chohoseong / p3-ims-obd-code-chaser

p3-ims-obd-code-chaser created by GitHub Classroom

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TEAM : Code Chaser

Task and solving strategies

  • data: a part of TACO dataset
  • problem and breakthrough:
    • overfitting -> multilabel stratified 5-fold cross validation, lr scheduling, weight decay
    • inconsistency of images -> augmentations(flip, grid mask, rotate, etc)
  • semantic segmentation
    • architecture: UNet3+ with depp supervision and class guide module
    • backbone: EfficientNet-B3(pretrained, noisy student)
    • validation: multilabel stratified 5-fold cross validation
    • ensemble: 5-fold soft voting
  • object detection
    • model1:
      • backbone: Swin Transformer
      • neck: FPN
      • detector: Hybrid Task Cascade
    • model2:
      • backbone: EfficientNet-B5
      • neck: BiFPN
      • detector: EfficientDet
    • model3:
      • backbone: CSP-Darknet (depth multiple: 1.33, width multiple: 1.25)
      • neck: PANet
      • detector: YOLO
    • multi head ensemble based on WBF

Result

  • semantic segmentation
    • mIoU - 0.6795
  • object detection
    • mAP@50 - 0.4171

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p3-ims-obd-code-chaser created by GitHub Classroom


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