{"id":20476010,"url":"https://github.com/mpoojithavigneswari/vegetable-classification-project","last_synced_at":"2026-04-13T15:33:40.550Z","repository":{"id":248906466,"uuid":"830068832","full_name":"MPoojithavigneswari/Vegetable-Classification-Project","owner":"MPoojithavigneswari","description":"Created a website of vegetable classification using vgg16 CNN model with 99% accuracy. 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The application is built using Streamlit for the front-end interface. The process includes data extraction, data preprocessing, model building with the VGG16 CNN model, and model evaluation. The objective is to classify images of vegetables into one of 15 categories: Bean, Bitter Gourd, Bottle Gourd, Brinjal, Broccoli, Cabbage, Capsicum, Carrot, Cauliflower, Cucumber, Papaya, Potato, Pumpkin, Radish, and Tomato.\n\n\n\n## Project Structure\n- `VeggiesClassification_modelTraining.ipynb`: Jupyter notebook used to train the VGG16 model on the vegetable dataset.\n\n- `veggiesClassification_model.keras`: Trained VGG16 model file.\n\n- `streamlit_app.py`: Streamlit web application .py file.\n\n- `requirements.txt`: List of Python packages required to run the application.\n\n- `Examples_ss/`: Directory containing example screenshots of the web app.\n\n\n\n## Installation\n1. Clone the repository:\n```bash\ngit clone https://github.com/MPoojithavigneswari/Vegetable-Classification-Project.git\n```\n2. Install the required packages:\n```bash\npip install -r requirements.txt\n```\n\n\n\n## Usage\nRun the Streamlit app:\n```bash\nstreamlit run streamlit_app.py\n```\nThe web application will open in your default web browser. You can upload an image of a vegetable either from your local system or by providing an image URL.\n\n\n\n## Dataset\nDataset is collected from kaggle. click [here](https://www.kaggle.com/datasets/misrakahmed/vegetable-image-dataset) for dataset download\n\n\n\n## Application Features\n- **Image Upload:** Users can upload images of vegetables directly from their local machine.\n\n- **URL Input:** Users can provide a URL to an image of a vegetable.\n\n- **Top-3 Predictions:** The application displays the top-3 predicted classes along with their probabilities.\n\n- **Responsive Design:** The application layout adjusts based on the screen size.\n\n\n\n## Contributing\nContributions are welcome! Please fork the repository and create a pull request for any improvements or bug fixes.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmpoojithavigneswari%2Fvegetable-classification-project","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmpoojithavigneswari%2Fvegetable-classification-project","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmpoojithavigneswari%2Fvegetable-classification-project/lists"}