Image Annotation for Swiss Food Waste Assessment Services

High-performance image training dataset built accurate Machine Learning techniques

Project Overview

Business Needs

Accurately label all interest areas in kitchen waste images and food images for training machine learning models of the client so that the models can interpret visual information like humans.

The Challenges

  • Hiring and training data annotation specialists with experience in food and beverages space
  • Accurately recognizing food images with multiple food items
  • Understanding, working and labeling a wide range of diverse European food products

Solutions and Results

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  • We followed a multi-step annotation process comprising segmentation, labelling, audit, and review. A web-based tool enabled secure login to client’s portal.
  • Garbage throwing process’s visual data was iteratively analyzed. Interest items in the images were reviewed from various angles.
  • Images were segmented and tagged product list-wise and as per standards followed by different food outlets.
  • An audit mechanism was implemented, involving the client conducting a parallel image labeling activity in-house.
  • Fuzzy or low confidence images were routed for separate validation. While re-labelling was initiated for erroneously labeled or missed out images.
  • Training data was automatically fed to machine learning models that rapidly matured them.
  • Real-time intelligence on important metrics like food wastage type, wastage ratio etc. significantly decreased the food wastage.

Business Impact

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