{"id":18258007,"url":"https://github.com/alessandroryo/rock-paper-scissors-classification","last_synced_at":"2026-04-16T14:07:48.835Z","repository":{"id":199667616,"uuid":"609072778","full_name":"alessandroryo/rock-paper-scissors-classification","owner":"alessandroryo","description":"A deep learning project using Convolutional Neural Networks (CNNs) built with TensorFlow to classify hand gestures in the game of Rock-Paper-Scissors. This project showcases image processing techniques, data augmentation, and the power of CNNs for visual recognition tasks.","archived":false,"fork":false,"pushed_at":"2024-08-22T10:42:45.000Z","size":210,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-02-14T17:57:51.266Z","etag":null,"topics":["computer-vision","convolutional-neural-networks","data-augmentation","deep-learning","image-classification","machine-learning","python","rock-paper-scissors","tensorflow"],"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/alessandroryo.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":"2023-03-03T10:16:08.000Z","updated_at":"2024-08-22T10:45:02.000Z","dependencies_parsed_at":"2023-10-11T13:29:12.621Z","dependency_job_id":"86f48746-5e4b-49ca-b67d-929671ae5107","html_url":"https://github.com/alessandroryo/rock-paper-scissors-classification","commit_stats":null,"previous_names":["alessandroryo/rock-paper-scissors-classification"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/alessandroryo%2Frock-paper-scissors-classification","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/alessandroryo%2Frock-paper-scissors-classification/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/alessandroryo%2Frock-paper-scissors-classification/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/alessandroryo%2Frock-paper-scissors-classification/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/alessandroryo","download_url":"https://codeload.github.com/alessandroryo/rock-paper-scissors-classification/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":247941719,"owners_count":21022037,"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","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":["computer-vision","convolutional-neural-networks","data-augmentation","deep-learning","image-classification","machine-learning","python","rock-paper-scissors","tensorflow"],"created_at":"2024-11-05T10:28:34.463Z","updated_at":"2026-04-16T14:07:48.769Z","avatar_url":"https://github.com/alessandroryo.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Rock-Paper-Scissors Classification\n\nThis repository contains a machine learning project designed to classify images of hand gestures into one of three categories: rock, paper, or scissors. The project demonstrates the use of convolutional neural networks (CNNs) for image classification tasks, utilizing Python and TensorFlow.\n\n## Table of Contents\n\n- [Rock-Paper-Scissors Classification](#rock-paper-scissors-classification)\n\t- [Table of Contents](#table-of-contents)\n\t- [Project Overview](#project-overview)\n\t- [Dataset](#dataset)\n\t- [Model Architecture](#model-architecture)\n\t- [Training](#training)\n\t- [Results](#results)\n\t- [Contributing](#contributing)\n\t- [License](#license)\n\t- [Contact](#contact)\n\n## Project Overview\n\nThe goal of this project is to build a model that can accurately classify images as either rock, paper, or scissors. The model is trained on a dataset of labeled images, and its performance is evaluated based on its accuracy in predicting the correct hand gesture. This project serves as a practical application of convolutional neural networks in the field of image recognition.\n\n## Dataset\n\nThe dataset used in this project consists of images labeled as either \"rock\", \"paper\", or \"scissors\". The images are preprocessed to a consistent size and format before being fed into the neural network for training. The dataset includes a diverse set of hand gestures to ensure robust model performance.\n\n- **Number of images**: Approximately 2,188 images.\n- **Image format**: PNG, with each image resized to 300x200 pixels.\n\n## Model Architecture\n\nThe model is built using a convolutional neural network (CNN) with the following architecture:\n\n- **Input Layer**: Handles input images of size 300x200x3 (height x width x channels).\n- **Convolutional Layers**: Multiple layers to capture spatial features from the images.\n- **Pooling Layers**: Max-pooling layers to reduce the spatial dimensions and computational load.\n- **Fully Connected Layers**: Dense layers for final classification.\n- **Output Layer**: A softmax layer that outputs probabilities for the three classes: rock, paper, and scissors.\n\n## Training\n\nThe model is trained using the following parameters:\n\n- **Optimizer**: Adam optimizer\n- **Loss Function**: Categorical cross-entropy\n- **Metrics**: Accuracy\n- **Epochs**: 20 (adjustable)\n- **Batch Size**: 32 (adjustable)\n\nThe training process includes data augmentation to improve the model's generalization ability.\n\n## Results\n\nThe model achieves an accuracy of over 95% on the test dataset, demonstrating its effectiveness in classifying hand gestures for the game of rock-paper-scissors.\n\n## Contributing\n\nContributions are welcome! If you have any suggestions for improvements or find any bugs, please open an issue or submit a pull request.\n\n## License\n\nThis project is licensed under the MIT License. See the [LICENSE](./LICENSE) file for details.\n\n## Contact\n\nFor any questions or inquiries, please contact me via email:\n\n- **Email**: \u003calessandroryo@gmail.com\u003e\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Falessandroryo%2Frock-paper-scissors-classification","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Falessandroryo%2Frock-paper-scissors-classification","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Falessandroryo%2Frock-paper-scissors-classification/lists"}