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

• URL: http://arxiv.org/abs/2310.08182v2
• Date: Thu, 18 Apr 2024 11:57:49 GMT
• Title: XIMAGENET-12: An Explainable AI Benchmark Dataset for Model Robustness Evaluation
• Authors: Qiang Li, Dan Zhang, Shengzhao Lei, Xun Zhao, Porawit Kamnoedboon, WeiWei Li, Junhao Dong, Shuyan Li,
• Abstract summary: XIMAGENET-12 consists of over 200K images with 15,410 manual semantic annotations. We develop a quantitative criterion for robustness assessment, allowing for a nuanced understanding of how visual models perform under varying conditions.
• Score: 19.399688660643367
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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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