ScratchDet: Exploring to Train Single Shot Object Detectors from Scratch
Current state of the art object objectors are fine tuned from the off the shelf networks pretrained on large scale classification dataset ImageNet, which incurs some additional problems: 1) The classification and detection have different degrees of sensitivity to translation, resulting in the learning objective bias; 2) The architecture is limited by the classification network, leading to the inconvenience of modification. To cope with these problems, training detectors from scratch is a feasible solution. However, the detectors trained from scratch generally perform worse than the pretrained ones, even suffer from the convergence issue in training. In this paper, we explore to train object detectors from scratch robustly. By analysing the previous work on optimization landscape, we find that one of the overlooked points in current trained from scratch detector is the BatchNorm. Resorting to the stable and predictable gradient brought by BatchNorm, detectors can be trained from scratch stably while keeping the favourable performance independent to the network architecture. Taking this advantage, we are able to explore various types of networks for object detection, without suffering from the poor convergence. By extensive experiments and analysis on downsampling factor, we propose the Root ResNet backbone network, which makes full use of the information from original images. Our ScratchDet achieves the state of the art accuracy on PASCAL VOC 2007, 2012 and MS COCO among all the train from scratch detectors and even performs better than several one stage pretrained methods. Codes will be made publicly available at https:/github.com/KimSoybean/ScratchDet
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