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코드 이슈 풀 리퀘스트 Actions 위키 활동
dfe0ac8efe
wing-ops/prediction/image/mx15hdi/Detect/mmsegmentation/configs/knet/knet.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: KNet
Metadata:
Training Data:
- ADE20K
Paper:
URL: https://arxiv.org/abs/2106.14855
Title: 'K-Net: Towards Unified Image Segmentation'
README: configs/knet/README.md
Code:
URL: https://github.com/open-mmlab/mmsegmentation/blob/v0.23.0/mmseg/models/decode_heads/knet_head.py#L392
Version: v0.23.0
Converted From:
Code: https://github.com/ZwwWayne/K-Net/
Models:
- Name: knet_s3_fcn_r50-d8_8x2_512x512_adamw_80k_ade20k
In Collection: KNet
Metadata:
backbone: R-50-D8
crop size: (512,512)
lr schd: 80000
inference time (ms/im):
- value: 51.98
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 7.01
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 43.6
mIoU(ms+flip): 45.12
Config: configs/knet/knet_s3_fcn_r50-d8_8x2_512x512_adamw_80k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/knet/knet_s3_fcn_r50-d8_8x2_512x512_adamw_80k_ade20k/knet_s3_fcn_r50-d8_8x2_512x512_adamw_80k_ade20k_20220228_043751-abcab920.pth
- Name: knet_s3_pspnet_r50-d8_8x2_512x512_adamw_80k_ade20k
In Collection: KNet
Metadata:
backbone: R-50-D8
crop size: (512,512)
lr schd: 80000
inference time (ms/im):
- value: 49.9
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 6.98
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 44.18
mIoU(ms+flip): 45.58
Config: configs/knet/knet_s3_pspnet_r50-d8_8x2_512x512_adamw_80k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/knet/knet_s3_pspnet_r50-d8_8x2_512x512_adamw_80k_ade20k/knet_s3_pspnet_r50-d8_8x2_512x512_adamw_80k_ade20k_20220228_054634-d2c72240.pth
- Name: knet_s3_deeplabv3_r50-d8_8x2_512x512_adamw_80k_ade20k
In Collection: KNet
Metadata:
backbone: R-50-D8
crop size: (512,512)
lr schd: 80000
inference time (ms/im):
- value: 82.64
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 7.42
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 45.06
mIoU(ms+flip): 46.11
Config: configs/knet/knet_s3_deeplabv3_r50-d8_8x2_512x512_adamw_80k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/knet/knet_s3_deeplabv3_r50-d8_8x2_512x512_adamw_80k_ade20k/knet_s3_deeplabv3_r50-d8_8x2_512x512_adamw_80k_ade20k_20220228_041642-00c8fbeb.pth
- Name: knet_s3_upernet_r50-d8_8x2_512x512_adamw_80k_ade20k
In Collection: KNet
Metadata:
backbone: R-50-D8
crop size: (512,512)
lr schd: 80000
inference time (ms/im):
- value: 58.45
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 7.34
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 43.45
mIoU(ms+flip): 44.07
Config: configs/knet/knet_s3_upernet_r50-d8_8x2_512x512_adamw_80k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/knet/knet_s3_upernet_r50-d8_8x2_512x512_adamw_80k_ade20k/knet_s3_upernet_r50-d8_8x2_512x512_adamw_80k_ade20k_20220304_125657-215753b0.pth
- Name: knet_s3_upernet_swin-t_8x2_512x512_adamw_80k_ade20k
In Collection: KNet
Metadata:
backbone: Swin-T
crop size: (512,512)
lr schd: 80000
inference time (ms/im):
- value: 64.27
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 7.57
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 45.84
mIoU(ms+flip): 46.27
Config: configs/knet/knet_s3_upernet_swin-t_8x2_512x512_adamw_80k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/knet/knet_s3_upernet_swin-t_8x2_512x512_adamw_80k_ade20k/knet_s3_upernet_swin-t_8x2_512x512_adamw_80k_ade20k_20220303_133059-7545e1dc.pth
- Name: knet_s3_upernet_swin-l_8x2_512x512_adamw_80k_ade20k
In Collection: KNet
Metadata:
backbone: Swin-L
crop size: (512,512)
lr schd: 80000
inference time (ms/im):
- value: 120.63
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,512)
Training Memory (GB): 13.5
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 52.05
mIoU(ms+flip): 53.24
Config: configs/knet/knet_s3_upernet_swin-l_8x2_512x512_adamw_80k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/knet/knet_s3_upernet_swin-l_8x2_512x512_adamw_80k_ade20k/knet_s3_upernet_swin-l_8x2_512x512_adamw_80k_ade20k_20220303_154559-d8da9a90.pth
- Name: knet_s3_upernet_swin-l_8x2_640x640_adamw_80k_ade20k
In Collection: KNet
Metadata:
backbone: Swin-L
crop size: (640,640)
lr schd: 80000
inference time (ms/im):
- value: 120.63
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (640,640)
Training Memory (GB): 13.54
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 52.21
mIoU(ms+flip): 53.34
Config: configs/knet/knet_s3_upernet_swin-l_8x2_640x640_adamw_80k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/knet/knet_s3_upernet_swin-l_8x2_640x640_adamw_80k_ade20k/knet_s3_upernet_swin-l_8x2_640x640_adamw_80k_ade20k_20220301_220747-8787fc71.pth
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