gc/wing-ops
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코드 이슈 풀 리퀘스트 Actions 위키 활동
20890fe8a9
wing-ops/prediction/image/mx15hdi/Detect/mmsegmentation/configs/bisenetv1/bisenetv1.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

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Collections:
- Name: BiSeNetV1
Metadata:
Training Data:
- Cityscapes
- COCO-Stuff 164k
Paper:
URL: https://arxiv.org/abs/1808.00897
Title: 'BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation'
README: configs/bisenetv1/README.md
Code:
URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.18.0/mmseg/models/backbones/bisenetv1.py#L266
Version: v0.18.0
Converted From:
Code: https://github.com/ycszen/TorchSeg/tree/master/model/bisenet
Models:
- Name: bisenetv1_r18-d32_4x4_1024x1024_160k_cityscapes
In Collection: BiSeNetV1
Metadata:
backbone: R-18-D32
crop size: (1024,1024)
lr schd: 160000
inference time (ms/im):
- value: 31.48
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (1024,1024)
Training Memory (GB): 5.69
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 74.44
mIoU(ms+flip): 77.05
Config: configs/bisenetv1/bisenetv1_r18-d32_4x4_1024x1024_160k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_4x4_1024x1024_160k_cityscapes/bisenetv1_r18-d32_4x4_1024x1024_160k_cityscapes_20210922_172239-c55e78e2.pth
- Name: bisenetv1_r18-d32_in1k-pre_4x4_1024x1024_160k_cityscapes
In Collection: BiSeNetV1
Metadata:
backbone: R-18-D32
crop size: (1024,1024)
lr schd: 160000
inference time (ms/im):
- value: 31.48
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (1024,1024)
Training Memory (GB): 5.69
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 74.37
mIoU(ms+flip): 76.91
Config: configs/bisenetv1/bisenetv1_r18-d32_in1k-pre_4x4_1024x1024_160k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_in1k-pre_4x4_1024x1024_160k_cityscapes/bisenetv1_r18-d32_in1k-pre_4x4_1024x1024_160k_cityscapes_20210905_220251-8ba80eff.pth
- Name: bisenetv1_r18-d32_in1k-pre_4x8_1024x1024_160k_cityscapes
In Collection: BiSeNetV1
Metadata:
backbone: R-18-D32
crop size: (1024,1024)
lr schd: 160000
inference time (ms/im):
- value: 31.48
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (1024,1024)
Training Memory (GB): 11.17
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 75.16
mIoU(ms+flip): 77.24
Config: configs/bisenetv1/bisenetv1_r18-d32_in1k-pre_4x8_1024x1024_160k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_in1k-pre_4x8_1024x1024_160k_cityscapes/bisenetv1_r18-d32_in1k-pre_4x8_1024x1024_160k_cityscapes_20210905_220322-bb8db75f.pth
- Name: bisenetv1_r50-d32_4x4_1024x1024_160k_cityscapes
In Collection: BiSeNetV1
Metadata:
backbone: R-50-D32
crop size: (1024,1024)
lr schd: 160000
inference time (ms/im):
- value: 129.7
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (1024,1024)
Training Memory (GB): 15.39
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 76.92
mIoU(ms+flip): 78.87
Config: configs/bisenetv1/bisenetv1_r50-d32_4x4_1024x1024_160k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r50-d32_4x4_1024x1024_160k_cityscapes/bisenetv1_r50-d32_4x4_1024x1024_160k_cityscapes_20210923_222639-7b28a2a6.pth
- Name: bisenetv1_r50-d32_in1k-pre_4x4_1024x1024_160k_cityscapes
In Collection: BiSeNetV1
Metadata:
backbone: R-50-D32
crop size: (1024,1024)
lr schd: 160000
inference time (ms/im):
- value: 129.7
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (1024,1024)
Training Memory (GB): 15.39
Results:
- Task: Semantic Segmentation
Dataset: Cityscapes
Metrics:
mIoU: 77.68
mIoU(ms+flip): 79.57
Config: configs/bisenetv1/bisenetv1_r50-d32_in1k-pre_4x4_1024x1024_160k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r50-d32_in1k-pre_4x4_1024x1024_160k_cityscapes/bisenetv1_r50-d32_in1k-pre_4x4_1024x1024_160k_cityscapes_20210917_234628-8b304447.pth
- Name: bisenetv1_r18-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k
In Collection: BiSeNetV1
Metadata:
backbone: R-18-D32
crop size: (512,512)
lr schd: 160000
Results:
- Task: Semantic Segmentation
Dataset: COCO-Stuff 164k
Metrics:
mIoU: 25.45
mIoU(ms+flip): 26.15
Config: configs/bisenetv1/bisenetv1_r18-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r18-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211022_054328-046aa2f2.pth
- Name: bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k
In Collection: BiSeNetV1
Metadata:
backbone: R-18-D32
crop size: (512,512)
lr schd: 160000
inference time (ms/im):
- value: 13.47
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 6.33
Results:
- Task: Semantic Segmentation
Dataset: COCO-Stuff 164k
Metrics:
mIoU: 28.55
mIoU(ms+flip): 29.26
Config: configs/bisenetv1/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r18-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211023_013100-f700dbf7.pth
- Name: bisenetv1_r50-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k
In Collection: BiSeNetV1
Metadata:
backbone: R-50-D32
crop size: (512,512)
lr schd: 160000
Results:
- Task: Semantic Segmentation
Dataset: COCO-Stuff 164k
Metrics:
mIoU: 29.82
mIoU(ms+flip): 30.33
Config: configs/bisenetv1/bisenetv1_r50-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r50-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r50-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211101_040616-d2bb0df4.pth
- Name: bisenetv1_r50-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k
In Collection: BiSeNetV1
Metadata:
backbone: R-50-D32
crop size: (512,512)
lr schd: 160000
inference time (ms/im):
- value: 30.67
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 9.28
Results:
- Task: Semantic Segmentation
Dataset: COCO-Stuff 164k
Metrics:
mIoU: 34.88
mIoU(ms+flip): 35.37
Config: configs/bisenetv1/bisenetv1_r50-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r50-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r50-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211101_181932-66747911.pth
- Name: bisenetv1_r101-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k
In Collection: BiSeNetV1
Metadata:
backbone: R-101-D32
crop size: (512,512)
lr schd: 160000
Results:
- Task: Semantic Segmentation
Dataset: COCO-Stuff 164k
Metrics:
mIoU: 31.14
mIoU(ms+flip): 31.76
Config: configs/bisenetv1/bisenetv1_r101-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r101-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r101-d32_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211102_164147-c6b32c3b.pth
- Name: bisenetv1_r101-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k
In Collection: BiSeNetV1
Metadata:
backbone: R-101-D32
crop size: (512,512)
lr schd: 160000
inference time (ms/im):
- value: 39.6
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 10.36
Results:
- Task: Semantic Segmentation
Dataset: COCO-Stuff 164k
Metrics:
mIoU: 37.38
mIoU(ms+flip): 37.99
Config: configs/bisenetv1/bisenetv1_r101-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/bisenetv1/bisenetv1_r101-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k/bisenetv1_r101-d32_in1k-pre_lr5e-3_4x4_512x512_160k_coco-stuff164k_20211101_225220-28c8f092.pth
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