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
104 lines
3.2 KiB
YAML
104 lines
3.2 KiB
YAML
Collections:
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- Name: EMANet
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Metadata:
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Training Data:
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- Cityscapes
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Paper:
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URL: https://arxiv.org/abs/1907.13426
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Title: Expectation-Maximization Attention Networks for Semantic Segmentation
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README: configs/emanet/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/ema_head.py#L80
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Version: v0.17.0
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Converted From:
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Code: https://xialipku.github.io/EMANet
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Models:
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- Name: emanet_r50-d8_512x1024_80k_cityscapes
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In Collection: EMANet
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Metadata:
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backbone: R-50-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: 218.34
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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): 5.4
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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: 77.59
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mIoU(ms+flip): 79.44
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Config: configs/emanet/emanet_r50-d8_512x1024_80k_cityscapes.py
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/emanet/emanet_r50-d8_512x1024_80k_cityscapes/emanet_r50-d8_512x1024_80k_cityscapes_20200901_100301-c43fcef1.pth
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- Name: emanet_r101-d8_512x1024_80k_cityscapes
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In Collection: EMANet
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Metadata:
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backbone: R-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: 348.43
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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): 6.2
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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: 79.1
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mIoU(ms+flip): 81.21
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Config: configs/emanet/emanet_r101-d8_512x1024_80k_cityscapes.py
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/emanet/emanet_r101-d8_512x1024_80k_cityscapes/emanet_r101-d8_512x1024_80k_cityscapes_20200901_100301-2d970745.pth
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- Name: emanet_r50-d8_769x769_80k_cityscapes
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In Collection: EMANet
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Metadata:
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backbone: R-50-D8
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crop size: (769,769)
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lr schd: 80000
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inference time (ms/im):
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- value: 507.61
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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: (769,769)
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Training Memory (GB): 8.9
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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: 79.33
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mIoU(ms+flip): 80.49
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Config: configs/emanet/emanet_r50-d8_769x769_80k_cityscapes.py
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/emanet/emanet_r50-d8_769x769_80k_cityscapes/emanet_r50-d8_769x769_80k_cityscapes_20200901_100301-16f8de52.pth
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- Name: emanet_r101-d8_769x769_80k_cityscapes
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In Collection: EMANet
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Metadata:
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backbone: R-101-D8
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crop size: (769,769)
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lr schd: 80000
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inference time (ms/im):
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- value: 819.67
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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): 10.1
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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): 81.0
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Config: configs/emanet/emanet_r101-d8_769x769_80k_cityscapes.py
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/emanet/emanet_r101-d8_769x769_80k_cityscapes/emanet_r101-d8_769x769_80k_cityscapes_20200901_100301-47a324ce.pth
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