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

17 KiB

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UPerNet

Unified Perceptual Parsing for Scene Understanding

Introduction

Official Repo

Code Snippet

Abstract

Humans recognize the visual world at multiple levels: we effortlessly categorize scenes and detect objects inside, while also identifying the textures and surfaces of the objects along with their different compositional parts. In this paper, we study a new task called Unified Perceptual Parsing, which requires the machine vision systems to recognize as many visual concepts as possible from a given image. A multi-task framework called UPerNet and a training strategy are developed to learn from heterogeneous image annotations. We benchmark our framework on Unified Perceptual Parsing and show that it is able to effectively segment a wide range of concepts from images. The trained networks are further applied to discover visual knowledge in natural scenes. Models are available at this https URL.

Citation

@inproceedings{xiao2018unified,
  title={Unified perceptual parsing for scene understanding},
  author={Xiao, Tete and Liu, Yingcheng and Zhou, Bolei and Jiang, Yuning and Sun, Jian},
  booktitle={Proceedings of the European Conference on Computer Vision (ECCV)},
  pages={418--434},
  year={2018}
}

Results and models

Cityscapes

Method	Backbone	Crop Size	Lr schd	Mem (GB)	Inf time (fps)	mIoU	mIoU(ms+flip)	config	download
UPerNet	R-18	512x1024	40000	4.8	4.47	75.39	77.0	config	model \|log
UPerNet	R-50	512x1024	40000	6.4	4.25	77.10	78.37	config	model \| log
UPerNet	R-101	512x1024	40000	7.4	3.79	78.69	80.11	config	model \| log
UPerNet	R-50	769x769	40000	7.2	1.76	77.98	79.70	config	model \| log
UPerNet	R-101	769x769	40000	8.4	1.56	79.03	80.77	config	model \| log
UPerNet	R-18	512x1024	80000	-	-	76.02	77.38	config	model \| log
UPerNet	R-50	512x1024	80000	-	-	78.19	79.19	config	model \| log
UPerNet	R-101	512x1024	80000	-	-	79.40	80.46	config	model \| log
UPerNet	R-50	769x769	80000	-	-	79.39	80.92	config	model \| log
UPerNet	R-101	769x769	80000	-	-	80.10	81.49	config	model \| log

ADE20K

Method	Backbone	Crop Size	Lr schd	Mem (GB)	Inf time (fps)	mIoU	mIoU(ms+flip)	config	download
UPerNet	R-18	512x512	80000	6.6	24.76	38.76	39.81	config	model \| log
UPerNet	R-50	512x512	80000	8.1	23.40	40.70	41.81	config	model \| log
UPerNet	R-101	512x512	80000	9.1	20.34	42.91	43.96	config	model \| log
UPerNet	R-18	512x512	160000	-	-	39.23	39.97	config	model \| log
UPerNet	R-50	512x512	160000	-	-	42.05	42.78	config	model \| log
UPerNet	R-101	512x512	160000	-	-	43.82	44.85	config	model \| log

Pascal VOC 2012 + Aug

Method	Backbone	Crop Size	Lr schd	Mem (GB)	Inf time (fps)	mIoU	mIoU(ms+flip)	config	download
UPerNet	R-18	512x512	20000	4.8	25.80	72.9	74.71	config	model \| log
UPerNet	R-50	512x512	20000	6.4	23.17	74.82	76.35	config	model \| log
UPerNet	R-101	512x512	20000	7.5	19.98	77.10	78.29	config	model \| log
UPerNet	R-18	512x512	40000	-	-	73.71	74.61	config	model \| log
UPerNet	R-50	512x512	40000	-	-	75.92	77.44	config	model \| log
UPerNet	R-101	512x512	40000	-	-	77.43	78.56	config	model \| log

17 KiB Raw Blame 히스토리