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yolov5-face-detection

face-detection-with-yolov5

https://github.com/marlinsiraegi/yolov5-face-detection

Science Score: 44.0%

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    .zenodo.json file
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    Scientific vocabulary similarity
    Low similarity (2.6%) to scientific vocabulary
Last synced: 6 months ago · JSON representation ·

Repository

face-detection-with-yolov5

Basic Info
  • Host: GitHub
  • Owner: MarlinSiraegi
  • License: agpl-3.0
  • Language: Python
  • Default Branch: main
  • Size: 51 MB
Statistics
  • Stars: 0
  • Watchers: 1
  • Forks: 0
  • Open Issues: 0
  • Releases: 0
Created over 1 year ago · Last pushed over 1 year ago
Metadata Files
Readme Contributing License Citation

README.md

https://www.kaggle.com/datasets/iamtushara/face-detection-dataset/code
캐글에서 데이터셋을 다운로드 받고 루트 디렉토리에 추가 데이터셋의 디렉토리 구조는
input/face-data 안에 images,labels 폴더가 있고

input 폴더가 루트 디렉토리(yolov5-test 폴더)에 있으면 됨

train.py를 실행시키면 학습이 시작됨

별도의 터미널 명령어 입력 필요없이 바로 되도록 코드를 구성해놨음

if __name__ == "__main__":

    opt = parse_opt()
    opt.epochs = 50  # 총 학습 에포크 수를 50으로 설정
    opt.data = str(Path(__file__).resolve().parent / 'data' / 'face.yaml')  # 사용할 데이터셋 구성 파일 경로를 설정
    opt.weights = 'yolov5s.pt'  # 사전 학습된 가중치 파일 경로를 설정
    opt.cache = True  # 데이터셋을 캐시하여 학습 속도를 높임
    opt.save_period = -1  # 베스트 모델만 저장
    opt.batch_size = 16  # 배치 크기를 16으로 설정
    opt.cos_lr = True  # 코사인 학습률 스케줄러 활성화
    opt.amp = True  # Auto Mixed Precision Training 활성화

    main(opt)

이부분을 조정함으로써 여러가지 조작가능

추가 yolov5 사전학습 모델 yolov5l 사용
이미지 사이즈 800으로 확장 (실행 명령어 : python3 train.py --img 800)

베스트 에포크 평가 지표
- Precision: 0.87094
- Recall: 0.74891
- mAP@0.5: 0.80812
- mAP@0.5:0.95: 0.47171
- Objectness Loss: 0.029933
- Box Loss: 0.03023

Owner

  • Login: MarlinSiraegi
  • Kind: user

Citation (CITATION.cff) 

cff-version: 1.2.0
preferred-citation:
  type: software
  message: If you use YOLOv5, please cite it as below.
  authors:
  - family-names: Jocher
    given-names: Glenn
    orcid: "https://orcid.org/0000-0001-5950-6979"
  title: "YOLOv5 by Ultralytics"
  version: 7.0
  doi: 10.5281/zenodo.3908559
  date-released: 2020-5-29
  license: AGPL-3.0
  url: "https://github.com/ultralytics/yolov5"

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Dependencies

utils/docker/Dockerfile docker
  • pytorch/pytorch 2.0.0-cuda11.7-cudnn8-runtime build
utils/google_app_engine/Dockerfile docker
  • gcr.io/google-appengine/python latest build
pyproject.toml pypi
  • matplotlib >=3.3.0
  • numpy >=1.22.2
  • opencv-python >=4.6.0
  • pandas >=1.1.4
  • pillow >=7.1.2
  • psutil *
  • py-cpuinfo *
  • pyyaml >=5.3.1
  • requests >=2.23.0
  • scipy >=1.4.1
  • seaborn >=0.11.0
  • thop >=0.1.1
  • torch >=1.8.0
  • torchvision >=0.9.0
  • tqdm >=4.64.0
  • ultralytics >=8.1.47
requirements.txt pypi
  • PyYAML >=5.3.1
  • gitpython >=3.1.30
  • matplotlib >=3.3
  • numpy >=1.23.5
  • opencv-python >=4.1.1
  • pandas >=1.1.4
  • pillow >=10.3.0
  • psutil *
  • requests >=2.32.0
  • scipy >=1.4.1
  • seaborn >=0.11.0
  • setuptools >=65.5.1
  • thop >=0.1.1
  • torchvision >=0.9.0
  • tqdm >=4.64.0
utils/google_app_engine/additional_requirements.txt pypi
  • Flask ==2.3.2
  • gunicorn ==22.0.0
  • pip ==23.3
  • werkzeug >=3.0.1
  • zipp >=3.19.1