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

14 KiB

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ISANet

Interlaced Sparse Self-Attention for Semantic Segmentation

Introduction

Official Repo

Code Snippet

Abstract

In this paper, we present a so-called interlaced sparse self-attention approach to improve the efficiency of the \emph{self-attention} mechanism for semantic segmentation. The main idea is that we factorize the dense affinity matrix as the product of two sparse affinity matrices. There are two successive attention modules each estimating a sparse affinity matrix. The first attention module is used to estimate the affinities within a subset of positions that have long spatial interval distances and the second attention module is used to estimate the affinities within a subset of positions that have short spatial interval distances. These two attention modules are designed so that each position is able to receive the information from all the other positions. In contrast to the original self-attention module, our approach decreases the computation and memory complexity substantially especially when processing high-resolution feature maps. We empirically verify the effectiveness of our approach on six challenging semantic segmentation benchmarks.

Citation

@article{huang2019isa,
  title={Interlaced Sparse Self-Attention for Semantic Segmentation},
  author={Huang, Lang and Yuan, Yuhui and Guo, Jianyuan and Zhang, Chao and Chen, Xilin and Wang, Jingdong},
  journal={arXiv preprint arXiv:1907.12273},
  year={2019}
}

The technical report above is also presented at:

@article{yuan2021ocnet,
  title={OCNet: Object Context for Semantic Segmentation},
  author={Yuan, Yuhui and Huang, Lang and Guo, Jianyuan and Zhang, Chao and Chen, Xilin and Wang, Jingdong},
  journal={International Journal of Computer Vision},
  pages={1--24},
  year={2021},
  publisher={Springer}
}

Results and models

Cityscapes

Method	Backbone	Crop Size	Lr schd	Mem (GB)	Inf time (fps)	mIoU	mIoU(ms+flip)	config	download
ISANet	R-50-D8	512x1024	40000	5.869	2.91	78.49	79.44	config	model \| log
ISANet	R-50-D8	512x1024	80000	5.869	2.91	78.68	80.25	config	model \| log
ISANet	R-50-D8	769x769	40000	6.759	1.54	78.70	80.28	config	model \| log
ISANet	R-50-D8	769x769	80000	6.759	1.54	79.29	80.53	config	model \| log
ISANet	R-101-D8	512x1024	40000	9.425	2.35	79.58	81.05	config	model \| log
ISANet	R-101-D8	512x1024	80000	9.425	2.35	80.32	81.58	config	model \| log
ISANet	R-101-D8	769x769	40000	10.815	0.92	79.68	80.95	config	model \| log
ISANet	R-101-D8	769x769	80000	10.815	0.92	80.61	81.59	config	model \| log

ADE20K

Method	Backbone	Crop Size	Lr schd	Mem (GB)	Inf time (fps)	mIoU	mIoU(ms+flip)	config	download
ISANet	R-50-D8	512x512	80000	9.0	22.55	41.12	42.35	config	model \| log
ISANet	R-50-D8	512x512	160000	9.0	22.55	42.59	43.07	config	model \| log
ISANet	R-101-D8	512x512	80000	12.562	10.56	43.51	44.38	config	model \| log
ISANet	R-101-D8	512x512	160000	12.562	10.56	43.80	45.4	config	model \| log

Pascal VOC 2012 + Aug

Method	Backbone	Crop Size	Lr schd	Mem (GB)	Inf time (fps)	mIoU	mIoU(ms+flip)	config	download
ISANet	R-50-D8	512x512	20000	5.9	23.08	76.78	77.79	config	model \| log
ISANet	R-50-D8	512x512	40000	5.9	23.08	76.20	77.22	config	model \| log
ISANet	R-101-D8	512x512	20000	9.465	7.42	78.46	79.16	config	model \| log
ISANet	R-101-D8	512x512	40000	9.465	7.42	78.12	79.04	config	model \| log

14 KiB Raw Blame 히스토리