level1-image-classification-level1-recsys-09 created by GitHub Classroom

Overview

level1-image-classification-level1-recsys-09

주제 설명

  • COVID-19 Pandemic 상황 속 마스크 착용 유무 판단 시스템 구축
  • 마스크 착용 여부, 성별, 나이 총 세가지 기준에 따라 총 18개의 class로 구분하는 모델

👋 팀원 소개

김혜지 이아현 김동우 김은선 김연요
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🔨 Installation

  • torch == 1.6.0
  • torchvision == 0.7.0
  • tensorboard == 2.4.1
  • pandas == 1.1.5
  • opencv-python == 4.5.1.48
  • scikit-learn ~= 0.24.1
  • matplotlib == 3.2.1
  • efficientnet_pytorch
$ pip install -r $ROOT/level1-image-classification-level1-recsys-09/requirements.txt

Function Description

model.py: EfficientNet-b4와 GoogLeNet을 Ensemble하여 모델링

dataset.py: data augmentation, labeling 등 model training에 사용되는 dataset 생성

loss.py: cross entropy, f1 score, arcface를 이용해 loss 값을 계산

train.py: model을 사용자가 지정한 parameter에 따라 실행하여 training

🏢 Structure

level1-image-classification-level1-recsys-09
│
├── README.md
├── requirements.txt
├── EDA
│   ├── data_EDA.ipynb
│   ├── image_EDA.ipynb
│   └── torchvision_transforms.ipynb
└── python
    ├── dataset.py
    ├── loss.py
    ├── model.py
    └── train.py

⚙️ Training 명령어

python train.py --model 'Ensemble' --TTA True --name 'final model' --epoch 3

image

🖼️ 실행 결과

모델명 F1-Score Accuracy 최종 순위
EfficientNet-b4 + GoogLeNet 0.7269 77.3016 private 35등

📜 참고자료

EfficientNet-PyTorch

GoogLeNet

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