DetectoRS

Introduction

[ALGORITHM]

We provide the config files for DetectoRS: Detecting Objects with Recursive Feature Pyramid and Switchable Atrous Convolution.

@article{qiao2020detectors,
  title={DetectoRS: Detecting Objects with Recursive Feature Pyramid and Switchable Atrous Convolution},
  author={Qiao, Siyuan and Chen, Liang-Chieh and Yuille, Alan},
  journal={arXiv preprint arXiv:2006.02334},
  year={2020}
}

Dataset

DetectoRS requires COCO and COCO-stuff dataset for training. You need to download and extract it in the COCO dataset path. The directory should be like this.

mmdetection
├── mmdet
├── tools
├── configs
├── data
│   ├── coco
│   │   ├── annotations
│   │   ├── train2017
│   │   ├── val2017
│   │   ├── test2017
|   |   ├── stuffthingmaps

Results and Models

DetectoRS includes two major components:

Recursive Feature Pyramid (RFP).
Switchable Atrous Convolution (SAC).

They can be used independently. Combining them together results in DetectoRS. The results on COCO 2017 val are shown in the below table.

Method	Detector	Lr schd	Mem (GB)	Inf time (fps)	box AP	mask AP	Config	Download
RFP	Cascade + ResNet-50	1x	7.5	-	44.8		config	model \| log
SAC	Cascade + ResNet-50	1x	5.6	-	45.0		config	model \| log
DetectoRS	Cascade + ResNet-50	1x	9.9	-	47.4		config	model \| log
RFP	HTC + ResNet-50	1x	11.2	-	46.6	40.9	config	model \| log
SAC	HTC + ResNet-50	1x	9.3	-	46.4	40.9	config	model \| log
DetectoRS	HTC + ResNet-50	1x	13.6	-	49.1	42.6	config	model \| log

Note: This is a re-implementation based on MMDetection-V2. The original implementation is based on MMDetection-V1.