code_test/configs/panoptic_fpn

• Panoptic feature pyramid networks
• • Introduction
  • Dataset
  • Results and Models

Panoptic feature pyramid networks

Introduction

The base method for panoptic segmentation task.

@inproceedings{kirillov2018panopticfpn,
  author = {
    Alexander Kirillov,
    Ross Girshick,
    Kaiming He,
    Piotr Dollar,
  },
  title = {Panoptic Feature Pyramid Networks},
  booktitle = {Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2019}
}

Dataset

PanopticFPN requires COCO and COCO-panoptic dataset for training and evaluation. 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
│   │   │   ├── panoptic_train2017.json
│   │   │   ├── panoptic_train2017
│   │   │   ├── panoptic_val2017.json
│   │   │   ├── panoptic_val2017
│   │   ├── train2017
│   │   ├── val2017
│   │   ├── test2017

Results and Models

Backbone	style	Lr schd	Mem (GB)	Inf time (fps)	PQ	SQ	RQ	PQ_th	SQ_th	RQ_th	PQ_st	SQ_st	RQ_st	Config	Download
R-50-FPN	pytorch	1x	4.7		40.2	77.8	49.3	47.8	80.9	57.5	28.9	73.1	37.0	config	model | log
R-50-FPN	pytorch	3x	-	-	42.5	78.1	51.7	50.3	81.5	60.3	30.7	73.0	38.8	config	model | log
R-101-FPN	pytorch	1x	6.7		42.2	78.3	51.4	50.1	81.4	59.9	30.3	73.6	38.5	config	model | log
R-101-FPN	pytorch	3x	-	-	44.1	78.9	53.6	52.1	81.7	62.3	32.0	74.6	40.3	config	model | log