Summary of Creation and Evaluation Of a Food Product Image Dataset For Product Property Extraction, by Christoph Brosch et al.
Creation and Evaluation of a Food Product Image Dataset for Product Property Extraction
by Christoph Brosch, Alexander Bouwens, Sebastian Bast, Swen Haab, Rolf Krieger
First submitted to arxiv on: 15 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel annotated dataset for product recognition and classification tasks in the retail domain is introduced, comprising 1,034 studio-captured images of single food products with GS1-standardized labels. This dataset aims to support machine learning model development and provide a reference process for creating high-quality training data, ultimately enabling AI-based automation approaches in retail. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine having a super-smart way to recognize and classify different food products just like the ones you find at your favorite store! Researchers created a special collection of 1,034 photos of single food items taken under controlled conditions. They also added labels telling what’s in each picture (like “apple” or “chicken nugget”). This dataset can help develop better machine learning models and provide a blueprint for making high-quality training data. |
Keywords
* Artificial intelligence * Classification * Machine learning
