gc/wing-ops
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코드 이슈 풀 리퀘스트 Actions 위키 활동
6b9ed4e06e
wing-ops/prediction/image/mx15hdi/Detect/mmsegmentation/configs/vit/vit.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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Models:
- Name: upernet_vit-b16_mln_512x512_80k_ade20k
In Collection: UPerNet
Metadata:
backbone: ViT-B + MLN
crop size: (512,512)
lr schd: 80000
inference time (ms/im):
- value: 144.09
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 9.2
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 47.71
mIoU(ms+flip): 49.51
Config: configs/vit/upernet_vit-b16_mln_512x512_80k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_vit-b16_mln_512x512_80k_ade20k/upernet_vit-b16_mln_512x512_80k_ade20k_20210624_130547-0403cee1.pth
- Name: upernet_vit-b16_mln_512x512_160k_ade20k
In Collection: UPerNet
Metadata:
backbone: ViT-B + MLN
crop size: (512,512)
lr schd: 160000
inference time (ms/im):
- value: 131.93
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 9.2
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 46.75
mIoU(ms+flip): 48.46
Config: configs/vit/upernet_vit-b16_mln_512x512_160k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_vit-b16_mln_512x512_160k_ade20k/upernet_vit-b16_mln_512x512_160k_ade20k_20210624_130547-852fa768.pth
- Name: upernet_vit-b16_ln_mln_512x512_160k_ade20k
In Collection: UPerNet
Metadata:
backbone: ViT-B + LN + MLN
crop size: (512,512)
lr schd: 160000
inference time (ms/im):
- value: 146.63
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 9.21
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 47.73
mIoU(ms+flip): 49.95
Config: configs/vit/upernet_vit-b16_ln_mln_512x512_160k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_vit-b16_ln_mln_512x512_160k_ade20k/upernet_vit-b16_ln_mln_512x512_160k_ade20k_20210621_172828-f444c077.pth
- Name: upernet_deit-s16_512x512_80k_ade20k
In Collection: UPerNet
Metadata:
backbone: DeiT-S
crop size: (512,512)
lr schd: 80000
inference time (ms/im):
- value: 33.5
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 4.68
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 42.96
mIoU(ms+flip): 43.79
Config: configs/vit/upernet_deit-s16_512x512_80k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_deit-s16_512x512_80k_ade20k/upernet_deit-s16_512x512_80k_ade20k_20210624_095228-afc93ec2.pth
- Name: upernet_deit-s16_512x512_160k_ade20k
In Collection: UPerNet
Metadata:
backbone: DeiT-S
crop size: (512,512)
lr schd: 160000
inference time (ms/im):
- value: 34.26
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 4.68
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 42.87
mIoU(ms+flip): 43.79
Config: configs/vit/upernet_deit-s16_512x512_160k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_deit-s16_512x512_160k_ade20k/upernet_deit-s16_512x512_160k_ade20k_20210621_160903-5110d916.pth
- Name: upernet_deit-s16_mln_512x512_160k_ade20k
In Collection: UPerNet
Metadata:
backbone: DeiT-S + MLN
crop size: (512,512)
lr schd: 160000
inference time (ms/im):
- value: 89.45
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 5.69
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 43.82
mIoU(ms+flip): 45.07
Config: configs/vit/upernet_deit-s16_mln_512x512_160k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_deit-s16_mln_512x512_160k_ade20k/upernet_deit-s16_mln_512x512_160k_ade20k_20210621_161021-fb9a5dfb.pth
- Name: upernet_deit-s16_ln_mln_512x512_160k_ade20k
In Collection: UPerNet
Metadata:
backbone: DeiT-S + LN + MLN
crop size: (512,512)
lr schd: 160000
inference time (ms/im):
- value: 80.71
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 5.69
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 43.52
mIoU(ms+flip): 45.01
Config: configs/vit/upernet_deit-s16_ln_mln_512x512_160k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_deit-s16_ln_mln_512x512_160k_ade20k/upernet_deit-s16_ln_mln_512x512_160k_ade20k_20210621_161021-c0cd652f.pth
- Name: upernet_deit-b16_512x512_80k_ade20k
In Collection: UPerNet
Metadata:
backbone: DeiT-B
crop size: (512,512)
lr schd: 80000
inference time (ms/im):
- value: 103.2
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 7.75
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 45.24
mIoU(ms+flip): 46.73
Config: configs/vit/upernet_deit-b16_512x512_80k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_deit-b16_512x512_80k_ade20k/upernet_deit-b16_512x512_80k_ade20k_20210624_130529-1e090789.pth
- Name: upernet_deit-b16_512x512_160k_ade20k
In Collection: UPerNet
Metadata:
backbone: DeiT-B
crop size: (512,512)
lr schd: 160000
inference time (ms/im):
- value: 96.25
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 7.75
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 45.36
mIoU(ms+flip): 47.16
Config: configs/vit/upernet_deit-b16_512x512_160k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_deit-b16_512x512_160k_ade20k/upernet_deit-b16_512x512_160k_ade20k_20210621_180100-828705d7.pth
- Name: upernet_deit-b16_mln_512x512_160k_ade20k
In Collection: UPerNet
Metadata:
backbone: DeiT-B + MLN
crop size: (512,512)
lr schd: 160000
inference time (ms/im):
- value: 128.53
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 9.21
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 45.46
mIoU(ms+flip): 47.16
Config: configs/vit/upernet_deit-b16_mln_512x512_160k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_deit-b16_mln_512x512_160k_ade20k/upernet_deit-b16_mln_512x512_160k_ade20k_20210621_191949-4e1450f3.pth
- Name: upernet_deit-b16_ln_mln_512x512_160k_ade20k
In Collection: UPerNet
Metadata:
backbone: DeiT-B + LN + MLN
crop size: (512,512)
lr schd: 160000
inference time (ms/im):
- value: 129.03
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 9.21
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 45.37
mIoU(ms+flip): 47.23
Config: configs/vit/upernet_deit-b16_ln_mln_512x512_160k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/vit/upernet_deit-b16_ln_mln_512x512_160k_ade20k/upernet_deit-b16_ln_mln_512x512_160k_ade20k_20210623_153535-8a959c14.pth
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