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--- |
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license: mit |
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task_categories: |
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- time-series-forecasting |
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- image-classification |
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- text-to-3d |
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- image-to-3d |
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tags: |
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- 3d-gaussian-splatting |
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- point-clouds |
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- mesh |
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- nerf |
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- depth-estimation |
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- robotics |
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- neural-network |
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- ANN |
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- synthetic |
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- simulator |
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- multi-modal |
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--- |
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[](https://webxos.netlify.app) |
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[](https://github.com/webxos/webxos) |
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[](https://huggingface.co/webxos) |
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[](https://x.com/webxos) |
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# 3D Neural Network Dataset |
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## Dataset Information |
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- **Generated**: 2026-01-09T00:01:04.850Z |
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- **Session ID**: session_1767916636309_sg9adv10z |
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- **Total Steps**: 1641 |
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- **Total Images**: 1641 |
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- **Total Nodes**: 153 |
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- **Total Connections**: 303 |
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- **Session Duration**: 0h 3m 48s |
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## Dataset Structure |
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``` |
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neural_network_3d_2026-01-09T00-00-59-568Z/ |
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├── README.md |
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├── data/ |
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│ ├── dataset.json # Complete dataset (JSON) |
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│ ├── steps.csv # Time-series step data |
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│ ├── labels.csv # Classification labels |
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│ ├── node_stats.csv # Node statistics |
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│ └── connection_stats.csv # Connection statistics |
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├── images/ |
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│ ├── step_*.png # High-resolution images |
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│ ├── thumb_*.jpg # Thumbnails |
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│ └── *.png.meta.json # Image metadata |
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└── metadata/ |
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├── session_metadata.json # Session information |
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└── network_metadata.json # Network structure |
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``` |
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## Time-Sequenced Data Format |
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The dataset features high-resolution PNG images (1641 in total) depicting each step of the network's growth, |
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alongside CSV files for steps, labels, node stats, and connection stats. JSON files provide metadata, including |
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session details and image specifics. Thumbnails in JPEG format (320x240) are included for quick previews. |
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Classification labels cover aspects like complexity, topology, density, symmetry, and growth patterns, making |
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it suitable for supervised learning tasks. |
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Potential applications extend to time-series prediction of network expansion, where models could forecast node |
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additions based on historical steps; image-to-graph neural networks, converting visual representations into |
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structured data; topology classification using provided labels; 3D visualization learning for rendering complex |
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structures; and generative models for simulating network architectures. Its alignment by timestamps and FPS |
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intervals ensures usability in dynamic, real-time ML tasks. |
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## Potential Applications |
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It can support various ML workflows, such as developing image-to-graph models or studying 3D visualizations of neural |
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networks. Users can load it via the Hugging Face Datasets library for integration into projects involving time-series |
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analysis or graph generation. |
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### Steps Data (CSV/JSON) |
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Each entry represents a precise moment in network construction: |
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- `step`: Sequential construction step |
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- `timestamp`: Unix timestamp (milliseconds) |
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- `session_time_ms`: Time from session start |
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- `time_iso`: ISO 8601 timestamp |
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- `total_nodes`: Current node count |
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- `total_connections`: Current connection count |
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- `network_depth`: Estimated network depth |
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- `image_file`: Associated image filename |
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### Classification Labels |
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- `complexity`: Network complexity (simple/moderate/complex/very_complex) |
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- `topology`: Network topology (shallow/sparse/dense/deep/balanced) |
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- `density`: Connection density level |
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- `symmetry`: Spatial symmetry |
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- `growth_pattern`: Growth behavior |
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### Node & Connection Statistics |
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Detailed statistics for each construction step including: |
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- Node type distribution |
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- Connection type distribution |
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- Average weights and layers |
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- Activation function usage |
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## Image Data |
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- Resolution: Native canvas resolution |
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- Format: PNG (lossless) |
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- Timestamps: Precisely aligned with tabular data |
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- Metadata: Includes classification labels |
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- Thumbnails: 320x240 JPEG |
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## Multimodal Data Alignment |
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All data streams are precisely synchronized: |
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1. Tabular data captured at exact timestamps |
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2. Images captured at specified FPS intervals |
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3. Classifications computed for each step |
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4. Metadata preserved for each image |
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## Usage Examples |
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- Time-series prediction of network growth |
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- Image-to-graph neural networks |
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- Classification of network topologies |
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- 3D visualization learning |
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- Graph generation models |
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## Citation |
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``` |
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@dataset{3d_neural_network, |
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title = {3D Neural Network Dataset}, |
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author = {webXOS}, |
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year = {2026}, |
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url = {webxos.netlify.app} |
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} |
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``` |
