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
134 lines
4.4 KiB
YAML
134 lines
4.4 KiB
YAML
Models:
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- Name: upernet_convnext_tiny_fp16_512x512_160k_ade20k
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In Collection: UPerNet
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Metadata:
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backbone: ConvNeXt-T
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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: 50.25
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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: (512,512)
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Training Memory (GB): 4.23
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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: 46.11
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mIoU(ms+flip): 46.62
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Config: configs/convnext/upernet_convnext_tiny_fp16_512x512_160k_ade20k.py
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_tiny_fp16_512x512_160k_ade20k/upernet_convnext_tiny_fp16_512x512_160k_ade20k_20220227_124553-cad485de.pth
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- Name: upernet_convnext_small_fp16_512x512_160k_ade20k
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In Collection: UPerNet
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Metadata:
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backbone: ConvNeXt-S
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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: 65.88
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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: (512,512)
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Training Memory (GB): 5.16
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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: 48.56
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mIoU(ms+flip): 49.02
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Config: configs/convnext/upernet_convnext_small_fp16_512x512_160k_ade20k.py
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_small_fp16_512x512_160k_ade20k/upernet_convnext_small_fp16_512x512_160k_ade20k_20220227_131208-1b1e394f.pth
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- Name: upernet_convnext_base_fp16_512x512_160k_ade20k
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In Collection: UPerNet
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Metadata:
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backbone: ConvNeXt-B
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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: 69.4
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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: (512,512)
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Training Memory (GB): 6.33
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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: 48.71
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mIoU(ms+flip): 49.54
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Config: configs/convnext/upernet_convnext_base_fp16_512x512_160k_ade20k.py
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_base_fp16_512x512_160k_ade20k/upernet_convnext_base_fp16_512x512_160k_ade20k_20220227_181227-02a24fc6.pth
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- Name: upernet_convnext_base_fp16_640x640_160k_ade20k
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In Collection: UPerNet
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Metadata:
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backbone: ConvNeXt-B
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crop size: (640,640)
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lr schd: 160000
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inference time (ms/im):
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- value: 91.91
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hardware: V100
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backend: PyTorch
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batch size: 1
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mode: FP16
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resolution: (640,640)
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Training Memory (GB): 8.53
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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: 52.13
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mIoU(ms+flip): 52.66
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Config: configs/convnext/upernet_convnext_base_fp16_640x640_160k_ade20k.py
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_base_fp16_640x640_160k_ade20k/upernet_convnext_base_fp16_640x640_160k_ade20k_20220227_182859-9280e39b.pth
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- Name: upernet_convnext_large_fp16_640x640_160k_ade20k
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In Collection: UPerNet
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Metadata:
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backbone: ConvNeXt-L
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crop size: (640,640)
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lr schd: 160000
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inference time (ms/im):
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- value: 130.04
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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: (640,640)
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Training Memory (GB): 12.08
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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: 53.16
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mIoU(ms+flip): 53.38
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Config: configs/convnext/upernet_convnext_large_fp16_640x640_160k_ade20k.py
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_large_fp16_640x640_160k_ade20k/upernet_convnext_large_fp16_640x640_160k_ade20k_20220226_040532-e57aa54d.pth
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- Name: upernet_convnext_xlarge_fp16_640x640_160k_ade20k
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In Collection: UPerNet
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Metadata:
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backbone: ConvNeXt-XL
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crop size: (640,640)
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lr schd: 160000
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inference time (ms/im):
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- value: 157.98
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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: (640,640)
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Training Memory (GB): 26.16
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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: 53.58
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mIoU(ms+flip): 54.11
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Config: configs/convnext/upernet_convnext_xlarge_fp16_640x640_160k_ade20k.py
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Weights: https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_xlarge_fp16_640x640_160k_ade20k/upernet_convnext_xlarge_fp16_640x640_160k_ade20k_20220226_080344-95fc38c2.pth
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