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

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