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https://github.com/navin772/retail_product_segmentation

Segment typical products on a retail shelf through Machine Learning techniques and find similar fashion items in a retail fashion store through vector embeddings
https://github.com/navin772/retail_product_segmentation

gsoc-23 machine-learning milvus yolo

Last synced: 6 months ago
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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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README

          

# 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](https://summerofcode.withgoogle.com/programs/2023/organizations/opensuse-project) organization.

Here's the link to my gsoc project - [Analytics Edge Ecosystem Workloads](https://summerofcode.withgoogle.com/programs/2023/projects/YD5TW1gM).

To read more about this project and my learnings during the google summer of code program, checkout my [Medium blog](https://medium.com/@navinchandra772/google-summer-of-code-2023-opensuse-project-8bd74e38d819).

## Mentors
This project was mentored by [Bryan Gartner](https://github.com/bwgartner), [Ann Davis](https://github.com/andavissuse) and [Terry Smith](https://github.com/tlssuse). I would like to thank them for their guidance and support throughout the program.