README.md

---
license: mit
task_categories:
- image-to-text
- visual-question-answering
- image-classification
- video-classification
tags:
- computer-vision
- visual-question-answering
- visual-sequence-learning
- video-frame-prediction
- regression-from-images
- multimodal-regression
- conditional-image-generation
- mathematical-visualization
---

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 _   _ _   _______ ___________ _    _  ___________ _    ______ 
| | | | \ | |  _  \  ___| ___ \ |  | ||  _  | ___ \ |   |  _  \
| | | |  \| | | | | |__ | |_/ / |  | || | | | |_/ / |   | | | |
| | | | . ` | | | |  __||    /| |/\| || | | |    /| |   | | | |
| |_| | |\  | |/ /| |___| |\ \\  /\  /\ \_/ / |\ \| |___| |/ / 
 \___/\_| \_/___/ \____/\_| \_|\/  \/  \___/\_| \_\_____/___/  
      
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# UNDERWORLD Dataset v3

- Visualizes the Fast Inverse Square Root (FISR / Quake III) algorithm.
- 120 rows total (train split only).
- Main content: 1280px PNG image frames showing bit hacks, Newton-Raphson steps, error surfaces, 3D math plots.
- Numerical_data.csv (regression), metadata.json (conditional).
- Size ~3.5 MB. Generated via UNDERGROUND: FISR tool (downloadable in repo).
- Magic Number: 0x5f23aac5  
- Newton Iterations: 3
- Input Range: 0.1 to 1000
- Maximum Error: 1.1742636926798086e+287%

**Generated with UNDERWORLD: FISR by webXOS, Educational visualization of the Quake III Arena optimization algorithm.**

**The UNDERWORLD app by webXOS is available for download in the /underworld/ folder of this repo so users can create their own datasets.**

## Use cases:

- Training ML models for fault detection / anomaly detection in time-series or sensor data.
  
- Simulating hardware faults (bit flips, stuck-at, etc.) for robust AI / embedded ML.
  
- Reliability engineering: predict system failures under errors.
  
- Synthetic data for safety-critical systems (automotive, aerospace, IoT) where real fault data is rare.
  
- Benchmarking error-correction / resilient algorithms.
  
- Visual sequence learning → train models on math visualization sequences (frame prediction, video understanding).
  
- Image-to-text / captioning → describe FISR steps from images.
  
- Visual question answering → QA on algorithm visuals.
  
- Regression from images → predict error metrics from visualization frames.
  
- Educational multimodal models → teach bit manipulation / fast math approx.
  
- Conditional generation → use metadata to condition on input range/error.
  
- 3D math function visualization benchmark → compare rendering / understanding.

## Education:

1. The Fast Inverse Square Root algorithm implementation
2. Error analysis of the approximation
3. 3D visualization of mathematical functions
4. Bit-level manipulation techniques

### Usage for Training:

1. Use frames/ for visual sequence learning
2. Use numerical_data.csv for regression tasks
3. Use metadata.json for conditional generation
4. Train models to understand optimization algorithms

### Citation:

If you use this dataset, please cite:
UNDERGROUND: FISR by webXOS, 2027

### License:

MIT