datasets

Overview

The datasets module provides PyTorch Dataset implementations for loading and preprocessing the MVTec Anomaly Detection dataset. It includes the MVTecDataset class for efficient data loading with built-in image transformations and train/test split handling.

Classes

MVTecDataset

PyTorch Dataset implementation for the MVTec Anomaly Detection dataset.

from patchcore.datasets.mvtec import MVTecDataset, DatasetSplit

dataset = MVTecDataset(
    source="/path/to/mvtec",
    classname="bottle",
    resize=256,
    imagesize=224,
    split=DatasetSplit.TRAIN
)

Constructor Parameters

str

required

Path to the MVTec data folder containing the dataset classes.

str | None

required

Name of the MVTec class to load (e.g., “bottle”, “cable”, “capsule”). If None, the dataset iterates over all 15 available classes.

int

default:"256"

Square size that loaded images are initially resized to before cropping.

int

default:"224"

Square size that resized images are center-cropped to. This is the final output dimension.

DatasetSplit

default:"DatasetSplit.TRAIN"

Indicates whether to use training or test split. Options:

DatasetSplit.TRAIN - Training images (only “good” samples)
DatasetSplit.VAL - Validation split
DatasetSplit.TEST - Test images with anomalies and ground truth masks

float

default:"1.0"

Ratio for splitting training data into train/validation sets. Value of 1.0 uses all data for training.

Methods

`getitem(idx)`

Retrieves a single sample from the dataset.

int

required

Index of the sample to retrieve.

dict

Dictionary containing:

image (torch.Tensor): Preprocessed image tensor of shape (3, imagesize, imagesize)
mask (torch.Tensor): Ground truth mask for test split, zero tensor for training
classname (str): MVTec class name
anomaly (str): Anomaly type (e.g., “good”, “broken_large”, “scratch”)
is_anomaly (int): Binary label (0 for “good”, 1 for anomalous)
image_name (str): Relative path of the image
image_path (str): Full path to the image file

sample = dataset[0]
image = sample['image']  # torch.Tensor (3, 224, 224)
mask = sample['mask']    # torch.Tensor (1, 224, 224)
is_anomaly = sample['is_anomaly']  # 0 or 1

`len()`

Returns the total number of samples in the dataset.

int

Number of images in the dataset.

`get_image_data()`

Internal method that scans the dataset directory and organizes image and mask paths.

tuple

Tuple of (imgpaths_per_class, data_to_iterate):

imgpaths_per_class (dict): Nested dictionary mapping classname → anomaly type → list of image paths
data_to_iterate (list): Flattened list of tuples (classname, anomaly, image_path, mask_path)

Enums

DatasetSplit

Enum for specifying dataset split mode.

from patchcore.datasets.mvtec import DatasetSplit

# Available options:
DatasetSplit.TRAIN  # "train"
DatasetSplit.VAL    # "val"
DatasetSplit.TEST   # "test"

Constants

Available Classes

The MVTec dataset includes 15 object categories:

_CLASSNAMES = [
    "bottle", "cable", "capsule", "carpet", "grid",
    "hazelnut", "leather", "metal_nut", "pill", "screw",
    "tile", "toothbrush", "transistor", "wood", "zipper"
]

ImageNet Normalization

Images are normalized using ImageNet statistics:

IMAGENET_MEAN = [0.485, 0.456, 0.406]
IMAGENET_STD = [0.229, 0.224, 0.225]

Image Transformations

The dataset applies the following preprocessing pipeline: For Images:

Resize to specified resize dimension
Center crop to imagesize
Convert to tensor
Normalize with ImageNet mean and std

For Masks (test split only):

Resize to specified resize dimension
Center crop to imagesize
Convert to tensor (no normalization)

Usage Example

import torch
from torch.utils.data import DataLoader
from patchcore.datasets.mvtec import MVTecDataset, DatasetSplit

# Create training dataset for "bottle" class
train_dataset = MVTecDataset(
    source="./datasets/mvtec",
    classname="bottle",
    resize=256,
    imagesize=224,
    split=DatasetSplit.TRAIN
)

# Create test dataset
test_dataset = MVTecDataset(
    source="./datasets/mvtec",
    classname="bottle",
    resize=256,
    imagesize=224,
    split=DatasetSplit.TEST
)

# Create DataLoaders
train_loader = DataLoader(
    train_dataset,
    batch_size=32,
    shuffle=True,
    num_workers=4
)

test_loader = DataLoader(
    test_dataset,
    batch_size=32,
    shuffle=False,
    num_workers=4
)

# Iterate through batches
for batch in train_loader:
    images = batch['image']  # (batch_size, 3, 224, 224)
    labels = batch['is_anomaly']  # (batch_size,)
    # Training logic here...

Multi-Class Loading

Load all 15 classes simultaneously:

# Pass None as classname to iterate over all classes
full_dataset = MVTecDataset(
    source="./datasets/mvtec",
    classname=None,  # Load all classes
    split=DatasetSplit.TRAIN
)

print(f"Total samples across all classes: {len(full_dataset)}")

Train/Validation Split

Split training data for validation:

# Use 80% for training
train_dataset = MVTecDataset(
    source="./datasets/mvtec",
    classname="bottle",
    split=DatasetSplit.TRAIN,
    train_val_split=0.8
)

# Use remaining 20% for validation
val_dataset = MVTecDataset(
    source="./datasets/mvtec",
    classname="bottle",
    split=DatasetSplit.VAL,
    train_val_split=0.8
)

print(f"Train samples: {len(train_dataset)}")
print(f"Val samples: {len(val_dataset)}")

Core Components

Utilities

CLI Tools

Overview

Classes

MVTecDataset

Constructor Parameters

Methods

`getitem(idx)`

`len()`

`get_image_data()`

Enums

DatasetSplit

Constants

Available Classes

ImageNet Normalization

Image Transformations

Usage Example

Multi-Class Loading

Train/Validation Split

​Overview

​Classes

​MVTecDataset

​Constructor Parameters

​Methods

__getitem__(idx)

__len__()

get_image_data()

​Enums

​DatasetSplit

​Constants

​Available Classes

​ImageNet Normalization

​Image Transformations

​Usage Example

​Multi-Class Loading

​Train/Validation Split

Overview

Classes

MVTecDataset

Constructor Parameters

Methods

`getitem(idx)`

`len()`

`get_image_data()`

Enums

DatasetSplit

Constants

Available Classes

ImageNet Normalization

Image Transformations

Usage Example

Multi-Class Loading

Train/Validation Split