Practical training techniques including AMP, label smoothing, ArcFace loss, class pivot adjustment, and Wandb logging for image classification.

Clarifying common mistakes in PyTorch training loops, validation ordering, and K-fold cross validation usage.

Applying vertical CutMix augmentation for face mask classification to focus patches on facial regions.

CutMix data augmentation in PyTorch: loss computation, accuracy, and F1 score calculation.

Week 4 retrospective of Naver Boostcamp AI Tech covering image classification competition, CNN model comparisons, and CutMix augmentation.

TIL notes on wandb logging, fine-tuning pre-trained models, early stopping, and ensemble learning during an image classification competition.