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In this technical report, we present our approaches for the continual objectdetection track of the SODA10M challenge. We adapt ResNet50-FPN as the baselineand try several improvements for the final submission model. We find thattask-specific replay scheme, learning rate scheduling, model calibration, andusing original image scale helps to improve performance for both large andsmall objects in images. Our team `hypertune28' secured the second positionamong 52 participants in the challenge. This work will be presented at the ICCV2021 Workshop on Self-supervised Learning for Next-Generation Industry-levelAutonomous Driving (SSLAD).

2nd Place Solution for SODA10M Challenge 2021 -- Continual Detection Track