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.
@article{arxiv.2310.08182,
title = {XIMAGENET-12: An Explainable AI Benchmark Dataset for Model Robustness Evaluation},
author = {Qiang Li and Dan Zhang and Shengzhao Lei and Xun Zhao and Porawit Kamnoedboon and WeiWei Li and Junhao Dong and Shuyan Li},
journal= {arXiv preprint arXiv:2310.08182},
year = {2024}
}
Comments
Paper accepted by Synthetic Data for Computer Vision Workshop @ IEEE CVPR 2024