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SIIM-ISIC Melanoma Classification いのうえ

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Overview → →

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Last year’s solution (Link) ↓

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My approach summary Region model EfficientNetB3 TPU models EfficientNets TPU models EfficientNets TPU models EfficientNets TPU models EfficientNets TPU models EfficientNets TPU models EfficientNets TPU models EfficientNets TPU models Effnets Crop-model RegNet16, 80 Crop model RegNet16, 80 Patch-Attention-model RegNets Patch-Attention-model RegNets Patch-Attention-model RegNets Patch Attention model Effnets, RegNets Ridge Stacking TPU models EfficientNets TPU models EfficientNets TPU models EfficientNets TPU models EfficientNets TPU models Effnets Ridge Stacking

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Interesting Discussion (Link) ↓

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Region model ↓

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Patch-Based Attention model (link)

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Postprocessing?

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Postprocessing? 以上の予測値を 倍 →

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Key discussion (link)

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1st place (link) 2018+2019+2020's data or 2020 only →

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2nd place (link) → EfficientNet-B6, 512x512, BS64, no metadata (CV 0.9336 / public 0.9534) EfficientNet-B7, 640x640, BS32, gradient accumulation 2, no metadata (CV 0.9389 / public 0.9525) Model 1, trained on combined training and pseudolabeled test data (CV 0.9438 / public 0.9493)

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2nd place (link) → EfficientNet-B6, 512x512, BS64, no metadata (CV 0.9336 / public 0.9534) EfficientNet-B7, 640x640, BS32, gradient accumulation 2, no metadata (CV 0.9389 / public 0.9525) Model 1, trained on combined training and pseudolabeled test data (CV 0.9438 / public 0.9493)

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Others