Dataset setup

The Image Classification Dataset

Fashion-MNIST as a reusable dataset

Fashion-MNIST is the workhorse dataset for the rest of this chapter:

• 10 classes (T-shirt / trouser / pullover / …) of 28×28 grayscale images; 60 k train, 10 k test.
• Drop-in replacement for MNIST — same shape, same API, harder.
• We’ll wrap it in a DataModule so every classifier we build can reuse the same loaders.

Imports and the FashionMNIST DataModule shell:

%matplotlib inline
import time
from d2l import mxnet as d2l
from mxnet import gluon, npx
from mxnet.gluon.data.vision import transforms
npx.set_np()

d2l.use_svg_display()

class FashionMNIST(d2l.DataModule):
    """The Fashion-MNIST dataset."""
    def __init__(self, batch_size=64, resize=(28, 28)):
        super().__init__()
        self.save_hyperparameters()
        trans = transforms.Compose([transforms.Resize(resize),
                                    transforms.ToTensor()])
        self.train = gluon.data.vision.FashionMNIST(
            train=True).transform_first(trans)
        self.val = gluon.data.vision.FashionMNIST(
            train=False).transform_first(trans)

Instantiate Fashion-MNIST

Instantiate (resizing to 32×32 to match later ConvNet inputs):

data = FashionMNIST(resize=(32, 32))
len(data.train), len(data.val)

What does one example look like?

Each train item is a (C, H, W) image tensor + an integer label:

data.train[0][0].shape

A 1×32×32 grayscale image — single channel, after the resize.

Human-readable labels

The dataset stores labels as integers 0–9. A small helper turns each label into its English name (T-shirt, Trouser, Pullover, …):

@d2l.add_to_class(FashionMNIST)
def text_labels(self, indices):
    """Return text labels."""
    labels = ['t-shirt', 'trouser', 'pullover', 'dress', 'coat',
              'sandal', 'shirt', 'sneaker', 'bag', 'ankle boot']
    return [labels[int(i)] for i in indices]

Minibatches

Wrap the framework dataloader so train and val each yield batches in the same shape:

@d2l.add_to_class(FashionMNIST)
def get_dataloader(self, train):
    data = self.train if train else self.val
    return gluon.data.DataLoader(data, self.batch_size, shuffle=train,
                                 num_workers=self.num_workers)

X, y = next(iter(data.train_dataloader()))
print(X.shape, X.dtype, y.shape, y.dtype)

A batch of 64 32×32 grayscale images plus 64 integer labels.

Throughput sanity check

Time one full epoch through the loader. Slow loading bottlenecks training as much as the model itself:

tic = time.time()
for X, y in data.train_dataloader():
    continue
f'{time.time() - tic:.2f} sec'

Visualization helpers

A grid plotter we’ll reuse for spot-checks:

def show_images(imgs, num_rows, num_cols, titles=None, scale=1.5):
    """Plot a list of images."""
    raise NotImplementedError

Bound to the dataset as a method that pulls one batch and labels each tile with the class name:

@d2l.add_to_class(FashionMNIST)
def visualize(self, batch, nrows=1, ncols=8, labels=None):
    X, y = batch
    if not labels:
        labels = self.text_labels(y)
    d2l.show_images(X.squeeze(1), nrows, ncols, titles=labels)
batch = next(iter(data.val_dataloader()))
data.visualize(batch)

Recap

• Fashion-MNIST: 10 classes, 28×28 grayscale, harder than MNIST.
• A DataModule subclass owns the framework’s train / val_dataloader, label decoding, and a visualize helper.
• Always sanity-check throughput — slow I/O caps training speed.
• Same data API drives every model in this chapter.