| # Double Heads | |
| > [Rethinking Classification and Localization for Object Detection](https://arxiv.org/abs/1904.06493) | |
| <!-- [ALGORITHM] --> | |
| ## Abstract | |
| Two head structures (i.e. fully connected head and convolution head) have been widely used in R-CNN based detectors for classification and localization tasks. However, there is a lack of understanding of how does these two head structures work for these two tasks. To address this issue, we perform a thorough analysis and find an interesting fact that the two head structures have opposite preferences towards the two tasks. Specifically, the fully connected head (fc-head) is more suitable for the classification task, while the convolution head (conv-head) is more suitable for the localization task. Furthermore, we examine the output feature maps of both heads and find that fc-head has more spatial sensitivity than conv-head. Thus, fc-head has more capability to distinguish a complete object from part of an object, but is not robust to regress the whole object. Based upon these findings, we propose a Double-Head method, which has a fully connected head focusing on classification and a convolution head for bounding box regression. Without bells and whistles, our method gains +3.5 and +2.8 AP on MS COCO dataset from Feature Pyramid Network (FPN) baselines with ResNet-50 and ResNet-101 backbones, respectively. | |
| <div align=center> | |
| <img src="https://user-images.githubusercontent.com/40661020/143879010-e30f654b-f93e-44b2-a186-c251fdca5bda.png"/> | |
| </div> | |
| ## Results and Models | |
| | Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | | |
| | :------: | :-----: | :-----: | :------: | :------------: | :----: | :-------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | | |
| | R-50-FPN | pytorch | 1x | 6.8 | 9.5 | 40.0 | [config](./dh-faster-rcnn_r50_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/double_heads/dh_faster_rcnn_r50_fpn_1x_coco/dh_faster_rcnn_r50_fpn_1x_coco_20200130-586b67df.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/double_heads/dh_faster_rcnn_r50_fpn_1x_coco/dh_faster_rcnn_r50_fpn_1x_coco_20200130_220238.log.json) | | |
| ## Citation | |
| ```latex | |
| @article{wu2019rethinking, | |
| title={Rethinking Classification and Localization for Object Detection}, | |
| author={Yue Wu and Yinpeng Chen and Lu Yuan and Zicheng Liu and Lijuan Wang and Hongzhi Li and Yun Fu}, | |
| year={2019}, | |
| eprint={1904.06493}, | |
| archivePrefix={arXiv}, | |
| primaryClass={cs.CV} | |
| } | |
| ``` | |