| license: mit | |
| language: en | |
| tags: | |
| - autoencoder | |
| - computer-vision | |
| - image-reconstruction | |
| - celeba | |
| - deep-learning | |
| - face-processing | |
| datasets: | |
| - celeba | |
| pipeline_tag: image-to-image | |
| # CelebA Autoencoder | |
| ## Overview | |
| This project implements a **Convolutional Autoencoder** trained on the [*CelebA dataset*](https://www.kaggle.com/datasets/jessicali9530/celeba-dataset) for image compression and reconstruction. | |
| ## Features | |
| - Learns compressed latent representation of face images | |
| - Reconstructs images from compressed representation | |
| - Evaluated using PSNR and SSIM metrics | |
| ## Dataset | |
| - [CelebA Dataset (Kaggle)](https://www.kaggle.com/datasets/jessicali9530/celeba-dataset) | |
| ## Model | |
| - Encoder: Convolutional layers with downsampling | |
| - Decoder: Transposed convolution layers for reconstruction | |
| ## Results | |
| - Average PSNR: 31.126471439997356 | |
| - Average SSIM: 0.9329655667146047 | |
| # Notes | |
| - Model performs lossy compression | |
| - Some blurring is expected due to reconstruction loss | |
| # Please Fell Free to Use this Project in what ever way you like. |