{"id":19825001,"url":"https://github.com/mjahmadee/vae","last_synced_at":"2026-06-13T05:32:57.578Z","repository":{"id":182028330,"uuid":"667831747","full_name":"MJAHMADEE/VAE","owner":"MJAHMADEE","description":"Variational Autoencoder","archived":false,"fork":false,"pushed_at":"2024-03-16T12:54:53.000Z","size":14277,"stargazers_count":2,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-02-28T20:45:39.776Z","etag":null,"topics":["isomap","latent-space","pca","vae","variational-autoencoder"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/MJAHMADEE.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2023-07-18T11:58:26.000Z","updated_at":"2023-07-21T09:15:17.000Z","dependencies_parsed_at":null,"dependency_job_id":"9ea94715-b383-4845-b5bc-dc8a947de7cb","html_url":"https://github.com/MJAHMADEE/VAE","commit_stats":null,"previous_names":["mjahmadee/vae"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/MJAHMADEE/VAE","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/MJAHMADEE%2FVAE","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/MJAHMADEE%2FVAE/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/MJAHMADEE%2FVAE/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/MJAHMADEE%2FVAE/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/MJAHMADEE","download_url":"https://codeload.github.com/MJAHMADEE/VAE/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/MJAHMADEE%2FVAE/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":34273788,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-26T15:22:16.424Z","status":"online","status_checked_at":"2026-06-13T02:00:06.617Z","response_time":62,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["isomap","latent-space","pca","vae","variational-autoencoder"],"created_at":"2024-11-12T11:06:40.923Z","updated_at":"2026-06-13T05:32:57.540Z","avatar_url":"https://github.com/MJAHMADEE.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Variational Autoencoders for Image Classification 🤖👚\n\n![Python](https://img.shields.io/badge/Python-3.x-blue.svg)\n![TensorFlow](https://img.shields.io/badge/TensorFlow-2.x-orange.svg)\n![Machine Learning](https://img.shields.io/badge/Machine%20Learning-VAE-green.svg)\n\nThis repository contains implementations of Variational Autoencoders (VAE) and their application in image classification tasks, primarily focusing on the Fashion MNIST dataset.\n\n## Features 🌟\n- Implements Variational Autoencoders (VAE) for generating and reconstructing images.\n- Utilizes TensorFlow and Keras for building and training models.\n- Supports dimensionality reduction for improving image classification using K-Nearest Neighbors (KNN).\n- Includes detailed performance evaluation with confusion matrices and classification reports.\n- Provides visualizations of training losses, latent spaces, and generated images.\n\n## Setup and Installation 🛠️\n1. Clone the repository.\n2. Install the necessary dependencies using `pip install -r requirements.txt`.\n3. Ensure TensorFlow with GPU support is installed if GPU processing is desired.\n\n## Datasets 📁\nThe primary dataset used is Fashion MNIST, which includes 60,000 training images and 10,000 testing images of 10 fashion categories.\n\n## Training the Model 🚀\n- Execute the VAE training script to learn latent representations of images.\n- The model automatically performs image reconstruction and generation.\n\n## Image Classification 🧪\n- Use the encoded representations from VAE as features for training a KNN classifier.\n- Evaluate the classifier's performance using the test dataset and calculate various metrics like accuracy, precision, recall, and F1-score.\n\n## Results and Evaluation 📊\n- Check the output directory for training logs, model checkpoints, and generated images.\n- Review the classification reports and confusion matrices to understand model performance.\n\n## Contributing 🤝\nContributions, issues, and feature requests are welcome! Feel free to check the issues page.\n\n## License 📜\nThis project is available under the MIT License. See the LICENSE file for more details.\n\n## Acknowledgements 🙌\n- TensorFlow and Keras documentation for providing extensive guides and API documentation.\n- Fashion MNIST dataset creators for providing a benchmark dataset for image classification tasks.\n\nFor more details, please visit the [GitHub repository](https://github.com/MJAHMADEE/VAE/).\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmjahmadee%2Fvae","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmjahmadee%2Fvae","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmjahmadee%2Fvae/lists"}