{"id":21321697,"url":"https://github.com/cloaky233/keraslab","last_synced_at":"2026-05-15T01:36:52.800Z","repository":{"id":263787091,"uuid":"874889123","full_name":"CLoaKY233/KerasLab","owner":"CLoaKY233","description":"A hands-on guide to building and training deep learning models using Keras and Streamlit. 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The application features a Streamlit-based user interface for training the model, visualizing the training process, and making predictions.\n\n---\nTo Learn more about the project, click [Here](LearnWithPrompts.md)\n\n\n## Table of Contents\n\n- [Features](#features)\n- [Installation](#installation)\n- [Usage](#usage)\n- [Code Structure](#code-structure)\n- [Model Architecture](#model-architecture)\n- [License](#license)\n\n## Features\n\n- User-friendly Streamlit interface for model training and prediction\n- Customizable training parameters (learning rate, epochs, batch size)\n- Real-time training progress visualization\n- Interactive charts for loss and accuracy metrics\n- Image prediction functionality\n\n## Installation\n\n1. Clone the repository:\n   ```\n   git clone https://github.com/cloaky233/KerasLab.git\n   cd KerasLab\n   ```\n\n2. Install `uv` for dependency management:\n   ```\n   pip install uv\n   ```\n\n3. Create a virtual environment and activate it:\n   ```\n   uv venv\n   .venv/scripts/activate  # On Windows\n   source .venv/bin/activate  # On Unix or MacOS\n   ```\n\n4. Install the required packages:\n   ```\n   uv pip install -r requirements.txt\n   ```\n\n## Usage\n\n1. Prepare your dataset:\n   - Place your training images in `dataset/training_set/training_set`\n   - Place your testing images in `dataset/test_set/test_set`\n   - Ensure each set has subdirectories for `cats` and `dogs`\n\n2. Run the Streamlit app:\n   ```\n   streamlit run app.py\n   ```\n\n3. Use the Streamlit interface to:\n   - Set data paths\n   - Configure training parameters\n   - Start the training process\n   - Visualize training progress and results\n   - Make predictions on new images\n\n## Code Structure\n\nThe main script `app.py` is organized into several sections:\n(now kept in pyfiles directory)\n\n1. **Imports and Constants**: Required libraries and global variables.\n2. **Data Loading and Preprocessing**: Functions to load and prepare image data.\n3. **Model Creation**: Definition of the CNN architecture.\n4. **Visualization Functions**: Methods for plotting training history and predictions.\n5. **StreamlitCallback**: Custom Keras callback for updating the Streamlit UI during training.\n6. **Main Function**: Streamlit UI setup and main execution flow.\n\n## Model Architecture\n\nFor detailed information about the model architecture, please refer to the [ModelArchitecture.md](ModelArchitecture.md) file.\n\n## License\n\nThis project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.\n\n---\n\nFeel free to contribute to this project by submitting issues or pull requests. For any questions or support, please open an issue in the GitHub repository.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcloaky233%2Fkeraslab","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fcloaky233%2Fkeraslab","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcloaky233%2Fkeraslab/lists"}