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
89 lines
3.0 KiB
YAML
89 lines
3.0 KiB
YAML
Collections:
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- Name: BiSeNetV2
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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/2004.02147
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Title: 'Bisenet v2: Bilateral Network with Guided Aggregation for Real-time Semantic
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Segmentation'
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README: configs/bisenetv2/README.md
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Code:
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URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/backbones/bisenetv2.py#L545
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Version: v0.18.0
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Models:
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- Name: bisenetv2_fcn_4x4_1024x1024_160k_cityscapes
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In Collection: BiSeNetV2
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Metadata:
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backbone: BiSeNetV2
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crop size: (1024,1024)
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lr schd: 160000
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inference time (ms/im):
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- value: 31.48
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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: (1024,1024)
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Training Memory (GB): 7.64
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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: 73.21
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mIoU(ms+flip): 75.74
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Config: configs/bisenetv2/bisenetv2_fcn_4x4_1024x1024_160k_cityscapes.py
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv2/bisenetv2_fcn_4x4_1024x1024_160k_cityscapes/bisenetv2_fcn_4x4_1024x1024_160k_cityscapes_20210902_015551-bcf10f09.pth
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- Name: bisenetv2_fcn_ohem_4x4_1024x1024_160k_cityscapes
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In Collection: BiSeNetV2
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Metadata:
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backbone: BiSeNetV2
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crop size: (1024,1024)
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lr schd: 160000
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Training Memory (GB): 7.64
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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: 73.57
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mIoU(ms+flip): 75.8
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Config: configs/bisenetv2/bisenetv2_fcn_ohem_4x4_1024x1024_160k_cityscapes.py
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv2/bisenetv2_fcn_ohem_4x4_1024x1024_160k_cityscapes/bisenetv2_fcn_ohem_4x4_1024x1024_160k_cityscapes_20210902_112947-5f8103b4.pth
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- Name: bisenetv2_fcn_4x8_1024x1024_160k_cityscapes
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In Collection: BiSeNetV2
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Metadata:
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backbone: BiSeNetV2
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crop size: (1024,1024)
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lr schd: 160000
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Training Memory (GB): 15.05
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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: 75.76
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mIoU(ms+flip): 77.79
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Config: configs/bisenetv2/bisenetv2_fcn_4x8_1024x1024_160k_cityscapes.py
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv2/bisenetv2_fcn_4x8_1024x1024_160k_cityscapes/bisenetv2_fcn_4x8_1024x1024_160k_cityscapes_20210903_000032-e1a2eed6.pth
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- Name: bisenetv2_fcn_fp16_4x4_1024x1024_160k_cityscapes
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In Collection: BiSeNetV2
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Metadata:
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backbone: BiSeNetV2
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crop size: (1024,1024)
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lr schd: 160000
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inference time (ms/im):
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- value: 27.29
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hardware: V100
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backend: PyTorch
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batch size: 1
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mode: FP16
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resolution: (1024,1024)
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Training Memory (GB): 5.77
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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: 73.07
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mIoU(ms+flip): 75.13
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Config: configs/bisenetv2/bisenetv2_fcn_fp16_4x4_1024x1024_160k_cityscapes.py
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv2/bisenetv2_fcn_fp16_4x4_1024x1024_160k_cityscapes/bisenetv2_fcn_fp16_4x4_1024x1024_160k_cityscapes_20210902_045942-b979777b.pth
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