OpenMMLab Detection Toolbox and Benchmark
https://mmdetection.readthedocs.io/
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Guangchen Lin
9874180a01
|
3 years ago | |
|---|---|---|
| .. | ||
| README.md | ||
| metafile.yml | ||
| panoptic_fpn_r50_fpn_1x_coco.py | 3 years ago | |
| panoptic_fpn_r50_fpn_mstrain_3x_coco.py | ||
| panoptic_fpn_r101_fpn_1x_coco.py | ||
| panoptic_fpn_r101_fpn_mstrain_3x_coco.py | ||
README.md
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 |