https://github.com/rathod-shubham/augmentation

Data augmentation is a technique of artificially increasing the training set by creating modified copies of a dataset using existing data. Here is a notebook of different augmentation techniques.
https://github.com/rathod-shubham/augmentation

albumentations computer-vision convolutional-neural-networks deep-learning image-classification keras python3

Last synced: 2 months ago
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Data augmentation is a technique of artificially increasing the training set by creating modified copies of a dataset using existing data. Here is a notebook of different augmentation techniques.

• Host: GitHub
• URL: https://github.com/rathod-shubham/augmentation
• Owner: RATHOD-SHUBHAM
• Created: 2023-05-17T20:52:26.000Z (about 3 years ago)
• Default Branch: master
• Last Pushed: 2023-05-24T16:05:44.000Z (about 3 years ago)
• Last Synced: 2025-01-22T07:43:19.480Z (over 1 year ago)
• Topics: albumentations, computer-vision, convolutional-neural-networks, deep-learning, image-classification, keras, python3
• Language: Jupyter Notebook
• Homepage:
• Size: 46.7 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# 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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![vz7gkdD](https://github.com/RATHOD-SHUBHAM/Augmentation/assets/58945964/baf9e9d8-bcd3-4d59-ad87-9308b4d2d73d)

Image source: Kaggle

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![download](https://github.com/RATHOD-SHUBHAM/Augmentation/assets/58945964/5d8f1a65-5d11-4dbd-8b2b-8116499067ed)