Knowing that I was going to write a tutorial on data augmentation, two weekends ago I decided to have some fun and purposely post a semi-trick question on my Twitter feed. Instead, the ImageDataGenerator accepts the original data, randomly transforms it, and returns only the new, transformed data.
Technicallyall the answers are correct — but the only way you know if a given definition of data augmentation is correct is via the context of its application.
Our goal when applying data augmentation is to increase the generalizability of the model. Given that our network is constantly seeing new, slightly modified versions of the input data, the network is able to learn more robust features.
Training a machine learning model on this data may result in us modeling the distribution exactly — however, in real-world applications, data rarely follows such a nice, neat distribution. Instead, to increase the generalizability of our classifier, we may first randomly jitter points along the distribution by adding some random values drawn from a random distribution right.
A model trained on this modified, augmented data is more likely to generalize to example data points not included in the training set.
For example, we can obtain augmented data from the original images by applying simple geometric transforms, such as random:. Applying a small amount of the transformations to an input image will change its appearance slightly, but it does not change the class label — thereby making data augmentation a very natural, easy method to apply for computer vision tasks.
There are three types of data augmentation you will likely encounter when applying deep learning in the context of computer vision applications. Take the time to read this section carefully as I see many deep learning practitioners confuse what data augmentation does and does not do. The first type of data augmentation is what I call dataset generation or dataset expansion. You more than likely have more than a single image — you probably have 10s or s of images and now your goal is to turn that smaller set into s of images for training.
Yes, we have increased our training data by generating additional examples, but all of these examples are based on a super small dataset. We cannot expect to train a NN on a small amount of data and then expect it to generalize to data it was never trained on and has never seen before.
The second type of data augmentation is called in-place data augmentation or on-the-fly data augmentation. Using this type of data augmentation we want to ensure that our network, when trained, sees new variations of our data at each and every epoch.
But Adrian, what about the original training data? Why is it not used?
Change input shape dimensions for fine-tuning with Keras
Secondly, recall that the overall goal of data augmentation is to increase the generalizability of the model. The final type of data augmentation seeks to combine both dataset generation and in-place augmentation — you may see this type of data augmentation when performing behavioral cloning.
Creating self-driving car datasets can be extremely time consuming and expensive — a way around the issue is to instead use video games and car driving simulators. Once you have your training data you can go back and apply Type 2 data augmentation i. Open up the train. This completes our preprocessing. For this sample of data, there are two cats [1. Line 71 initializes our empty data augmentation object i. This is the default operation of this script. Line 75 checks to see if we are performing data augmentation.
If so, we re-initialize the data augmentation object with random transformation parameters Lines Lines then train our model.
Line makes predictions on the test set for evaluation purposes. Our dataset will contain 2 classes and initially, the dataset will trivially contain only 1 image per class:. We will use these example images to generate new training images per class images in total. Lines import our necessary packages. Image loading and processing is handled via Keras functionality i. This object will facilitate performing random rotations, zooms, shifts, shears, and flips on our input image.
It only takes a minute to sign up. I want to train a CNN for image recognition. Images for training have not fixed size. I want the input size for the CNN to be 50x height x widthfor example. When I resize some small sized images for example 32x32 to input size, the content of the image is stretched horizontally too much, but for some medium size images it looks okay. I am thinking about padding images with 0s to complete size after resizing them to some degree keeping ratio of width and height.
Would it be okay with this method? This question on stackoverflow might help you. To sum up, some deep learning researchers think that padding a big part of the image is not a good practice, since the neural network has to learn that the padded area is not relevant for classification, and it does not have to learn that if you use interpolation, for instance.
If you are unable to maintain the aspect ratio via upsampling, you can upsample and also crop the excess pixels in the largest dimension. Of course this would result in losing data, but you can repeatedly shift the center of your crop. This would help your model be more robust. You can do the following First resize the images up to certain extent and then pad the image from all sides ,which could help in maintaining the features in the image. Sign up to join this community.
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Viewed 18k times. What is the proper method for resizing images while avoiding the content being destroyed? David Masip 3, 1 1 gold badge 8 8 silver badges 40 40 bronze badges.Last Updated on July 5, The Keras deep learning library provides a sophisticated API for loading, preparing, and augmenting image data.
Also included in the API are some undocumented functions that allow you to quickly and easily load, convert, and save image files. These functions can be convenient when getting started on a computer vision deep learning project, allowing you to use the same Keras API initially to inspect and handle image data. In this tutorial, you will discover how to use the basic image handling functions provided by the Keras API. Discover how to build models for photo classification, object detection, face recognition, and more in my new computer vision bookwith 30 step-by-step tutorials and full source code.
We will use a photograph of Bondi Beach, Sydneytaken by Isabell Schulz, released under a permissive creative commons license. We will not cover the ImageDataGenerator class in this tutorial. Instead, we will take a closer look at a few less-documented or undocumented functions that may be useful when working with image data and modeling with the Keras API.
Specifically, Keras provides functions for loading, converting, and saving image data. The functions are in the utils. These functions can be useful convenience functions when getting started on a new deep learning computer vision project or when you need to inspect specific images. Some of these functions are demonstrated when working with pre-trained models in the Applications section of the API documentation.
All image handling in Keras requires that the Pillow library is installed. If it is not installed, you can review the installation instructions. The example below loads the Bondi Beach photograph from file as a PIL image and reports details about the loaded image.
The loaded image is then displayed using the default application on the workstation, in this case, the Preview application on macOS. This can be useful if the pixel data is modified while the image is in array format and can then be saved or viewed. The example below loads the test image, converts it to a NumPy array, and then converts it back into a PIL image.
Running the example first loads the photograph in PIL format, then converts the image to a NumPy array and reports the data type and shape. We can see that the pixel values are converted from unsigned integers to bit floating point values, and in this case, converted to the array format [ height, width, channels ].
Finally, the image is converted back into PIL format.GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community. Already on GitHub? Sign in to your account. I am trying to use the preprocessing function to take a network sized crop out of inconsistently sized input images instead of resizing to the network size. I have tried to do this using the preprocessing function but found that it is not easily possible.
Using Keras 2. While looking into this I saw that the preprocessing function runs at the start of standardize, which is after the random transforms are applied. To me this sounds like preprocssing is a bad name since it isn't actually happening first.
Agreed; Having the same problem. No, it's just using self. I do understand the dilemma here though. You don't want to run the batch process before setting self. It's tricky. I would like to help you guys out. I just want to get the point clear.
In the end, I wrote a generator specific to the application. I think this will be solved in the new design? Currently I would need to implement my own Sequence object, but if there was a resizing function that could be called before the pre-processing function that is supposed to return images of the same size as the input and before the augmentation, I could just use that.
The new design Dref pointed out was closed I think sine die.Identifying dog breeds is an interesting computer vision problem due to fine-scale differences that visually separate dog breeds from one another. This article is designed to be a tutorial for those who are just getting started with Convolutional Neural Networks for Image Classification and want to see how to experiment with network architecture, hyperparameters, data augmentations, and how to deal with loading custom data for test and train.
The dataset we are using is from the Dog Breed identification challenge on Kaggle. Kaggle competitions are a great way to level up your Machine Learning skills and this tutorial will help you get comfortable with the way image data is formatted on the site. This challenge listed on Kaggle had 1, different teams participating. The overall challenge is to identify dog breeds amongst different classes. For example, one-hot encoding the labels would require very sparse vectors for each class such as: [0, 0, …,0, 1, 0,0, …, 0].
This tutorial randomly selects two classes, Golden Retrievers and Shetland Sheepdogs and focuses on the task of binary classification.
An additional challenge that newcomers to Programming and Data Science might encounter, is the format of this data from Kaggle. I have found that python string function. The first part of this tutorial will show you how to parse this data and format it to be inputted to a Keras model. We will then focus on a subsection of the problem, Golden Retrievers vs. Shetland Sheepdogs, chosen arbitrarily. The second part of this tutorial will show you how to load custom data into Keras and build a Convolutional Neural Network to classify them.
The third part of this tutorial will discuss bias-variance tradeoff and look into different architectures, dropout layers, and data augmentations to achieve a better score on the test set. Match them with the breed from the naming dictionary.
We will then name them based on how many of this breed we have already counted. Finally, increment the count with this new instance. Very useful for loading into the CNN and assigning one-hot vector class labels using the image naming. Note on Train-Test Split: In this tutorial, I have decided to use a train set and test set instead of cross-validation. This is because I am running these CNNs on my CPU and therefore they take about 10—15 minutes to train, thus 5-fold cross validation would take about an hour.
I want the focus of this study to be on how the different ways to change your model structure to achieve a better result, and therefore fast iterations are important. In terms of the neural network structure, this means have 2 neurons in the output layer rather than 1, you will see this in the final line on the CNN code below:. In binary classification, the output is treated as 0 or 1 and there is only one output neuron, keras will correct this error during compilation.
When we are formatting images to be inputted to a Keras model, we must specify the input dimensions. However, in the ImageNet dataset and this dog breed challenge dataset, we have many different sizes of images.
First, we will write some code to loop through the images and gather some descriptive statistics on the maximum, mean, and minimum height and width of the dog images.
Test the Image loading to make sure it worked properly. Training Log with this model:. There are 5 strategies that I think would be the most effective in improving this test accuracy score:. Overfitting can be solved by adding dropout layers or simplifying the network architecture, a la bias-variance tradeoff.
However, this is still more of an art than a science and it is very hard to calculate exactly how to reduce overfitting without trial-and-error experimentation.
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We could experiment with removing or adding convolutional layers, changing the filter size, or even changing the activation functions. Convolutional networks work by convolving over images, creating a new representation, and then compressing this representation into a vector that is fed into a classic multilayer feed-forward neural network.
We can try adding more hidden layers or altering the number of neurons in each of these hidden layers.CNNs are a type of neural network which build progressively higher level features out of groups of pixels commonly found in the images. How an image scores on these features is then weighted to generate a final classification result. CNNs are the best image classifier algorithm we know of, and they work particularly well when given lots and lots of data to work with.
Progressive resizing is a technique for building CNNs that can be very helpful during the training and optimization phases of a machine learning project. You can follow along with the code and learn how to download the data on GitHub. This post assumes familiarity with CNNs. Recall that our dataset consists of images of 12 different kinds of fruits taken from Google Open Imageswhich is in turn based on permissively-licensed images from Flickr. Right away we see that this dataset is very problematic.
It includes tiny images; occluded images that only depict parts of the sample; samples depicting groups of objects instead of individual ones; and bounding boxes that are just plain noisy and may not even be constrained to a single type of fruit. To add to the difficulty, the dataset is highly imbalanced, with some image classes appearing far more often than others:.
Between the low image and label quality and the class sparsity this classification problem is a very, very difficult one. Now that we understand the contents of our dataset, we need to make choices about the network we will train.
One trouble is that a single neural network can only work with standardly-sized images; too-small images must be scaled up and too-large images must be scaled down. But what image size should be pick? If your goal is model accuracy, larger is obviously better. But there is a lot of advantage to starting small. Properly tuned gradient descent naturally favors robust, well-supported features in its decision-making.
In the image classification case this translates into features occupying as many pixels in as many of the sample images as possible. For example, suppose we teach a neural network to distinguish between oranges and apples. The first model is robust: any image we score, no matter how small or misshaped, will have orange pixels and red pixels usable by the model.
The second model is not: we can image images so small that the stems are not easily distinguishable, or images with the stem cropped out, or images where the stems have been removed outright. The practical result is that while a model trained on very small images will learn fewer features than one trained on very large images, the ones that it does learn will be the most important ones. Thus a model architecture that works on small images will generalize to larger ones.
Meanwhile, small-image models are much faster to train. After all an image input size twice as large has four times as many pixels to learn on! In fact, that is exactly what progressive resizing is!
We now understand the main idea behind progressive resizing. We start out by building our first small-scale model. We see here that the images peak at around x in size. What kind of model you end up building in this phase of the project is entirely up to you.
Not stellar, but remember, this is a very simple model trained on very small very noisy images, with a lot of classes to choose from. We could do better by working on this model further, but I only had so much time to iterate on this model. We now apply progressive resizing to the problem. We started by building a classifier that performs well on tiny n x n 48 x 48 images.This example uses a convolutional stack followed by a recurrent stack and a CTC logloss function to perform optical character recognition of generated text images.
I have no evidence of whether it actually learns general shapes of text, or just is able to recognize all the different fonts thrown at it Note that the font list may need to be updated for the particular OS in use. This starts off with 4 letter words. After 20 epochs, longer sequences are thrown at it by recompiling the model to handle a wider image and rebuilding the word list to include two words separated by a space. The table below shows normalized edit distance values.
Theano uses a slightly different CTC implementation, hence the different results. Keras Documentation. Optical character recognition This example uses a convolutional stack followed by a recurrent stack and a CTC logloss function to perform optical character recognition of generated text images. Epoch TF TH 10 0. ImageSurface cairo. Context surface as context: context. Image renderings and text are created on the fly each time with random perturbations class TextImageGenerator keras.
For this example, best path is sufficient.