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.

End to End Whale Identification Model

Recent Update

2019.03.13

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| | ---------------- | ---- | |resnet101fold0256x512|0.9696| |seresnet101fold0256x512|0.9691| |seresnext101fold0256x512|0.9692| |resnet101fold0512x512|0.9682| |seresnet101fold0512x512|0.9664| |seresnext101fold0512x512|-|

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| | ---------------- | ---- | |resnet101fold0256x512|0.9705| |seresnet101fold0256x512|0.9704| |seresnext101fold0256x512|-|

Model ensemble performace

| single model | privare LB| | ---------------- | ---- | |resnet101seresnet101seresnext101fold0256x512|0.97113| |resnet101seresnet101seresnext101fold0512x512_pseudo|0.97072| |10 models(final submisson)|0.97209|

Path Setup

Set the following path to your own in ./process/datahelper.py ``` PJDIR = r'/KaggleWhale20192ndplacesolution'#project path traindf = pd.readcsv('train.csv') #train.csv path TRNIMGSDIR = '/train/'#train data path TSTIMGSDIR = '/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