gc/wing-ops
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코드 이슈 풀 리퀘스트 Actions 위키 활동
6b9ed4e06e
wing-ops/prediction/image/mx15hdi/Detect/mmsegmentation/configs/dmnet/dmnet.yml
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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Collections:
- Name: DMNet
Metadata:
Training Data:
- Cityscapes
- ADE20K
Paper:
URL: https://openaccess.thecvf.com/content_ICCV_2019/papers/He_Dynamic_Multi-Scale_Filters_for_Semantic_Segmentation_ICCV_2019_paper.pdf
Title: Dynamic Multi-scale Filters for Semantic Segmentation
README: configs/dmnet/README.md
Code:
URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/dm_head.py#L93
Version: v0.17.0
Converted From:
Code: https://github.com/Junjun2016/DMNet
Models:
- Name: dmnet_r50-d8_512x1024_40k_cityscapes
In Collection: DMNet
Metadata:
backbone: R-50-D8
crop size: (512,1024)
lr schd: 40000
inference time (ms/im):
- value: 273.22
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,1024)
Training Memory (GB): 7.0
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 77.78
mIoU(ms+flip): 79.14
Config: configs/dmnet/dmnet_r50-d8_512x1024_40k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_512x1024_40k_cityscapes/dmnet_r50-d8_512x1024_40k_cityscapes_20201215_042326-615373cf.pth
- Name: dmnet_r101-d8_512x1024_40k_cityscapes
In Collection: DMNet
Metadata:
backbone: R-101-D8
crop size: (512,1024)
lr schd: 40000
inference time (ms/im):
- value: 393.7
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,1024)
Training Memory (GB): 10.6
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 78.37
mIoU(ms+flip): 79.72
Config: configs/dmnet/dmnet_r101-d8_512x1024_40k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_512x1024_40k_cityscapes/dmnet_r101-d8_512x1024_40k_cityscapes_20201215_043100-8291e976.pth
- Name: dmnet_r50-d8_769x769_40k_cityscapes
In Collection: DMNet
Metadata:
backbone: R-50-D8
crop size: (769,769)
lr schd: 40000
inference time (ms/im):
- value: 636.94
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (769,769)
Training Memory (GB): 7.9
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 78.49
mIoU(ms+flip): 80.27
Config: configs/dmnet/dmnet_r50-d8_769x769_40k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_769x769_40k_cityscapes/dmnet_r50-d8_769x769_40k_cityscapes_20201215_093706-e7f0e23e.pth
- Name: dmnet_r101-d8_769x769_40k_cityscapes
In Collection: DMNet
Metadata:
backbone: R-101-D8
crop size: (769,769)
lr schd: 40000
inference time (ms/im):
- value: 990.1
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (769,769)
Training Memory (GB): 12.0
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 77.62
mIoU(ms+flip): 78.94
Config: configs/dmnet/dmnet_r101-d8_769x769_40k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_769x769_40k_cityscapes/dmnet_r101-d8_769x769_40k_cityscapes_20201215_081348-a74261f6.pth
- Name: dmnet_r50-d8_512x1024_80k_cityscapes
In Collection: DMNet
Metadata:
backbone: R-50-D8
crop size: (512,1024)
lr schd: 80000
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.07
mIoU(ms+flip): 80.22
Config: configs/dmnet/dmnet_r50-d8_512x1024_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_512x1024_80k_cityscapes/dmnet_r50-d8_512x1024_80k_cityscapes_20201215_053728-3c8893b9.pth
- Name: dmnet_r101-d8_512x1024_80k_cityscapes
In Collection: DMNet
Metadata:
backbone: R-101-D8
crop size: (512,1024)
lr schd: 80000
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.64
mIoU(ms+flip): 80.67
Config: configs/dmnet/dmnet_r101-d8_512x1024_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_512x1024_80k_cityscapes/dmnet_r101-d8_512x1024_80k_cityscapes_20201215_031718-fa081cb8.pth
- Name: dmnet_r50-d8_769x769_80k_cityscapes
In Collection: DMNet
Metadata:
backbone: R-50-D8
crop size: (769,769)
lr schd: 80000
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.22
mIoU(ms+flip): 80.55
Config: configs/dmnet/dmnet_r50-d8_769x769_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_769x769_80k_cityscapes/dmnet_r50-d8_769x769_80k_cityscapes_20201215_034006-6060840e.pth
- Name: dmnet_r101-d8_769x769_80k_cityscapes
In Collection: DMNet
Metadata:
backbone: R-101-D8
crop size: (769,769)
lr schd: 80000
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.19
mIoU(ms+flip): 80.65
Config: configs/dmnet/dmnet_r101-d8_769x769_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_769x769_80k_cityscapes/dmnet_r101-d8_769x769_80k_cityscapes_20201215_082810-7f0de59a.pth
- Name: dmnet_r50-d8_512x512_80k_ade20k
In Collection: DMNet
Metadata:
backbone: R-50-D8
crop size: (512,512)
lr schd: 80000
inference time (ms/im):
- value: 47.73
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 9.4
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 42.37
mIoU(ms+flip): 43.62
Config: configs/dmnet/dmnet_r50-d8_512x512_80k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_512x512_80k_ade20k/dmnet_r50-d8_512x512_80k_ade20k_20201215_144744-f89092a6.pth
- Name: dmnet_r101-d8_512x512_80k_ade20k
In Collection: DMNet
Metadata:
backbone: R-101-D8
crop size: (512,512)
lr schd: 80000
inference time (ms/im):
- value: 72.05
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 13.0
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 45.34
mIoU(ms+flip): 46.13
Config: configs/dmnet/dmnet_r101-d8_512x512_80k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_512x512_80k_ade20k/dmnet_r101-d8_512x512_80k_ade20k_20201215_104812-bfa45311.pth
- Name: dmnet_r50-d8_512x512_160k_ade20k
In Collection: DMNet
Metadata:
backbone: R-50-D8
crop size: (512,512)
lr schd: 160000
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 43.15
mIoU(ms+flip): 44.17
Config: configs/dmnet/dmnet_r50-d8_512x512_160k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r50-d8_512x512_160k_ade20k/dmnet_r50-d8_512x512_160k_ade20k_20201215_115313-025ab3f9.pth
- Name: dmnet_r101-d8_512x512_160k_ade20k
In Collection: DMNet
Metadata:
backbone: R-101-D8
crop size: (512,512)
lr schd: 160000
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 45.42
mIoU(ms+flip): 46.76
Config: configs/dmnet/dmnet_r101-d8_512x512_160k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/dmnet/dmnet_r101-d8_512x512_160k_ade20k/dmnet_r101-d8_512x512_160k_ade20k_20201215_111145-a0bc02ef.pth
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