  Efficiently learning visual representations of items is vital for large-scale
recommendations. In this article we compare several pretrained efficient
backbone architectures, both in the convolutional neural network (CNN) and in
the vision transformer (ViT) family. We describe challenges in e-commerce
vision applications at scale and highlight methods to efficiently train,
evaluate, and serve visual representations. We present ablation studies
evaluating visual representations in several downstream tasks. To this end, we
present a novel multilingual text-to-image generative offline evaluation method
for visually similar recommendation systems. Finally, we include online results
from deployed machine learning systems in production on a large scale
e-commerce platform.

Computer Vision and Pattern Recognition;Machine Learning