mmdetection/configs/tridentnet

TridentNet

Scale-Aware Trident Networks for Object Detection

Abstract

Scale variation is one of the key challenges in object detection. In this work, we first present a controlled experiment to investigate the effect of receptive fields for scale variation in object detection. Based on the findings from the exploration experiments, we propose a novel Trident Network (TridentNet) aiming to generate scale-specific feature maps with a uniform representational power. We construct a parallel multi-branch architecture in which each branch shares the same transformation parameters but with different receptive fields. Then, we adopt a scale-aware training scheme to specialize each branch by sampling object instances of proper scales for training. As a bonus, a fast approximation version of TridentNet could achieve significant improvements without any additional parameters and computational cost compared with the vanilla detector. On the COCO dataset, our TridentNet with ResNet-101 backbone achieves state-of-the-art single-model results of 48.4 mAP.

Results and Models

We reports the test results using only one branch for inference.

Backbone	Style	mstrain	Lr schd	Mem (GB)	Inf time (fps)	box AP	Download
R-50	caffe	N	1x			37.7	model | log
R-50	caffe	Y	1x			37.6	model | log
R-50	caffe	Y	3x			40.3	model | log

Note

Similar to Detectron2, we haven't implemented the Scale-aware Training Scheme in section 4.2 of the paper.

Citation

@InProceedings{li2019scale,
  title={Scale-Aware Trident Networks for Object Detection},
  author={Li, Yanghao and Chen, Yuntao and Wang, Naiyan and Zhang, Zhaoxiang},
  journal={The International Conference on Computer Vision (ICCV)},
  year={2019}
}