Object detection from images
Summary of the entity:
Migros is one of the largest FMCG retailers in Turkey. With more than 2000 stores and 30.000 employees, Migros is also the pioneer of organized retailing in Turkey. Migros today offers spacious stores in a wide range of formats and locations whose vast selection of cosmetics, stationery, glass and kitchenware, electronic appliances, book, textiles, and other items along with groceries and other necessities give it the ability to satisfy the shopping needs of its customers.
The company aims to be always the first choice of customers by providing a unique convenience and trustworthy shopping experience through its ultimate service approach, pioneer applications, broad product portfolio and family budget friendly pricing strategy.
Summary of the challenge:
In this challenge we are looking for a machine learning model that can be used to detect objects picked up by the customers from refrigerators using camera images from the refrigerators. The problem contains mainly two challenges. The images contain obstructions such as customer’s hands and they should be able to run on a Jetson Nano 4Gb, even though they can be trained on a different environment.
In this challenge, we are going to share images taken from the cameras in the refrigeration unit in our lab. The images capture customers picking up various products from the refrigerator. Given a small set predefined of SKUs, we aim to detect the object/objects in the images that are picked up by the customers. The images are taken using fixed cameras and may be blurry or contain obstructions. The machine learning model used for this purpose has to run on a Jetson Nano 4Gb as the objective is to use them for wide scale adoption of the project.
A model that can run on Jetson Nano 4Gb (see https://developer.nvidia.com/embedded/jetson-nano-developer-kit for specifications).
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