Team Nishki consisted of 11th-graders is presenting a Hackathon ML solution to a Kaufland Airmap case for which they won a Datathon special award. Used methodologies and algorithms: OCR, DarkFlow, YOLO The solution can be found at: https://www.datasciencesociety.net/datathon/kaufland-case-datathon-2019/ Team: Evgeni Dimov, Kalin Doichev, Kostadin Kostadinov and Aneta Tsvetkova

1. DATATHON 2019 KAUFLAND AIRMAP SOLUTION
TEAM “NISHKI” – EVGENI DIMOV, ANETA TSVETKOVA, KOSTADIN KOSTADINOV, KALIN DOICHEV

2. THE CASE

3. THE DATA – GROUND TRUTH

4. THE DATA - WORKING

5. THE DATA - WORKING

6. THE DATA - WORKING

7. DATA PREPARATION
 Label processing (reducing)
 Missing
 Wrong
 Difficult
 Image & Label processing

8. USING THE DATA
 Detect objects & labels
 Extract the product number from labels
 Use the predictions to identity problems

9. DETECTING OBJECTS AND LABELS

10. YOLO – REAL TIME OBJECT DETECTION
 You Only Look Once

11. YOLO – REAL TIME OBJECT DETECTION
 How does it work?
 Example: Detect TV, Bicycle, Monitor

12. YOLO – REAL TIME OBJECT DETECTION
 Split image into sections
 Bounding boxes per section
 Predictions per bounding box
 Central location (within the sqare)
 Width
 Height
 Confidence (any object)
 Confidence(each class)
 Remove boxes with no object
 Remove redundancy – Non Max
Suppression & Intersection over Union

13. YOLO – REAL TIME OBJECT DETECTION

14. READING LABELS - OCR

15. IDENTIFYING AN ISSUE
 Pipeline:
 Identify products & labels
 Extract product numbers from labels
 Apply good-old-fashioned algorithms to detect issues
 Grouping into rows
 Checking for unique subgroups in rows
 Checking for labels with no items
 Dismissing some detected objects
 Running everything on the test dataset got a score of 0.677 (out of 1)

16. QUESTIONS TIME

17. THANK YOU
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ET/DATATHON-DATATHON-
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CASE-SOLUTION-NISHKI/