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Kaggle Humpback Whale Identification Challenge 2019 2nd place code
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README.md

Kaggle Humpback Whale Identification Challenge 2019 2nd place code

This is the source code for my part of the 2nd place solution to the Humpback Whale Identification Challenge hosted by Kaggle.com.

image

End to End Whale Identification Model

Recent Update

2019.03.13: code upload.

Dependencies

  • imgaug == 0.2.8
  • opencv-python==3.4.2
  • scikit-image==0.14.0
  • scikit-learn==0.19.1
  • scipy==1.1.0
  • torch==1.0.1.
  • torchvision==0.2.2

Solution Development

single model design

  • Input: 256x512 or 512*512 cropped images;
  • Backbone: resnet101, seresnet101, seresnext101;
  • Loss function: arcface loss + triplet loss + focal loss;
  • optimizer: adam with warm up lr strategy;
  • Augmentation: blur,grayscale,noise,shear,rotate,perspective transform;
  • Horizontal flip to create more ids -> 5004*2
  • Pseudo Labeling

Single model performace

single model privare LB
resnet101_fold0_256x512 0.9696
seresnet101_fold0_256x512 0.9691
seresnext101_fold0_256x512 0.9692
resnet101_fold0_512x512 0.9682
seresnet101_fold0_512x512 0.9664
seresnext101_fold0_512x512 -

Single model performace with pseudo labeling

I generate a pseudo label list containing 1.5k samples when I reached 0.940 in public LB, and I kept using this list till the competition ended. I used the bottleneck feature of the arcface model (my baseline model) to calculate cosine distance of train test images. For those few shot classes (less than 2 samples), I choose 0.65 as the threshold to filter high confidence samples. I think it will be better result using 0.970 LB model to find pseudo label.

single model privare LB
resnet101_fold0_256x512 0.9705
seresnet101_fold0_256x512 0.9704
seresnext101_fold0_256x512 -

Model ensemble performace

single model privare LB
resnet101_seresnet101_seresnext101_fold0_256x512 0.97113
resnet101_seresnet101_seresnext101_fold0_512x512_pseudo 0.97072
10 models(final submisson) 0.97209

Path Setup

Set the following path to your own in ./process/data_helper.py

PJ_DIR = r'/Kaggle_Whale2019_2nd_place_solution'#project path
train_df = pd.read_csv('train.csv') #train.csv path
TRN_IMGS_DIR = '/train/'#train data path
TST_IMGS_DIR = '/test/' #test data path

Single Model Training

train resnet101 256x512 fold 0:

CUDA_VISIBLE_DEVICES=0,1,2,3 python main.py --mode=train --model=resnet101 --image_h=256 --image_w=512 --fold_index=0 --batch_size=128

train resnet101 512x512 fold 0:

CUDA_VISIBLE_DEVICES=0,1,2,3 python main.py --mode=train --model=resnet101 --image_h=512 --image_w=512 --fold_index=0 --batch_size=128

predict resnet101 256x512 fold 0 model:

CUDA_VISIBLE_DEVICES=0,1,2,3 python main.py --mode=test --model=resnet101 --image_h=256 --image_w=512 --fold_index=0 --batch_size=128 --pretrained_mode=max_valid_model.pth

train resnet101 256x512 fold 0 with pseudo labeling:

CUDA_VISIBLE_DEVICES=0,1,2,3 python main.py --mode=train --model=resnet101 --image_h=256 --image_w=512 --fold_index=0 --batch_size=128 --is_pseudo=True

predict resnet101 256x512 fold 0 model with pseudo labeling:

CUDA_VISIBLE_DEVICES=0,1,2,3 python main.py --mode=test --model=resnet101 --image_h=256 --image_w=512 --fold_index=0 --batch_size=128 --is_pseudo=True --pretrained_mode=max_valid_model.pth

Final Ensemble

the final submission is the weight average result of 10 ckpts

python ensemble.py
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