SCNet

Introduction

We provide the code for reproducing experiment results of SCNet.

@inproceedings{vu2019cascade,
  title={SCNet: Training Inference Sample Consistency for Instance Segmentation},
  author={Vu, Thang and Haeyong, Kang and Yoo, Chang D},
  booktitle={AAAI},
  year={2021}
}

Dataset

SCNet 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

The results on COCO 2017val are shown in the below table. (results on test-dev are usually slightly higher than val)

Backbone	Style	Lr schd	Mem (GB)	Inf speed (fps)	box AP	mask AP	TTA box AP	TTA mask AP	Config	Download
R-50-FPN	pytorch	1x	7.0	6.2	43.5	39.2	44.8	40.9	config	model \| log
R-50-FPN	pytorch	20e	7.0	6.2	44.5	40.0	45.8	41.5	config	model \| log
R-101-FPN	pytorch	20e	8.9	5.8	45.8	40.9	47.3	42.7	config	model \| log
X-101-64x4d-FPN	pytorch	20e	13.2	4.9	47.5	42.3	48.9	44.0	config	model \| log

Notes

Training hyper-parameters are identical to those of HTC.
TTA means Test Time Augmentation, which applies horizonal flip and multi-scale testing. Refer to config.