Releasing-Research-Code

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releasing-research-code is an Open Source & Models tool that provides best practices for releasing machine learning research code. It offers official NeurIPS 2021 guidelines to facilitate reproducibility and increase GitHub stars.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is releasing-research-code?

releasing-research-code offers comprehensive tips and guidelines for effectively releasing machine learning research code. These recommendations are collated from an analysis of over 200 popular ML research repositories and are now official guidelines at NeurIPS 2021. The project emphasizes practices that facilitate reproducibility and correlate with higher GitHub stars. Key components include a README.md template, an ML Code Completeness Checklist covering dependencies, training code, evaluation code, pre-trained models, and detailed result tables. The resource also provides additional awesome resources for hosting pre-trained models, managing model files, standardized model interfaces, results leaderboards, and making project pages and demos.

Best used for

Ideal for developers, data scientists, and professors who need to publish machine learning research code, ensure its reproducibility, and maximize its impact. Especially valuable for structuring ML projects according to best practices and official NeurIPS guidelines.

Common actions

publish research code

improve code reproducibility

structure ML projects

share pre-trained models

low-code/no-codeopen-sourcedeepfakeautomated workflowworkflowscollaborationgithub copilotface swapping"AI Agents"

Capabilities

Key features

README.md template
ML Code Completeness Checklist
Dependency specification guidelines
Training code recommendations
Evaluation code recommendations
Pre-trained model advice
Results leaderboard integration

Target Audience

developerdata scientistprofessorstudent

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What is the ML Code Completeness Checklist?

The ML Code Completeness Checklist is a set of five items compiled from popular ML research repositories. It includes specifying dependencies, providing training and evaluation code, offering pre-trained models, and including a detailed README with results and reproduction commands.

Are these guidelines mandatory for NeurIPS submissions?

For NeurIPS 2021 code submissions, it is recommended but not mandatory to use the provided README.md template and check as many items on the ML Code Completeness Checklist as possible. These guidelines aim to improve reproducibility.

Where can I host pre-trained models according to these guidelines?

The guidelines suggest several platforms for hosting pre-trained models, including Zenodo, GitHub Releases, OneDrive, Google Drive, Dropbox, AWS S3, huggingface_hub, and DAGsHub, each with varying features and limitations.

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