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

arXiv:2310.08182v2 Announce Type: replace-cross
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 …

ai benchmark arxiv benchmark cs.cv cs.lg dataset evaluation explainable ai model robustness robustness type