CCAiM – Cloud Classification AI Model

project links

CCAiM hugging face collection 🤗

📌 Project Status

Currently in the dataset collection phase.

🎯 Project Goal

CCAiM aims to develop an AI-powered model for classifying clouds based on ground-level photographs. The project uses image recognition techniques to identify cloud types according to the WMO International Cloud Atlas classification.

☁️ Target Cloud Classes

The model will be trained to recognize the following cloud types:

1. Cirrus (Ci) – Thin, wispy clouds high in the sky
2. Cirrostratus (Cs) – Transparent, whitish veil clouds
3. Cirrocumulus (Cc) – Small, white patchy clouds
4. Altostratus (As) – Gray/blue layer clouds preceding storms
5. Altocumulus (Ac) – White/gray layered clouds with shading
6. Stratus (St) – Uniform gray cloud blanket
7. Stratocumulus (Sc) – Low lumpy clouds with blue sky gaps
8. Nimbostratus (Ns) – Dark precipitation clouds
9. Cumulus (Cu) – Fluffy white clouds with flat bases
10. Cumulonimbus (Cb) – Towering thunderstorm clouds

🛠 Planned Features

dreams

• 📷 Dataset expansion – Using contributor photographs
• 🧠 Multiple model versions:
• V1: Initial first stable model (minimal viable dataset)
• 🔍 Image classification API for integrations
• 🌐 Interactive web demo
• 📊 Model evaluation tools

now can

• collect dataset
• create first model V0.0.1

📄 License Information

• All data exist in hugging face
• Code in this repository is licensed under the MIT License (see LICENSE).
• Photographs located in folders named clouds_<dataset_number> are licensed under CC0 1.0 Universal (Public Domain), meaning they can be used freely for any purpose, including commercial use, without attribution.
• If other datasets are added in the future, their license terms will be specified in a separate license file inside their respective folder.

models

All models exist in hugging face

models list

• v_0.0.1 # first model learned by 23 photo.
• v_0.0.2 # second model learned by 42 photo
• v_0.0.3 # model learned by 88 photo
• v_0.0.4 # model learned by 165 photo

🤝 How to Contribute

• The most helpful contribution at the moment: If you find a discrepancy between the cloud class specified in the JSON label and the actual image content, correcting it will greatly improve dataset quality.