DSOD: Learning Deeply Supervised Object Detectors from Scratch

Key Problems

Limited structure design space.

Learning bias

As both the loss functions and the category distributions between classification and detection tasks are different, we argue that this will lead to different searching/optimization spaces. Therefore, learning may be biased towards a local minimum which is not the best for detection task.

Domain mismatch

State-of-the-art object objectors rely heavily on the offthe-shelf networks pre-trained on large-scale classification datasets like ImageNet

transferring pre-trained models from classification to detection between discrepant domains is even more difficult

Architecture

Principles

training detection network from scratch requires a proposal-free framework.

Deep Supervision

Transition w/o Pooling Layer. We introduce this layer in order to increase the number of dense blocks without reducing the final feature map resolution.

Stem Block

stem block can reduce the information loss from raw input images.

Dense Prediction Structure

*

Contributions

DSOD is a simple yet efficient framework which could learn object detectors from scratch

DSOD is fairly flexible, so that we can tailor various network structures for different computing platforms such as server, desktop, mobile and even embedded devices.

We present DSOD, to the best of our knowledge, world first framework that can train object detection networks from scratch with state-of-the-art performance.

We introduce and validate a set of principles to design efficient object detection networks from scratch through step-by-step ablation studies.

We show that our DSOD can achieve state-of-the-art performance on three standard benchmarks (PASCAL VOC 2007, 2012 and MS COCO datasets) with realtime processing speed and more compact models.

Experiments

Others

a well-designed network structure can outperform state-ofthe-art solutions without using the pre-trained models

only the proposal-free method (the 3rd category) can converge successfully without the pre-trained models.

RoI pooling generates features for each region proposals, which hinders the gradients being smoothly back-propagated from region-level to convolutional feature maps.

The proposal-based methods work well with pretrained network models because the parameter initialization

is good for those layers before RoI pooling, while this is not

true for training from scratch