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Second prize Winner's at Hackathon organized by Loblaws Digital and Mircosoft

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Hackbyte Preparation

Overview

This repository contains all resources and files created during the Hackbyte event, jointly organized by Loblaws and Microsoft.

Resources

Meet Our Team on LinkedIn

Project Phases

Phase 1: Business Problem Framing

Phase 2: Layout and Wireframing

Phase 3: Data Preparation

Phase 4: Modeling

  • Developed five versions of the model with the synthetically created and manually labeled data using Azure Custom Vision API.

Phase 5: Deployment

  • Successful deployment on Microsoft Custom Vision API.
  • Continued dataset building by capturing and labeling product images.
  • Impressive model performance after seven iterations.

Model Performance Metrics

The model showed outstanding results, as evidenced by the following metrics:

Tag Precision Recall A.P Image Count
Chocomini 96.8% 81.1% 84.4 100
Vegetable Baby Food 94.7% 72.0% 86.9 110
Apple Baby Food 94.6% 62.5% 82.0 153

Conclusion

Our solution offers multiple benefits:

  1. Streamlines the process for in-store shoppers and pickers.
  2. Provides real-time item availability from the latest shelf images.
  3. Eliminates the need for extensive camera installations by leveraging crowd-sourced data.

This innovative approach simplifies the process of locating and checking item availability, utilizing advanced computer vision techniques and Azure Custom Vision.

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Second prize Winner's at Hackathon organized by Loblaws Digital and Mircosoft

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