{"id":23727800,"url":"https://github.com/mktechai-0786/imagexclassify","last_synced_at":"2026-02-18T04:30:16.054Z","repository":{"id":267331899,"uuid":"900922064","full_name":"MKTechAI-0786/ImageXclassifY","owner":"MKTechAI-0786","description":"Image Classification using CNN Model ","archived":false,"fork":false,"pushed_at":"2024-12-10T09:36:53.000Z","size":18681,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":2,"default_branch":"main","last_synced_at":"2024-12-31T01:45:19.083Z","etag":null,"topics":["cifar10","convolutional-neural-networks","deep-learning","machine-learning","mobilenetv2","tensorflow-keras"],"latest_commit_sha":null,"homepage":"","language":"Python","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/MKTechAI-0786.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":"2024-12-09T18:04:16.000Z","updated_at":"2024-12-16T16:41:06.000Z","dependencies_parsed_at":"2024-12-10T10:26:37.691Z","dependency_job_id":null,"html_url":"https://github.com/MKTechAI-0786/ImageXclassifY","commit_stats":null,"previous_names":["mktechai-0786/imagexclassy","mktechai-0786/imagexclassify"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/MKTechAI-0786%2FImageXclassifY","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/MKTechAI-0786%2FImageXclassifY/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/MKTechAI-0786%2FImageXclassifY/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/MKTechAI-0786%2FImageXclassifY/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/MKTechAI-0786","download_url":"https://codeload.github.com/MKTechAI-0786/ImageXclassifY/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":239832086,"owners_count":19704550,"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":["cifar10","convolutional-neural-networks","deep-learning","machine-learning","mobilenetv2","tensorflow-keras"],"created_at":"2024-12-31T01:45:30.678Z","updated_at":"2026-02-18T04:30:15.989Z","avatar_url":"https://github.com/MKTechAI-0786.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# ImageXclassY\nImage Classification using CNN Model \n\nThis is a innovative Streamlit application seamlessly integrates the powerful MobileNetV2 and CIFAR-10 models for advanced image classification. Users can effortlessly upload their images and receive detailed predictions along with confidence scores from either model. The app boasts a sleek and intuitive navigation bar, allowing for easy switching between models and real-time results. This makes it an ideal tool for both learning and practical applications, providing a user-friendly experience for all.\n\n## Key Features\n\n- **Dual Model Support**:\n  - **MobileNetV2 (ImageNet)**: Recognizes 1,000 different classes from the ImageNet dataset, including everyday objects, animals, and vehicles.\n  - **Custom CIFAR-10 Model**: Specializes in classifying images into one of ten specific categories such as airplanes, automobiles, and birds.\n\n- **Intuitive Interface**:\n  - **Navigation Bar**: TO switch between MobileNetV2 and CIFAR-10 models using a sleek sidebar menu.\n  - **Real-Time Classification**: Upload an image to receive immediate predictions with confidence scores.\n\n- **Educational and Practical Use**:\n  - Ideal for learning about deep learning models and their performance.\n  - Useful for practical applications where image classification is needed for better performance.\n\n## Getting Started ###\n\n### Prerequisites\n\n- Python 3.7 or later\n- A web browser (Google Chrome, Mozilla Firefox, or equivalent to access the Streamlit interface.)\n\n### Installation steps\n\n1. **Create and activate a virtual environment**:\n    #open cmd promot where the source code is saved\n    python -m venv venv\n    venv\\Scripts\\activate  #for Windows\n2. **Install the required packages**:\n    pip install -r requirements.txt\n\n3. **Start the Streamlit app**:\n    streamlit run app.py\n\n4. **Open the app**: \n    The app will open in your default web browser. If not, navigate to http://localhost:8501\n\n\n### Usage\n  1. Use the navigation bar to select either the MobileNetV2 or CIFAR-10 model.\n  2. Upload an image file (JPG or PNG or JPEG) FORMAT.\n  3. View the classification results and confidence scores WITH ACCURACY VALUE.\n\n\n### Acknowledgements\n  - Streamlit\n  - TensorFlow\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmktechai-0786%2Fimagexclassify","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmktechai-0786%2Fimagexclassify","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmktechai-0786%2Fimagexclassify/lists"}