Add Savage X3D Generation Dataset

- 19,712 training examples
- Derived from 1,232 Savage X3D models
- 16x augmentation with color, scale, rotation variations
- Generated with 32 parallel threads on 64-core system
- Ready for LLM fine-tuning for X3D generation

.gitattributes

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+*.jsonl filter=lfs diff=lfs merge=lfs -text
+*.json filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+language:
+- en
+license: apache-2.0
+task_categories:
+- text-generation
+- text2text-generation
+tags:
+- 3D
+- X3D
+- code-generation
+- structured-data
+- xml
+- computer-graphics
+- synthetic
+- web3d
+- military
+- simulation
+pretty_name: Savage X3D Model Generation Dataset
+size_categories:
+- 10K<n<100K
+dataset_info:
+  features:
+  - name: instruction
+    dtype: string
+  - name: input
+    dtype: string
+  - name: output
+    dtype: string
+  - name: metadata
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 1785868800
+    num_examples: 19712
+  download_size: 1785868800
+  dataset_size: 1785868800
+---
+
+# Savage X3D Model Generation Dataset
+
+## Dataset Description
+
+### Purpose
+
+This dataset enables training of Large Language Models (LLMs) to generate structured 3D models in X3D format from natural language descriptions. It contains **19,712 instruction-following examples** derived from the prestigious **Savage 3D Model Repository** maintained by the Naval Postgraduate School.
+
+### Key Statistics
+
+| Metric | Value |
+|--------|-------|
+| **Total Training Examples** | 19,712 |
+| **Base X3D Models** | 1,232 |
+| **Augmentation Factor** | 16x |
+| **Dataset Size** | 1.7 GB |
+| **Average Model Size** | ~90 KB |
+| **Total Downloaded Models** | 3,696 |
+| **Processing Time** | ~30 minutes |
+| **Hardware Used** | 64-core, 5x RTX 4090 |
+
+## Quick Start
+
+```python
+from datasets import load_dataset
+
+# Load the dataset
+dataset = load_dataset("stratplans/savage-x3d-generation")
+
+# Example usage
+example = dataset['train'][0]
+print(f"Instruction: {example['instruction']}")
+print(f"Input metadata: {example['input']}")
+print(f"Output X3D: {example['output'][:500]}...")  # First 500 chars
+```
+
+## Dataset Structure
+
+### Data Fields
+
+Each training example contains four fields:
+
+1. **`instruction`** (string): Natural language prompt requesting X3D generation
+   - Example: *"Create an X3D model of military ground vehicle with 4 shapes using Box, Cylinder geometries"*
+
+2. **`input`** (string): JSON-encoded metadata providing context
+   ```json
+   {
+     "category": "vehicle",
+     "keywords": ["military", "hmmwv", "armored"],
+     "complexity": "medium",
+     "profile": "Immersive",
+     "source_file": "HMMWV"
+   }
+   ```
+
+3. **`output`** (string): Complete X3D XML model
+   ```xml
+   <?xml version="1.0" encoding="UTF-8"?>
+   <!DOCTYPE X3D PUBLIC "ISO//Web3D//DTD X3D 3.0//EN" 
+            "https://www.web3d.org/specifications/x3d-3.0.dtd">
+   <X3D profile='Immersive' version='3.0'>
+     <Scene>
+       <Transform>
+         <Shape>
+           <Appearance>
+             <Material diffuseColor='0.5 0.5 0.5'/>
+           </Appearance>
+           <Box size='2 1 4'/>
+         </Shape>
+       </Transform>
+     </Scene>
+   </X3D>
+   ```
+
+4. **`metadata`** (string): JSON-encoded additional information
+   - Model complexity metrics (node count, shapes, materials)
+   - Viewpoint information
+   - Animation details
+   - Original model ID
+
+### Dataset Categories
+
+The dataset covers 9 major categories from the Savage repository:
+
+| Category | Description | Example Models |
+|----------|-------------|----------------|
+| **GroundVehicles** | Military ground vehicles | HMMWV, M1A1, Jeep |
+| **AircraftFixedWing** | Fixed-wing aircraft | F-16, F-18, AV8B Harrier |
+| **AircraftRotaryWing** | Helicopters | Apache, BlackHawk, CH-46 |
+| **ShipsMilitary** | Naval vessels | Destroyers, Frigates, Carriers |
+| **ShipsCivilian** | Civilian vessels | Tankers, Ferries, Tugboats |
+| **Buildings** | Structures | Hangars, Houses, Stadiums |
+| **Sensors** | Detection equipment | Radar, Sonar, Satellites |
+| **Weapons** | Military ordnance | Missiles, Bombs |
+| **AmphibiousVehicles** | Amphibious craft | LCAC, AAV |
+
+## Dataset Creation Process
+
+### 1. Data Collection (Parallel Download)
+
+```python
+# Used 32 parallel threads for efficient downloading
+with concurrent.futures.ThreadPoolExecutor(max_workers=32) as executor:
+    # Downloaded 3,696 X3D files from savage.nps.edu
+    # Crawled 1,439 index pages
+    # Total download time: ~10 minutes
+```
+
+### 2. Data Processing Pipeline
+
+```python
+# Parse X3D files and extract structured information
+for x3d_file in x3d_files:
+    metadata = extract_metadata(x3d_file)      # Title, creator, keywords
+    scene_info = extract_scene_info(x3d_file)  # Shapes, materials, transforms
+    complexity = calculate_complexity(x3d_file) # Node counts, animations
+    description = generate_description(...)     # Natural language description
+```
+
+### 3. Data Augmentation Techniques
+
+Applied 5 augmentation strategies to increase dataset diversity:
+
+1. **Color Variations**: Modified HSV values of materials
+2. **Scale Transformations**: Applied 0.5x to 2x scaling
+3. **Rotation Modifications**: Added random rotations on axes
+4. **Viewpoint Adjustments**: Modified camera positions
+5. **Lighting Variations**: Adjusted light intensities and colors
+
+### 4. Instruction Generation
+
+Created diverse instruction templates:
+
+```python
+templates = [
+    "Create an X3D model of {description}",
+    "Generate a 3D scene showing {description}",
+    "Build an X3D representation of {description}",
+    "Design an X3D model featuring {description}",
+    "Construct a 3D model with {description}"
+]
+```
+
+## Recommended Models for Fine-tuning
+
+Based on the structured nature of X3D generation:
+
+1. **Qwen2.5-Coder-7B-Instruct** (Best Overall)
+   - Excellent for code/structured data
+   - Strong XML understanding
+
+2. **CodeLlama-13B-Instruct**
+   - Specialized for code generation
+   - Good spatial reasoning
+
+3. **Mistral-7B-Instruct-v0.3**
+   - Efficient and fast
+   - Good instruction following
+
+4. **DeepSeek-Coder-7B**
+   - Strong at structured output
+   - Good for long sequences
+
+## Training Configuration Example
+
+```python
+from transformers import AutoModelForCausalLM, TrainingArguments
+
+model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-Coder-7B")
+
+training_args = TrainingArguments(
+    output_dir="./x3d-generator",
+    num_train_epochs=3,
+    per_device_train_batch_size=4,
+    gradient_accumulation_steps=2,
+    learning_rate=2e-5,
+    bf16=True,
+    gradient_checkpointing=True,
+    logging_steps=100,
+    save_strategy="epoch",
+    evaluation_strategy="epoch"
+)
+```
+
+## Use Cases
+
+1. **3D Content Generation**: Automatically generate 3D models from text descriptions
+2. **Simulation & Training**: Create scenarios for military/defense simulations
+3. **Education**: Teach 3D graphics and X3D standards
+4. **Research**: Benchmark structured data generation in LLMs
+5. **Game Development**: Rapid prototyping of 3D assets
+
+## Limitations
+
+- **Domain Specific**: Primarily military/defense-oriented models
+- **Format Specific**: Only X3D format (not OBJ, FBX, GLTF)
+- **No Textures**: Models don't include texture image files
+- **Geometric Focus**: Limited artistic/organic shapes
+- **X3D Version**: Mostly X3D 3.0/3.3 (not latest 4.0)
+
+## Citation
+
+```bibtex
+@dataset{savage_x3d_generation_2024,
+  title={Savage X3D Model Generation Dataset},
+  author={Web3D Consortium and Naval Postgraduate School},
+  year={2024},
+  publisher={Hugging Face},
+  journal={Hugging Face Datasets},
+  howpublished={\url{https://huggingface.co/datasets/stratplans/savage-x3d-generation}}
+}
+```
+
+## Acknowledgments
+
+- **Naval Postgraduate School** for maintaining the Savage repository
+- **Web3D Consortium** for X3D standards and tools
+- **Don Brutzman** and the Savage development team
+- All original model creators and contributors
+
+## License
+
+Apache 2.0 - The dataset is provided under Apache 2.0 license. Original Savage models are provided under their respective licenses (see [Savage License](https://savage.nps.edu/Savage/license.html)).
+
+## Links
+
+- [Savage Repository](https://savage.nps.edu/Savage/)
+- [X3D Specifications](https://www.web3d.org/standards)
+- [Web3D Consortium](https://www.web3d.org/)
+- [Dataset Generation Code](https://github.com/stratplans/x3d-llm-training)
+
+## Dataset Samples
+
+### Simple Example (Box):
+```xml
+<Shape>
+  <Appearance>
+    <Material diffuseColor='1 0 0'/>
+  </Appearance>
+  <Box size='2 2 2'/>
+</Shape>
+```
+
+### Complex Example (Vehicle with multiple parts):
+```xml
+<Transform translation='0 0.5 0'>
+  <Shape>
+    <Appearance>
+      <Material diffuseColor='0.3 0.3 0.3'/>
+    </Appearance>
+    <Box size='4 1 2'/>
+  </Shape>
+</Transform>
+<Transform translation='1.5 0 0.8'>
+  <Shape>
+    <Appearance>
+      <Material diffuseColor='0.1 0.1 0.1'/>
+    </Appearance>
+    <Cylinder radius='0.3' height='0.2'/>
+  </Shape>
+</Transform>
+<!-- Additional vehicle components... -->
+```
+
+---
+
+**Dataset created by**: [Your Name/Organization]  
+**Contact**: [Your contact information]  
+**Last updated**: August 26, 2024

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