A Deep Learning Pipeline For Product Recognition On Store Shelves

Abstract

Recognition of grocery products in store shelves poses peculiar challenges. Firstly, the task mandates the recognition of an extremely high number of different items, in the order of several thousands for medium-small shops, with many of them featuring small inter and intra class variability. Then, available product databases usually include just one or a few studio-quality images per product (referred to herein as reference images), whilst at test time recognition is performed on pictures displaying a portion of a shelf containing several products and taken in the store by cheap cameras (referred to as query images). Moreover, as the items on sale in a store as well as their appearance change frequently over time, a practical recognition system should handle seamlessly new products/packages. Inspired by recent advances in object detection and image retrieval, we propose to leverage on state of the art object detectors based on deep learning to obtain an initial productagnostic item de

Stats

• citations37
• S2 citations—
• github stars0
• HF likes0
• heat score11.85
• arxiv keytonioni2018a

Related papers

• Retailklip : Finetuning Openclip Backbone Using Metric Learning On A Single GPU For Zero-shot Retail Product Image Classification (2023)2.26
• Large-scale Product Retrieval With Weakly Supervised Representation Learning (2022)0.00
• Snap And Find: Deep Discrete Cross-domain Garment Image Retrieval (2019)0.00
• An Effective Pipeline For A Real-world Clothes Retrieval System (2020)0.00
• Deep Learning Based Large Scale Visual Recommendation And Search For E-commerce (2017)0.00
• Image Based Fashion Product Recommendation With Deep Learning (2018)12.74
• Searching For Apparel Products From Images In The Wild (2019)0.00
• Scalable Logo Recognition Using Proxies (2018)11.93