wing-ops/prediction/image/mx15hdi/Detect/mmsegmentation/configs/point_rend
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
..
point_rend.yml feat(prediction): 이미지 분석 서버 Docker 패키징 + DB 코드 제거 2026-03-10 18:37:36 +09:00
pointrend_r50_512x512_160k_ade20k.py feat(prediction): 이미지 분석 서버 Docker 패키징 + DB 코드 제거 2026-03-10 18:37:36 +09:00
pointrend_r50_512x1024_80k_cityscapes.py feat(prediction): 이미지 분석 서버 Docker 패키징 + DB 코드 제거 2026-03-10 18:37:36 +09:00
pointrend_r101_512x512_160k_ade20k.py feat(prediction): 이미지 분석 서버 Docker 패키징 + DB 코드 제거 2026-03-10 18:37:36 +09:00
pointrend_r101_512x1024_80k_cityscapes.py 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

PointRend

PointRend: Image Segmentation as Rendering

Introduction

Official Repo

Code Snippet

Abstract

We present a new method for efficient high-quality image segmentation of objects and scenes. By analogizing classical computer graphics methods for efficient rendering with over- and undersampling challenges faced in pixel labeling tasks, we develop a unique perspective of image segmentation as a rendering problem. From this vantage, we present the PointRend (Point-based Rendering) neural network module: a module that performs point-based segmentation predictions at adaptively selected locations based on an iterative subdivision algorithm. PointRend can be flexibly applied to both instance and semantic segmentation tasks by building on top of existing state-of-the-art models. While many concrete implementations of the general idea are possible, we show that a simple design already achieves excellent results. Qualitatively, PointRend outputs crisp object boundaries in regions that are over-smoothed by previous methods. Quantitatively, PointRend yields significant gains on COCO and Cityscapes, for both instance and semantic segmentation. PointRend's efficiency enables output resolutions that are otherwise impractical in terms of memory or computation compared to existing approaches. Code has been made available at this https URL.

Citation

@inproceedings{kirillov2020pointrend,
  title={Pointrend: Image segmentation as rendering},
  author={Kirillov, Alexander and Wu, Yuxin and He, Kaiming and Girshick, Ross},
  booktitle={Proceedings of the IEEE/CVF conference on computer vision and pattern recognition},
  pages={9799--9808},
  year={2020}
}

Results and models

Cityscapes

Method Backbone Crop Size Lr schd Mem (GB) Inf time (fps) mIoU mIoU(ms+flip) config download
PointRend R-50 512x1024 80000 3.1 8.48 76.47 78.13 config model | log
PointRend R-101 512x1024 80000 4.2 7.00 78.30 79.97 config model | log

ADE20K

Method Backbone Crop Size Lr schd Mem (GB) Inf time (fps) mIoU mIoU(ms+flip) config download
PointRend R-50 512x512 160000 5.1 17.31 37.64 39.17 config model | log
PointRend R-101 512x512 160000 6.1 15.50 40.02 41.60 config model | log