README.md

---
license: mit
task_categories:
- time-series-forecasting
- image-classification
- text-to-3d
- image-to-3d
tags:
- 3d-gaussian-splatting
- point-clouds
- mesh
- nerf
- depth-estimation
- robotics
- neural-network
- ANN
- synthetic
- simulator
- multi-modal
---

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# 3D Neural Network Dataset

## Dataset Information

- **Generated**: 2026-01-09T00:01:04.850Z
- **Session ID**: session_1767916636309_sg9adv10z
- **Total Steps**: 1641
- **Total Images**: 1641
- **Total Nodes**: 153
- **Total Connections**: 303
- **Session Duration**: 0h 3m 48s

## Dataset Structure

```
neural_network_3d_2026-01-09T00-00-59-568Z/
├── README.md
├── data/
│   ├── dataset.json              # Complete dataset (JSON)
│   ├── steps.csv                 # Time-series step data
│   ├── labels.csv               # Classification labels
│   ├── node_stats.csv           # Node statistics
│   └── connection_stats.csv     # Connection statistics
├── images/
│   ├── step_*.png              # High-resolution images
│   ├── thumb_*.jpg             # Thumbnails
│   └── *.png.meta.json         # Image metadata
└── metadata/
    ├── session_metadata.json   # Session information
    └── network_metadata.json   # Network structure
```

## Time-Sequenced Data Format

The dataset features high-resolution PNG images (1641 in total) depicting each step of the network's growth, 
alongside CSV files for steps, labels, node stats, and connection stats. JSON files provide metadata, including 
session details and image specifics. Thumbnails in JPEG format (320x240) are included for quick previews. 
Classification labels cover aspects like complexity, topology, density, symmetry, and growth patterns, making 
it suitable for supervised learning tasks.

Potential applications extend to time-series prediction of network expansion, where models could forecast node 
additions based on historical steps; image-to-graph neural networks, converting visual representations into 
structured data; topology classification using provided labels; 3D visualization learning for rendering complex 
structures; and generative models for simulating network architectures. Its alignment by timestamps and FPS 
intervals ensures usability in dynamic, real-time ML tasks.

## Potential Applications

It can support various ML workflows, such as developing image-to-graph models or studying 3D visualizations of neural 
networks. Users can load it via the Hugging Face Datasets library for integration into projects involving time-series 
analysis or graph generation.

### Steps Data (CSV/JSON)

Each entry represents a precise moment in network construction:
- `step`: Sequential construction step
- `timestamp`: Unix timestamp (milliseconds)
- `session_time_ms`: Time from session start
- `time_iso`: ISO 8601 timestamp
- `total_nodes`: Current node count
- `total_connections`: Current connection count
- `network_depth`: Estimated network depth
- `image_file`: Associated image filename

### Classification Labels

- `complexity`: Network complexity (simple/moderate/complex/very_complex)
- `topology`: Network topology (shallow/sparse/dense/deep/balanced)
- `density`: Connection density level
- `symmetry`: Spatial symmetry
- `growth_pattern`: Growth behavior

### Node & Connection Statistics

Detailed statistics for each construction step including:
- Node type distribution
- Connection type distribution
- Average weights and layers
- Activation function usage

## Image Data

- Resolution: Native canvas resolution
- Format: PNG (lossless)
- Timestamps: Precisely aligned with tabular data
- Metadata: Includes classification labels
- Thumbnails: 320x240 JPEG

## Multimodal Data Alignment

All data streams are precisely synchronized:
1. Tabular data captured at exact timestamps
2. Images captured at specified FPS intervals
3. Classifications computed for each step
4. Metadata preserved for each image

## Usage Examples

- Time-series prediction of network growth
- Image-to-graph neural networks
- Classification of network topologies
- 3D visualization learning
- Graph generation models

## Citation
```
@dataset{3d_neural_network,
  title = {3D Neural Network Dataset},
  author = {webXOS},
  year = {2026},
  url = {webxos.netlify.app}
}
```