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.
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.
@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}
}