https://github.com/infinitode/open-arc
Open-source Platform for Engineering Neural Architectures and Research Collaboration. Developing and improving AI tools for everyone.
https://github.com/infinitode/open-arc
ai ai-models ai-tools artificial colab-notebook collaboration community engineering experiments free jupyter kaggle ml neural-network nlp notebooks open-source python research tutorials
Last synced: 2 months ago
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Open-source Platform for Engineering Neural Architectures and Research Collaboration. Developing and improving AI tools for everyone.
- Host: GitHub
- URL: https://github.com/infinitode/open-arc
- Owner: Infinitode
- License: other
- Created: 2024-06-17T11:38:56.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-10-10T11:29:48.000Z (about 1 year ago)
- Last Synced: 2024-10-11T14:42:31.015Z (about 1 year ago)
- Topics: ai, ai-models, ai-tools, artificial, colab-notebook, collaboration, community, engineering, experiments, free, jupyter, kaggle, ml, neural-network, nlp, notebooks, open-source, python, research, tutorials
- Language: Jupyter Notebook
- Homepage: https://infinitode.netlify.app
- Size: 3.81 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE.md
- Code of conduct: CODE_OF_CONDUCT.md
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README
# OPEN-ARC
### *Open-source Platform for Engineering Neural Architectures and Research Collaboration*
[](https://open-arc.netlify.app)
[](https://github.com/Infinitode/OPEN-ARC/pulls)
[](https://github.com/Infinitode/OPEN-ARC/stargazers)
## 👋 Welcome to OPEN-ARC
OPEN-ARC is an open-source initiative to advance AI research through collaboration and community-driven development. Each project presents a challenge, a dataset, and a leaderboard. All contributions are welcome, whether you're a hobbyist, researcher, student, or curious coder.
- 🧠 Build models
- 🧪 Share notebooks
- 🏆 Update the leaderboard
- 🚀 Further the field
## 🔧 How to Participate
1. **Pick a project** from the list below.
2. **Create your model implementation** from scratch or by improving the base model.
3. **Fork this repo and update `LEADERBOARD.md`** with:
* Your name or alias
* Architecture type
* Platform (e.g. Kaggle/Colab)
* A link to your notebook or GitHub files
4. **Make a pull request.** Done! You’re on the board!
> [!NOTE]
> You do **not** need to touch anything other than `LEADERBOARD.md` to participate. Keep it simple!
## 📁 Project Leaderboards (Top 5 Only)
### 🩺 Project 1: Liver Cirrhosis Stage Classification
[🔗 Dataset](https://www.kaggle.com/datasets/aadarshvelu/liver-cirrhosis-stage-classification)
| Rank | Contributor | Architecture | Platform | Base Model | Accuracy | Link |
| ---- | ----------- | ---------------------- | -------- | ---------- | -------- | ----------------------------------------------- |
| 🥇 | Our Model | RandomForestClassifier | Kaggle | ✗ | 95.6% | [Notebook](Project-1-LCSC/project-1-lcsc.ipynb) |
### 🌦️ Project 2: Weather Type Classification
[🔗 Dataset](https://www.kaggle.com/datasets/nikhil7280/weather-type-classification)
| Rank | Contributor | Architecture | Platform | Base Model | Accuracy | Link |
| ---- | ----------- | ---------------------- | -------- | ---------- | -------- | --------------------------------------------- |
| 🥇 | Our Model | RandomForestClassifier | Kaggle | ✗ | 91.2% | [Notebook](Project-2-WTC/project-2-wtc.ipynb) |
### 🥔 Project 3: Potato Plant Disease Classification
[🔗 Dataset](https://www.kaggle.com/datasets/hafiznouman786/potato-plant-diseases-data)
| Rank | Contributor | Architecture | Platform | Base Model | Accuracy | Link |
| ---- | ----------- | ------------ | -------- | ---------- | -------- | ----------------------------------------------- |
| 🥇 | Our Model | CustomCNN | Kaggle | ✗ | 95.1% | [Notebook](Project-3-PPDC/project-3-ppdc.ipynb) |
### 🍷 Project 4: Red Wine Quality Classification
[🔗 Dataset](https://www.kaggle.com/datasets/uciml/red-wine-quality-cortez-et-al-2009)
| Rank | Contributor | Architecture | Platform | Base Model | Accuracy | Link |
| ---- | ----------- | -------------------------- | -------- | ---------- | -------- | ----------------------------------------------- |
| 🥇 | Our Model | GradientBoostingClassifier | Kaggle | ✗ | 72.8% | [Notebook](Project-4-RWQC/project-4-rwqc.ipynb) |
### ⚔️ Project 5: Terraria Weapon Name Generation
[🔗 Dataset](https://www.kaggle.com/datasets/acr1209/all-terraria-weapons-dps-v-1449)
| Rank | Contributor | Architecture | Platform | Base Model | Accuracy | Link |
| ---- | ----------- | ------------ | -------- | ---------- | -------- | ----------------------------------------------- |
| 🥇 | Our Model | SimpleRNN | Kaggle | ✔️ | 78.6% | [Notebook](Project-5-TWNG/project-5-twng.ipynb) |
### 📰 Project 6: News Headline Generation
[🔗 Dataset](https://www.kaggle.com/datasets/sunnysai12345/news-summary)
| Rank | Contributor | Architecture | Platform | Base Model | BLEU Score | Link |
| ---- | ----------- | ------------ | -------- | ---------- | ---------- | --------------------------------------------- |
| 🥇 | Our Model | DistilBART | Kaggle | ✗ | 52.8% | [Notebook](Project-6-NHG/project-6-nhg.ipynb) |
### 🌾 Project 7: Crop Recommendation
[🔗 Dataset](https://www.kaggle.com/datasets/varshitanalluri/crop-recommendation-dataset)
| Rank | Contributor | Architecture | Platform | Base Model | Accuracy | Link |
| ---- | ----------- | ------------- | -------- | ---------- | -------- | ------------------------------------------- |
| 🥇 | Our Model | XGBClassifier | Kaggle | ✔️ | 98.6% | [Notebook](Project-7-CR/project-7-cr.ipynb) |
### 🪴 Project 8: Plant Stress Prediction
[🔗 Dataset](https://www.kaggle.com/datasets/ziya07/plant-health-data)
| Rank | Contributor | Architecture | Platform | Base Model | Accuracy | Link |
| ---- | ----------- | ------------- | -------- | ---------- | -------- | ----------------------------------------------- |
| 🥇 | Our Model | XGBClassifier | Kaggle | ✔️ | 99.1% | [Notebook](Project-8-PSPM/project-8-pspm.ipynb) |
### 🚗 Project 9: Traffic Accident Prediction
[🔗 Dataset](https://www.kaggle.com/datasets/denkuznetz/traffic-accident-prediction)
| Rank | Contributor | Architecture | Platform | Base Model | Accuracy | Link |
| ---- | ----------- | ------------- | -------- | ---------- | -------- | ----------------------------------------------- |
| 🥇 | Our Model | XGBClassifier | Kaggle | ✔️ | 85.2% | [Notebook](Project-9-TAPM/project-9-tapm.ipynb) |
### 🍄 Project 10: Mushroom Classification
[🔗 Dataset](https://www.kaggle.com/datasets/uciml/mushroom-classification)
| Rank | Contributor | Architecture | Platform | Base Model | Accuracy | Link |
| ---- | ----------- | ---------------------- | -------- | ---------- | -------- | ----------------------------------------------- |
| 🥇 | Our Model | RandomForestClassifier | Kaggle | ✔️ | 91.1% | [Notebook](Project-10-MCM/project-10-mcm.ipynb) |
### Project 11: Basic Personality Prediction Model
[🔗 Dataset](https://www.kaggle.com/datasets/hardikchhipa28/personality-dataset-introvert-or-extrovert)
| Rank | Contributor | Architecture | Platform | Base Model | Accuracy | Link |
| ---- | ----------- | ---------------------- | -------- | ---------- | -------- | ----------------------------------------------- |
| 🥇 | Our Model | XGBClassifier | Kaggle | ✔️ | 92% | [Notebook](Project-11-BPPM/project-11-bppm.ipynb) |
### Project 12: Spam Mail Classification Model
[🔗 Dataset](https://www.kaggle.com/datasets/hardikchhipa28/personality-dataset-introvert-or-extrovert)
| Rank | Contributor | Architecture | Platform | Base Model | Accuracy | Link |
| ---- | ----------- | ---------------------- | -------- | ---------- | -------- | ----------------------------------------------- |
| 🥇 | Our Model | MultinomialNB | Kaggle | ✔️ | 98.4% | [Notebook](Project-12/notebook.ipynb) |
## 💬 Questions or Ideas?
Feel free to open an issue, start a discussion, or just make a PR. This project is made to be collaborative, welcoming, and constantly evolving.
## 🪪 License
OPEN-ARC is licensed under the [MIT License](LICENSE). Go wild, be cool, and credit contributors where credit's due. Contributors' implementations and models may be subject to different licenses. Be sure to check them before using.