Visual Product Search Benchmark
Karthik Sulthanpete Govindappa

TL;DR
This paper introduces a comprehensive benchmark for evaluating modern visual embedding models in industrial product image retrieval, focusing on real-world conditions and exact instance matching to guide practitioners and researchers.
Contribution
It provides a structured evaluation protocol, curated datasets, and comparative analysis of foundation, proprietary, and domain-specific models for industrial image retrieval.
Findings
Foundation models transfer well to fine-grained retrieval tasks.
Explicitly trained industrial models outperform general-purpose models.
Benchmark results highlight strengths and limitations of current approaches.
Abstract
Reliable product identification from images is a critical requirement in industrial and commercial applications, particularly in maintenance, procurement, and operational workflows where incorrect matches can lead to costly downstream failures. At the core of such systems lies the visual search component, which must retrieve and rank the exact object instance from large and continuously evolving catalogs under diverse imaging conditions. This report presents a structured benchmark of modern visual embedding models for instance-level image retrieval, with a focus on industrial applications. A curated set of open-source foundation embedding models, proprietary multi-modal embedding systems, and domain-specific vision-only models are evaluated under a unified image-to-image retrieval protocol. The benchmark includes curated datasets, which includes industrial datasets derived from…
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Taxonomy
TopicsImage Retrieval and Classification Techniques · Advanced Image and Video Retrieval Techniques · Multimodal Machine Learning Applications
