This is the code for 30th place solution in kaggle Seversteal steel detection.
This might be a relatively simple approach. Just apply 5 class classification including background class and then 4 class segmenteation.
For classification task, I trained resnet50, efficientnet-b3 and se-resnext50.
For segmentation task, Unet with resnet18, PSPNet with resnet18 and FPN with resnet50.
$ python split_fold.py --config config/base_config.yml
$ python train_cls.py --config config/cls/001_resnet50_BCE_5class_fold0.yml
$ python train_cls.py --config config/cls/002_efnet_b3_cls_BCE_5class_fold1.yml
$ python train_cls.py --config config/cls/003_seresnext50_cls_BCE_5class_fold2.yml
$ python train_seg.py --config config/seg/001_resnet18_Unet_fold0.yml
$ python train_seg.py --config config/seg/002_resnet18_PSPNet_fold0.yml
$ python train_seg.py --config config/seg/003_resnet50_fpn_fold0.yml
$ python ensemble.py --config_dir config/
My code for this competition specific part is based on this great starter kernel
this kernel also inspired me a lot.
And I borrowed many idea from Heng's great disscussion topics.
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