{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/ximagenet-12-an-explainable-ai-benchmark","title":"XIMAGENET-12: An Explainable AI Benchmark Dataset for Model Robustness Evaluation","arxiv_id":"2310.08182","date":"2023-10-12","proceeding":null,"authors":["Qiang Li","Dan Zhang","Shengzhao Lei","Xun Zhao","Porawit Kamnoedboon","Weiwei Li","Junhao Dong","Shuyan Li"],"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.","url_abs":"https://arxiv.org/abs/2310.08182v2","url_pdf":"https://arxiv.org/pdf/2310.08182v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[],"tasks":[{"task_slug":"classification-1","task_name":"Classification"}],"methods":[],"datasets_introduced":[{"slug":"ximagenet","name":"XImageNet","full_name":"XIMAGENET-12: An Explainable AI Benchmark Dataset for Model Robustness Evaluation"},{"slug":"ximagenet-12","name":"XImageNet-12","full_name":"XIMAGENET-12: An Explainable AI Benchmark Dataset for Model Robustness Evaluation"}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}