Upload README.md

README.md

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 ---
+language: en
+tags:
+- vae
+- generative-model
+- pytorch
+- swiss-roll
+- unsupervised-learning
 license: mit
+datasets:
+- make_swiss_roll
 ---
+
+# VAE Model for Swiss Roll
+
+This is a Variational Autoencoder (VAE) model trained on the Swiss Roll dataset.
+
+## Model Description
+
+This repository contains a complete implementation of a Variational Autoencoder (VAE) trained on the Swiss Roll 2D manifold dataset. The model learns to encode 2D points from the Swiss Roll into a lower-dimensional latent space and decode them back, enabling both dimensionality reduction and generation of new points that lie on the Swiss Roll manifold.
+
+The architecture is based on the implementation outlined in **Auto-Encoding Variational Bayes by Diederik et al., 2022**
+
+### Architecture Details
+
+- **Model Type**: Variational Autoencoder (VAE)
+- **Framework**: PyTorch
+- **Input**: 2-dimensional points from Swiss Roll (x, z coordinates after projection)
+- **Latent Space**: 2 dimensions 
+- **Encoder and Decoder Layers**: 2 
+- **Encoder and Decoder Hidden Units**: 96 → 48 (encoder), 96 → 48 (decoder)
+- **Total Parameters**: 15,994
+- **Data type:** Binary/Continous (automatically detected)
+- **Current Implementation:** Continous (un-normalised)
+
+### Key Components
+
+1. **Encoder Network**: Maps input images to latent distribution parameters (μ, σ²)
+2. **Reparameterization Trick**: Enables differentiable sampling from the latent distribution
+3. **Decoder Network**: Reconstructs images from latent space samples
+4. **Loss Function**: Combines reconstruction loss ELBO (Bernoulli: binary cross-entropy,  Gaussian: negative log-likelihood) + KL divergence
+
+## Training Details
+
+- **Dataset**: Swiss Roll (10,000 points generated using scikit-learn's make_swiss_roll)
+- **Train/Test Split**: 80/20
+- **Batch Size**: 128
+- **Epochs**: 150
+- **Optimizer**: Adam
+- **Learning Rate**: 1e-3
+- **Gamma**: 1e-1
+
+## Model Performance
+
+### Metrics
+- **Final Training Loss**: ~6.16
+- **Reconstruction Loss**: ~3.42
+- **KL Divergence**: ~2.74
+
+- **Final Validation Loss**: ~5.94
+- **Reconstruction Loss**: ~3.23
+- **KL Divergence**: ~2.71
+
+### Capabilities
+- ✅ High-quality reconstruction of Swiss Roll points
+- ✅ Smooth latent space interpolation
+- ✅ Generation of new points along the Swiss Roll manifold
+- ✅ Well-organized latent space capturing the underlying manifold structure
+
+
+## Usage
+
+### Using Transformers
+
+```python
+from transformers import AutoModel
+import torch
+import torchvision.transforms as transforms
+
+# Load model
+model = AutoModel.from_pretrained("uday9k/SwissRoll_VAE")
+
+# Generate samples
+with torch.no_grad():
+    z = torch.randn(1, 20)  # Sample from prior
+    generated = model.generate(z=z)
+```
+
+### Visualizations Available
+
+1. **Latent Space Visualization**: 2D projection of the 2D latent space showing manifold structure
+2. **Reconstructions**: Original vs. reconstructed Swiss Roll points  
+3. **Generated Samples**: New digits sampled from the latent space
+4. **Interpolations**: Smooth transitions between different regions of the Swiss Roll
+5. **Training Curves**: Loss components over training epochs
+
+## Files and Outputs
+
+- `SwissRoll_VAE_Train.ipynb`: Complete implementation with training and visualization
+- `customVAE_model.pth`: Trained model weights
+- `generated_samples`: Scatter plot of generated samples as part of notebook
+- `latent_space_visualization`: 2D latent space plot as part of notebook
+- `reconstruction_comparison`: Original vs reconstructed images as part of notebook
+- `latent_interpolation`: Interpolation between points as part of notebook
+- `comprehensive_training_curves`: Training loss curves as part of notebook
+
+## Applications
+
+This VAE implementation can be used for:
+
+- **Generative Modeling**: Create new points lying on the Swiss Roll manifold
+- **Dimensionality Reduction**: Compress 2D points to 2D latent representations
+- **Manifold Learning**: Learn the underlying structure of the Swiss Roll data
+- **Interpolation**: Generate smooth transitions between points on the manifold
+- **Educational Purposes**: Understand VAE concepts and implementation
+
+## Research and Educational Value
+
+This implementation serves as an excellent educational resource for:
+
+- Understanding Variational Autoencoders theory and practice
+- Visualizing how VAEs learn manifold structures
+- Learning PyTorch implementation techniques
+- Exploring latent space representations on simple data
+- Studying the balance between reconstruction and regularization
+
+## Citation
+
+If you use this implementation in your research or projects, please cite:
+
+```bibtex
+@misc{vae_mnist_implementation,
+  title={Variational Autoencoder Implementation for Swiss Roll},
+  author={Uday Jain},
+  year={2026},
+  url={https://huggingface.co/uday9k/SwissRoll_VAE}
+}
+```
+
+## License
+
+This project is licensed under the MIT License - see the LICENSE file for details.
+
+## Additional Resources
+
+- **GitHub Repository**: [Profile](https://github.com/SpikeStriker/)
+
+---
+
+**Tags**: deep-learning, generative-ai, pytorch, vae, swiss-roll, unsupervised-learning
+
+**Model Card Authors**: Uday Jain