Transform Library Comparison Guide 🔗

This guide helps you find equivalent transforms between Albumentations and other popular libraries (torchvision and Kornia).

Key Differences 🔗

Compared to TorchVision 🔗

• Albumentations operates on numpy arrays (TorchVision uses PyTorch tensors)
• More parameters for fine-tuning transformations
• Built-in support for mask augmentation
• Better handling of bounding boxes and keypoints

Compared to Kornia 🔗

• CPU-based numpy operations (Kornia uses GPU tensors)
• More comprehensive support for detection/segmentation
• Generally better CPU performance
• Simpler API for common tasks

Common Transform Mappings 🔗

Basic Geometric Transforms 🔗

Kornia to Albumentations 🔗

Key Differences 🔗

Compared to TorchVision 🔗

• Albumentations operates on numpy arrays instead of PyTorch tensors
• Albumentations typically provides more parameters for fine-tuning transformations
• Most Albumentations transforms support both image and mask augmentation
• Better support for bounding box and keypoint augmentation

Compared to Kornia 🔗

• Kornia operates directly on GPU tensors, while Albumentations works with numpy arrays
• Albumentations provides more comprehensive support for object detection and segmentation tasks
• Albumentations typically offers better performance for CPU-based augmentations

Performance Comparison 🔗

According to benchmarking results, Albumentations generally offers superior CPU performance compared to TorchVision and Kornia for most transforms. Here are some key highlights: Common Transforms Performance (images/second, higher is better)

Key Performance Insights: 🔗

• Basic Operations: Albumentations excels at basic transforms like flips and crops, often being 5-20x faster than alternatives
• Complex Operations: For more complex transforms like elastic deformation, the performance gap narrows
• Memory Efficiency: Working with numpy arrays (Albumentations) is generally more memory efficient than tensor operations (Kornia/TorchVision) on CPU

When to Choose Each Library: 🔗

• Albumentations: Best choice for CPU-based preprocessing pipelines and when maximum performance is needed
• Kornia: Consider when doing augmentation on GPU with existing PyTorch tensors
• TorchVision: Good choice when deeply integrated into PyTorch ecosystem and GPU performance isn't critical

Note: Benchmarks performed on macOS-15.0.1-arm64 with Python 3.12.7. Your results may vary based on hardware and setup.

Code Examples 🔗

TorchVision to Albumentations 🔗

# TorchVision
transforms = T.Compose([
    T.RandomHorizontalFlip(p=0.5),
    T.RandomRotation(10),
    T.Normalize(mean=[0.485, 0.456, 0.406],
                std=[0.229, 0.224, 0.225])
])
 
# Albumentations equivalent
transforms = A.Compose([
    A.HorizontalFlip(p=0.5),
    A.Rotate(limit=10),
    A.Normalize(mean=[0.485, 0.456, 0.406],
                std=[0.229, 0.224, 0.225])
])

Kornia to Albumentations 🔗

# Kornia
transforms = K.AugmentationSequential(
    K.RandomHorizontalFlip(p=0.5),
    K.RandomRotation(degrees=10),
    K.Normalize(mean=[0.485, 0.456, 0.406],
                std=[0.229, 0.224, 0.225])
)
 
# Albumentations equivalent
transforms = A.Compose([
    A.HorizontalFlip(p=0.5),
    A.Rotate(limit=10),
    A.Normalize(mean=[0.485, 0.456, 0.406],
                std=[0.229, 0.224, 0.225])
])

Additional Resources 🔗

• TorchVision Transforms Documentation
• Kornia Augmentation Documentation
• Albumentations Documentation