Data augmentation artificially increases your dataset size by creating modified versions of existing images. This helps improve model generalization and reduces overfitting.
🔄
Rotation
Rotate images by random angles
↔️
Flipping
Horizontal/vertical mirroring
🔍
Scaling
Zoom in/out randomly
📐
Translation
Shift images in x/y direction
☀️
Brightness
Adjust image brightness
🌓
Contrast
Modify contrast levels
💡 Best Practices
• Keep augmentations realistic for your domain
• Don't over-augment - maintain class integrity
• Test different combinations systematically
• Use validation set to evaluate effectiveness
Advanced Techniques
📡
Noise Addition
Add Gaussian/salt-pepper noise
🌫️
Blur
Apply Gaussian blur effects
✂️
Cutout
Randomly mask image patches
🎨
Mixup
Blend images and labels
🌊
Elastic Transform
Non-linear deformations
🌈
Color Jittering
Hue, saturation variations
Domain-Specific Considerations:
Medical images: Be careful with orientation changes
Text recognition: Avoid rotations that make text unreadable
Satellite imagery: Consider geographic constraints