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

Raw Blame 히스토리

ANN

Asymmetric Non-local Neural Networks for Semantic Segmentation

Introduction

Official Repo

Code Snippet

Abstract

The non-local module works as a particularly useful technique for semantic segmentation while criticized for its prohibitive computation and GPU memory occupation. In this paper, we present Asymmetric Non-local Neural Network to semantic segmentation, which has two prominent components: Asymmetric Pyramid Non-local Block (APNB) and Asymmetric Fusion Non-local Block (AFNB). APNB leverages a pyramid sampling module into the non-local block to largely reduce the computation and memory consumption without sacrificing the performance. AFNB is adapted from APNB to fuse the features of different levels under a sufficient consideration of long range dependencies and thus considerably improves the performance. Extensive experiments on semantic segmentation benchmarks demonstrate the effectiveness and efficiency of our work. In particular, we report the state-of-the-art performance of 81.3 mIoU on the Cityscapes test set. For a 256x128 input, APNB is around 6 times faster than a non-local block on GPU while 28 times smaller in GPU running memory occupation. Code is available at: this https URL.

Citation

@inproceedings{zhu2019asymmetric,
  title={Asymmetric non-local neural networks for semantic segmentation},
  author={Zhu, Zhen and Xu, Mengde and Bai, Song and Huang, Tengteng and Bai, Xiang},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={593--602},
  year={2019}
}

Results and models

Cityscapes

Method	Backbone	Crop Size	Lr schd	Mem (GB)	Inf time (fps)	mIoU	mIoU(ms+flip)	config	download
ANN	R-50-D8	512x1024	40000	6	3.71	77.40	78.57	config	model \| log
ANN	R-101-D8	512x1024	40000	9.5	2.55	76.55	78.85	config	model \| log
ANN	R-50-D8	769x769	40000	6.8	1.70	78.89	80.46	config	model \| log
ANN	R-101-D8	769x769	40000	10.7	1.15	79.32	80.94	config	model \| log
ANN	R-50-D8	512x1024	80000	-	-	77.34	78.65	config	model \| log
ANN	R-101-D8	512x1024	80000	-	-	77.14	78.81	config	model \| log
ANN	R-50-D8	769x769	80000	-	-	78.88	80.57	config	model \| log
ANN	R-101-D8	769x769	80000	-	-	78.80	80.34	config	model \| log

ADE20K

Method	Backbone	Crop Size	Lr schd	Mem (GB)	Inf time (fps)	mIoU	mIoU(ms+flip)	config	download
ANN	R-50-D8	512x512	80000	9.1	21.01	41.01	42.30	config	model \| log
ANN	R-101-D8	512x512	80000	12.5	14.12	42.94	44.18	config	model \| log
ANN	R-50-D8	512x512	160000	-	-	41.74	42.62	config	model \| log
ANN	R-101-D8	512x512	160000	-	-	42.94	44.06	config	model \| log

Pascal VOC 2012 + Aug

Method	Backbone	Crop Size	Lr schd	Mem (GB)	Inf time (fps)	mIoU	mIoU(ms+flip)	config	download
ANN	R-50-D8	512x512	20000	6	20.92	74.86	76.13	config	model \| log
ANN	R-101-D8	512x512	20000	9.5	13.94	77.47	78.70	config	model \| log
ANN	R-50-D8	512x512	40000	-	-	76.56	77.51	config	model \| log
ANN	R-101-D8	512x512	40000	-	-	76.70	78.06	config	model \| log

14 KiB Raw Blame 히스토리

ANN