papers-with-everything

KO | EN

gitlite — search

#javascript #python #hacktoberfest #react #ai #typescript #llm #go #golang #android #machine-learning #rust #deep-learning #linux

papers-with-everything

(★ 29)

Machine Learning papers with code, data and models

papers-with-everything Latest Version Download

Download Latest Version (.zip)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# Papers With Everything

[Hacktoberfest](https://hacktoberfest.com) is a month-long virtual festival of open source! Participants are giving back to the community by completing pull requests, participating in events, and donating to open-source projects. This project is part of Hacktoberfest, where **participants enrich the [Open Source Data Science](https://dagshub.com/blog/a-case-for-open-source-data-science/) domain by adding datasets and models to existing code repos.**

![banner](https://user-images.githubusercontent.com/66431403/193002906-e50617ad-bb9d-4942-98c1-7b9684df9ddc.png)

## What is DagsHub?

[DagsHub](https://dagshub.com/) is a centralized platform to host and manage machine learning projects including code, data, models, experiments, annotations, model registry, and more! DagsHub does the MLOps heavy lifting for its users. Every repository comes with configured S3 storage, an experiment tracking server, and an annotation workspace - all using popular open-source tools like MLflow, DVC, Git, and Label Studio.

## **Quick Start to Contribution**

- Sign-up to [Hacktoberfest](https://hacktoberfest.com/auth/) & [DagsHub](https://dagshub.com/user/sign_up?redirect_to=).
- Join our [Hacktoberfest Discord channel](https://discord.gg/6SsqDCUVeq).
- Read the [contribution guidelines](https://hacktoberfest.com/participation/).
- Create a Pull Requests on the [Papers With Everything](https://github.com/DagsHub/papers-with-everything) with the README of your DagsHub Project.

## ****What does the DagsHub community contribute?****

Promoting [Open Source Data Science](https://dagshub.com/blog/a-case-for-open-source-data-science/) has always been a core value for us at DagsHub. We've promoted it in various ways, including [sponsoring the Reproducibility Challenge](https://dagshub.com/DAGsHub-Official/reproducibility-challenge/reports/ML+Reproducibility+Challenge+Spring+2021) by Papers with Code to make SOTA paper accessible to the ML community.

**This year, we decided to combine the open source festival of Hacktoberfest and the Reproducibility Challenge to create THE Grand Festival of Open Source Data Science!**

In this challenge, participants will connect repos from GitHub to DagsHub that host reproduced papers from NeurIPS, ICML, ICLR, ACL, EMNLP, CVPR, and ECCV, and add their datasets and model's weights to DagsHub Storage.

## ****How to contribute?****

- Claim a paper you wish to contribute from the SOTA [3D](https://paperswithcode.com/area/computer-vision/3d) or [object-detection](https://paperswithcode.com/area/computer-vision/object-detection) papers (KUDOS to [Papers With Code](https://paperswithcode.com/)) by opening a new issue on the [GitHub repository](https://github.com/DagsHub/papers-with-everything) and name it after the paper. **Please make sure that the paper wasn't claimed**.
- [Connect the paper’s repository from GitHub to DagsHub](https://dagshub.com/docs/integration_guide/github/).
- Upload the data and model to its [DagsHub storage](https://dagshub.com/docs/feature_guide/dagshub_storage/).
- Add relevant tags to the repository and files.
- Add the following labels to the repository:
    - `3D Model` / `object detection`
    - `hacktoberfest`
- In the [GitHub papers-with-everything project](https://github.com/DagsHub/papers-with-everything):
    - Open a new branch named after the paper.
    - Add a directory named after the paper with its README file.
    - Commit and push the changes to GitHub.
    - Create a pull request on GitHub.
- Optional: Connect the DagsHub repo to [Papers with Code](https://paperswithcode.com/).

papers-with-everything

papers-with-everything Latest Version Download

Today's picks

Was this content helpful?

Recent Feedback

papers-with-everything

papers-with-everything Latest Version Download

Today's picks

Was this content helpful?

Recent Feedback

Download README