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README.md

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+---
+license: cc-by-4.0
+task_categories:
+- other
+tags:
+- pathfinding
+- gpu-computing
+- benchmark
+- neuromorphic
+- navigation
+- eikonal-equation
+- robotics
+- real-time
+size_categories:
+- n<1K
+---
+
+# Optical Neuromorphic Eikonal Solver - Benchmark Datasets
+
+## Overview
+
+Benchmark datasets for evaluating the **Optical Neuromorphic Eikonal Solver**, a GPU-accelerated pathfinding algorithm achieving **30-300× speedup** over CPU Dijkstra.
+
+## 🎯 Key Results
+
+- **134.9× average speedup** vs CPU Dijkstra
+- **0.64% mean error** (sub-1% accuracy)
+- **1.025× path length** (near-optimal paths)
+- **2-4ms per query** on 512×512 grids
+
+## 📊 Dataset Content
+
+5 synthetic pathfinding test cases covering diverse scenarios:
+
+| File | Grid Size | Cells | Obstacles | Speed Field | Difficulty |
+|------|-----------|-------|-----------|-------------|------------|
+| sparse_128.npz | 128×128 | 16,384 | 10% | Uniform | Easy |
+| medium_256.npz | 256×256 | 65,536 | 20% | Uniform | Medium |
+| gradient_256.npz | 256×256 | 65,536 | 20% | Gradient | Medium |
+| maze_511.npz | 511×511 | 261,121 | 30% (maze) | Uniform | Hard |
+| complex_512.npz | 512×512 | 262,144 | 30% | Random | Hard |
+
+Plus: `benchmark_results.csv` with performance metrics
+
+## 📋 Format
+
+Each `.npz` file contains:
+
+```python
+{
+    'obstacles': np.ndarray,  # (H,W) float32, 1.0=blocked, 0.0=free
+    'speeds': np.ndarray,     # (H,W) float32, propagation speed
+    'source': np.ndarray,     # (2,) int32, [x,y] start coordinates
+    'target': np.ndarray,     # (2,) int32, [x,y] goal coordinates
+    'metadata': str           # JSON with provenance info
+}
+```
+
+## 🔧 Loading Data
+
+```python
+import numpy as np
+from huggingface_hub import hf_hub_download
+
+# Download dataset
+file_path = hf_hub_download(
+    repo_id="Agnuxo/optical-neuromorphic-eikonal-benchmarks",
+    filename="maze_511.npz",
+    repo_type="dataset"
+)
+
+# Load
+data = np.load(file_path, allow_pickle=True)
+obstacles = data['obstacles']
+speeds = data['speeds']
+source = tuple(data['source'])
+target = tuple(data['target'])
+
+print(f"Grid: {obstacles.shape}")
+print(f"Start: {source}, Goal: {target}")
+```
+
+## 🎮 Interactive Demo
+
+Try the interactive pathfinding demo: [Space Link](https://huggingface.co/spaces/Agnuxo/optical-neuromorphic-pathfinding-demo)
+
+## 📄 Paper & Code
+
+- **Paper**: [GitHub](https://github.com/Agnuxo1/optical-neuromorphic-eikonal-solver)
+- **Code**: [GitHub Repository](https://github.com/Agnuxo1/optical-neuromorphic-eikonal-solver)
+- **Author**: [Francisco Angulo de Lafuente](https://huggingface.co/Agnuxo)
+
+## 📚 Citation
+
+```bibtex
+@misc{angulo2025optical,
+  title={Optical Neuromorphic Eikonal Solver Benchmark Datasets},
+  author={Angulo de Lafuente, Francisco},
+  year={2025},
+  publisher={Hugging Face},
+  url={https://huggingface.co/datasets/Agnuxo/optical-neuromorphic-eikonal-benchmarks}
+}
+```
+
+## 📜 License
+
+CC BY 4.0 (Creative Commons Attribution 4.0 International)
+
+## 🔗 Links
+
+- Code: https://github.com/Agnuxo1/optical-neuromorphic-eikonal-solver
+- Kaggle: https://www.kaggle.com/franciscoangulo
+- ResearchGate: https://www.researchgate.net/profile/Francisco-Angulo-Lafuente-3