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While this model exhibits strong performance, particularly in improving the accuracy of microbial classification for diagnostic purposes, it is generally outperformed by the Gemini bacteria classification benchmark.\n\n## Tech Stack\n\nThe following technologies were used in the development of Microlens:\n\n- **Frontend:**\n  - React, Styled-component, Typescript\n  - Firebase Auth, Storage and Firestore\n  - react-i18next\n\n- **Backend:**\n  - Python\n  - Flask\n\n- **Machine Learning:**\n  - Python\n  - TensorFlow/PyTorch\n  - DiBas Dataset\n  - Gemini Benchmark\n\n- **Database:**\n  - Firebase\n\n- **Other Tools:**\n  - Docker\n\n## Getting Started\n\nTo get started with Microlens, clone the repository and follow the instructions below.\n\n### Running the Client\n\n1. Navigate to the `client` directory:\n   ```bash\n   cd client\n   ```\n\n2. Install dependencies:\n   ```bash\n   npm install\n   ```\n\n3. Run the development server:\n   ```bash\n   npm run dev\n   ```\n\n### Running the Server\n\n1. Navigate to the `server` directory:\n   ```bash\n   cd server\n   ```\n\n2. Activate the Python virtual environment:\n   ```bash\n   source venv/bin/activate   # On Linux/MacOS\n   venv\\Scripts\\activate      # On Windows\n   ```\n\n3. Install the required Python packages:\n   ```bash\n   pip install -r requirements.txt\n   ```\n\n4. Run the Python server:\n   ```bash\n   python main.py\n   ```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjhmeel%2Fmicrolens","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fjhmeel%2Fmicrolens","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjhmeel%2Fmicrolens/lists"}