Topic: Python – Reading Images from Datasets and Organizing Them (Part 2): Using PyTorch and TensorFlow Data Loaders

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1. Using PyTorch’s `ImageFolder` and `DataLoader`

PyTorch provides an easy way to load image datasets organized in folders by classes.

from torchvision import datasets, transforms
from torch.utils.data import DataLoader

# Define transformations (resize, normalize, convert to tensor)
transform = transforms.Compose([
    transforms.Resize((128, 128)),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406],
                         std=[0.229, 0.224, 0.225])
])

dataset = datasets.ImageFolder(root='dataset/', transform=transform)

# Create DataLoader for batching and shuffling
dataloader = DataLoader(dataset, batch_size=32, shuffle=True)

# Access class names
class_names = dataset.classes
print(class_names)

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2. Iterating Through DataLoader

for images, labels in dataloader:
    print(images.shape)  # (batch_size, 3, 128, 128)
    print(labels)
    # Use images and labels for training or validation
    break

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3. Using TensorFlow `image_dataset_from_directory`

TensorFlow Keras also provides utilities for loading datasets organized in folders.

import tensorflow as tf

dataset = tf.keras.preprocessing.image_dataset_from_directory(
    'dataset/',
    image_size=(128, 128),
    batch_size=32,
    label_mode='int'  # can be 'categorical', 'binary', or None
)

class_names = dataset.class_names
print(class_names)

for images, labels in dataset.take(1):
    print(images.shape)
    print(labels)

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4. Dataset Splitting

You can split datasets into training and validation sets easily:

train_ds = tf.keras.preprocessing.image_dataset_from_directory(
    'dataset/',
    validation_split=0.2,
    subset="training",
    seed=123,
    image_size=(128, 128),
    batch_size=32
)

val_ds = tf.keras.preprocessing.image_dataset_from_directory(
    'dataset/',
    validation_split=0.2,
    subset="validation",
    seed=123,
    image_size=(128, 128),
    batch_size=32
)

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5. Summary

• PyTorch’s ImageFolder + DataLoader offers a quick way to load and batch datasets.

• TensorFlow’s image\_dataset\_from\_directory provides similar high-level dataset loading.

• Both allow easy transformations, batching, and shuffling.

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Exercise

• Write code to normalize images in TensorFlow dataset using map() with Rescaling.

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#Python #DatasetHandling #PyTorch #TensorFlow #ImageProcessing

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