Update README.md

README.md

@@ -1,3 +1,24 @@
+---
+title: SimEIT Datasets Visualizer
+emoji: 🔬
+colorFrom: blue
+colorTo: purple
+sdk: gradio
+sdk_version: 5.0.0
+app_file: app.py
+pinned: false
+license: apache-2.0
+tags:
+  - medical-imaging
+  - electrical-impedance-tomography
+  - eit
+  - dataset-visualization
+  - simulation
+  - pytorch
+  - computer-vision
+short_description: Visualizer for SimEIT synthetic EIT datasets
+---
+
 # SimEIT: Large-Scale Electrical Impedance Tomography Dataset Visualizer
 
 **A Scalable Simulation Framework for Generating Physically Consistent, AI-Ready EIT Training Data**
@@ -9,51 +30,90 @@
 
 ---
 
-This repository contains an interactive visualization tool for exploring large-scale synthetic EIT (Electrical Impedance Tomography) datasets generated using the **SimEIT framework**—a scalable simulation platform for creating physically consistent, AI-ready training data.
+## 🎯 About This Demo
+
+This interactive demo allows you to explore large-scale synthetic EIT (Electrical Impedance Tomography) datasets generated using the **SimEIT framework**—a scalable simulation platform for creating physically consistent, AI-ready training data.
 
-## About SimEIT
+## 🔬 What is SimEIT?
 
 Electrical Impedance Tomography (EIT) offers advantages over conventional imaging methods, such as X-ray and MRI, but suffers from an ill-posed inverse problem. Deep learning can alleviate this challenge, yet progress is limited by the lack of large, diverse, and reproducible datasets. 
 
 **SimEIT** enables high-throughput creation of diverse geometries and conductivity maps using parallelized finite element simulations, reproducible seeding, and automated validation. The framework provides multi-resolution, AI-ready HDF5 outputs with PyTorch integration, bridging the gap between physical simulation and AI training.
 
-## Features
-
-- 🔄 **Streaming Mode**: Load datasets without downloading them entirely
-- 🖼️ **Multi-resolution Images**: View images at different resolutions (256x256, 128x128, 64x64, 32x32)
-- 📊 **Voltage Plots**: Visualize voltage data per electrode
-- 🎲 **Random/Manual Selection**: Choose samples randomly or by index
-
-## Setup
-
-```bash
-conda create -n SimEIT python=3.13
-
-conda activate SimEIT
-
-conda install pip 
-
-pip install -r requirements.txt
+## ✨ Features
+
+- 🔄 **Streaming Mode**: Load datasets directly from Hugging Face Hub without downloading
+- 🖼️ **Multi-resolution Images**: View images at different resolutions (256×256, 128×128, 64×64, 32×32)
+- 📊 **Interactive Voltage Plots**: Visualize voltage data per electrode with Plotly
+- 🎨 **Customizable Colormaps**: Choose from 18 different scientific colormaps
+- 🎲 **Flexible Selection**: Choose samples randomly or by specific index
+- 💾 **Efficient Caching**: LRU cache for fast repeated access to samples
+- 📈 **Two Dataset Variants**: Explore 'FourObjects' or 'CirclesOnly' subsets
+
+## 🚀 How to Use
+
+1. **Select Dataset Configuration**: Choose between 'FourObjects' or 'CirclesOnly' subsets
+2. **Choose a Sample**: 
+   - Click "Generate Random Index" for a random sample
+   - Or enter a specific index (0-100,000)
+3. **Customize Visualization**:
+   - Select your preferred image resolution
+   - Choose a colormap for visualization
+   - Toggle between linear and log scales
+4. **View Results**: 
+   - Explore multi-resolution conductivity and permittivity maps
+   - Analyze electrode voltage measurements
+   - Examine the domain geometry
+
+## 📊 Dataset Information
+
+The SimEIT dataset contains:
+- **100,000+ samples** per subset
+- **Multi-resolution images**: 256×256, 128×128, 64×64, 32×32
+- **Physical parameters**: Conductivity, permittivity, electrode voltages
+- **Geometry information**: Object positions and boundaries
+- **Two subsets**:
+  - `FourObjects`: Complex scenes with up to 4 objects
+  - `CirclesOnly`: Simplified circular objects
+
+## 🔗 Links
+
+- 📦 **Dataset**: [AymanAmeen/SimEIT-dataset](https://huggingface.co/datasets/AymanAmeen/SimEIT-dataset)
+- 💻 **Code Repository**: [GitHub](https://github.com/Ayman-Ameen/SimEIT)
+- 📄 **Paper**: [ayman-ameen.github.io/SimEIT_page](https://ayman-ameen.github.io/SimEIT_page)
+
+## 🛠️ Technical Details
+
+This visualizer uses:
+- **Gradio** for the interactive interface
+- **HDF5** for efficient data storage and streaming
+- **Plotly** for interactive plots
+- **Hugging Face Hub** for seamless dataset access
+- **NumPy** and **OpenCV** for image processing
+
+## 📝 Citation
+
+If you use the SimEIT dataset or framework in your research, please cite:
+
+```bibtex
+@misc{simeit2025,
+  title={SimEIT: A Scalable Simulation Framework for Generating Physically Consistent, AI-Ready EIT Training Data},
+  author={Ameen, Ayman A. and Mathis-Ullrich, Franziska and Kainz, Bernhard},
+  year={2025},
+  institution={Friedrich-Alexander University Erlangen-Nürnberg, Imperial College London}
+}
 ```
 
-## Usage
+## 📧 Contact
 
-Run the application:
+For questions or feedback:
+- **Ayman A. Ameen**: just drop an email. 
+- **Issues**: Please open an issue on [GitHub](https://github.com/Ayman-Ameen/SimEIT-demo)
 
-```bash
-python appfile.py
-```
+## 📄 License
 
-The Gradio interface will launch in your browser where you can:
-- Generate random sample indices or enter specific ones (0-100,000)
-- Click "Show Images" to visualize the selected sample
-- View images at different resolutions (256x256, 128x128, 64x64, 32x32)
-- View voltage plots per electrode
+This project is licensed under the apache-2.0 License. See the LICENSE file for details.
 
-## File Structure
+---
 
-```
-├── appfile.py          # Main application (all-in-one)
-├── requirements.txt    # Python dependencies
-└── README.md          # This file
-```
+*Built with ❤️ for the research community*