Popular articles by tony
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 […]
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.
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: http://norris.imagga.com/demos/kaufland-case/
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 […]
Popular comments by tony
From the article for me it is not clear if the training data set provided by Kaufland is used to report the accuracy.
The models results are good and I particularly like the simplicity of the model!
Very well done!
However, there was no attempt to compare the results with similar model created with fully convolution network, that can be used directly with data set with different image sizes.
Overall I am not impressed by the achieved accuracy.
One of the reasons is the expiration of the Azure vouchers and the troubles with the VM.
Other possible reason is that the team used relatively old CNN architecture.
I will like to see the reported accuracy against the provided test data set by Kaufland, not just on 20% of the training set.
Since datascience is experimental science I very much appreciate the approach by the team to compare wide range of CNN architectures!
Very well done!