Upload 3 files

uploading model and related files

README.md

@@ -1,3 +1,148 @@
 ---
+language: en
+tags:
+- vae
+- generative-model
+- pytorch
+- mnist
+- unsupervised-learning
 license: mit
+datasets:
+- mnist
 ---
+
+# VAE Model for MNIST
+
+This is a Variational Autoencoder (VAE) model trained on the MNIST dataset.
+
+## Model Description
+
+This repository contains a complete implementation of a Variational Autoencoder (VAE) trained on the MNIST handwritten digits dataset. The model learns to encode images into a 2-dimensional latent space and decode them back to reconstructed images, enabling both data compression and generation of new digit-like images.
+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**: 28×28 grayscale images (784 dimensions)
+- **Latent Space**: 20 dimensions 
+- **Encoder and Decoder Layers**: 2 
+- **Encoder and Decoder Hidden Units**: 1024 → 512 (encoder), 1024 → 512 (decoder)
+- **Total Parameters**: ~4.8M
+- **Data type:** Binary/Continous (automatically detected)
+- **Current Implementation:** Binary (pixel>0.5)
+
+### 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**: MNIST (60,000 training images, 10,000 test images) torchvision.datasets.MNIST
+- **Batch Size**: 128
+- **Epochs**: 44
+- **Optimizer**: Adam
+- **Learning Rate**: 1e-3
+
+## Model Performance
+
+### Metrics
+- **Final Training Loss**: ~79.6
+- **Final Validation Loss**: ~84.3
+- **Reconstruction Loss**: ~48.0
+- **KL Divergence**: ~31.5
+
+### Capabilities
+- ✅ High-quality digit reconstruction
+- ✅ Smooth latent space interpolation
+- ✅ Generation of new digit-like samples
+- ✅ Well-organized latent space with digit clusters
+
+
+## Usage
+
+### Using Transformers
+
+```python
+from transformers import AutoModel
+import torch
+import torchvision.transforms as transforms
+
+# Load model
+model = AutoModel.from_pretrained("uday9k/Binarized_MNIST_VAE")
+
+# Generate samples
+with torch.no_grad():
+    z = torch.randn(1, 20)  # Sample from prior
+    generated = model.generate(z=z)
+    # Reshape to image
+    image = generated.view(28, 28).cpu().numpy()
+
+### Visualizations Available
+
+1. **Latent Space Visualization**: 2D scatter plot showing digit clusters
+2. **Reconstructions**: Original vs. reconstructed digit comparisons  
+3. **Generated Samples**: New digits sampled from the latent space
+4. **Interpolations**: Smooth transitions between different digits
+5. **Training Curves**: Loss components over training epochs
+
+## Files and Outputs
+
+- `MNIST_VAE_Train.ipynb`: Complete implementation with training and visualization
+- `best_vae_model.pth`: Trained model weights
+- `generated_samples`: Grid of generated digit samples as part of notebook
+- `latent_space_visualization.png`: 2D latent space plot as part of notebook
+- `reconstruction_comparison.png`: Original vs reconstructed images as part of notebook
+- `latent_interpolation.png`: Interpolation between digit pairs as part of notebook
+- `comprehensive_training_curves.png`: Training loss curves as part of notebook
+
+## Applications
+
+This VAE implementation can be used for:
+
+- **Generative Modeling**: Create new handwritten digit images
+- **Dimensionality Reduction**: Compress images to 2D representations
+- **Anomaly Detection**: Identify unusual digits using reconstruction error
+- **Data Augmentation**: Generate synthetic training data
+- **Representation Learning**: Learn meaningful features for downstream tasks
+- **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
+- Learning PyTorch implementation techniques
+- Exploring generative modeling concepts
+- Analyzing latent space representations
+- 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 MNIST},
+  author={Uday Jain},
+  year={2026},
+  url={https://huggingface.co/uday9k/Binarized_MNIST_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, mnist, computer-vision, unsupervised-learning
+
+**Model Card Authors**: Uday Jain 

customVAE_model2.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4c6fac73e7c30ffb37e71029f6f8d319507bb055b22d4e2f536227ead417d806
+size 36823498