Update README.md

README.md

@@ -1,3 +1,26 @@
+---
+license: mit
+task_categories:
+- time-series-forecasting
+- image-to-text
+- image-to-image
+- image-classification
+- image-segmentation
+- tabular-classification
+- text-to-3d
+- image-to-3d
+tags:
+- 3d-gaussian-splatting
+- point-clouds
+- mesh
+- nerf
+- depth-estimation
+- robotics
+- neural-network
+- ANN
+- synthetic
+- simulator
+---
 # 3D Neural Network Construction Dataset: neural_network_3d
 
 ## Dataset Information
@@ -30,7 +53,26 @@ neural_network_3d_2026-01-09T00-00-59-568Z/
 
 ## 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)
@@ -42,6 +84,7 @@ Each entry represents a precise moment in network construction:
 - `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
@@ -49,6 +92,7 @@ Each entry represents a precise moment in network construction:
 - `growth_pattern`: Growth behavior
 
 ### Node & Connection Statistics
+
 Detailed statistics for each construction step including:
 - Node type distribution
 - Connection type distribution
@@ -56,6 +100,7 @@ Detailed statistics for each construction step including:
 - Activation function usage
 
 ## Image Data
+
 - Resolution: Native canvas resolution
 - Format: PNG (lossless)
 - Timestamps: Precisely aligned with tabular data
@@ -63,6 +108,7 @@ Detailed statistics for each construction step including:
 - 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
@@ -70,6 +116,7 @@ All data streams are precisely synchronized:
 4. Metadata preserved for each image
 
 ## Usage Examples
+
 - Time-series prediction of network growth
 - Image-to-graph neural networks
 - Classification of network topologies
@@ -78,11 +125,11 @@ All data streams are precisely synchronized:
 
 ## Citation
 ```
-@dataset{neural_network_3d_2026-01-09T00-00-59-568Z,
+@dataset{3d_neural_network,
   title = {3D Neural Network Construction Dataset},
-  author = {Generated by Multimodal Dataset Builder v3.0},
+  author = {webXOS},
   year = {2026},
   version = {3.0},
-  url = {Generated locally}
+  url = {Generated locally in Three JS}
 }
-```
+```