gc/wing-ops
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코드 이슈 풀 리퀘스트 Actions 위키 활동
734ebeeaab
wing-ops/prediction/image/mx15hdi/Detect/mmsegmentation/configs/isanet/isanet.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: ISANet
Metadata:
Training Data:
- Cityscapes
- ADE20K
- Pascal VOC 2012 + Aug
Paper:
URL: https://arxiv.org/abs/1907.12273
Title: Interlaced Sparse Self-Attention for Semantic Segmentation
README: configs/isanet/README.md
Code:
URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/decode_heads/isa_head.py#L58
Version: v0.18.0
Converted From:
Code: https://github.com/openseg-group/openseg.pytorch
Models:
- Name: isanet_r50-d8_512x1024_40k_cityscapes
In Collection: ISANet
Metadata:
backbone: R-50-D8
crop size: (512,1024)
lr schd: 40000
inference time (ms/im):
- value: 343.64
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,1024)
Training Memory (GB): 5.869
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 78.49
mIoU(ms+flip): 79.44
Config: configs/isanet/isanet_r50-d8_512x1024_40k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r50-d8_512x1024_40k_cityscapes/isanet_r50-d8_512x1024_40k_cityscapes_20210901_054739-981bd763.pth
- Name: isanet_r50-d8_512x1024_80k_cityscapes
In Collection: ISANet
Metadata:
backbone: R-50-D8
crop size: (512,1024)
lr schd: 80000
inference time (ms/im):
- value: 343.64
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,1024)
Training Memory (GB): 5.869
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 78.68
mIoU(ms+flip): 80.25
Config: configs/isanet/isanet_r50-d8_512x1024_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r50-d8_512x1024_80k_cityscapes/isanet_r50-d8_512x1024_80k_cityscapes_20210901_074202-89384497.pth
- Name: isanet_r50-d8_769x769_40k_cityscapes
In Collection: ISANet
Metadata:
backbone: R-50-D8
crop size: (769,769)
lr schd: 40000
inference time (ms/im):
- value: 649.35
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (769,769)
Training Memory (GB): 6.759
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 78.7
mIoU(ms+flip): 80.28
Config: configs/isanet/isanet_r50-d8_769x769_40k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r50-d8_769x769_40k_cityscapes/isanet_r50-d8_769x769_40k_cityscapes_20210903_050200-4ae7e65b.pth
- Name: isanet_r50-d8_769x769_80k_cityscapes
In Collection: ISANet
Metadata:
backbone: R-50-D8
crop size: (769,769)
lr schd: 80000
inference time (ms/im):
- value: 649.35
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (769,769)
Training Memory (GB): 6.759
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.29
mIoU(ms+flip): 80.53
Config: configs/isanet/isanet_r50-d8_769x769_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r50-d8_769x769_80k_cityscapes/isanet_r50-d8_769x769_80k_cityscapes_20210903_101126-99b54519.pth
- Name: isanet_r101-d8_512x1024_40k_cityscapes
In Collection: ISANet
Metadata:
backbone: R-101-D8
crop size: (512,1024)
lr schd: 40000
inference time (ms/im):
- value: 425.53
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,1024)
Training Memory (GB): 9.425
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.58
mIoU(ms+flip): 81.05
Config: configs/isanet/isanet_r101-d8_512x1024_40k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r101-d8_512x1024_40k_cityscapes/isanet_r101-d8_512x1024_40k_cityscapes_20210901_145553-293e6bd6.pth
- Name: isanet_r101-d8_512x1024_80k_cityscapes
In Collection: ISANet
Metadata:
backbone: R-101-D8
crop size: (512,1024)
lr schd: 80000
inference time (ms/im):
- value: 425.53
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,1024)
Training Memory (GB): 9.425
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 80.32
mIoU(ms+flip): 81.58
Config: configs/isanet/isanet_r101-d8_512x1024_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r101-d8_512x1024_80k_cityscapes/isanet_r101-d8_512x1024_80k_cityscapes_20210901_145243-5b99c9b2.pth
- Name: isanet_r101-d8_769x769_40k_cityscapes
In Collection: ISANet
Metadata:
backbone: R-101-D8
crop size: (769,769)
lr schd: 40000
inference time (ms/im):
- value: 1086.96
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (769,769)
Training Memory (GB): 10.815
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.68
mIoU(ms+flip): 80.95
Config: configs/isanet/isanet_r101-d8_769x769_40k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r101-d8_769x769_40k_cityscapes/isanet_r101-d8_769x769_40k_cityscapes_20210903_111320-509e7224.pth
- Name: isanet_r101-d8_769x769_80k_cityscapes
In Collection: ISANet
Metadata:
backbone: R-101-D8
crop size: (769,769)
lr schd: 80000
inference time (ms/im):
- value: 1086.96
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (769,769)
Training Memory (GB): 10.815
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 80.61
mIoU(ms+flip): 81.59
Config: configs/isanet/isanet_r101-d8_769x769_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r101-d8_769x769_80k_cityscapes/isanet_r101-d8_769x769_80k_cityscapes_20210903_111319-24f71dfa.pth
- Name: isanet_r50-d8_512x512_80k_ade20k
In Collection: ISANet
Metadata:
backbone: R-50-D8
crop size: (512,512)
lr schd: 80000
inference time (ms/im):
- value: 44.35
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 9.0
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 41.12
mIoU(ms+flip): 42.35
Config: configs/isanet/isanet_r50-d8_512x512_80k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r50-d8_512x512_80k_ade20k/isanet_r50-d8_512x512_80k_ade20k_20210903_124557-6ed83a0c.pth
- Name: isanet_r50-d8_512x512_160k_ade20k
In Collection: ISANet
Metadata:
backbone: R-50-D8
crop size: (512,512)
lr schd: 160000
inference time (ms/im):
- value: 44.35
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 9.0
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 42.59
mIoU(ms+flip): 43.07
Config: configs/isanet/isanet_r50-d8_512x512_160k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r50-d8_512x512_160k_ade20k/isanet_r50-d8_512x512_160k_ade20k_20210903_104850-f752d0a3.pth
- Name: isanet_r101-d8_512x512_80k_ade20k
In Collection: ISANet
Metadata:
backbone: R-101-D8
crop size: (512,512)
lr schd: 80000
inference time (ms/im):
- value: 94.7
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 12.562
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 43.51
mIoU(ms+flip): 44.38
Config: configs/isanet/isanet_r101-d8_512x512_80k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r101-d8_512x512_80k_ade20k/isanet_r101-d8_512x512_80k_ade20k_20210903_162056-68b235c2.pth
- Name: isanet_r101-d8_512x512_160k_ade20k
In Collection: ISANet
Metadata:
backbone: R-101-D8
crop size: (512,512)
lr schd: 160000
inference time (ms/im):
- value: 94.7
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 12.562
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 43.8
mIoU(ms+flip): 45.4
Config: configs/isanet/isanet_r101-d8_512x512_160k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r101-d8_512x512_160k_ade20k/isanet_r101-d8_512x512_160k_ade20k_20210903_211431-a7879dcd.pth
- Name: isanet_r50-d8_512x512_20k_voc12aug
In Collection: ISANet
Metadata:
backbone: R-50-D8
crop size: (512,512)
lr schd: 20000
inference time (ms/im):
- value: 43.33
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 5.9
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 76.78
mIoU(ms+flip): 77.79
Config: configs/isanet/isanet_r50-d8_512x512_20k_voc12aug.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r50-d8_512x512_20k_voc12aug/isanet_r50-d8_512x512_20k_voc12aug_20210901_164838-79d59b80.pth
- Name: isanet_r50-d8_512x512_40k_voc12aug
In Collection: ISANet
Metadata:
backbone: R-50-D8
crop size: (512,512)
lr schd: 40000
inference time (ms/im):
- value: 43.33
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 5.9
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 76.2
mIoU(ms+flip): 77.22
Config: configs/isanet/isanet_r50-d8_512x512_40k_voc12aug.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r50-d8_512x512_40k_voc12aug/isanet_r50-d8_512x512_40k_voc12aug_20210901_151349-7d08a54e.pth
- Name: isanet_r101-d8_512x512_20k_voc12aug
In Collection: ISANet
Metadata:
backbone: R-101-D8
crop size: (512,512)
lr schd: 20000
inference time (ms/im):
- value: 134.77
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 9.465
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 78.46
mIoU(ms+flip): 79.16
Config: configs/isanet/isanet_r101-d8_512x512_20k_voc12aug.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r101-d8_512x512_20k_voc12aug/isanet_r101-d8_512x512_20k_voc12aug_20210901_115805-3ccbf355.pth
- Name: isanet_r101-d8_512x512_40k_voc12aug
In Collection: ISANet
Metadata:
backbone: R-101-D8
crop size: (512,512)
lr schd: 40000
inference time (ms/im):
- value: 134.77
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 9.465
Results:
- Task: Semantic Segmentation
Dataset: Pascal VOC 2012 + Aug
Metrics:
mIoU: 78.12
mIoU(ms+flip): 79.04
Config: configs/isanet/isanet_r101-d8_512x512_40k_voc12aug.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/isanet/isanet_r101-d8_512x512_40k_voc12aug/isanet_r101-d8_512x512_40k_voc12aug_20210901_145814-bc71233b.pth
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