# Core Deep Learning Framework
tensorflow>=2.12.0,<2.16.0
# Note: tensorflow-gpu is deprecated since TF 2.1+, GPU support is included in tensorflow

# Computer Vision and Image Processing
opencv-python>=4.8.0
albumentations>=1.3.0
Pillow>=9.5.0

# Scientific Computing and Data Manipulation
numpy>=1.21.0,<2.0.0
pandas>=1.5.0
scipy>=1.9.0

# Machine Learning and Model Evaluation
scikit-learn>=1.3.0
imbalanced-learn>=0.11.0

# Data Visualization and Plotting
matplotlib>=3.6.0
seaborn>=0.12.0

# Progress Bars and Utilities
tqdm>=4.65.0

# Kaggle API for Dataset Download
kaggle>=1.5.16

# Path Handling (built-in for Python 3.4+)
# pathlib2>=2.3.7; python_version < "3.4"  # Not needed for Python 3.8+

# JSON handling (usually built-in, but for completeness)
# json - built-in module

# Additional Scientific Libraries
# (These are typically installed with tensorflow/scikit-learn but listing for completeness)
h5py>=3.7.0  # For model saving/loading
protobuf>=3.20.0,<4.21.0  # TensorFlow compatibility

# Optional: For better performance and additional features
# Uncomment the following if needed:
# tensorflow-addons>=0.20.0  # Additional TensorFlow operations
# tensorboard>=2.12.0  # For training visualization
# jupyter>=1.0.0  # For notebook development
# ipywidgets>=8.0.0  # For interactive widgets in notebooks

# Development and Testing (Optional)
pytest>=7.0.0
black>=23.0.0  # Code formatting
flake8>=6.0.0  # Linting
torchvision

# System Requirements Notes:
# - Python 3.8+ recommended
# - CUDA 11.8+ and cuDNN 8.6+ for GPU acceleration
# - Minimum 8GB RAM, 16GB+ recommended
# - Minimum 10GB free disk space for dataset and models