Kaggle_Whale2019_2nd_palce_solution
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.
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