  In this technical report, we present our approaches for the continual object
detection track of the SODA10M challenge. We adapt ResNet50-FPN as the baseline
and try several improvements for the final submission model. We find that
task-specific replay scheme, learning rate scheduling, model calibration, and
using original image scale helps to improve performance for both large and
small objects in images. Our team `hypertune28' secured the second position
among 52 participants in the challenge. This work will be presented at the ICCV
2021 Workshop on Self-supervised Learning for Next-Generation Industry-level
Autonomous Driving (SSLAD).
