What is competition-baseline?

datawhalechina/competition-baseline — explained in plain English

Analysis updated 2026-06-26

★ 4,738Jupyter NotebookAudience · dataComplexity · 2/5Setup · easy

Mindmap

mindmap
  root((competition-baseline))
    Competition areas
      Computer vision
      NLP
      Data mining
      Recommendation
    Task types
      Image classification
      Text matching
      Fraud detection
      Time-series forecasting
    Audience
      Competition beginners
      AI learners
    Format
      Jupyter Notebooks
      Annotated code

mindmap root((competition-baseline)) Competition areas Computer vision NLP Data mining Recommendation Task types Image classification Text matching Fraud detection Time-series forecasting Audience Competition beginners AI learners Format Jupyter Notebooks Annotated code

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What do people build with it?

USE CASE 1

Study a working baseline for an image classification or NLP competition and learn how experts structure the code

USE CASE 2

Use an existing baseline as a starting point to iterate and improve your competition score

USE CASE 3

Learn how to process data, train a model, and format a competition submission from a readable example

USE CASE 4

Find a recommendation system or time-series forecasting baseline to adapt for a new contest

What is it built with?

PythonJupyter Notebook

How does it compare?

	datawhalechina/competition-baseline	lixin4ever/conference-acceptance-rate	boyu-ai/hands-on-rl
Stars	4,738	4,742	4,728
Language	Jupyter Notebook	Jupyter Notebook	Jupyter Notebook
Setup difficulty	easy	easy	moderate
Complexity	2/5	1/5	3/5
Audience	data	researcher	researcher

Figures from each repo's GitHub metadata at analysis time.

How do you get it running?

Difficulty · easy Time to first run · 30min

Content and documentation are primarily in Chinese.

License not specified in the explanation.

So what is it?

This repository collects baseline solutions for Chinese AI and data science competitions. A baseline is a working starting-point solution that shows one way to approach a contest problem. The goal is not to provide winning code but to give beginners a clear, readable example they can learn from and build on. The repository covers competitions in several areas: data mining, computer vision, natural language processing, and recommendation systems. Each competition folder contains code and notes explaining the approach. Competitions listed span several years and include events hosted by major Chinese technology companies and academic organizations, covering tasks like image classification, text matching, time-series forecasting, fraud detection, and deepfake detection. The primary audience is people new to data competitions who want to see how experienced practitioners set up a project, process data, train a model, and submit predictions. The README links to a competition calendar and related social media channels where new competition information and fresh baseline code are announced. All code samples are in Jupyter Notebooks, which let readers read explanations and run code side by side. A mirror of the repository is hosted inside China for faster access. The project is maintained by DataWhale China, a community focused on AI education and open collaboration. The full README is longer than what was shown.

Copy-paste prompts

Prompt 1

Take this competition-baseline fraud detection notebook and help me add three new feature engineering steps to improve the model

Prompt 2

Using the DataWhale competition-baseline approach, help me set up a text matching pipeline for a new NLP competition I'm entering

Prompt 3

Explain how this image classification baseline works step by step and suggest three model improvements I could try first

Prompt 4

Help me adapt the time-series forecasting baseline to a new dataset with different column names and a weekly instead of daily frequency

Frequently asked questions