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# 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 |