allensmile/kaggle_tgs2018_4th_solution

Kaggle TGS Salt Identification Challenge 2018 4th place code

Kaggle TGS Salt Identification Challenge 2018 4th place code

This is the source code for my part of the 4th place solution to the TGS Salt Identification Challenge hosted by Kaggle.com.

Recent Update

2018.10.22: single model training code updated.

2018.10.20: We achieved the 4th place on Kaggle TGS Salt Identification Challenge.

Dependencies

pytorch 0.3

Solution Development

Single model design

• input: 101 random pad to 128*128, random LRflip;
• encoder: resnet34, se-resnext50, resnext101_ibna, se-resnet101, se-resnet152, se resnet154;
• decoder: scse, hypercolumn (not used in network with resnext101_ibna, se_resnext101 backbone), ibn block, dropout;
• Deep supervision structure with Lovasz softmax (a great idea from Heng); We designed 6 single models for the final submission;

Single model performace

single model（10fold 7cycle）	valid LB	public LB	privare LB
model_50	0.873	0.873	0.891
model_50_slim	0.871	0.872	0.891
model_101A	0.868	0.870	0.889
model_101B	0.870	0.871	0.891
model_152	0.868	0.869	0.888
model_154	0.869	0.871	0.890

Model ensemble performace

ensemble model（cycle voting）	public LB	privare LB
50+50_slim	0.873	0.891
50+50_slim+101B	0.873	0.892
50+50_slim+101A	0.873	0.892
50+50_slim+101A+101B	0.874	0.892
50+50_slim+101A+101B+154	0.874	0.892
50+50_slim+101A+101B+152+154	0.874	0.892

Post processing

According to the 2D and 3D jigsaw results (amazing ideas and great job from @CHAN), we applied around 10 handcraft rules that gave a 0.010~0.011 public LB boost and 0.001 private LB boost.

model	public LB	privare LB
50+50_slim+101A+101B with post processing	0.884	0.893

Data distill (Pseudo Labeling)

We started to do this part since the middle of the competetion. As Heng posts, pseudo labeling is pretty tricky and has the risk of overfitting. I am not sure whether it would boost the private LB untill the result is published. I just post our results here, the implementation details will be updated.

model with datadistill	public LB	privare LB
model_34	0.877	0.893
model_50	0.880	0.893
model_101	0.880	0.894
model 34+50+101	0.879	0.895
model_34 with post processing	0.885	0.893
model_50 with post processing	0.886	0.894
model_101 with post processing	0.886	0.895
model 34+50+101 with post processing (final sub)	0.887	0.895

Training

Train model_34

CUDA_VISIBLE_DEVICES=0 python main.py --mode=train --model=model_34 --model_name=model_34_try --train_fold_index=0

Test model_34

CUDA_VISIBLE_DEVICES=0 python main.py --mode=test --model=model_34 --model_name=model_34_try --train_fold_index=0

Suggestions.

Refference