kisejin/repo_mywork

benchmark_agriculture

Description

Brief description of your research project.

Project Structure

benchmark_agriculture/
├── assets/              # Data files, images, datasets
├── config/              # Configuration files
├── dataloader/          # Data loading utilities
├── init_env/            # Environment setup files
│   ├── environment.yaml # Conda environment configuration
│   ├── README.md        # Environment setup instructions
│   └── setup.sh         # Quick setup script
├── model/               # Model definitions and architectures
├── notebook/            # Jupyter notebooks for exploration
├── results/             # Experimental results
│   ├── checkpoints/     # Model checkpoints
│   └── analysis/        # Analysis outputs, visualizations
├── src/                 # Main source code
├── test/                # Unit tests and test data
├── utils/               # Utility functions and helpers
├── .env                 # Environment variables (template)
├── .gitignore           # Git ignore rules
├── Makefile             # Common commands
├── README.md            # This file
└── requirements.txt     # Pip dependencies (alternative to conda)

Quick Start

1. Clone the Repository

git clone <repository-url>
cd benchmark_agriculture

2. Set Up the Environment

Option A: Using the setup script (Recommended)

bash init_env/setup.sh
conda activate torch311

Option B: Using Makefile

make conda-env
conda activate torch311

Option C: Manual setup

conda env create -f init_env/environment.yaml
conda activate torch311

For detailed environment setup instructions, see init_env/README.md.

3. Configure Environment Variables

cp .env .env.local
# Edit .env.local with your configuration (API keys, paths, etc.)

4. Verify Installation

python -c "import torch; print(f'PyTorch: {torch.__version__}, CUDA: {torch.cuda.is_available()}')"

Usage

Training

make train
# or
python src/train.py --config config/config.yaml

Evaluation

make evaluate
# or
python src/evaluate.py

Running Jupyter Notebooks

make jupyter
# or
jupyter lab --notebook-dir=notebook

Testing

make test
# or
pytest test/ -v

Configuration

Edit configuration files in the config/ directory:

• config/config.yaml: Main configuration file
• .env.local: Environment variables (API keys, secrets)

Results

Results and checkpoints are saved in the results/ directory:

• results/checkpoints/: Model checkpoints and saved weights
• results/analysis/: Analysis outputs, plots, and visualizations

Development

Code Formatting

make format

Linting

make lint

Clean Temporary Files

make clean

Requirements

• Python 3.11
• PyTorch 2.0+ with CUDA 12.4
• See init_env/environment.yaml for complete dependency list

Contributing

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

License

[Add license information here]

Citation

If you use this code in your research, please cite:

@misc{benchmark_agriculture,
  author = {Your Name},
  title = {benchmark_agriculture},
  year = {2026},
  publisher = {GitHub},
  url = {https://github.com/username/benchmark_agriculture}
}

Acknowledgments

[Add acknowledgments here]