This guide is a hands-on tutorial to build an image dataset for deep learning in TensorFlow. You’ll be familiar with all possible ways to accomplish this task in TensorFlow

Using ImageDataGenerator

This is the most easiest way to prepare an image dataset. You just need to specify the dataset folder and it’ll retrieve all subfolders and images within each subfolder. Each subfolder will represent a class.

Sub-folder as Image Class

Source Code

import tensorflow as tf
images_generator = tf.keras.preprocessing.image.ImageDataGenerator(rescale=1./255)
train_images, train_labels = next(images_generator.flow_from_directory("DIRECTORY_NAME_HERE"))

The output will be “Found 15406 images belonging to 12 classes.” because there are 12 sub folders in the main folder.

Using API

The api is useful to create input pipleline and avoid memory overflow in large datasets.

DATASET_PATH = 'images'
image_classes = glob.glob(DATASET_PATH+'/*')
all_images = []
all_labels = []
for image_class in image_classes:
    class_name = os.path.split(image_class)[-1]
    class_images = glob.glob(image_class + '/*.jpg')
    all_labels.extend([class_name for i in range(len(class_images))])

image_paths = tf.convert_to_tensor(all_images, dtype=tf.string)
labels = tf.convert_to_tensor(all_labels)

# Build a TF Queue, shuffle data
dataset =, labels))
def im_file_to_tensor(file, label):
    def _im_file_to_tensor(file, label):
        path = file
        im = tf.image.decode_jpeg(, channels=3)
        im = tf.cast(im, tf.float32) / 255.0
        return im, label
    return tf.py_function(_im_file_to_tensor, 
                          inp=(file, label), 
                          Tout=(tf.float32, tf.string))

dataset =

def show(image, label):

image,label = next(iter(dataset))