eProduct: A Million-Scale Visual Search Benchmark to Address Product Recognition Challenges

TL;DR

eProduct is a large-scale benchmark dataset with 2.5 million product images designed to advance visual search and fine-grained recognition in e-commerce, addressing challenges of subtle visual differences and dataset creation.

Contribution

The paper introduces eProduct, a comprehensive dataset for product recognition and visual search, including training and evaluation sets to facilitate research in super fine-grained recognition.

Findings

Baseline models show promising performance on eProduct.

eProduct covers diverse product categories and visual variations.

The dataset accelerates research in self-supervised and multimodal learning.

Abstract

Large-scale product recognition is one of the major applications of computer vision and machine learning in the e-commerce domain. Since the number of products is typically much larger than the number of categories of products, image-based product recognition is often cast as a visual search rather than a classification problem. It is also one of the instances of super fine-grained recognition, where there are many products with slight or subtle visual differences. It has always been a challenge to create a benchmark dataset for training and evaluation on various visual search solutions in a real-world setting. This motivated creation of eProduct, a dataset consisting of 2.5 million product images towards accelerating development in the areas of self-supervised learning, weakly-supervised learning, and multimodal learning, for fine-grained recognition. We present eProduct as a training set and an evaluation set, where the training set contains 1.3M+ listing images with titles and hierarchical category labels, for model development, and the evaluation set includes 10,000 query and 1.1 million index images for visual search evaluation. We will present eProduct's construction steps, provide analysis about its diversity and cover the performance of baseline models trained on it.