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
178 lines
5.5 KiB
YAML
178 lines
5.5 KiB
YAML
Models:
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- Name: fcn_s101-d8_512x1024_80k_cityscapes
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In Collection: FCN
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Metadata:
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backbone: S-101-D8
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crop size: (512,1024)
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lr schd: 80000
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inference time (ms/im):
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- value: 418.41
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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): 11.4
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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: 77.56
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mIoU(ms+flip): 78.98
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Config: configs/resnest/fcn_s101-d8_512x1024_80k_cityscapes.py
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/resnest/fcn_s101-d8_512x1024_80k_cityscapes/fcn_s101-d8_512x1024_80k_cityscapes_20200807_140631-f8d155b3.pth
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- Name: pspnet_s101-d8_512x1024_80k_cityscapes
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In Collection: PSPNet
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Metadata:
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backbone: S-101-D8
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crop size: (512,1024)
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lr schd: 80000
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inference time (ms/im):
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- value: 396.83
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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): 11.8
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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: 78.57
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mIoU(ms+flip): 79.19
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Config: configs/resnest/pspnet_s101-d8_512x1024_80k_cityscapes.py
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/resnest/pspnet_s101-d8_512x1024_80k_cityscapes/pspnet_s101-d8_512x1024_80k_cityscapes_20200807_140631-c75f3b99.pth
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- Name: deeplabv3_s101-d8_512x1024_80k_cityscapes
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In Collection: DeepLabV3
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Metadata:
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backbone: S-101-D8
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crop size: (512,1024)
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lr schd: 80000
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inference time (ms/im):
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- value: 531.91
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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): 11.9
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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: 79.67
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mIoU(ms+flip): 80.51
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Config: configs/resnest/deeplabv3_s101-d8_512x1024_80k_cityscapes.py
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/resnest/deeplabv3_s101-d8_512x1024_80k_cityscapes/deeplabv3_s101-d8_512x1024_80k_cityscapes_20200807_144429-b73c4270.pth
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- Name: deeplabv3plus_s101-d8_512x1024_80k_cityscapes
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In Collection: DeepLabV3+
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Metadata:
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backbone: S-101-D8
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crop size: (512,1024)
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lr schd: 80000
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inference time (ms/im):
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- value: 423.73
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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): 13.2
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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: 79.62
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mIoU(ms+flip): 80.27
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Config: configs/resnest/deeplabv3plus_s101-d8_512x1024_80k_cityscapes.py
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/resnest/deeplabv3plus_s101-d8_512x1024_80k_cityscapes/deeplabv3plus_s101-d8_512x1024_80k_cityscapes_20200807_144429-1239eb43.pth
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- Name: fcn_s101-d8_512x512_160k_ade20k
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In Collection: FCN
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Metadata:
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backbone: S-101-D8
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crop size: (512,512)
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lr schd: 160000
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inference time (ms/im):
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- value: 77.76
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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): 14.2
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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: 45.62
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mIoU(ms+flip): 46.16
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Config: configs/resnest/fcn_s101-d8_512x512_160k_ade20k.py
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/resnest/fcn_s101-d8_512x512_160k_ade20k/fcn_s101-d8_512x512_160k_ade20k_20200807_145416-d3160329.pth
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- Name: pspnet_s101-d8_512x512_160k_ade20k
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In Collection: PSPNet
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Metadata:
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backbone: S-101-D8
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crop size: (512,512)
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lr schd: 160000
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inference time (ms/im):
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- value: 76.8
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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): 14.2
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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: 45.44
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mIoU(ms+flip): 46.28
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Config: configs/resnest/pspnet_s101-d8_512x512_160k_ade20k.py
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/resnest/pspnet_s101-d8_512x512_160k_ade20k/pspnet_s101-d8_512x512_160k_ade20k_20200807_145416-a6daa92a.pth
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- Name: deeplabv3_s101-d8_512x512_160k_ade20k
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In Collection: DeepLabV3
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Metadata:
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backbone: S-101-D8
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crop size: (512,512)
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lr schd: 160000
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inference time (ms/im):
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- value: 107.76
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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): 14.6
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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: 45.71
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mIoU(ms+flip): 46.59
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Config: configs/resnest/deeplabv3_s101-d8_512x512_160k_ade20k.py
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/resnest/deeplabv3_s101-d8_512x512_160k_ade20k/deeplabv3_s101-d8_512x512_160k_ade20k_20200807_144503-17ecabe5.pth
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- Name: deeplabv3plus_s101-d8_512x512_160k_ade20k
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In Collection: DeepLabV3+
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Metadata:
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backbone: S-101-D8
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crop size: (512,512)
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lr schd: 160000
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inference time (ms/im):
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- value: 83.61
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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): 16.2
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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: 46.47
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mIoU(ms+flip): 47.27
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Config: configs/resnest/deeplabv3plus_s101-d8_512x512_160k_ade20k.py
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/resnest/deeplabv3plus_s101-d8_512x512_160k_ade20k/deeplabv3plus_s101-d8_512x512_160k_ade20k_20200807_144503-27b26226.pth
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