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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BiSeNetV1

BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation

Introduction

Official Repo

Code Snippet

Abstract

Semantic segmentation requires both rich spatial information and sizeable receptive field. However, modern approaches usually compromise spatial resolution to achieve real-time inference speed, which leads to poor performance. In this paper, we address this dilemma with a novel Bilateral Segmentation Network (BiSeNet). We first design a Spatial Path with a small stride to preserve the spatial information and generate high-resolution features. Meanwhile, a Context Path with a fast downsampling strategy is employed to obtain sufficient receptive field. On top of the two paths, we introduce a new Feature Fusion Module to combine features efficiently. The proposed architecture makes a right balance between the speed and segmentation performance on Cityscapes, CamVid, and COCO-Stuff datasets. Specifically, for a 2048x1024 input, we achieve 68.4% Mean IOU on the Cityscapes test dataset with speed of 105 FPS on one NVIDIA Titan XP card, which is significantly faster than the existing methods with comparable performance.

Citation

@inproceedings{yu2018bisenet,
  title={Bisenet: Bilateral segmentation network for real-time semantic segmentation},
  author={Yu, Changqian and Wang, Jingbo and Peng, Chao and Gao, Changxin and Yu, Gang and Sang, Nong},
  booktitle={Proceedings of the European conference on computer vision (ECCV)},
  pages={325--341},
  year={2018}
}

Results and models

Cityscapes

Method	Backbone	Crop Size	Lr schd	Mem (GB)	Inf time (fps)	mIoU	mIoU(ms+flip)	config	download
BiSeNetV1 (No Pretrain)	R-18-D32	1024x1024	160000	5.69	31.77	74.44	77.05	config	model \| log
BiSeNetV1	R-18-D32	1024x1024	160000	5.69	31.77	74.37	76.91	config	model \| log
BiSeNetV1 (4x8)	R-18-D32	1024x1024	160000	11.17	31.77	75.16	77.24	config	model \| log
BiSeNetV1 (No Pretrain)	R-50-D32	1024x1024	160000	15.39	7.71	76.92	78.87	config	model \| log
BiSeNetV1	R-50-D32	1024x1024	160000	15.39	7.71	77.68	79.57	config	model \| log

COCO-Stuff 164k

Method	Backbone	Crop Size	Lr schd	Mem (GB)	Inf time (fps)	mIoU	mIoU(ms+flip)	config	download
BiSeNetV1 (No Pretrain)	R-18-D32	512x512	160000	-	-	25.45	26.15	config	model \| log
BiSeNetV1	R-18-D32	512x512	160000	6.33	74.24	28.55	29.26	config	model \| log
BiSeNetV1 (No Pretrain)	R-50-D32	512x512	160000	-	-	29.82	30.33	config	model \| log
BiSeNetV1	R-50-D32	512x512	160000	9.28	32.60	34.88	35.37	config	model \| log
BiSeNetV1 (No Pretrain)	R-101-D32	512x512	160000	-	-	31.14	31.76	config	model \| log
BiSeNetV1	R-101-D32	512x512	160000	10.36	25.25	37.38	37.99	config	model \| log

Note:

4x8: Using 4 GPUs with 8 samples per GPU in training.
For BiSeNetV1 on Cityscapes dataset, default setting is 4 GPUs with 4 samples per GPU in training.
No Pretrain means the model is trained from scratch.

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