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DetectoRS

Introduction

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
DetectoRS HTC + ResNet-101 20e 19.6 50.5 43.9 config model | log

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