jeonghyo.k 3946ff6a25 feat(prediction): 이미지 분석 서버 Docker 패키징 + DB 코드 제거

- prediction/image/ FastAPI 서버 Docker 환경 구성
  - Dockerfile: PyTorch 2.1 + CUDA 12.1 기반 GPU 이미지
  - docker-compose.yml: GPU 할당 + 데이터 볼륨 마운트
  - requirements.txt: 서버 의존성 목록
  - .env.example: 환경변수 템플릿
  - DOCKER_USAGE.md: 빌드/실행/API 사용법 문서
  - Dockerfile에 .dockerignore 제외 폴더 mkdir -p 추가
- .gitignore: prediction/image 결과물 및 모델 가중치(.pth) 제외 추가
- dbInsert_csv.py, dbInsert_shp.py 삭제 (미사용 DB 로직)
- api.py: dbInsert import 및 주석 처리된 DB 호출 코드 제거
- aerialRouter.ts: req.params 타입 오류 수정

2026-03-10 18:37:36 +09:00

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PSANet

PSANet: Point-wise Spatial Attention Network for Scene Parsing

Introduction

Official Repo

Code Snippet

Abstract

We notice information flow in convolutional neural networksis restricted inside local neighborhood regions due to the physical de-sign of convolutional filters, which limits the overall understanding ofcomplex scenes. In this paper, we propose thepoint-wise spatial atten-tion network(PSANet) to relax the local neighborhood constraint. Eachposition on the feature map is connected to all the other ones througha self-adaptively learned attention mask. Moreover, information propa-gation in bi-direction for scene parsing is enabled. Information at otherpositions can be collected to help the prediction of the current positionand vice versa, information at the current position can be distributedto assist the prediction of other ones. Our proposed approach achievestop performance on various competitive scene parsing datasets, includ-ing ADE20K, PASCAL VOC 2012 and Cityscapes, demonstrating itseffectiveness and generality.

Citation

@inproceedings{zhao2018psanet,
  title={Psanet: Point-wise spatial attention network for scene parsing},
  author={Zhao, Hengshuang and Zhang, Yi and Liu, Shu and Shi, Jianping and Change Loy, Chen and Lin, Dahua and Jia, Jiaya},
  booktitle={Proceedings of the European Conference on Computer Vision (ECCV)},
  pages={267--283},
  year={2018}
}

Results and models

Cityscapes

Method	Backbone	Crop Size	Lr schd	Mem (GB)	Inf time (fps)	mIoU	mIoU(ms+flip)	config	download
PSANet	R-50-D8	512x1024	40000	7	3.17	77.63	79.04	config	model \| log
PSANet	R-101-D8	512x1024	40000	10.5	2.20	79.14	80.19	config	model \| log
PSANet	R-50-D8	769x769	40000	7.9	1.40	77.99	79.64	config	model \| log
PSANet	R-101-D8	769x769	40000	11.9	0.98	78.43	80.26	config	model \| log
PSANet	R-50-D8	512x1024	80000	-	-	77.24	78.69	config	model \| log
PSANet	R-101-D8	512x1024	80000	-	-	79.31	80.53	config	model \| log
PSANet	R-50-D8	769x769	80000	-	-	79.31	80.91	config	model \| log
PSANet	R-101-D8	769x769	80000	-	-	79.69	80.89	config	model \| log

ADE20K

Method	Backbone	Crop Size	Lr schd	Mem (GB)	Inf time (fps)	mIoU	mIoU(ms+flip)	config	download
PSANet	R-50-D8	512x512	80000	9	18.91	41.14	41.91	config	model \| log
PSANet	R-101-D8	512x512	80000	12.5	13.13	43.80	44.75	config	model \| log
PSANet	R-50-D8	512x512	160000	-	-	41.67	42.95	config	model \| log
PSANet	R-101-D8	512x512	160000	-	-	43.74	45.38	config	model \| log

Pascal VOC 2012 + Aug

Method	Backbone	Crop Size	Lr schd	Mem (GB)	Inf time (fps)	mIoU	mIoU(ms+flip)	config	download
PSANet	R-50-D8	512x512	20000	6.9	18.24	76.39	77.34	config	model \| log
PSANet	R-101-D8	512x512	20000	10.4	12.63	77.91	79.30	config	model \| log
PSANet	R-50-D8	512x512	40000	-	-	76.30	77.35	config	model \| log
PSANet	R-101-D8	512x512	40000	-	-	77.73	79.05	config	model \| log

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