenor Social Media Not Found

Posted 1 CommentPosted in SNA, Team solutions

Social networks are characterized by the links between the nodes. We employed six different link analysis algorithms to rank the nodes in the network by their importance. For the task of leader detection, the best link analysis algorithm proved to be the vanilla PageRank. Out of the top 2000 nodes, 1725 are leaders, achieving a precision of 0.8625 and a recall ot 0.8792.
We also explored an alternative solution based on embeddings. We trained a Skipgram model where we set the context of a given node to its neighbourhood. To avoid the assumption for word order made by the model we repeated and reshuffled longer neighbourhoods. These embeddings were then used to train a softmax classifier, achieving a macro-f1 score of 0.6667.
We futher provide a visualization tool which can be used to explore the graph manually.

The coalas Identrics

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Identrics     Results_Dependency_Trees  Syntactic Parsing or Dependency Parsing is the task of recognizing a sentence and assigning a syntactic structure to it. The most widely used syntactic structure is the parse tree which can be generated using some parsing algorithms. These parse trees are useful in various applications like grammar checking or more importantly it plays a critical role in the […]