In-person

EMEA

Hackathons

Food Waste Estimation Hackathon - Computer Vision for Sustainability - 1 August, 2025

Aug 1, 2025

10:00 AM - 8:00 PM

Hasso Plattner Institute Main building H, d-space Prof.-Dr.-Helmert-Str. 2-3, D-14482 Potsdam

About this event

Join us for a hands-on computer vision hackathon focused on tackling food waste through image analysis. Participants will build systems that estimate food waste by analyzing post-meal images, contributing to sustainability efforts through machine learning.

Host

Target Audience

Data Scientists
Machine Learning Engineers
Computer vision programmers

Prerequisites: Participants must understand computer vision fundamentals. Familiarity with the open source dataset curation tool FiftyOne is highly beneficial but not required.

We recommend reviewing the documentation of FiftyOne and the following openHPI MOOC link for self-study preparation before the event:

The particularly relevant sections are neural network fundamentals, image-based regression, image augmentation, object detection, and image segmentation.

Tutorial notebooks are available on GitHub.

Challenge: Food Waste Estimation from Images

Build computer vision systems that analyze food images to estimate waste quantities.

Teams will work with post-meal photographs to develop accurate food waste measurement algorithms.

Technical Tasks

Object Detection and Segmentation

Zero-shot food item segmentation with YOLO-E and YOLO-W and Grounding DINO + SAM

Dataset Creation and Management

Image dataset exploration and quality assessment with FiftyOne
Dataset curation using CVAT annotation tools for food images
Custom segmentation dataset creation with food waste labels
Validation set construction for before/after images
Data augmentation techniques for diverse food scenarios

Food Waste Estimation Modeling

Volume estimation algorithms (primary focus): Calculate remaining food quantities from segmented regions
Waste percentage calculation: Compare before/after states to determine consumption ratios
Multi-food item tracking: Handle plates with multiple food types
Portion size standardization: Account for varying plate sizes

Advanced Features

Synthetic food image generation for training enhancement
Fine-tuning models for specific food categories

Rewards

1st Place: Amazon gift card with a value of 300 EUR
2nd Place: Amazon gift card with a value of 150 EUR
3rd Place: Amazon gift card with a value of 50 EUR

Scoring Rubric

Participants earn points based on implementation quality and food waste estimation accuracy:

Technical Implementation (50 points)

Food Detection/Segmentation (25 points): Accurate identification and boundary detection of food items and use of model ensembles and manual annotation
Waste Estimation Accuracy and Experiment Tracking (25 points): Precision in calculating food waste percentages and volumes, and use of appropriate metrics.

Best Practices Adherence (30 points)

Dataset curation (30 points): Enrichment of the dataset through labeling, identification and resolution of leaky duplicates across train and validation splits, identification of uniqueness and representativeness of samples, richness of embeddings visualizations, data augmentation strategies, sharing of curated version of the dataset on HuggingFace
Documentation and reproducibility (5 points): quality of public GitHub repository, inclusion of requirements.txt file and / or Docker image with reproducible solution, use of wandb to track experiments

Creative solutions and deployment (15 points)

Creative Solutions (10 points): Novel approaches to food volume estimation (e.g. augmentation of the dataset with diffusion models).
Deployment (5 points): creation of a deployed version of the app in a Gradio endpoint on HuggingFace

Resources Provided

Development Platform

Google Colab: Pre-configured notebooks with quickstart tutorials

Technical Support

Mentors with expertise in computer vision and food analysis
Access to pre-trained food detection models and baseline implementations
Documentation on food waste estimation techniques

Schedule

10:00 AM - Registration and team formation
10:30 AM - Technical briefing: Food waste estimation challenges
11:00 AM - Hacking begins
1:00 PM - Lunch break (observe your own food waste!)
2:00 PM - Mid-event check-in and mentor consultations
6:00 PM - Project submissions deadline
6:30 PM - Team presentations: Demo your food waste estimation system
7:30 PM - Judging and awards ceremony
8:00 PM - Event conclusion

Impact

Your work will contribute to understanding and reducing food waste through technology. The best solutions could help restaurants, cafeterias, and households track and minimize their environmental impact.

What to Bring

Laptop with VSCode installed
Basic knowledge of Python and machine learning frameworks
Interest in sustainability and environmental impact

Join us to combine computer vision expertise with environmental consciousness in the heart of Potsdam's tech ecosystem!