SODA10M
Introduced by Han et al. in SODA10M: A Large-Scale 2D Self/Semi-Supervised Object Detection Dataset for Autonomous Driving
SODA10M is a large-scale object detection benchmark for standardizing the evaluation of different self-supervised and semi-supervised approaches by learning from raw data. SODA10M contains 10 million unlabeled images and 20K images labeled with 6 representative object categories. To improve diversity, the images are collected every ten seconds per frame within 32 different cities under different weather conditions, periods and location scenes.
Papers
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