$ show datawhalechina/competition-baseline
This repository is a curated collection of baseline solutions for data science competitions, primarily from Chinese platforms (科大讯飞 AI开发者大赛, Tianchi, DataFountain, AIWIN, etc.) and some international ones (Kaggle). It contains hundreds of Jupyter notebooks and Python scripts covering a wide range of tasks: structured data classification/regression (LightGBM, XGBoost), computer vision (ResNet, EfficientNet, YOLO, CRNN), natural language processing (BERT, TF-IDF, Qwen), time series forecasting, and multimodal challenges. Each baseline is self-contained, providing a quick-start pipeline from data loading to submission generation. The repository serves as a practical learning resource for competition participants, demonstrating common feature engineering techniques, model training loops, and ensemble strategies. However, the code quality is uneven: many notebooks are exploratory with hardcoded paths, outdated library versions, and minimal error handling. The repo lacks centralized dependency management, environment definitions, or modular reusable libraries, making reproducibility low without manual adaptation. Despite these limitations, it offers a valuable catalog of patterns and templates that can be adapted for new competitions.
The LLM identified 2 files worth highlighting for reuse:
Contains reusable image augmentation functions `elastic_transform_fast` and `grid_distortion` for stroke-based or deformation-sensitive image tasks. These functions are self-contained, well-documented (with references to Simard2003), and can be extracted into a standalone augmentation library. They demonstrate non-obvious skill in implementing elastic deformations with OpenCV and random affine warping, applicable beyond the Quick Draw competition.
Provides a clear, self-contained tutorial on rank-based ensemble methods (average rank, highest rank, etc.) for combining multiple model predictions. The pattern is independent of any specific model or dataset, making it directly reusable in any classification or ranking competition. It demonstrates a non-obvious technique that often improves leaderboard scores beyond simple averaging.