  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.
