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ProbSpace Competition: Ukiyo-e Author Prediction

Maxwell
January 14, 2020

ProbSpace Competition: Ukiyo-e Author Prediction

This slide shows the 2nd place overall solution of the ProbSpace Ukiyo-e competition which was held from Nov.2019 to Jan.2020.

Competition URL: https://prob.space/competitions/ukiyoe-author

Updated on Jul. 2022

Maxwell

January 14, 2020
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  1. Ukiyo-e Author Prediction
    Open Review Competition
    Maxwell

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  2. 0
    1
    2
    3
    4
    5
    6
    7
    8
    9
    3158 train images
    10 classes
    224 x 224
    397 test images
    10 classes
    224 x 224
    Remove 10 images
    But this was less helphul for me...
    (might be regularization?)
    3158 => 3148 images
    ProbSpace
    DenseNet 121
    (GR=32)
    DenseNet 169
    (GR=32)
    SE-ResNeXt 50
    SE-ResNeXt 101
    ResNet 18
    Inception - ResNetV2
    Common to All Models
    - Stratified 8 folds
    - Images divided by 255
    - AdamW
    - 3 Stage Learning
    CyclicLR (1e-3~1e-4) => LRonPlateau (1e-5) => LRonPlateau (5e-6)
    - Batch Size: 36
    - Augmenatation:
    RandomSizedCrop, Shift, Scale, Brightness, Rotate
    HorizontalFlip, GridDistortion
    - Softmax with 10 classes
    Predict
    FC 1024
    FC 128
    FC 10
    DenseNet 121
    Psuedo threshold: 0.99
    1st stage
    Geometric Weighted Blending
    CV: 0.8974, LB: 0.902
    2nd stage
    Model 1 (TTA)
    CV: 0.8736, LB: 0.914
    Model 2 (TTA)
    CV: 0.8792, LB: 0.914
    Model 3 (TTA)
    CV: 0.8743, LB: 0.899
    DenseNet 121
    Psuedo threshold: 0.95
    DenseNet 169
    Psuedo threshold: 0.99
    Common to All Models
    - Stratified 8 folds
    - Images divided by 255
    - AdamW
    - 3 Stage Learning
    CyclicLR => LRonPlateau => LRonPlateau
    - Batch Size: 36
    - Augmenatation:
    RandomSizedCrop, Shift, Scale, Brightness,
    Rotate, HorizontalFlip, GridDistortion
    - CutMix: p=0.5, alpha=0.5
    - Softmax with 10 classes
    FC 1024
    FC 128
    FC 10
    397 test images
    10 classes
    224 x 224
    Predict
    Geometric Weighted Blending
    Model 1 : Model 2 : Model 3 =
    40% : 25% : 35%
    Prediction with TTA
    - Augmentation:
    - Shift, Scale, Rotate, ...
    - HorizontalFlip
    - 50-times averaging
    Post-Processing
    Image No.150
    No.150 is all white.
    Classified as `0`
    Model 1
    Model 2
    Model 3
    CV: 0.883
    LB: 0.929
    Author
    Class 2 nd
    Ubuntu 18.04
    - Keras
    - TITAN RTX x 1
    - Geforce GTX 1080 Ti x 2
    @ Maxwell_110

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  3. What worked
     DenseNet 121, 169
    - DenseNet outperformed other model architectures
    - Color information seemed to be important
     Data Augmentation
    - Crop, Shift, Scale, Rotate, Brightness, HorizontalFlip, GridDistortion
     CutMix
    - alpha=0.5
     Psuedo Labeling (Hard Labeling)
    - Threshold 0.99/0.95
    - Threshold ensemble in 2nd stage
     3 stage Learning
    - AdamW
    - CyclicLR (1e-3-1e-4, 200 epoch) => RLRonP (1e-5, 100 epoch) => RLRonP (5e-6, 100 epoch)
     TTA
    - 50 times, Shift, Scale, Rotate, HorizontalFlip
    Confusion matrix of DenseNet121
    GradCAM
    (In this case, CNN looks paying attention to
    the back ground and umbrellas)
    ProbSpace

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  4. What did `NOT` worked
     Metric Learning
    - Arcface
     Stacking
    - Arcface features as additional features in 2nd layer
    - Statistical values (mean, std, CoV, ...) as additional features in 2nd layer
     A custom function to improve class imbalance
    - I have never succeeded to this kind of tricks... you?
    Arcface with t-SNE ( 4 of 8 folds )
    ProbSpace

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