Augmentation

Deep convolutional neural networks have performed remarkably well on many Computer Vision tasks.
However, these networks are heavily reliant on big data to avoid overfitting.
Overfitting refers to the phenomenon when a network learns a function with very high variance such as to perfectly model the training data.

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Increasing the amount and diversity of data.

• what do you do when you have limited data?

  • Ans: Data Augmentation.

• What is Data Augmentation?

  • Data augmentation is a technique of artificially increasing the training set by creating modified copies of a dataset using existing data.
  • It includes making minor changes to the dataset or using deep learning to generate new data points.

• Commonly used Augmentation Techniques
  Some of the commonly used Image data augmentations techniques are:

  1. Flipping

     • This means flipping the image horizontally or vertically.

  2. Rotation

     • This means to rotate the image by a given angle in the clockwise or anticlockwise direction.

  3. Cropping

     • During cropping, a section of the image is sampled randomly.

  4. Brightness

     • Increase or decrease the brightness of the image.

  5. Scaling

     • Scaling Images can be scaled outward or inward. When scaled outward, the image size increases while the image size decreases when scaled inwards.

  6. Noise Addition

     • We can also add gaussian noise to the existing images.

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• Popular Image Augmentation packages

  • skimage
  • opencv
  • imgaug
  • Albumentations
  • Augmentor
  • Keras(ImageDataGenerator class)
  • SOLT

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Image source: Kaggle

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