ImageNet-PD is a new benchmark dataset for robustness evaluation against unprecedented changes in shape, size, orientation, angles, and other spatial relationships of visual concepts in images due to perspective distortion. ImageNet-PD is dedicated benchmark to evaluate robustness of models for perspective distortion, derived from the ImageNet validation set by synthesizing distortions in different orientations . ImageNet-PD has eight subsets four corresponding to four orientations (left, right, top, bottom) with black background (PD-L, PD-R, PD-T, PD-B). The other four subsets are with the same orientations but with integrated padding background using boundary pixels (PD-LI, PD-RI, PD-TI, PD-BI).
Why ImageNet-PD?
Perspective distortion (PD) is pervasive in real-world imagery and presents significant challenges for developing computer vision applications. PD res…
