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

Enlarge the dataset to understand how image background effect the Computer Vision ML model. With the following topics: Blur Background / Segmented Background / AI generated Background/ Bias of tools during annotation/ Color in Background / Dependent Factor in Background/ LatenSpace Distance of Foreground/ Random Background with Real Environment!

We introduce XIMAGENET-12, an explainable benchmark dataset with over 200K images and 15,600 manual semantic annotations. Covering 12 categories from ImageNet to represent objects commonly encountered in practical life and simulating six diverse scenarios, including overexposure, blurring, color changing, etc.,

Our research builds upon the foundation laid by "Noise or Signal: The Role of Image Backgrounds in Object Recognition" (Xiao et al., ICLR 2022), "Explainable AI: Object Recognition With Help From Background" (Qiang et al., ICLR Workshop 2022), reinforced the notion that models trained solely on backgrounds can substantially improve accuracy. One noteworthy discovery highlighted in their studies is that more accurate models tend to rely less on backgrounds.