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
  
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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