{"id":28290345,"url":"https://github.com/monish-nallagondalla/cnn_classifier","last_synced_at":"2026-06-30T22:31:49.423Z","repository":{"id":290424553,"uuid":"974403149","full_name":"Monish-Nallagondalla/CNN_Classifier","owner":"Monish-Nallagondalla","description":"This project focuses on building an end-to-end system for classifying chicken diseases using Convolutional Neural Networks (CNNs). The model is trained to distinguish between healthy chickens and those affected by Coccidiosis based on fecal images. 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The model is trained to distinguish between healthy chickens and those affected by *Coccidiosis* based on fecal images. The project includes deployment and testing workflows on AWS and Azure for practical usability.\n\n---\n\n## 📂 Project Overview\n\nChicken diseases, especially *Coccidiosis*, are a significant challenge in poultry farming. Early and accurate detection is crucial to minimizing losses. This project leverages deep learning to automate the classification process, offering a scalable solution for poultry health management.\n\n### Key Features:\n- **Deep Learning Model**: CNN-based classifier trained on chicken fecal images.\n- **End-to-End System**: Includes model training, evaluation, deployment, and testing workflows.\n- **Multi-Cloud Deployment**: Integrated deployment pipelines for AWS and Azure.\n- **DVC Integration**: Tracks data and model versioning efficiently.\n\n---\n\n## 🚀 Workflows\n\n1. Update `config.yaml`\n2. Update `secrets.yaml` *(optional)*\n3. Update `params.yaml`\n4. Define entities\n5. Configure the `ConfigurationManager` in the `src/config` directory\n6. Implement core components\n7. Create and test the pipeline\n8. Update `main.py`\n9. Modify the `dvc.yaml` for pipeline versioning\n\n---\n\n## 🔧 How to Run\n\n### Clone the Repository\n```bash\ngit clone https://github.com/Monish-Nallagondalla/CNN_Classifier.git\ncd CNN_Classifier\n````\n\n### STEP 01: Set Up the Environment\n\nCreate and activate a Conda environment:\n\n```bash\nconda create -n cnncls python=3.12 -y\nconda activate cnncls\n```\n\n### STEP 02: Install Dependencies\n\n```bash\npip install -r requirements.txt\n```\n\n### STEP 03: Run the Application\n\n```bash\npython app.py\n```\n\n### Access the Application\n\nOpen your browser and navigate to your local host and port as specified by the application.\n\n---\n\n## 🛠️ DVC Commands\n\n1. Initialize DVC:\n\n   ```bash\n   dvc init\n   ```\n2. Reproduce the pipeline:\n\n   ```bash\n   dvc repro\n   ```\n3. Visualize the pipeline DAG:\n\n   ```bash\n   dvc dag\n   ```\n\n---\n\n## 📊 Model Pipeline Overview\n\n1. **Data Preprocessing**:\n\n   * Cleanses and augments input images.\n   * Ensures data consistency for training.\n\n2. **Model Training**:\n\n   * CNN architecture optimized for image classification.\n   * Trained on a labeled dataset of healthy and *Coccidiosis*-affected chicken fecal images.\n\n3. **Evaluation**:\n\n   * Validates model performance using precision, recall, and accuracy metrics.\n   * Includes visualization of training and validation loss curves.\n\n4. **Deployment**:\n\n   * Packaged as a Docker container.\n   * Tested on AWS (ECR + EC2) and Azure (Azure Container Registry + Web App).\n\n---\n\n## 🖥️ Deployment Workflows\n\n### AWS Deployment Steps:\n\n1. Build Docker image.\n2. Push the image to Amazon ECR.\n3. Launch an EC2 instance.\n4. Pull the image from ECR to the EC2 instance.\n5. Run the application in the EC2 instance.\n\n### Azure Deployment Steps:\n\n1. Build Docker image.\n2. Push the image to Azure Container Registry.\n3. Deploy the image to an Azure Web App Server.\n4. Launch the application from the container registry.\n\n---\n\n## 📜 Future Enhancements\n\n* Add support for additional chicken diseases.\n* Optimize the model for real-time inference.\n* Incorporate more advanced deployment pipelines (e.g., Kubernetes).\n* Explore edge computing for on-site disease classification.\n\n---\n\n## 🤝 Contributing\n\nContributions are welcome! Please fork this repository and submit a pull request with your proposed changes.\n\n---\n\n## 💡 Acknowledgments\n\nThis project is inspired by the need for automated disease detection in poultry farming, ensuring healthier chickens and reducing economic losses.\n\n---\n\n## 📄 License\n\nThis project is licensed under the MIT License. See the [LICENSE](LICENSE) file for more details.\n\n---\n\nHappy Coding! 🐔\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmonish-nallagondalla%2Fcnn_classifier","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmonish-nallagondalla%2Fcnn_classifier","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmonish-nallagondalla%2Fcnn_classifier/lists"}