  We'd like to share a simple tweak of Single Shot Multibox Detector (SSD)
family of detectors, which is effective in reducing model size while
maintaining the same quality. We share box predictors across all scales, and
replace convolution between scales with max pooling. This has two advantages
over vanilla SSD: (1) it avoids score miscalibration across scales; (2) the
shared predictor sees the training data over all scales. Since we reduce the
number of predictors to one, and trim all convolutions between them, model size
is significantly smaller. We empirically show that these changes do not hurt
model quality compared to vanilla SSD.
