gc/wing-ops
8
0
포크 0
코드 이슈 풀 리퀘스트 Actions 위키 활동
c7c7537dbb
wing-ops/prediction/image/mx15hdi/Detect/mmsegmentation/configs/fastfcn/fastfcn.yml
jeonghyo.k 3946ff6a25 feat(prediction): 이미지 분석 서버 Docker 패키징 + DB 코드 제거 
- 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
2026-03-10 18:37:36 +09:00

236 lines
8.1 KiB
YAML
Raw Blame 히스토리

Collections:
- Name: FastFCN
Metadata:
Training Data:
- Cityscapes
- ADE20K
Paper:
URL: https://arxiv.org/abs/1903.11816
Title: 'FastFCN: Rethinking Dilated Convolution in the Backbone for Semantic Segmentation'
README: configs/fastfcn/README.md
Code:
URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/necks/jpu.py#L12
Version: v0.18.0
Converted From:
Code: https://github.com/wuhuikai/FastFCN
Models:
- Name: fastfcn_r50-d32_jpu_aspp_512x1024_80k_cityscapes
In Collection: FastFCN
Metadata:
backbone: R-50-D32
crop size: (512,1024)
lr schd: 80000
inference time (ms/im):
- value: 378.79
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,1024)
Training Memory (GB): 5.67
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.12
mIoU(ms+flip): 80.58
Config: configs/fastfcn/fastfcn_r50-d32_jpu_aspp_512x1024_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_aspp_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_aspp_512x1024_80k_cityscapes_20210928_053722-5d1a2648.pth
- Name: fastfcn_r50-d32_jpu_aspp_4x4_512x1024_80k_cityscapes
In Collection: FastFCN
Metadata:
backbone: R-50-D32
crop size: (512,1024)
lr schd: 80000
Training Memory (GB): 9.79
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.52
mIoU(ms+flip): 80.91
Config: configs/fastfcn/fastfcn_r50-d32_jpu_aspp_4x4_512x1024_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_aspp_4x4_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_aspp_4x4_512x1024_80k_cityscapes_20210924_214357-72220849.pth
- Name: fastfcn_r50-d32_jpu_psp_512x1024_80k_cityscapes
In Collection: FastFCN
Metadata:
backbone: R-50-D32
crop size: (512,1024)
lr schd: 80000
inference time (ms/im):
- value: 227.27
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,1024)
Training Memory (GB): 5.67
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 79.26
mIoU(ms+flip): 80.86
Config: configs/fastfcn/fastfcn_r50-d32_jpu_psp_512x1024_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_psp_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_psp_512x1024_80k_cityscapes_20210928_053722-57749bed.pth
- Name: fastfcn_r50-d32_jpu_psp_4x4_512x1024_80k_cityscapes
In Collection: FastFCN
Metadata:
backbone: R-50-D32
crop size: (512,1024)
lr schd: 80000
Training Memory (GB): 9.94
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 78.76
mIoU(ms+flip): 80.03
Config: configs/fastfcn/fastfcn_r50-d32_jpu_psp_4x4_512x1024_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_psp_4x4_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_psp_4x4_512x1024_80k_cityscapes_20210925_061841-77e87b0a.pth
- Name: fastfcn_r50-d32_jpu_enc_512x1024_80k_cityscapes
In Collection: FastFCN
Metadata:
backbone: R-50-D32
crop size: (512,1024)
lr schd: 80000
inference time (ms/im):
- value: 209.64
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,1024)
Training Memory (GB): 8.15
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 77.97
mIoU(ms+flip): 79.92
Config: configs/fastfcn/fastfcn_r50-d32_jpu_enc_512x1024_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_enc_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_enc_512x1024_80k_cityscapes_20210928_030036-78da5046.pth
- Name: fastfcn_r50-d32_jpu_enc_4x4_512x1024_80k_cityscapes
In Collection: FastFCN
Metadata:
backbone: R-50-D32
crop size: (512,1024)
lr schd: 80000
Training Memory (GB): 15.45
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 78.6
mIoU(ms+flip): 80.25
Config: configs/fastfcn/fastfcn_r50-d32_jpu_enc_4x4_512x1024_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_enc_4x4_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_enc_4x4_512x1024_80k_cityscapes_20210926_093217-e1eb6dbb.pth
- Name: fastfcn_r50-d32_jpu_aspp_512x512_80k_ade20k
In Collection: FastFCN
Metadata:
backbone: R-50-D32
crop size: (512,512)
lr schd: 80000
inference time (ms/im):
- value: 82.92
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 8.46
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 41.88
mIoU(ms+flip): 42.91
Config: configs/fastfcn/fastfcn_r50-d32_jpu_aspp_512x512_80k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_aspp_512x512_80k_ade20k/fastfcn_r50-d32_jpu_aspp_512x512_80k_ade20k_20211013_190619-3aa40f2d.pth
- Name: fastfcn_r50-d32_jpu_aspp_512x512_160k_ade20k
In Collection: FastFCN
Metadata:
backbone: R-50-D32
crop size: (512,512)
lr schd: 160000
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 43.58
mIoU(ms+flip): 44.92
Config: configs/fastfcn/fastfcn_r50-d32_jpu_aspp_512x512_160k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_aspp_512x512_160k_ade20k/fastfcn_r50-d32_jpu_aspp_512x512_160k_ade20k_20211008_152246-27036aee.pth
- Name: fastfcn_r50-d32_jpu_psp_512x512_80k_ade20k
In Collection: FastFCN
Metadata:
backbone: R-50-D32
crop size: (512,512)
lr schd: 80000
inference time (ms/im):
- value: 52.06
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 8.02
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 41.4
mIoU(ms+flip): 42.12
Config: configs/fastfcn/fastfcn_r50-d32_jpu_psp_512x512_80k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_psp_512x512_80k_ade20k/fastfcn_r50-d32_jpu_psp_512x512_80k_ade20k_20210930_225137-993d07c8.pth
- Name: fastfcn_r50-d32_jpu_psp_512x512_160k_ade20k
In Collection: FastFCN
Metadata:
backbone: R-50-D32
crop size: (512,512)
lr schd: 160000
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 42.63
mIoU(ms+flip): 43.71
Config: configs/fastfcn/fastfcn_r50-d32_jpu_psp_512x512_160k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_psp_512x512_160k_ade20k/fastfcn_r50-d32_jpu_psp_512x512_160k_ade20k_20211008_105455-e8f5a2fd.pth
- Name: fastfcn_r50-d32_jpu_enc_512x512_80k_ade20k
In Collection: FastFCN
Metadata:
backbone: R-50-D32
crop size: (512,512)
lr schd: 80000
inference time (ms/im):
- value: 58.04
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 9.67
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 40.88
mIoU(ms+flip): 42.36
Config: configs/fastfcn/fastfcn_r50-d32_jpu_enc_512x512_80k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_enc_512x512_80k_ade20k/fastfcn_r50-d32_jpu_enc_512x512_80k_ade20k_20210930_225214-65aef6dd.pth
- Name: fastfcn_r50-d32_jpu_enc_512x512_160k_ade20k
In Collection: FastFCN
Metadata:
backbone: R-50-D32
crop size: (512,512)
lr schd: 160000
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 42.5
mIoU(ms+flip): 44.21
Config: configs/fastfcn/fastfcn_r50-d32_jpu_enc_512x512_160k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_enc_512x512_160k_ade20k/fastfcn_r50-d32_jpu_enc_512x512_160k_ade20k_20211008_105456-d875ce3c.pth
Reference in New Issue View Git Blame Copy Permalink