Datathon NSI Solution – Team Lemurs

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The NSI case – Predicting Household Budgets by team Lemurs   Team Tsvetan (cecopld) Radoslav (rdimitrov) (mr-reliable) (khadeer) Business Understanding The survey’s data on expenditure of household according to COICOP (quarterly and annually) are used for the purposes of producing macroeconomic statistics – National Accounts and Consumer Price Index. In order to optimize the cost […]

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.