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LICENSE

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+MIT License
+
+Copyright (c) 2023 Hussam Alafandi
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

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+---
+library_name: pytorch
+tags:
+  - vae
+  - celeba
+  - generative-model
+  - latent-space
+  - image-reconstruction
+  - attribute-manipulation
+datasets:
+  - celeba
+metrics:
+  - reconstruction-loss
+  - kl-divergence
+license: mit
+---
+
+# Variational Autoencoder (VAE) - CelebA Dataset
+
+This repository contains a trained Variational Autoencoder (VAE) model on the CelebA dataset. The model is designed to encode and decode facial images, enabling tasks such as image reconstruction, latent space interpolation, and attribute manipulation.
+
+## Model Details
+
+- **Architecture**: Variational Autoencoder (VAE)
+- **Dataset**: CelebA
+- **Latent Dimension**: 200
+- **Training Subset Size**: 80,000 images
+- **Batch Size**: 64
+- **Learning Rate**: 1e-3
+- **Epochs**: 10
+
+
+### Weights and Biases Run
+
+The training process was tracked using [Weights and Biases](https://wandb.ai). You can view the full training logs and metrics [here](https://wandb.ai/hussam-alafandi/vae-celeba/runs/cv01woz1?nw=nwuserhussamalafandi).
+
+## Usage
+
+### Loading the Model
+
+To load the trained model, use the following code snippet:
+```python
+import torch
+from vae_model import VAE  # Ensure the VAE class is defined in vae_model.py
+
+# Define the latent dimension
+latent_dim = 200
+
+# Initialize the model
+model = VAE(latent_dim=latent_dim)
+
+# Load the trained weights
+model_path = "./vae_celeba_latent_200_epochs_10_batch_64_subset_80000.pth"
+model.load_state_dict(torch.load(model_path))
+model.eval()
+```
+
+### Applications
+
+1. **Image Reconstruction**: Reconstruct input images using the encoder and decoder.
+2. **Latent Space Interpolation**: Generate smooth transitions between two images by interpolating in the latent space.
+3. **Attribute Manipulation**: Modify specific attributes (e.g., smiling, hair color) by moving along attribute directions in the latent space.
+
+## Example Results
+
+### Reconstruction
+Below is a reconstruction example where the first row represents the original images, and the second row represents the reconstructed images:
+
+![Reconstruction](./reconstruction_images_13_9c2c9b76dee391efede2.png)
+
+### Latent Space Interpolation
+Below is an example of interpolating between two images in the latent space:
+
+![Latent Space Interpolation](./latent_space_interpolation.png)
+
+### Attribute Manipulation
+Manipulating the "Smiling" attribute in the latent space:
+
+![Attribute Manipulation](./attribute_manipulation.png)
+
+
+## License
+
+This project is licensed under the MIT License. See the LICENSE file for details.

vae_celeba_latent_200_epochs_10_batch_64_subset_80000.pth

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