Quick shell script for batch-downloading Kaggle competition datasets using the Kaggle API for local server training.

Week 11 retrospective of Naver Boostcamp AI Tech covering the Relation Extraction competition, model experiments, and leaderboard submissions.

Practical tips for Kaggle competitions covering ranking systems, competition types, validation strategies, ensemble methods, and hardware recommendations.

Retrospective on a mask/age/gender image classification competition: model selection, augmentation, CutMix, and lessons learned.

Using the Albumentations library for image augmentation in PyTorch, with example pipelines for training and TTA.

Last-minute competition score boosting: test-time augmentation (TTA) with soft voting and half-precision training.

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.

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

Day 2 competition log covering ensemble strategies (bagging vs. boosting) and CNN model selection for image classification.

Tips on efficient data feeding, transform ordering for performance, and using Albumentations for image augmentation.

Image preprocessing and data augmentation techniques for deep learning competitions, including resizing, bounding box cropping, and torchvision transforms.

Training plan for a mask classification competition: per-feature models with ensemble learning, plus Jupyter vs. CLI workflow notes.

Guide to approaching AI competitions with EDA strategies for image classification tasks.