Bird Detection Pipeline

A machine learning pipeline for detecting and annotating birds in aerial imagery.

Project Structure

project_root/
│
├── src/                      # Source code for the ML pipeline
│   ├── __init__.py
│   ├── data_ingestion.py    # Data loading and preparation
│   ├── data_processing.py   # Data preprocessing and transformations
│   ├── model_training.py    # Model training functionality
│   ├── pipeline_evaluation.py # Pipeline and model evaluation metrics
│   ├── model_deployment.py  # Model deployment utilities
│   ├── monitoring.py        # Monitoring and logging functionality
│   ├── reporting.py         # Report generation for pipeline results
│   ├── pre_annotation_prediction.py  # Pre-annotation model predictions
│   └── annotation/          # Annotation-related functionality
│       ├── __init__.py
│       └── pipeline.py      # Annotation pipeline implementation
│
├── tests/                   # Test files for each component
│   ├── test_data_ingestion.py
│   ├── test_data_processing.py
│   ├── test_model_training.py
│   ├── test_pipeline_evaluation.py
│   ├── test_model_deployment.py
│   ├── test_monitoring.py
│   ├── test_reporting.py
│
├── conf/                    # Configuration files
│   └── config.yaml         # Main configuration file
│
├── main.py                 # Main entry point for the pipeline
├── run_ml_workflow.sh      # Script to run pipeline in Serenity container
├── requirements.txt        # Project dependencies
├── .gitignore             # Git ignore file
├── CONTRIBUTING.md        # Contributing guidelines
├── LICENSE                # Project license
└── README.md             # This file

Components

Source Code (src/)

• data_ingestion.py: Handles data loading and initial preparation
• data_processing.py: Implements data preprocessing and transformations
• model_training.py: Contains model training logic
• pipeline_evaluation.py: Evaluates pipeline performance and model metrics
• model_deployment.py: Manages model deployment
• monitoring.py: Provides monitoring and logging capabilities
• reporting.py: Generates reports for pipeline results
• annotation/: Contains annotation-related functionality
  • pipeline.py: Implements the annotation pipeline

Tests (tests/)

Contains test files corresponding to each component in src/. Uses pytest for testing.

Configuration (conf/)

Contains YAML configuration files managed by Hydra:

• config.yaml: Main configuration file defining pipeline parameters

Installation

1. Clone the repository:

git clone https://github.com/your-username/project-name.git
cd project-name

2. Install dependencies:

pip install -r requirements.txt

Usage

Running the Pipeline

Using the Serenity container:

./run_ml_workflow.sh your-branch-name

Or directly with Python:

python main.py

Running Tests

pytest tests/

Configuration

The pipeline uses Hydra for configuration management. Main configuration options are defined in conf/config.yaml.

Example configuration:

data:
  input_dir: "path/to/input"
  output_dir: "path/to/output"

model:
  type: "classification"
  parameters:
    learning_rate: 0.001
    batch_size: 32

pipeline:
  steps:
    - data_ingestion
    - data_processing
    - model_training
    - evaluation

Contributing

Please read CONTRIBUTING.md for details on our code of conduct and the process for submitting pull requests.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Dependencies

Key dependencies include:

• Hydra
• PyTorch
• NumPy
• Pandas
• Pytest

See requirements.txt for a complete list.

Development

Code Organization

• Each component is a separate module in the src/ directory
• Tests mirror the source code structure in the tests/ directory
• Configuration is managed through Hydra
• Monitoring and logging are integrated throughout the pipeline using comet

Testing

• Tests are written using pytest
• Each component has its own test file
• Run tests with pytest tests/

Adding New Components

1. Create a new module in src/
2. Add corresponding test file in tests/
3. Update configuration in conf/config.yaml
4. Update main.py to integrate the new component
5. Create a branch and push your changes to the remote repository
6. Create a pull request to merge your changes into the main branch