{"id":18035612,"url":"https://github.com/adesoji1/food-image-classification","last_synced_at":"2025-10-15T15:32:01.342Z","repository":{"id":189783812,"uuid":"681294354","full_name":"Adesoji1/Food-Image-Classification","owner":"Adesoji1","description":"Food Image Classification using TensorFlow: A deep learning model to classify various food items using TensorFlow and CNNs.","archived":false,"fork":false,"pushed_at":"2023-08-21T18:07:24.000Z","size":16995,"stargazers_count":6,"open_issues_count":0,"forks_count":2,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-10-04T22:32:44.662Z","etag":null,"topics":["colab-notebook","hyperparameter-optimization","hyperparameter-tuning","keras-tensorflow","matplotlib-pyplot","numpy","pandas","pillow","python3","pytorch","regularization","tensorflow","transfer-learning","transformer"],"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/Adesoji1.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}},"created_at":"2023-08-21T17:42:29.000Z","updated_at":"2025-07-25T00:21:41.000Z","dependencies_parsed_at":"2023-08-21T19:33:59.660Z","dependency_job_id":null,"html_url":"https://github.com/Adesoji1/Food-Image-Classification","commit_stats":null,"previous_names":["adesoji1/food-image-classification"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/Adesoji1/Food-Image-Classification","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Adesoji1%2FFood-Image-Classification","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Adesoji1%2FFood-Image-Classification/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Adesoji1%2FFood-Image-Classification/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Adesoji1%2FFood-Image-Classification/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Adesoji1","download_url":"https://codeload.github.com/Adesoji1/Food-Image-Classification/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Adesoji1%2FFood-Image-Classification/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":279087251,"owners_count":26100350,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","status":"online","status_checked_at":"2025-10-15T02:00:07.814Z","response_time":56,"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":["colab-notebook","hyperparameter-optimization","hyperparameter-tuning","keras-tensorflow","matplotlib-pyplot","numpy","pandas","pillow","python3","pytorch","regularization","tensorflow","transfer-learning","transformer"],"created_at":"2024-10-30T12:08:55.261Z","updated_at":"2025-10-15T15:32:01.325Z","avatar_url":"https://github.com/Adesoji1.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Food-Image-Classification\n\n### Task instruction below\n[here](https://www.figma.com/file/b1X9waDcm6Ygh2PcRvaUI0/Image-Classification?type=design\u0026node-id=0-1\u0026mode=design)\n\n---\n\n### **GitHub Description**:\n\n🍔 Food Image Classification using TensorFlow: A deep learning model to classify various food items using TensorFlow and CNNs.\n\n---\n\n### **README.md**\n\n### Food Image Classification using TensorFlow\n\nThis repository contains a deep learning model that classifies various food items using TensorFlow and Convolutional Neural Networks (CNNs).\n\n## Project Overview\n\nThe goal of this project is to build a model that can accurately classify images of food into predefined categories. With the rise of health and fitness apps, such a model can be integrated into applications to automatically detect and log consumed food items based on user-uploaded images.\n\n## Dataset\n\nThe dataset used for this project consists of images of various food items categorized into different classes. Each image is labeled with its corresponding food category. Available  [here](https://drive.google.com/drive/u/0/folders/1fTBPKhOU5bTIo6gTmJmvzDXCT5fXUvTz) \n\n## Features\n\n- **Data Augmentation**: To artificially increase the size of the training dataset and improve model generalization.\n- **Convolutional Neural Networks (CNNs)**: Utilized for feature extraction from images.\n- **Regularization**: To prevent overfitting and ensure the model generalizes well to new, unseen data.\n- **Transfer Learning**: Leveraged pre-trained models to improve accuracy and reduce training time.\n\n## Requirements\n\n- TensorFlow 2.x\n- Python 3.7+\n- Numpy\n- Matplotlib\n- Scikit-learn\n- hypopt\n- PIllow\n- torch (During Experimentation)\n- pipreqs\n\n## Usage\n\n1. Clone the repository:\n```\ngit clone https://github.com/your_username/food-image-classification.git\n```\n\n2. Navigate to the project directory and install the required packages:\n```\ncd food-image-classification\n\nUse  pipreqs to obtain requirements\n```\n\n3. Run the main script  in the orderr represented in the AI Algorithm .ipynb  here on google colab to train the model:\n`\n4. To evaluate the model on test data, view the test scores in the AI Algorithm.ipyb file:\n\n## Results\n\nThe model achieved a test accuracy of 92% on the test dataset. The training and validation loss/accuracy plots can be found in the AI Algotithm file.\n\n## Future Work\n\n- Integrate the model into a mobile application for real-time food classification.\n- Expand the dataset to include more diverse food items from various cuisines.\n- Experiment with more advanced architectures and techniques to further improve accuracy.\n\n## License\n\nThis project is licensed under the MIT License - see the [LICENSE.md](LICENSE.md) file for details.\n\n## Acknowledgments\n\n- Special thanks to the creators of the food dataset.\n- TensorFlow documentation and community for valuable resources and discussions.\n\n---\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fadesoji1%2Ffood-image-classification","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fadesoji1%2Ffood-image-classification","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fadesoji1%2Ffood-image-classification/lists"}