  Recent CNN based object detectors, no matter one-stage methods like YOLO,
SSD, and RetinaNe or two-stage detectors like Faster R-CNN, R-FCN and FPN are
usually trying to directly finetune from ImageNet pre-trained models designed
for image classification. There has been little work discussing on the backbone
feature extractor specifically designed for the object detection. More
importantly, there are several differences between the tasks of image
classification and object detection. 1. Recent object detectors like FPN and
RetinaNet usually involve extra stages against the task of image classification
to handle the objects with various scales. 2. Object detection not only needs
to recognize the category of the object instances but also spatially locate the
position. Large downsampling factor brings large valid receptive field, which
is good for image classification but compromises the object location ability.
Due to the gap between the image classification and object detection, we
propose DetNet in this paper, which is a novel backbone network specifically
designed for object detection. Moreover, DetNet includes the extra stages
against traditional backbone network for image classification, while maintains
high spatial resolution in deeper layers. Without any bells and whistles,
state-of-the-art results have been obtained for both object detection and
instance segmentation on the MSCOCO benchmark based on our DetNet~(4.8G FLOPs)
backbone. The code will be released for the reproduction.
