{"id":25076338,"url":"https://github.com/asrot0/pineaipple","last_synced_at":"2025-07-02T21:32:42.174Z","repository":{"id":275595698,"uuid":"914904072","full_name":"asRot0/PineAIpple","owner":"asRot0","description":"🍍PineAIpple – AI-powered fruit classification using CNNs! 🤖 Built with TensorFlow, Keras \u0026 a pinch of magic ✨ | 🔍 Fast. Accurate. 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It contains images of various fruits that are grouped into different categories. The dataset is divided into three folders:\n- **Training data**: Contains images for training the model.\n- **Validation data**: Used to validate the model during training.\n- **Test data**: Used to evaluate the model after training.\n\nYou can download the full dataset from [Fruits-360 Dataset on Kaggle](https://www.kaggle.com/datasets/moltean/fruits).\n\n## Project Overview\n\n- Preprocess and augment the images using TensorFlow’s `ImageDataGenerator`.\n- Define a Convolutional Neural Network (CNN) model for image classification.\n- Train the model using the training and validation sets.\n- Evaluate the model using the test set to measure its accuracy.\n- Visualize results including prediction examples.\n\n### Steps\n1. **Data Preprocessing**:\n   - Augment the images (rotate, shift, zoom, etc.) to increase dataset variety.\n   - Split the data into training, validation, and test sets.\n   \n2. **Model Definition**:\n   - A CNN model is built using TensorFlow/Keras layers to recognize fruit images.\n\n3. **Training**:\n   - The model is trained using the augmented data and validation sets.\n   - Early stopping and model checkpointing are used to avoid overfitting.\n\n4. **Evaluation**:\n   - The model is evaluated using the test dataset, and performance metrics are displayed.\n\n5. **Prediction Visualization**:\n   - Predictions are made on test images, and the results are visualized.\n\n## Technologies Used\n\n- **Python**: Programming language used for the project.\n- **TensorFlow/Keras**: For building and training the CNN model.\n- **Matplotlib/Seaborn**: For data visualization (plots and graphs).\n- **Scikit-learn**: For additional evaluation metrics like classification report.\n\n## Requirements\n\n- Python 3.x\n- TensorFlow\n- Keras\n- Matplotlib\n- Seaborn\n- Scikit-learn\n\n### Install Requirements\n\nTo install the required dependencies, run:\n\n```bash\npip install -r requirements.txt\n```","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fasrot0%2Fpineaipple","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fasrot0%2Fpineaipple","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fasrot0%2Fpineaipple/lists"}