WEDGE: A multi-weather autonomous driving dataset built from generative
  vision-language models
Aboli Marathe Deva Ramanan Rahee Walambe Ketan Kotecha
The open road poses many challenges to autonomous perception, including poor
visibility from extreme weather conditions. Models trained on good-weather
datasets frequently fail at detection in these out-of-distribution settings. To
aid adversarial robustness in perception, we introduce WEDGE (WEather images by
DALL-E GEneration): a synthetic dataset generated with a vision-language
generative model via prompting. WEDGE consists of 3360 images in 16 extreme
weather conditions manually annotated with 16513 bounding boxes, supporting
research in the tasks of weather classification and 2D object detection. We
have analyzed WEDGE from research standpoints, verifying its effectiveness for
extreme-weather autonomous perception. We establish baseline performance for
classification and detection with 53.87% test accuracy and 45.41 mAP. Most
importantly, WEDGE can be used to fine-tune state-of-the-art detectors,
improving SOTA performance on real-world weather benchmarks (such as DAWN) by
4.48 AP for well-generated classes like trucks. WEDGE has been collected under
OpenAI's terms of use and is released for public use under the CC BY-NC-SA 4.0
license. The repository for this work and dataset is available at
https://infernolia.github.io/WEDGE.