{"id":18237456,"url":"https://github.com/ayodimeji1/ai_convolutional_neural_network","last_synced_at":"2026-04-13T17:33:52.499Z","repository":{"id":261005949,"uuid":"882961926","full_name":"Ayodimeji1/AI_Convolutional_Neural_Network","owner":"Ayodimeji1","description":null,"archived":false,"fork":false,"pushed_at":"2024-11-04T06:16:32.000Z","size":2061,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2024-12-22T05:14:18.618Z","etag":null,"topics":["artificial-intelligence","classification","deep-neural-networks","keras","machine-learning","matplotlib","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":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Ayodimeji1.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"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":"2024-11-04T05:58:46.000Z","updated_at":"2024-11-04T06:19:02.000Z","dependencies_parsed_at":"2024-11-04T07:30:10.789Z","dependency_job_id":null,"html_url":"https://github.com/Ayodimeji1/AI_Convolutional_Neural_Network","commit_stats":null,"previous_names":["ayodimeji1/cnn"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Ayodimeji1%2FAI_Convolutional_Neural_Network","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Ayodimeji1%2FAI_Convolutional_Neural_Network/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Ayodimeji1%2FAI_Convolutional_Neural_Network/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Ayodimeji1%2FAI_Convolutional_Neural_Network/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Ayodimeji1","download_url":"https://codeload.github.com/Ayodimeji1/AI_Convolutional_Neural_Network/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":238837815,"owners_count":19539079,"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":["artificial-intelligence","classification","deep-neural-networks","keras","machine-learning","matplotlib","tensorflow"],"created_at":"2024-11-05T02:04:37.593Z","updated_at":"2026-04-13T17:33:52.470Z","avatar_url":"https://github.com/Ayodimeji1.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"\n# Convolutional Neural Network (CNN) Model for Image Classification\n\n## Overview\nThis project is a Convolutional Neural Network (CNN) built using TensorFlow and Keras for image classification tasks. The primary focus of the model is to classify images into different categories using a deep learning approach. The project is developed using Python and includes essential exploration, dataset preparation, model training, and evaluation steps.\n\n## Table of Contents\n1. [Introduction](#introduction)\n2. [Installation](#installation)\n3. [Dataset](#dataset)\n4. [Model Architecture](#model-architecture)\n5. [Training and Evaluation](#training-and-evaluation)\n6. [Results](#results)\n7. [Usage](#usage)\n10. [License](#license)\n\n## Installation\nEnsure that Python is installed on your system. Follow these steps to set up the environment:\n1. Clone this repository.\n ```\ngit clone https://github.com/Ayodimeji1/CNN.git\n```\n2. Install the necessary dependencies: \n\n## Dataset\nThe dataset used in this project consists of images of flowers, split into five categories. The data is loaded using TensorFlow's `tf.keras.utils.image_dataset_from_directory` method.\n\n- **Number of images**: 3670\n- **Number of categories**: 5\n- **Image size**: Resized to 256x256 pixels\n\n## Model Architecture\nThe CNN is built using TensorFlow's Keras API and consists of multiple convolutional and pooling layers followed by dense layers for classification. The key components include:\n\n- **Convolutional Layers**: Extract spatial features.\n- **Pooling Layers**: Reduce dimensionality.\n- **Dense Layers**: Perform the final classification.\n\n## Training and Evaluation\nThe model is trained on the dataset with a specified batch size and uses a validation split to monitor performance. The notebook contains details about the training configurations and metrics used for evaluation.\n\n## Results\nDetails about the model's performance, including accuracy and loss plots, are shown in the notebook. \n\n## Usage\nTo use this model for your own dataset:\n1. Ensure your images are organized in a directory structure similar to `dataset_name/class_name/image.jpg`.\n2. Adjust the dataset path in the code:\n   ```\n   python\n   dataset = tf.keras.utils.image_dataset_from_directory(\n       'your_dataset_path', batch_size=500,\n       image_size=(256, 256))\n   ```\n3. Run the training cells in the provided notebook.\n\n## Dependencies\n- Python\n- TensorFlow\n- Keras\n- NumPy\n- Matplotlib\n\n\n## License\nThis project is licensed under the MIT License. See the `LICENSE` file for more details.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fayodimeji1%2Fai_convolutional_neural_network","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fayodimeji1%2Fai_convolutional_neural_network","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fayodimeji1%2Fai_convolutional_neural_network/lists"}