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
38 lines
1.2 KiB
YAML
38 lines
1.2 KiB
YAML
Collections:
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- Name: ERFNet
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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: http://www.robesafe.uah.es/personal/eduardo.romera/pdfs/Romera17tits.pdf
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Title: 'ERFNet: Efficient Residual Factorized ConvNet for Real-time Semantic Segmentation'
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README: configs/erfnet/README.md
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Code:
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URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.20.0/mmseg/models/backbones/erfnet.py#L321
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Version: v0.20.0
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Converted From:
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Code: https://github.com/Eromera/erfnet_pytorch
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Models:
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- Name: erfnet_fcn_4x4_512x1024_160k_cityscapes
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In Collection: ERFNet
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Metadata:
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backbone: ERFNet
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crop size: (512,1024)
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lr schd: 160000
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inference time (ms/im):
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- value: 65.53
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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): 6.04
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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: 71.08
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mIoU(ms+flip): 72.6
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Config: configs/erfnet/erfnet_fcn_4x4_512x1024_160k_cityscapes.py
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/erfnet/erfnet_fcn_4x4_512x1024_160k_cityscapes/erfnet_fcn_4x4_512x1024_160k_cityscapes_20211126_082056-03d333ed.pth
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