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
..
fast_scnn_lr0.12_8x4_160k_cityscapes.py	feat(prediction): 이미지 분석 서버 Docker 패키징 + DB 코드 제거	2026-03-10 18:37:36 +09:00
fastscnn.yml	feat(prediction): 이미지 분석 서버 Docker 패키징 + DB 코드 제거	2026-03-10 18:37:36 +09:00
README.md	feat(prediction): 이미지 분석 서버 Docker 패키징 + DB 코드 제거	2026-03-10 18:37:36 +09:00

README.md

Fast-SCNN

Fast-SCNN for Semantic Segmentation

Introduction

Official Repo

Code Snippet

Abstract

The encoder-decoder framework is state-of-the-art for offline semantic image segmentation. Since the rise in autonomous systems, real-time computation is increasingly desirable. In this paper, we introduce fast segmentation convolutional neural network (Fast-SCNN), an above real-time semantic segmentation model on high resolution image data (1024x2048px) suited to efficient computation on embedded devices with low memory. Building on existing two-branch methods for fast segmentation, we introduce our `learning to downsample' module which computes low-level features for multiple resolution branches simultaneously. Our network combines spatial detail at high resolution with deep features extracted at lower resolution, yielding an accuracy of 68.0% mean intersection over union at 123.5 frames per second on Cityscapes. We also show that large scale pre-training is unnecessary. We thoroughly validate our metric in experiments with ImageNet pre-training and the coarse labeled data of Cityscapes. Finally, we show even faster computation with competitive results on subsampled inputs, without any network modifications.

Citation

@article{poudel2019fast,
  title={Fast-scnn: Fast semantic segmentation network},
  author={Poudel, Rudra PK and Liwicki, Stephan and Cipolla, Roberto},
  journal={arXiv preprint arXiv:1902.04502},
  year={2019}
}

Results and models

Cityscapes

Method	Backbone	Crop Size	Lr schd	Mem (GB)	Inf time (fps)	mIoU	mIoU(ms+flip)	config	download
FastSCNN	FastSCNN	512x1024	160000	3.3	56.45	70.96	72.65	config	model \| log