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제1회 코스포 x 데이콘 자동차 충돌 분석 AI경진대회(채용 연계형)

private 5등 솔루션

Capture-20230319-025604

실행

crash, ego, weather, timing 총 4개 변수에 대한 각각을 예측하는 모형을 생성.

각각의 변수를 학습하기 위한 예시는 아래와 같습니다.

다음은 crash를 예측하기 위한 실행 명령어 예시입니다.

Conv3D 베이스 모델(x3d_m)

python main.py --epoch 20 --bs 4 --lr 5e-5 --height 112 --width 112 --name cnn --seed 0 --fs 3 --gamma 2 --wandb 1 --model x3d_m --es 5 --kfold 1 --rs 1 --cuda 0 --target crash

ResNet18 + rnn 베이스 모델

python main.py --epoch 20 --bs 4 --lr 5e-5 --height 112 --width 112 --name rnn --seed 0 --fs 3 --gamma 2 --wandb 1 --model rnn --es 5 --kfold 1 --rs 1 --cuda 0 --target crash

arguments

  • height: 이미지 크기
  • fs: 프레임 스킵
  • gamma: Focal Loss 에 적용되는 감마 값
  • wandb: 1: 적용, 0: 미적용
  • model: 모델의 이름
  • es: 얼리스탑 적용 여부
  • kfold: KFold 적용 여부
  • rs: 랜덤 시드 변경 여부
  • cuda: CUDA 디바이스 (0, 1)
  • target: crash, ego, weather, timing 중 선택

Weights

링크: https://drive.google.com/drive/folders/1vT3Dkp_uKiKBK-6i3fbWiXeCLZwnBa7I?usp=share_link

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