In the flow_from_directory() method, there is only one specific argument: y_col = column in the dataframe that contains image classes.x_col = column in the dataframe that contains image paths.directory = if the paths declared in the dataframe aren’t absolute paths, the directory where images are stored should be declared here.dataframe = a Pandas dataframe containing image paths and classes.The most important ones to use in the flow_from_dataframe() method are: There are many different parameters to customize said methods, as pictured in the Keras documentation.
It is also possible to create more than one ImageDataGenerator object, if you intend to apply data augmentation techniques on your validation or test sets, but not on your training set, for example. For now, we will build a simple ImageDataGenerator object. According to the Keras documentation, it is possible to implement numerous data augmentation techniques, such as rotations, crops, zoom in/zoom out, etc., using the ImageDataGenerator object, as well as declaring a preprocessing function to be applied to each image. Good! Now, regardless of your data structure, the next step is building an ImageDataGenerator object. import pandas as pd from tensorflow import keras from import ImageDataGenerator
pip install tensorflowĪfter, start your script/notebook with the necessary imports. Pandas should come pre-installed, so no worries there. If you have not yet installed Tensorflow, you can install it through pip using the command prompt. The only dependencies needed for the occasion are mainly Tensorflow, and Pandas if you are using the dataframe approach.
The first step when building a generator is… you guessed it! Checking for dependencies. Now that your data is organized, we can finally start importing the images and build batches of data. Now that your data is organized, you’re all set! Let’s start building those generators. Note that you will need a dataframe for the training set as well as another one for the validation images! csv file containing text/numeric features as well as image paths.Įxample dataframe containing image paths and corresponding labels. This is particularly useful for datasets that provide a.
It requires, however, a dataframe with two columns: the first column should contain the images’ full paths and the second column corresponding classes. Keras’ ImageDataGenerator allows for another approach that doesn’t require a training folder and validation folder with all the different classes. If you are using an image dataset that comes organized in a particular manner and you are wondering how much of a hassle it is going to be to put together train and validation folders, worry not! There is an alternative that is just as functional. Dataframe with Image Paths and corresponding Classes medical data), you may want to consider the second approach, since it can be a pain in the head to move each image from their folder to another. Note: If your data is organized with a sub-folder for each image (i.e. Having images organized by class and by training and validation folders is the most common organization scheme in machine learning, and it is the go-to for large datasets. Some datasets are organized by this structure already, however, if you don’t have this data structure from the get-go, it is relatively easy to accomplish by defining a training split (normally 0.8) and then organizing your images accordingly (80% of your images will be used for training and 20% for validation). Īs it is clear to see above, there are two sub-folders inside the main folder, which in turn have sub-sub-folders corresponding to each class of the dataset. To make it all clearer, there is an example of the sub-folder approach below: image_data/ /train/ /class_1/ image1.png image2.png.
Inside the Train and Validation folders, there are more subfolders, as many as the number of classes of your data, and finally, inside each class subfolder, you can find the images. Having your data separated into two different folders for model training and model validation is the most straightforward and natural type of organization. Nevertheless, there are two main approaches provided by Keras to handle big image data.
Having a tailored and organized structure will certainly make your life easier, especially when using image data.Īccording to your image dataset, you might have loose images in a specific folder, or different sub-folders with each class of your data, or even a subfolder for each image, which is common when dealing with medical data, since each folder may represent a different patient. The data structure is very important to consider when training a deep learning model.