  We propose Deeply Supervised Object Detectors (DSOD), an object detection
framework that can be trained from scratch. Recent advances in object detection
heavily depend on the off-the-shelf models pre-trained on large-scale
classification datasets like ImageNet and OpenImage. However, one problem is
that adopting pre-trained models from classification to detection task may
incur learning bias due to the different objective function and diverse
distributions of object categories. Techniques like fine-tuning on detection
task could alleviate this issue to some extent but are still not fundamental.
Furthermore, transferring these pre-trained models across discrepant domains
will be more difficult (e.g., from RGB to depth images). Thus, a better
solution to handle these critical problems is to train object detectors from
scratch, which motivates our proposed method. Previous efforts on this
direction mainly failed by reasons of the limited training data and naive
backbone network structures for object detection. In DSOD, we contribute a set
of design principles for learning object detectors from scratch. One of the key
principles is the deep supervision, enabled by layer-wise dense connections in
both backbone networks and prediction layers, plays a critical role in learning
good detectors from scratch. After involving several other principles, we build
our DSOD based on the single-shot detection framework (SSD). We evaluate our
method on PASCAL VOC 2007, 2012 and COCO datasets. DSOD achieves consistently
better results than the state-of-the-art methods with much more compact models.
Specifically, DSOD outperforms baseline method SSD on all three benchmarks,
while requiring only 1/2 parameters. We also observe that DSOD can achieve
comparable/slightly better results than Mask RCNN + FPN (under similar input
size) with only 1/3 parameters, using no extra data or pre-trained models.
