Installation

This guide will help you set up Phenoscape on your system.

Prerequisites

  • Python 3.10 or higher
  • CUDA-compatible GPU (recommended for training)
  • Git

Clone the Repository

git clone https://github.com/ioannouE/bio-colouration.git
cd bio-colouration

Environment Setup

Create a new conda environment with the required packages:

conda create -n phenoclr python=3.10
conda activate phenoclr

Option 2: Virtual Environment

python -m venv phenoclr-env
source phenoclr-env/bin/activate  # On Windows: phenoclr-env\Scripts\activate

Install Dependencies

The project requires the following key packages:

pip install torch==2.6.0 torchvision==0.21.0
pip install albumentations==1.4.20
pip install matplotlib==3.10.1
pip install opencv-python==4.11.0.86
pip install scikit-image==0.25.2
pip install scikit-learn==1.6.1
pip install tensorboard==2.19.0
pip install timm==0.4.5
pip install tqdm==4.67.1
pip install pillow==11.1.0
pip install PyYAML==6.0.2
pip install rasterio==1.4.3

Or install from the requirements file:

cd simclr
pip install -r requirements.txt

Additional Dependencies for Specialized Data

For multispectral and hyperspectral data processing:

pip install mmcv-full==1.5.0
pip install mmsegmentation==0.27.0

Verify Installation

Test your installation by running:

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

Next Steps

Troubleshooting

Common Issues

CUDA Out of Memory: Reduce batch size in configuration files Import Errors: Ensure all dependencies are installed in the correct environment Permission Errors: Check file permissions and directory access