GPU Kernel < 1 hour run-time (Inference only) • External data and pre-trained models are not allowed • 2 stage kernel competition Light models ensemble or Heavy single model TTA or Ensemble Faster preprocess 8
Resized Crop • We use Inception-V3 model • Default Random Resized Crop • Low score in single fold → score jumping up in 5 folds 17 Point 4 torchvision.transforms.RandomResi zedCrop( size, scale=(0.08, 1.0), ratio=(0.75,1.3333333333333333), interpolation=2)
every methods in Discussion/ Public Kernel We investigated all discussion and high score kernel. We try most methods. 23 Number of experiments first (Not jobs, Not research) Understanding method is also important, but it is more important not to stop GPUs during the competition.
and Listen many data Some sounds are very dirty. Viewing and listening the data is most important for making hypothesis. 24 Try many famous architecture We try many famous architecture. (ex. Resnet, Densenet, Wideresnet, resnext, se-resnext...)