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arXiv · 2310.08182

XIMAGENET-12: An Explainable AI Benchmark Dataset for Model Robustness Evaluation

Qiang Li·Dan Zhang·Shengzhao Lei·Xun Zhao·Porawit Kamnoedboon·WeiWei Li·Junhao Dong·Shuyan Li
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Abstract

Despite the promising performance of existing visual models on public benchmarks, the critical assessment of their robustness for real-world applications remains an ongoing challenge. To bridge this gap, we propose an explainable visual dataset, XIMAGENET-12, to evaluate the robustness of visual models. XIMAGENET-12 consists of over 200K images with 15,410 manual semantic annotations. Specifically, we deliberately selected 12 categories from ImageNet, representing objects commonly encountered in practical life. To simulate real-world situations, we incorporated six diverse scenarios, such as overexposure, blurring, and color changes, etc. We further develop a quantitative criterion for robustness assessment, allowing for a nuanced understanding of how visual models perform under varying conditions, notably in relation to the background. We make the XIMAGENET-12 dataset and its corresponding code openly accessible at \url{https://sites.google.com/view/ximagenet-12/home}. We expect the introduction of the XIMAGENET-12 dataset will empower researchers to thoroughly evaluate the robustness of their visual models under challenging conditions.

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Qiang Li, Dan Zhang, Shengzhao Lei, Xun Zhao, Porawit Kamnoedboon, WeiWei Li, Junhao Dong, Shuyan Li. 2024-04-18. XIMAGENET-12: An Explainable AI Benchmark Dataset for Model Robustness Evaluation. https://arxiv.org/abs/2310.08182

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