{"id":19156231,"url":"https://github.com/kyegomez/cognetx","last_synced_at":"2025-05-07T07:36:24.182Z","repository":{"id":257788883,"uuid":"860706079","full_name":"kyegomez/CogNetX","owner":"kyegomez","description":"CogNetX is an advanced, multimodal neural network architecture inspired by human cognition. It integrates speech, vision, and video processing into one unified framework.","archived":false,"fork":false,"pushed_at":"2025-04-19T12:53:43.000Z","size":2270,"stargazers_count":14,"open_issues_count":1,"forks_count":0,"subscribers_count":2,"default_branch":"main","last_synced_at":"2025-04-19T20:16:55.789Z","etag":null,"topics":["agents","ai","cognitive","cognitive-architecture","gpt-10","gpt-5","llms","ml","multi-agent","robots","sentience","swarms"],"latest_commit_sha":null,"homepage":"https://discord.com/servers/agora-999382051935506503","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/kyegomez.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":".github/FUNDING.yml","license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null},"funding":{"github":["kyegomez"],"patreon":null,"open_collective":null,"ko_fi":null,"tidelift":null,"community_bridge":null,"liberapay":null,"issuehunt":null,"otechie":null,"lfx_crowdfunding":null,"custom":null}},"created_at":"2024-09-21T01:18:58.000Z","updated_at":"2025-01-27T18:45:10.000Z","dependencies_parsed_at":"2025-04-19T18:54:57.411Z","dependency_job_id":null,"html_url":"https://github.com/kyegomez/CogNetX","commit_stats":null,"previous_names":["kyegomez/cognetx"],"tags_count":0,"template":false,"template_full_name":"kyegomez/Python-Package-Template","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kyegomez%2FCogNetX","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kyegomez%2FCogNetX/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kyegomez%2FCogNetX/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/kyegomez%2FCogNetX/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/kyegomez","download_url":"https://codeload.github.com/kyegomez/CogNetX/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":252834455,"owners_count":21811385,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["agents","ai","cognitive","cognitive-architecture","gpt-10","gpt-5","llms","ml","multi-agent","robots","sentience","swarms"],"created_at":"2024-11-09T08:33:40.864Z","updated_at":"2025-05-07T07:36:24.162Z","avatar_url":"https://github.com/kyegomez.png","language":"Python","funding_links":["https://github.com/sponsors/kyegomez"],"categories":[],"sub_categories":[],"readme":"[![Multi-Modality](agorabanner.png)](https://discord.com/servers/agora-999382051935506503)\n\n# CogNetX\n\n[![Join our Discord](https://img.shields.io/badge/Discord-Join%20our%20server-5865F2?style=for-the-badge\u0026logo=discord\u0026logoColor=white)](https://discord.gg/agora-999382051935506503) [![Subscribe on YouTube](https://img.shields.io/badge/YouTube-Subscribe-red?style=for-the-badge\u0026logo=youtube\u0026logoColor=white)](https://www.youtube.com/@kyegomez3242) [![Connect on LinkedIn](https://img.shields.io/badge/LinkedIn-Connect-blue?style=for-the-badge\u0026logo=linkedin\u0026logoColor=white)](https://www.linkedin.com/in/kye-g-38759a207/) [![Follow on X.com](https://img.shields.io/badge/X.com-Follow-1DA1F2?style=for-the-badge\u0026logo=x\u0026logoColor=white)](https://x.com/kyegomezb)\n\n\n\nCogNetX is an advanced, multimodal neural network architecture inspired by human cognition. It integrates speech, vision, and video processing into one unified framework. Built with PyTorch, CogNetX leverages cutting-edge neural networks such as Transformers, Conformers, and CNNs to handle complex multimodal tasks. The architecture is designed to process inputs like speech, images, and video, and output coherent, human-like text.\n\n## Key Features\n- **Speech Processing**: Uses a Conformer network to handle speech inputs with extreme efficiency and accuracy.\n- **Vision Processing**: Employs a ResNet-based Convolutional Neural Network (CNN) for robust image understanding.\n- **Video Processing**: Utilizes a 3D CNN architecture for real-time video analysis and feature extraction.\n- **Text Generation**: Integrates a Transformer model to process and generate human-readable text, combining the features from speech, vision, and video.\n- **Multimodal Fusion**: Combines multiple input streams into a unified architecture, mimicking how humans process various types of sensory information.\n\n## Architecture Overview\n\nCogNetX brings together several cutting-edge neural networks:\n- **Conformer** for high-quality speech recognition.\n- **Transformer** for text generation and processing.\n- **ResNet** for vision and image recognition tasks.\n- **3D CNN** for video stream processing.\n\nThe architecture is designed to be highly modular, allowing easy extension and integration of additional modalities.\n\n### Neural Networks Used\n- **Speech**: [Conformer](https://arxiv.org/abs/2005.08100)\n- **Vision**: [ResNet50](https://arxiv.org/abs/1512.03385)\n- **Video**: [3D CNN (R3D-18)](https://arxiv.org/abs/1711.11248)\n- **Text**: [Transformer](https://arxiv.org/abs/1706.03762)\n\n## Installation\n\n\n```bash\n\n$ pip3 install -U cognetx\n\n```\n\n### Model Architecture\n\n```python\nimport torch\nfrom cognetx.model import CogNetX\n\nif __name__ == \"__main__\":\n    # Example configuration and usage\n    config = {\n        \"speech_input_dim\": 80,  # For example, 80 Mel-filterbank features\n        \"speech_num_layers\": 4,\n        \"speech_num_heads\": 8,\n        \"encoder_dim\": 256,\n        \"decoder_dim\": 512,\n        \"vocab_size\": 10000,\n        \"embedding_dim\": 512,\n        \"decoder_num_layers\": 6,\n        \"decoder_num_heads\": 8,\n        \"dropout\": 0.1,\n        \"depthwise_conv_kernel_size\": 31,\n    }\n\n    model = CogNetX(config)\n\n    # Dummy inputs\n    batch_size = 2\n    speech_input = torch.randn(\n        batch_size, 500, config[\"speech_input_dim\"]\n    )  # (batch_size, time_steps, feature_dim)\n    vision_input = torch.randn(\n        batch_size, 3, 224, 224\n    )  # (batch_size, 3, H, W)\n    video_input = torch.randn(\n        batch_size, 3, 16, 112, 112\n    )  # (batch_size, 3, time_steps, H, W)\n    tgt_input = torch.randint(\n        0, config[\"vocab_size\"], (20, batch_size)\n    )  # (tgt_seq_len, batch_size)\n\n    # Forward pass\n    output = model(speech_input, vision_input, video_input, tgt_input)\n    print(\n        output.shape\n    )  # Expected: (tgt_seq_len, batch_size, vocab_size)\n\n```\n\n### Example Pipeline\n\n1. **Speech Input**: Provide raw speech data or features extracted via an MFCC filter.\n2. **Vision Input**: Use images or frame snapshots from video.\n3. **Video Input**: Feed the network with video sequences.\n4. **Text Output**: The model will generate a text output based on the combined multimodal input.\n\n### Running the Example\n\nTo test CogNetX with some example data, run:\n\n```bash\npython example.py\n```\n\n### Train the model\n\n```bash\npython3 train.py\n```\n\n## Code Structure\n\n- `cognetx/`: Contains the core neural network classes.\n    - `model`: The entire model model architecture.\n- `example.py`: Example script to test the architecture with dummy data.\n\n## Future Work\n- Add support for additional modalities such as EEG signals or tactile data.\n- Optimize the model for real-time performance across edge devices.\n- Implement transfer learning and fine-tuning on various datasets.\n\n## Contributing\nContributions are welcome! Please submit a pull request or open an issue if you want to suggest an improvement.\n\n### Steps to Contribute\n1. Fork the repository\n2. Create a feature branch (`git checkout -b feature/awesome-feature`)\n3. Commit your changes (`git commit -am 'Add awesome feature'`)\n4. Push to the branch (`git push origin feature/awesome-feature`)\n5. Open a pull request\n\n## License\nThis project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkyegomez%2Fcognetx","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fkyegomez%2Fcognetx","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkyegomez%2Fcognetx/lists"}