IM A G ENE T-X: UNDERSTANDING MODEL MISTAKES WITH FACTOR OF VARIATION ANNOTATIONS

Abstract

Deep learning vision systems are widely deployed across applications where reliability is critical. However, even today's best models can fail to recognize an object when its pose, lighting, or background varies. While existing benchmarks surface examples that are challenging for models, they do not explain why such mistakes arise. To address this need, we introduce ImageNet-X-a set of sixteen human annotations of factors such as pose, background, or lighting for the entire ImageNet-1k validation set as well as a random subset of 12k training images. Equipped with ImageNet-X, we investigate 2,200 current recognition models and study the types of mistakes as a function of model's (1) architecture -e.g. transformer vs. convolutional -, (2) learning paradigm -e.g. supervised vs. self-supervised -, and (3) training procedures -e.g. data augmentation. Regardless of these choices, we find models have consistent failure modes across ImageNet-X categories. We also find that while data augmentation can improve robustness to certain factors, they induce spill-over effects to other factors. For example, color-jitter augmentation improves robustness to color and brightness, but surprisingly hurts robustness to pose. Together, these insights suggests that to advance the robustness of modern vision models, future research should focus on collecting additional diverse data and understanding data augmentation schemes. Along with these insights, we release a toolkit based on ImageNet-X to spur further study into the mistakes the image recognition systems make: https: //facebookresearch.github.io/imagenetx/site/home.

1. INTRODUCTION

Despite deep learning surpassing human performance on ImageNet (Russakovsky et al., 2015; He et al., 2015) , even today's best vision systems can fail in spectacular ways. Models are brittle to variation in object pose (Alcorn et al., 2019 ), background (Beery et al., 2018 ), texture (Geirhos et al., 2018 ), and lighting (Michaelis et al., 2019) . Model failures are of increasing importance as deep learning is deployed in critical systems spanning fields across medical imaging (Lundervold and Lundervold, 2019), autonomous driving (Grigorescu et al., 2020) , and satellite imagery (Zhu et al., 2017) . One example from the medical domain raises reasonable worry, as "recent deep learning systems to detect COVID-19 rely on confounding factors rather than medical pathology, creating an alarming situation in which the systems appear accurate, but fail when tested in new hospitals" (DeGrave et al., 2021) . Just as worrisome is evidence that model failures are pronounced for socially disadvantaged groups (Chasalow and Levy, 2021; Buolamwini and Gebru, 2018; DeVries et al., 2019; Idrissi et al., 2021) . Existing benchmarks such as ImageNet-A,-O, and -V2 surface more challenging classification examples, but do not reveal why models make such mistakes. Benchmarks don't indicate whether a model's failure is due to an unusual pose or an unseen color or dark lighting conditions. Researchers, instead, often measure robustness with respect to these examples' average accuracy. Average accuracy captures a model's mistakes, but does not reveal directions to reduce those mistakes. A hurdle to research progress is understanding not just that, but also why model failures occur. * hands-on and advising contributions 1

