CoreXDataSet is a comprehensive multi-modal dataset curated specifically for training and evaluating the OmniCoreX AI model — the ultimate AI brain designed for integrating infinite knowledge streams with adaptive reasoning and real-time decision-making capabilities.
This dataset includes diverse data modalities such as text, images, sensor readings, audio, and more, enabling OmniCoreX to learn cross-modal representations and perform advanced multi-stream reasoning across varied real-world scenarios.
- Text: Rich corpora including encyclopedic knowledge, technical documents, and conversational data.
- Images: High-resolution images covering a wide variety of domains such as nature, urban scenes, and technology.
- Sensor Data: Time-series sensor recordings from IoT devices, robotics, and mobile platforms.
- Audio: Speech and environmental audio clips for audio pattern understanding and integration.
- Labels/Annotations: Metadata and annotations required for supervised learning tasks.
CoreXDataSet/
├── metadata.json # Descriptions and references for dataset samples
├── text/ # Directory containing text files or JSON documents
├── images/ # Directory containing images in jpeg/png format
├── sensors/ # CSV or binary files for sensor data sequences
├── audio/ # Audio clips in WAV/MP3 format
└── annotations/ # Optional annotations for supervised tasks
CoreXDataSet is released under the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0). Please review the LICENSE file for more details.
Download and extract the CoreXDataSet archive. Use the provided metadata file to index and load samples efficiently using the OmniCoreX data loader utilities.
CoreXDataSet is designed for seamless integration with the OmniCoreX training pipelines and model architectures. Utilize the dataset modules and data loaders included within the OmniCoreX repository.
If you use CoreXDataSet in your research, please cite it as:
@dataset{corexdataset2024,
title={CoreXDataSet: Multi-Modal Dataset for OmniCoreX AI},
author={Kosasih, Team},
year={2024},
publisher={OmniCoreX Initiative},
url={https://github.com/KOSASIH/CoreXDataSet}
}
We welcome contributions to enhance CoreXDataSet with new modalities, expanded annotations, and improved quality. Please see the CONTRIBUTING.md file in the dataset repository for guidelines.
For inquiries, questions, or support related to CoreXDataSet:
- Email: support@omnicorex.ai
- GitHub: https://github.com/KOSASIH/CoreXDataSet
Empower your AI research with the rich and diverse CoreXDataSet — training the next generation AI brain.