How to Perform Data Augmentation with Augly Library

In machine learning and deep learning, having more data is very important to help you get good performance from your models. You can create more data by using a technique called data augmentation. Data augmentation is a technique used by practitioners to increase the data by creating modified data from the existing data.

 “We don’t have better algorithms. We just have more data.”-

In this article, you will learn how to perform data augmentation by using a new open-source library from Facebook called Augly.

What is Augly?

• Image Similarity Challenge - a NeurIPS 2021 competition run by Facebook AI with $200k in prizes. It has produced the DISC21 dataset, which will be made publicly available after the challenge concludes!
• DeepFake Detection Challenge - a Kaggle competition run by Facebook AI in 2020 with $1 million in prizes; also produced the DFDC dataset.
• SimSearchNet - a near-duplicate detection model developed at Facebook AI to identify infringing content on the platforms.

How to Install Augly

pip install augly

Note: The above command installs only base requirements to use the image and text modalities. For audio and video modalities, you can install the extra dependencies required with 

pip install augly[av]

conda install -c conda-forge python-magic

Data Augmentation Techniques for Text Data

The first step is to import text modality which contains augmentation techniques for text data.

import augly.text as textaugs

# Define input text
input_text = "Hello, world! Today we learn Data Augmentation techniques"

(a) Simulates Typos

print(textaugs.simulate_typos(input_text))

(b) Insert Punctuation Chars

print(textaugs.insert_punctuation_chars(input_text))

(c) Replace Bidirectional

print(textaugs.replace_bidirectional(input_text))

(d) Replace Similar Characters 

print(textaugs.replace_similar_chars(input_text))

As you can see the character “l” has been replaced with number 7, character “o” has been replaced with “()”, character “e” has been replaced with number 3 and then the character “o” has been replaced with “[]”.

(e) Replace Upside Down

print(textaugs.replace_upside_down(input_text))

(f) Split Words 

print(textaugs.split_words(input_text))

Data Augmentation Techniques for Image Data

The first step is to import image modality with its dependencies which contain augmentation techniques for image data.

import os
import augly.image as imaugs
import augly.utils as utils
from IPython.display import display

 (a) Image Scaling

The scale function can help you to alter the resolution of an image. You can use an argument called factor to define the ratio by which the image should be downscaled or upscaled.

input_img_path = "images/simple-image.jpg"

# We can use the AugLy scale augmentation

input_img = imaugs.scale(input_img_path, factor=0.2)
display(input_img)

(b) Blurs the Image

input_img = imaugs.blur(input_img, radius=5.0)
display(input_img)

(c) Change the Brightness of the Image

To change the brightness you need to adjust the factor argument in this function. Values less than 1.0 darken the image and values greater than 1.0 brighten the image. Setting the factor to 1.0 will not alter the image's brightness.

input_img = imaugs.brightness(input_img,factor=1.5)
display(input_img)

Then let’s set the factor's value to 0.5 to make it darker.

#make it darker 
input_img = imaugs.brightness(input_img,factor=0.5)
display(input_img)

(d) Changes the Aspect Ratio of the Image

In this function, the aspect ratio is the width/height of the new image you want to create.

input_img = imaugs.change_aspect_ratio(input_img, ratio=0.8)
display(input_img)

(e)Alters the Contrast of the Image 

In this function the factor argument handle everything, When you set the factor to zero, it gives a grayscale image, values below 1.0 decreases contrast,

input_img = imaugs.contrast(input_img,factor=1.7)
display(input_img)

(f) Crop the Image 

input_img = imaugs.crop(input_img,
                        x1=0.25,
                        x2=0.75,
                        y1=0.25,
                        y2=0.75
                        )
display(input_img

Final Thoughts on Data Augmentation with Augly Library

As I have explained before, the library has over 100 augmentation techniques and most of them were not covered in this article.

If you want to learn how to perform data augmentation for audio and video data, please read in the README for each modality!

• Audio -
• Video -

And you can read more articles like this here.

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