ObjectNet
ObjectNet is a dataset designed to challenge computer vision models by presenting images of objects in visually surprising contexts. Created by researchers at DeepMind, it aims to measure how well models generalize to out-of-distribution examples, which are common in real-world scenarios but often underrepresented in standard training datasets.
The dataset consists of approximately 300,000 images featuring 300 different object categories. What distinguishes ObjectNet from
The primary purpose of ObjectNet is to evaluate the robustness and generalization capabilities of visual recognition