Title: DSOD: Learning Deeply Supervised Object Detectors from Scratch,
Abstract: We present Deeply Supervised Object Detector (DSOD), a framework that can
learn object detectors from scratch. State-of-the-art object objectors rely
heavily on the off-the-shelf networks pre-trained on large-scale classification
datasets like ImageNet, which incurs learning bias due to the difference on
both the loss functions and the category distributions between classification
and detection tasks. Model fine-tuning for the detection task could alleviate
this bias to some extent but not fundamentally. Besides, transferring
pre-trained models from classification to detection between discrepant domains
is even more difficult (e.g. RGB to depth images). A better solution to tackle
these two critical problems is to train object detectors from scratch, which
motivates our proposed DSOD. Previous efforts in this direction mostly failed
due to much more complicated loss functions and limited training data in object
detection. In DSOD, we contribute a set of design principles for training
object detectors from scratch. One of the key findings is that deep
supervision, enabled by dense layer-wise connections, plays a critical role in
learning a good detector. Combining with several other principles, we develop
DSOD following the single-shot detection (SSD) framework. Experiments on PASCAL
VOC 2007, 2012 and MS COCO datasets demonstrate that DSOD can achieve better
results than the state-of-the-art solutions with much more compact models. For
instance, DSOD outperforms SSD on all three benchmarks with real-time detection
speed, while requires only 1/2 parameters to SSD and 1/10 parameters to Faster
RCNN. Our code and models are available at: this https URL .