jeonghyo.k
3946ff6a25
- 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
414 lines
13 KiB
YAML
414 lines
13 KiB
YAML
Collections:
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- Name: UPerNet
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Metadata:
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Training Data:
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- Cityscapes
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- ADE20K
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- Pascal VOC 2012 + Aug
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Paper:
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URL: https://arxiv.org/pdf/1807.10221.pdf
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Title: Unified Perceptual Parsing for Scene Understanding
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README: configs/upernet/README.md
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Code:
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URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.17.0/mmseg/models/decode_heads/uper_head.py#L13
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Version: v0.17.0
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Converted From:
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Code: https://github.com/CSAILVision/unifiedparsing
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Models:
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- Name: upernet_r18_512x1024_40k_cityscapes
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In Collection: UPerNet
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Metadata:
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backbone: R-18
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crop size: (512,1024)
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lr schd: 40000
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inference time (ms/im):
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- value: 223.71
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hardware: V100
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backend: PyTorch
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batch size: 1
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mode: FP32
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resolution: (512,1024)
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Training Memory (GB): 4.8
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Results:
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- Task: Semantic Segmentation
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Dataset: Cityscapes
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Metrics:
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mIoU: 75.39
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mIoU(ms+flip): 77.0
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Config: configs/upernet/upernet_r18_512x1024_40k_cityscapes.py
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r18_512x1024_40k_cityscapes/upernet_r18_512x1024_40k_cityscapes_20220615_113231-12ee861d.pth
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- Name: upernet_r50_512x1024_40k_cityscapes
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In Collection: UPerNet
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Metadata:
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backbone: R-50
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crop size: (512,1024)
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lr schd: 40000
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inference time (ms/im):
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- value: 235.29
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hardware: V100
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backend: PyTorch
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batch size: 1
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mode: FP32
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resolution: (512,1024)
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Training Memory (GB): 6.4
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Results:
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- Task: Semantic Segmentation
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Dataset: Cityscapes
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Metrics:
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mIoU: 77.1
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mIoU(ms+flip): 78.37
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Config: configs/upernet/upernet_r50_512x1024_40k_cityscapes.py
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x1024_40k_cityscapes/upernet_r50_512x1024_40k_cityscapes_20200605_094827-aa54cb54.pth
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- Name: upernet_r101_512x1024_40k_cityscapes
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In Collection: UPerNet
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Metadata:
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backbone: R-101
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crop size: (512,1024)
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lr schd: 40000
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inference time (ms/im):
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- value: 263.85
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hardware: V100
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backend: PyTorch
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batch size: 1
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mode: FP32
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resolution: (512,1024)
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Training Memory (GB): 7.4
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Results:
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- Task: Semantic Segmentation
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Dataset: Cityscapes
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Metrics:
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mIoU: 78.69
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mIoU(ms+flip): 80.11
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Config: configs/upernet/upernet_r101_512x1024_40k_cityscapes.py
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x1024_40k_cityscapes/upernet_r101_512x1024_40k_cityscapes_20200605_094933-ebce3b10.pth
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- Name: upernet_r50_769x769_40k_cityscapes
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In Collection: UPerNet
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Metadata:
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backbone: R-50
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crop size: (769,769)
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lr schd: 40000
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inference time (ms/im):
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- value: 568.18
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hardware: V100
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backend: PyTorch
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batch size: 1
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mode: FP32
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resolution: (769,769)
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Training Memory (GB): 7.2
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Results:
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- Task: Semantic Segmentation
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Dataset: Cityscapes
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Metrics:
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mIoU: 77.98
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mIoU(ms+flip): 79.7
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Config: configs/upernet/upernet_r50_769x769_40k_cityscapes.py
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_769x769_40k_cityscapes/upernet_r50_769x769_40k_cityscapes_20200530_033048-92d21539.pth
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- Name: upernet_r101_769x769_40k_cityscapes
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In Collection: UPerNet
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Metadata:
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backbone: R-101
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crop size: (769,769)
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lr schd: 40000
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inference time (ms/im):
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- value: 641.03
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hardware: V100
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backend: PyTorch
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batch size: 1
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mode: FP32
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resolution: (769,769)
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Training Memory (GB): 8.4
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Results:
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- Task: Semantic Segmentation
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Dataset: Cityscapes
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Metrics:
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mIoU: 79.03
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mIoU(ms+flip): 80.77
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Config: configs/upernet/upernet_r101_769x769_40k_cityscapes.py
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_769x769_40k_cityscapes/upernet_r101_769x769_40k_cityscapes_20200530_040819-83c95d01.pth
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- Name: upernet_r18_512x1024_80k_cityscapes
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In Collection: UPerNet
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Metadata:
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backbone: R-18
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crop size: (512,1024)
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lr schd: 80000
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Results:
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- Task: Semantic Segmentation
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Dataset: Cityscapes
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Metrics:
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mIoU: 76.02
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mIoU(ms+flip): 77.38
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Config: configs/upernet/upernet_r18_512x1024_80k_cityscapes.py
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r18_512x1024_80k_cityscapes/upernet_r18_512x1024_80k_cityscapes_20220614_110712-c89a9188.pth
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- Name: upernet_r50_512x1024_80k_cityscapes
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In Collection: UPerNet
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Metadata:
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backbone: R-50
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crop size: (512,1024)
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lr schd: 80000
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Results:
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- Task: Semantic Segmentation
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Dataset: Cityscapes
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Metrics:
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mIoU: 78.19
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mIoU(ms+flip): 79.19
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Config: configs/upernet/upernet_r50_512x1024_80k_cityscapes.py
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x1024_80k_cityscapes/upernet_r50_512x1024_80k_cityscapes_20200607_052207-848beca8.pth
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- Name: upernet_r101_512x1024_80k_cityscapes
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In Collection: UPerNet
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Metadata:
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backbone: R-101
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crop size: (512,1024)
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lr schd: 80000
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Results:
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- Task: Semantic Segmentation
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Dataset: Cityscapes
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Metrics:
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mIoU: 79.4
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mIoU(ms+flip): 80.46
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Config: configs/upernet/upernet_r101_512x1024_80k_cityscapes.py
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x1024_80k_cityscapes/upernet_r101_512x1024_80k_cityscapes_20200607_002403-f05f2345.pth
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- Name: upernet_r50_769x769_80k_cityscapes
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In Collection: UPerNet
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Metadata:
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backbone: R-50
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crop size: (769,769)
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lr schd: 80000
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Results:
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- Task: Semantic Segmentation
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Dataset: Cityscapes
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Metrics:
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mIoU: 79.39
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mIoU(ms+flip): 80.92
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Config: configs/upernet/upernet_r50_769x769_80k_cityscapes.py
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_769x769_80k_cityscapes/upernet_r50_769x769_80k_cityscapes_20200607_005107-82ae7d15.pth
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- Name: upernet_r101_769x769_80k_cityscapes
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In Collection: UPerNet
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Metadata:
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backbone: R-101
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crop size: (769,769)
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lr schd: 80000
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Results:
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- Task: Semantic Segmentation
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Dataset: Cityscapes
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Metrics:
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mIoU: 80.1
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mIoU(ms+flip): 81.49
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Config: configs/upernet/upernet_r101_769x769_80k_cityscapes.py
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_769x769_80k_cityscapes/upernet_r101_769x769_80k_cityscapes_20200607_001014-082fc334.pth
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- Name: upernet_r18_512x512_80k_ade20k
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In Collection: UPerNet
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Metadata:
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backbone: R-18
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crop size: (512,512)
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lr schd: 80000
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inference time (ms/im):
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- value: 40.39
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hardware: V100
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backend: PyTorch
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batch size: 1
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mode: FP32
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resolution: (512,512)
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Training Memory (GB): 6.6
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Results:
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- Task: Semantic Segmentation
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Dataset: ADE20K
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Metrics:
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mIoU: 38.76
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mIoU(ms+flip): 39.81
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Config: configs/upernet/upernet_r18_512x512_80k_ade20k.py
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r18_512x512_80k_ade20k/upernet_r18_512x512_80k_ade20k_20220614_110319-22e81719.pth
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- Name: upernet_r50_512x512_80k_ade20k
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In Collection: UPerNet
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Metadata:
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backbone: R-50
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crop size: (512,512)
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lr schd: 80000
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inference time (ms/im):
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- value: 42.74
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hardware: V100
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backend: PyTorch
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batch size: 1
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mode: FP32
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resolution: (512,512)
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Training Memory (GB): 8.1
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Results:
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- Task: Semantic Segmentation
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Dataset: ADE20K
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Metrics:
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mIoU: 40.7
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mIoU(ms+flip): 41.81
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Config: configs/upernet/upernet_r50_512x512_80k_ade20k.py
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_80k_ade20k/upernet_r50_512x512_80k_ade20k_20200614_144127-ecc8377b.pth
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- Name: upernet_r101_512x512_80k_ade20k
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In Collection: UPerNet
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Metadata:
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backbone: R-101
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crop size: (512,512)
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lr schd: 80000
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inference time (ms/im):
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- value: 49.16
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hardware: V100
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backend: PyTorch
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batch size: 1
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mode: FP32
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resolution: (512,512)
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Training Memory (GB): 9.1
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Results:
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- Task: Semantic Segmentation
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Dataset: ADE20K
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Metrics:
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mIoU: 42.91
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mIoU(ms+flip): 43.96
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Config: configs/upernet/upernet_r101_512x512_80k_ade20k.py
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_80k_ade20k/upernet_r101_512x512_80k_ade20k_20200614_185117-32e4db94.pth
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- Name: upernet_r18_512x512_160k_ade20k
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In Collection: UPerNet
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Metadata:
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backbone: R-18
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crop size: (512,512)
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lr schd: 160000
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Results:
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- Task: Semantic Segmentation
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Dataset: ADE20K
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Metrics:
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mIoU: 39.23
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mIoU(ms+flip): 39.97
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Config: configs/upernet/upernet_r18_512x512_160k_ade20k.py
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r18_512x512_160k_ade20k/upernet_r18_512x512_160k_ade20k_20220615_113300-791c3f3e.pth
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- Name: upernet_r50_512x512_160k_ade20k
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In Collection: UPerNet
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Metadata:
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backbone: R-50
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crop size: (512,512)
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lr schd: 160000
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Results:
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- Task: Semantic Segmentation
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Dataset: ADE20K
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Metrics:
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mIoU: 42.05
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mIoU(ms+flip): 42.78
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Config: configs/upernet/upernet_r50_512x512_160k_ade20k.py
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_160k_ade20k/upernet_r50_512x512_160k_ade20k_20200615_184328-8534de8d.pth
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- Name: upernet_r101_512x512_160k_ade20k
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In Collection: UPerNet
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Metadata:
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backbone: R-101
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crop size: (512,512)
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lr schd: 160000
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Results:
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- Task: Semantic Segmentation
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Dataset: ADE20K
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Metrics:
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mIoU: 43.82
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mIoU(ms+flip): 44.85
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Config: configs/upernet/upernet_r101_512x512_160k_ade20k.py
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_160k_ade20k/upernet_r101_512x512_160k_ade20k_20200615_161951-91b32684.pth
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- Name: upernet_r18_512x512_20k_voc12aug
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In Collection: UPerNet
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Metadata:
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backbone: R-18
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crop size: (512,512)
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lr schd: 20000
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inference time (ms/im):
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- value: 38.76
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hardware: V100
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backend: PyTorch
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batch size: 1
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mode: FP32
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resolution: (512,512)
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Training Memory (GB): 4.8
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Results:
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- Task: Semantic Segmentation
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Dataset: Pascal VOC 2012 + Aug
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Metrics:
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mIoU: 72.9
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mIoU(ms+flip): 74.71
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Config: configs/upernet/upernet_r18_512x512_20k_voc12aug.py
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r18_512x512_20k_voc12aug/upernet_r18_512x512_20k_voc12aug_20220614_123910-ed66e455.pth
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- Name: upernet_r50_512x512_20k_voc12aug
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In Collection: UPerNet
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Metadata:
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backbone: R-50
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crop size: (512,512)
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lr schd: 20000
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inference time (ms/im):
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- value: 43.16
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hardware: V100
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backend: PyTorch
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batch size: 1
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mode: FP32
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resolution: (512,512)
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Training Memory (GB): 6.4
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Results:
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- Task: Semantic Segmentation
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Dataset: Pascal VOC 2012 + Aug
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Metrics:
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mIoU: 74.82
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mIoU(ms+flip): 76.35
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Config: configs/upernet/upernet_r50_512x512_20k_voc12aug.py
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_20k_voc12aug/upernet_r50_512x512_20k_voc12aug_20200617_165330-5b5890a7.pth
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- Name: upernet_r101_512x512_20k_voc12aug
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In Collection: UPerNet
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Metadata:
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backbone: R-101
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crop size: (512,512)
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lr schd: 20000
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inference time (ms/im):
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- value: 50.05
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hardware: V100
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backend: PyTorch
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batch size: 1
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mode: FP32
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resolution: (512,512)
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Training Memory (GB): 7.5
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Results:
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- Task: Semantic Segmentation
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Dataset: Pascal VOC 2012 + Aug
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Metrics:
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mIoU: 77.1
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mIoU(ms+flip): 78.29
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Config: configs/upernet/upernet_r101_512x512_20k_voc12aug.py
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_20k_voc12aug/upernet_r101_512x512_20k_voc12aug_20200617_165629-f14e7f27.pth
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- Name: upernet_r18_512x512_40k_voc12aug
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In Collection: UPerNet
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Metadata:
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backbone: R-18
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crop size: (512,512)
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lr schd: 40000
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Results:
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- Task: Semantic Segmentation
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Dataset: Pascal VOC 2012 + Aug
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Metrics:
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mIoU: 73.71
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mIoU(ms+flip): 74.61
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Config: configs/upernet/upernet_r18_512x512_40k_voc12aug.py
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r18_512x512_40k_voc12aug/upernet_r18_512x512_40k_voc12aug_20220614_153605-fafeb868.pth
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- Name: upernet_r50_512x512_40k_voc12aug
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In Collection: UPerNet
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Metadata:
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backbone: R-50
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crop size: (512,512)
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lr schd: 40000
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Results:
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- Task: Semantic Segmentation
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Dataset: Pascal VOC 2012 + Aug
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Metrics:
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mIoU: 75.92
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mIoU(ms+flip): 77.44
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Config: configs/upernet/upernet_r50_512x512_40k_voc12aug.py
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r50_512x512_40k_voc12aug/upernet_r50_512x512_40k_voc12aug_20200613_162257-ca9bcc6b.pth
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- Name: upernet_r101_512x512_40k_voc12aug
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In Collection: UPerNet
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Metadata:
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backbone: R-101
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crop size: (512,512)
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lr schd: 40000
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Results:
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- Task: Semantic Segmentation
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Dataset: Pascal VOC 2012 + Aug
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Metrics:
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mIoU: 77.43
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mIoU(ms+flip): 78.56
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Config: configs/upernet/upernet_r101_512x512_40k_voc12aug.py
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/upernet/upernet_r101_512x512_40k_voc12aug/upernet_r101_512x512_40k_voc12aug_20200613_163549-e26476ac.pth
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