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Segment typical products on a retail shelf through Machine Learning techniques and find similar fashion items in a retail fashion store through vector embeddings

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navin772/Retail_product_segmentation

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Retail_Store_ML

This project aims to demonstrate machine learning usecases/applications in the Retail vertical and how it can help retail stores to improve their business and customer experience.

The directories such as Colgate_segmentation, retail_store_heatmap contains the code for the respective usecases. The web_application directory combines all these usecases into a single web application that can be used by the retail store to get insights. Checkout each folder for more details and implementations.

For deploying the web application read the README.md file in the web_application directory for various types of deployment.

Google Summer of Code 2023 Program

This project was part of the Google Summer of Code 2023 program under the openSUSE Project organization.

Here's the link to my gsoc project - Analytics Edge Ecosystem Workloads.

To read more about this project and my learnings during the google summer of code program, checkout my Medium blog.

Mentors

This project was mentored by Bryan Gartner, Ann Davis and Terry Smith. I would like to thank them for their guidance and support throughout the program.

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Segment typical products on a retail shelf through Machine Learning techniques and find similar fashion items in a retail fashion store through vector embeddings

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