Object detectors achieve strong performance on benchmark datasets, yet most are trained under the i.i.d. assumption, leading to significant degradation when deployed under real-world distribution shifts. Domain Generalisation (DG) addresses this challenge by enabling models to generalise to unseen, Out-Of-Distribution data without access to target domains during training. However, evaluating object detection under realistic DG conditions remains difficult due to the lack of standardised benchmarks. To fill this gap, we introduce Real-World Distribution Shifts (RWDS), a suite of three benchmark datasets designed to assess the robustness of state-of-the-art object detectors under realistic spatial domain shifts. Grounded in humanitarian and climate change applications, RWDS enables systematic evaluation across diverse climate zones, disaster types, and geographic regions, providing a timely benchmark for developing object detectors that generalise beyond the training setup.

Find out more about RWDS here: https://rwgai.com/rwds/