Inital commit for refactoring to lightning

81 files changed, 0 insertions, 7438 deletions

diff --git a/src/text_recognizer/__init__.py b/src/text_recognizer/__init__.py
deleted file mode 100644
index 3dc1f76..0000000
--- a/src/text_recognizer/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-__version__ = "0.1.0"
diff --git a/src/text_recognizer/character_predictor.py b/src/text_recognizer/character_predictor.py
deleted file mode 100644
index ad71289..0000000
--- a/src/text_recognizer/character_predictor.py
+++ /dev/null
@@ -1,29 +0,0 @@
-"""CharacterPredictor class."""
-from typing import Dict, Tuple, Type, Union
-
-import numpy as np
-from torch import nn
-
-from text_recognizer import datasets, networks
-from text_recognizer.models import CharacterModel
-from text_recognizer.util import read_image
-
-
-class CharacterPredictor:
-    """Recognizes the character in handwritten character images."""
-
-    def __init__(self, network_fn: str, dataset: str) -> None:
-        """Intializes the CharacterModel and load the pretrained weights."""
-        network_fn = getattr(networks, network_fn)
-        dataset = getattr(datasets, dataset)
-        self.model = CharacterModel(network_fn=network_fn, dataset=dataset)
-        self.model.eval()
-        self.model.use_swa_model()
-
-    def predict(self, image_or_filename: Union[np.ndarray, str]) -> Tuple[str, float]:
-        """Predict on a single images contianing a handwritten character."""
-        if isinstance(image_or_filename, str):
-            image = read_image(image_or_filename, grayscale=True)
-        else:
-            image = image_or_filename
-        return self.model.predict_on_image(image)
diff --git a/src/text_recognizer/datasets/__init__.py b/src/text_recognizer/datasets/__init__.py
deleted file mode 100644
index a6c1c59..0000000
--- a/src/text_recognizer/datasets/__init__.py
+++ /dev/null
@@ -1,39 +0,0 @@
-"""Dataset modules."""
-from .emnist_dataset import EmnistDataset
-from .emnist_lines_dataset import (
-    construct_image_from_string,
-    EmnistLinesDataset,
-    get_samples_by_character,
-)
-from .iam_dataset import IamDataset
-from .iam_lines_dataset import IamLinesDataset
-from .iam_paragraphs_dataset import IamParagraphsDataset
-from .iam_preprocessor import load_metadata, Preprocessor
-from .transforms import AddTokens, Transpose
-from .util import (
-    _download_raw_dataset,
-    compute_sha256,
-    DATA_DIRNAME,
-    download_url,
-    EmnistMapper,
-    ESSENTIALS_FILENAME,
-)
-
-__all__ = [
-    "_download_raw_dataset",
-    "AddTokens",
-    "compute_sha256",
-    "construct_image_from_string",
-    "DATA_DIRNAME",
-    "download_url",
-    "EmnistDataset",
-    "EmnistMapper",
-    "EmnistLinesDataset",
-    "get_samples_by_character",
-    "load_metadata",
-    "IamDataset",
-    "IamLinesDataset",
-    "IamParagraphsDataset",
-    "Preprocessor",
-    "Transpose",
-]
diff --git a/src/text_recognizer/datasets/dataset.py b/src/text_recognizer/datasets/dataset.py
deleted file mode 100644
index e794605..0000000
--- a/src/text_recognizer/datasets/dataset.py
+++ /dev/null
@@ -1,152 +0,0 @@
-"""Abstract dataset class."""
-from typing import Callable, Dict, List, Optional, Tuple, Union
-
-import torch
-from torch import Tensor
-from torch.utils import data
-from torchvision.transforms import ToTensor
-
-import text_recognizer.datasets.transforms as transforms
-from text_recognizer.datasets.util import EmnistMapper
-
-
-class Dataset(data.Dataset):
-    """Abstract class for with common methods for all datasets."""
-
-    def __init__(
-        self,
-        train: bool,
-        subsample_fraction: float = None,
-        transform: Optional[List[Dict]] = None,
-        target_transform: Optional[List[Dict]] = None,
-        init_token: Optional[str] = None,
-        pad_token: Optional[str] = None,
-        eos_token: Optional[str] = None,
-        lower: bool = False,
-    ) -> None:
-        """Initialization of Dataset class.
-
-        Args:
-            train (bool): If True, loads the training set, otherwise the validation set is loaded. Defaults to False.
-            subsample_fraction (float): The fraction of the dataset to use for training. Defaults to None.
-            transform (Optional[List[Dict]]): List of Transform types and args for input data. Defaults to None.
-            target_transform (Optional[List[Dict]]): List of Transform types and args for output data. Defaults to None.
-            init_token (Optional[str]): String representing the start of sequence token. Defaults to None.
-            pad_token (Optional[str]): String representing the pad token. Defaults to None.
-            eos_token (Optional[str]): String representing the end of sequence token. Defaults to None.
-            lower (bool): Only use lower case letters. Defaults to False.
-
-        Raises:
-            ValueError: If subsample_fraction is not None and outside the range (0, 1).
-
-        """
-        self.train = train
-        self.split = "train" if self.train else "test"
-
-        if subsample_fraction is not None:
-            if not 0.0 < subsample_fraction < 1.0:
-                raise ValueError("The subsample fraction must be in (0, 1).")
-        self.subsample_fraction = subsample_fraction
-
-        self._mapper = EmnistMapper(
-            init_token=init_token, eos_token=eos_token, pad_token=pad_token, lower=lower
-        )
-        self._input_shape = self._mapper.input_shape
-        self._output_shape = self._mapper._num_classes
-        self.num_classes = self.mapper.num_classes
-
-        # Set transforms.
-        self.transform = self._configure_transform(transform)
-        self.target_transform = self._configure_target_transform(target_transform)
-
-        self._data = None
-        self._targets = None
-
-    def _configure_transform(self, transform: List[Dict]) -> transforms.Compose:
-        transform_list = []
-        if transform is not None:
-            for t in transform:
-                t_type = t["type"]
-                t_args = t["args"] or {}
-                transform_list.append(getattr(transforms, t_type)(**t_args))
-        else:
-            transform_list.append(ToTensor())
-        return transforms.Compose(transform_list)
-
-    def _configure_target_transform(
-        self, target_transform: List[Dict]
-    ) -> transforms.Compose:
-        target_transform_list = [torch.tensor]
-        if target_transform is not None:
-            for t in target_transform:
-                t_type = t["type"]
-                t_args = t["args"] or {}
-                target_transform_list.append(getattr(transforms, t_type)(**t_args))
-        return transforms.Compose(target_transform_list)
-
-    @property
-    def data(self) -> Tensor:
-        """The input data."""
-        return self._data
-
-    @property
-    def targets(self) -> Tensor:
-        """The target data."""
-        return self._targets
-
-    @property
-    def input_shape(self) -> Tuple:
-        """Input shape of the data."""
-        return self._input_shape
-
-    @property
-    def output_shape(self) -> Tuple:
-        """Output shape of the data."""
-        return self._output_shape
-
-    @property
-    def mapper(self) -> EmnistMapper:
-        """Returns the EmnistMapper."""
-        return self._mapper
-
-    @property
-    def mapping(self) -> Dict:
-        """Return EMNIST mapping from index to character."""
-        return self._mapper.mapping
-
-    @property
-    def inverse_mapping(self) -> Dict:
-        """Returns the inverse mapping from character to index."""
-        return self.mapper.inverse_mapping
-
-    def _subsample(self) -> None:
-        """Only this fraction of the data will be loaded."""
-        if self.subsample_fraction is None:
-            return
-        num_subsample = int(self.data.shape[0] * self.subsample_fraction)
-        self._data = self.data[:num_subsample]
-        self._targets = self.targets[:num_subsample]
-
-    def __len__(self) -> int:
-        """Returns the length of the dataset."""
-        return len(self.data)
-
-    def load_or_generate_data(self) -> None:
-        """Load or generate dataset data."""
-        raise NotImplementedError
-
-    def __getitem__(self, index: Union[int, Tensor]) -> Tuple[Tensor, Tensor]:
-        """Fetches samples from the dataset.
-
-        Args:
-            index (Union[int, torch.Tensor]): The indices of the samples to fetch.
-
-        Raises:
-            NotImplementedError: If the method is not implemented in child class.
-
-        """
-        raise NotImplementedError
-
-    def __repr__(self) -> str:
-        """Returns information about the dataset."""
-        raise NotImplementedError
diff --git a/src/text_recognizer/datasets/emnist_dataset.py b/src/text_recognizer/datasets/emnist_dataset.py
deleted file mode 100644
index 9884fdf..0000000
--- a/src/text_recognizer/datasets/emnist_dataset.py
+++ /dev/null
@@ -1,131 +0,0 @@
-"""Emnist dataset: black and white images of handwritten characters (Aa-Zz) and digits (0-9)."""
-
-import json
-from pathlib import Path
-from typing import Callable, Optional, Tuple, Union
-
-from loguru import logger
-import numpy as np
-from PIL import Image
-import torch
-from torch import Tensor
-from torchvision.datasets import EMNIST
-from torchvision.transforms import Compose, ToTensor
-
-from text_recognizer.datasets.dataset import Dataset
-from text_recognizer.datasets.transforms import Transpose
-from text_recognizer.datasets.util import DATA_DIRNAME
-
-
-class EmnistDataset(Dataset):
-    """This is a class for resampling and subsampling the PyTorch EMNIST dataset."""
-
-    def __init__(
-        self,
-        pad_token: str = None,
-        train: bool = False,
-        sample_to_balance: bool = False,
-        subsample_fraction: float = None,
-        transform: Optional[Callable] = None,
-        target_transform: Optional[Callable] = None,
-        seed: int = 4711,
-    ) -> None:
-        """Loads the dataset and the mappings.
-
-        Args:
-            pad_token (str): The pad token symbol. Defaults to _.
-            train (bool): If True, loads the training set, otherwise the validation set is loaded. Defaults to False.
-            sample_to_balance (bool): Resamples the dataset to make it balanced. Defaults to False.
-            subsample_fraction (float): Description of parameter `subsample_fraction`. Defaults to None.
-            transform (Optional[Callable]): Transform(s) for input data. Defaults to None.
-            target_transform (Optional[Callable]): Transform(s) for output data. Defaults to None.
-            seed (int): Seed number. Defaults to 4711.
-
-        """
-        super().__init__(
-            train=train,
-            subsample_fraction=subsample_fraction,
-            transform=transform,
-            target_transform=target_transform,
-            pad_token=pad_token,
-        )
-
-        self.sample_to_balance = sample_to_balance
-
-        # Have to transpose the emnist characters, ToTensor norms input between [0,1].
-        if transform is None:
-            self.transform = Compose([Transpose(), ToTensor()])
-
-        self.target_transform = None
-
-        self.seed = seed
-
-    def __getitem__(self, index: Union[int, Tensor]) -> Tuple[Tensor, Tensor]:
-        """Fetches samples from the dataset.
-
-        Args:
-            index (Union[int, Tensor]): The indices of the samples to fetch.
-
-        Returns:
-            Tuple[Tensor, Tensor]: Data target tuple.
-
-        """
-        if torch.is_tensor(index):
-            index = index.tolist()
-
-        data = self.data[index]
-        targets = self.targets[index]
-
-        if self.transform:
-            data = self.transform(data)
-
-        if self.target_transform:
-            targets = self.target_transform(targets)
-
-        return data, targets
-
-    def __repr__(self) -> str:
-        """Returns information about the dataset."""
-        return (
-            "EMNIST Dataset\n"
-            f"Num classes: {self.num_classes}\n"
-            f"Input shape: {self.input_shape}\n"
-            f"Mapping: {self.mapper.mapping}\n"
-        )
-
-    def _sample_to_balance(self) -> None:
-        """Because the dataset is not balanced, we take at most the mean number of instances per class."""
-        np.random.seed(self.seed)
-        x = self._data
-        y = self._targets
-        num_to_sample = int(np.bincount(y.flatten()).mean())
-        all_sampled_indices = []
-        for label in np.unique(y.flatten()):
-            inds = np.where(y == label)[0]
-            sampled_indices = np.unique(np.random.choice(inds, num_to_sample))
-            all_sampled_indices.append(sampled_indices)
-        indices = np.concatenate(all_sampled_indices)
-        x_sampled = x[indices]
-        y_sampled = y[indices]
-        self._data = x_sampled
-        self._targets = y_sampled
-
-    def load_or_generate_data(self) -> None:
-        """Fetch the EMNIST dataset."""
-        dataset = EMNIST(
-            root=DATA_DIRNAME,
-            split="byclass",
-            train=self.train,
-            download=False,
-            transform=None,
-            target_transform=None,
-        )
-
-        self._data = dataset.data
-        self._targets = dataset.targets
-
-        if self.sample_to_balance:
-            self._sample_to_balance()
-
-        if self.subsample_fraction is not None:
-            self._subsample()
diff --git a/src/text_recognizer/datasets/emnist_essentials.json b/src/text_recognizer/datasets/emnist_essentials.json
deleted file mode 100644
index 2a0648a..0000000
--- a/src/text_recognizer/datasets/emnist_essentials.json
+++ /dev/null
@@ -1 +0,0 @@
-{"mapping": [[0, "0"], [1, "1"], [2, "2"], [3, "3"], [4, "4"], [5, "5"], [6, "6"], [7, "7"], [8, "8"], [9, "9"], [10, "A"], [11, "B"], [12, "C"], [13, "D"], [14, "E"], [15, "F"], [16, "G"], [17, "H"], [18, "I"], [19, "J"], [20, "K"], [21, "L"], [22, "M"], [23, "N"], [24, "O"], [25, "P"], [26, "Q"], [27, "R"], [28, "S"], [29, "T"], [30, "U"], [31, "V"], [32, "W"], [33, "X"], [34, "Y"], [35, "Z"], [36, "a"], [37, "b"], [38, "c"], [39, "d"], [40, "e"], [41, "f"], [42, "g"], [43, "h"], [44, "i"], [45, "j"], [46, "k"], [47, "l"], [48, "m"], [49, "n"], [50, "o"], [51, "p"], [52, "q"], [53, "r"], [54, "s"], [55, "t"], [56, "u"], [57, "v"], [58, "w"], [59, "x"], [60, "y"], [61, "z"]], "input_shape": [28, 28]}
diff --git a/src/text_recognizer/datasets/emnist_lines_dataset.py b/src/text_recognizer/datasets/emnist_lines_dataset.py
deleted file mode 100644
index 1992446..0000000
--- a/src/text_recognizer/datasets/emnist_lines_dataset.py
+++ /dev/null
@@ -1,359 +0,0 @@
-"""Emnist Lines dataset: synthetic handwritten lines dataset made from Emnist characters."""
-
-from collections import defaultdict
-from pathlib import Path
-from typing import Callable, Dict, List, Optional, Tuple, Union
-
-import click
-import h5py
-from loguru import logger
-import numpy as np
-import torch
-from torch import Tensor
-import torch.nn.functional as F
-from torchvision.transforms import ToTensor
-
-from text_recognizer.datasets.dataset import Dataset
-from text_recognizer.datasets.emnist_dataset import EmnistDataset, Transpose
-from text_recognizer.datasets.sentence_generator import SentenceGenerator
-from text_recognizer.datasets.util import (
-    DATA_DIRNAME,
-    EmnistMapper,
-    ESSENTIALS_FILENAME,
-)
-
-DATA_DIRNAME = DATA_DIRNAME / "processed" / "emnist_lines"
-
-MAX_WIDTH = 952
-
-
-class EmnistLinesDataset(Dataset):
-    """Synthetic dataset of lines from the Brown corpus with Emnist characters."""
-
-    def __init__(
-        self,
-        train: bool = False,
-        transform: Optional[Callable] = None,
-        target_transform: Optional[Callable] = None,
-        subsample_fraction: float = None,
-        max_length: int = 34,
-        min_overlap: float = 0,
-        max_overlap: float = 0.33,
-        num_samples: int = 10000,
-        seed: int = 4711,
-        init_token: Optional[str] = None,
-        pad_token: Optional[str] = None,
-        eos_token: Optional[str] = None,
-        lower: bool = False,
-    ) -> None:
-        """Set attributes and loads the dataset.
-
-        Args:
-            train (bool): Flag for the filename. Defaults to False. Defaults to None.
-            transform (Optional[Callable]): The transform of the data. Defaults to None.
-            target_transform (Optional[Callable]): The transform of the target. Defaults to None.
-            subsample_fraction (float): The fraction of the dataset to use for training. Defaults to None.
-            max_length (int): The maximum number of characters. Defaults to 34.
-            min_overlap (float): The minimum overlap between concatenated images. Defaults to 0.
-            max_overlap (float): The maximum overlap between concatenated images. Defaults to 0.33.
-            num_samples (int): Number of samples to generate. Defaults to 10000.
-            seed (int): Seed number. Defaults to 4711.
-            init_token (Optional[str]): String representing the start of sequence token. Defaults to None.
-            pad_token (Optional[str]): String representing the pad token. Defaults to None.
-            eos_token (Optional[str]): String representing the end of sequence token. Defaults to None.
-            lower (bool): If True, convert uppercase letters to lowercase. Otherwise, use both upper and lowercase.
-
-        """
-        self.pad_token = "_" if pad_token is None else pad_token
-
-        super().__init__(
-            train=train,
-            transform=transform,
-            target_transform=target_transform,
-            subsample_fraction=subsample_fraction,
-            init_token=init_token,
-            pad_token=self.pad_token,
-            eos_token=eos_token,
-            lower=lower,
-        )
-
-        # Extract dataset information.
-        self._input_shape = self._mapper.input_shape
-        self.num_classes = self._mapper.num_classes
-
-        self.max_length = max_length
-        self.min_overlap = min_overlap
-        self.max_overlap = max_overlap
-        self.num_samples = num_samples
-        self._input_shape = (
-            self.input_shape[0],
-            self.input_shape[1] * self.max_length,
-        )
-        self._output_shape = (self.max_length, self.num_classes)
-        self.seed = seed
-
-        # Placeholders for the dataset.
-        self._data = None
-        self._target = None
-
-    def __getitem__(self, index: Union[int, Tensor]) -> Tuple[Tensor, Tensor]:
-        """Fetches data, target pair of the dataset for a given and index or indices.
-
-        Args:
-            index (Union[int, Tensor]): Either a list or int of indices/index.
-
-        Returns:
-            Tuple[Tensor, Tensor]: Data target pair.
-
-        """
-        if torch.is_tensor(index):
-            index = index.tolist()
-
-        data = self.data[index]
-        targets = self.targets[index]
-
-        if self.transform:
-            data = self.transform(data)
-
-        if self.target_transform:
-            targets = self.target_transform(targets)
-
-        return data, targets
-
-    def __repr__(self) -> str:
-        """Returns information about the dataset."""
-        return (
-            "EMNIST Lines Dataset\n"  # pylint: disable=no-member
-            f"Max length: {self.max_length}\n"
-            f"Min overlap: {self.min_overlap}\n"
-            f"Max overlap: {self.max_overlap}\n"
-            f"Num classes: {self.num_classes}\n"
-            f"Input shape: {self.input_shape}\n"
-            f"Data: {self.data.shape}\n"
-            f"Tagets: {self.targets.shape}\n"
-        )
-
-    @property
-    def data_filename(self) -> Path:
-        """Path to the h5 file."""
-        filename = "train.pt" if self.train else "test.pt"
-        return DATA_DIRNAME / filename
-
-    def load_or_generate_data(self) -> None:
-        """Loads the dataset, if it does not exist a new dataset is generated before loading it."""
-        np.random.seed(self.seed)
-
-        if not self.data_filename.exists():
-            self._generate_data()
-        self._load_data()
-        self._subsample()
-
-    def _load_data(self) -> None:
-        """Loads the dataset from the h5 file."""
-        logger.debug("EmnistLinesDataset loading data from HDF5...")
-        with h5py.File(self.data_filename, "r") as f:
-            self._data = f["data"][()]
-            self._targets = f["targets"][()]
-
-    def _generate_data(self) -> str:
-        """Generates a dataset with the Brown corpus and Emnist characters."""
-        logger.debug("Generating data...")
-
-        sentence_generator = SentenceGenerator(self.max_length)
-
-        # Load emnist dataset.
-        emnist = EmnistDataset(
-            train=self.train, sample_to_balance=True, pad_token=self.pad_token
-        )
-        emnist.load_or_generate_data()
-
-        samples_by_character = get_samples_by_character(
-            emnist.data.numpy(), emnist.targets.numpy(), self.mapper.mapping,
-        )
-
-        DATA_DIRNAME.mkdir(parents=True, exist_ok=True)
-        with h5py.File(self.data_filename, "a") as f:
-            data, targets = create_dataset_of_images(
-                self.num_samples,
-                samples_by_character,
-                sentence_generator,
-                self.min_overlap,
-                self.max_overlap,
-            )
-
-            targets = convert_strings_to_categorical_labels(
-                targets, emnist.inverse_mapping
-            )
-
-            f.create_dataset("data", data=data, dtype="u1", compression="lzf")
-            f.create_dataset("targets", data=targets, dtype="u1", compression="lzf")
-
-
-def get_samples_by_character(
-    samples: np.ndarray, labels: np.ndarray, mapping: Dict
-) -> defaultdict:
-    """Creates a dictionary with character as key and value as the list of images of that character.
-
-    Args:
-        samples (np.ndarray): Dataset of images of characters.
-        labels (np.ndarray): The labels for each image.
-        mapping (Dict): The Emnist mapping dictionary.
-
-    Returns:
-        defaultdict: A dictionary with characters as keys and list of images as values.
-
-    """
-    samples_by_character = defaultdict(list)
-    for sample, label in zip(samples, labels.flatten()):
-        samples_by_character[mapping[label]].append(sample)
-    return samples_by_character
-
-
-def select_letter_samples_for_string(
-    string: str, samples_by_character: Dict
-) -> List[np.ndarray]:
-    """Randomly selects Emnist characters to use for the senetence.
-
-    Args:
-        string (str): The word or sentence.
-        samples_by_character (Dict): The dictionary of emnist images of each character.
-
-    Returns:
-        List[np.ndarray]: A list of emnist images of the string.
-
-    """
-    zero_image = np.zeros((28, 28), np.uint8)
-    sample_image_by_character = {}
-    for character in string:
-        if character in sample_image_by_character:
-            continue
-        samples = samples_by_character[character]
-        sample = samples[np.random.choice(len(samples))] if samples else zero_image
-        sample_image_by_character[character] = sample.reshape(28, 28).swapaxes(0, 1)
-    return [sample_image_by_character[character] for character in string]
-
-
-def construct_image_from_string(
-    string: str, samples_by_character: Dict, min_overlap: float, max_overlap: float
-) -> np.ndarray:
-    """Concatenates images of the characters in the string.
-
-    The concatination is made with randomly selected overlap so that some portion of the character will overlap.
-
-    Args:
-        string (str): The word or sentence.
-        samples_by_character (Dict): The dictionary of emnist images of each character.
-        min_overlap (float): Minimum amount of overlap between Emnist images.
-        max_overlap (float): Maximum amount of overlap between Emnist images.
-
-    Returns:
-        np.ndarray: The Emnist image of the string.
-
-    """
-    overlap = np.random.uniform(min_overlap, max_overlap)
-    sampled_images = select_letter_samples_for_string(string, samples_by_character)
-    length = len(sampled_images)
-    height, width = sampled_images[0].shape
-    next_overlap_width = width - int(overlap * width)
-    concatenated_image = np.zeros((height, width * length), np.uint8)
-    x = 0
-    for image in sampled_images:
-        concatenated_image[:, x : (x + width)] += image
-        x += next_overlap_width
-
-    if concatenated_image.shape[-1] > MAX_WIDTH:
-        concatenated_image = Tensor(concatenated_image).unsqueeze(0)
-        concatenated_image = F.interpolate(
-            concatenated_image, size=MAX_WIDTH, mode="nearest"
-        )
-        concatenated_image = concatenated_image.squeeze(0).numpy()
-
-    return np.minimum(255, concatenated_image)
-
-
-def create_dataset_of_images(
-    length: int,
-    samples_by_character: Dict,
-    sentence_generator: SentenceGenerator,
-    min_overlap: float,
-    max_overlap: float,
-) -> Tuple[np.ndarray, List[str]]:
-    """Creates a dataset with images and labels from strings generated from the SentenceGenerator.
-
-    Args:
-        length (int): The number of characters for each string.
-        samples_by_character (Dict): The dictionary of emnist images of each character.
-        sentence_generator (SentenceGenerator): A SentenceGenerator objest.
-        min_overlap (float): Minimum amount of overlap between Emnist images.
-        max_overlap (float): Maximum amount of overlap between Emnist images.
-
-    Returns:
-        Tuple[np.ndarray, List[str]]: A list of Emnist images and a list of the strings (labels).
-
-    Raises:
-        RuntimeError: If the sentence generator is not able to generate a string.
-
-    """
-    sample_label = sentence_generator.generate()
-    sample_image = construct_image_from_string(sample_label, samples_by_character, 0, 0)
-    images = np.zeros((length, sample_image.shape[0], sample_image.shape[1]), np.uint8)
-    labels = []
-    for n in range(length):
-        label = None
-        # Try several times to generate before actually throwing an error.
-        for _ in range(10):
-            try:
-                label = sentence_generator.generate()
-                break
-            except Exception:  # pylint: disable=broad-except
-                pass
-        if label is None:
-            raise RuntimeError("Was not able to generate a valid string.")
-        images[n] = construct_image_from_string(
-            label, samples_by_character, min_overlap, max_overlap
-        )
-        labels.append(label)
-    return images, labels
-
-
-def convert_strings_to_categorical_labels(
-    labels: List[str], mapping: Dict
-) -> np.ndarray:
-    """Translates a string of characters in to a target array of class int."""
-    return np.array([[mapping[c] for c in label] for label in labels])
-
-
-@click.command()
-@click.option(
-    "--max_length", type=int, default=34, help="Number of characters in a sentence."
-)
-@click.option(
-    "--min_overlap", type=float, default=0.0, help="Min overlap between characters."
-)
-@click.option(
-    "--max_overlap", type=float, default=0.33, help="Max overlap between characters."
-)
-@click.option("--num_train", type=int, default=10_000, help="Number of train examples.")
-@click.option("--num_test", type=int, default=1_000, help="Number of test examples.")
-def create_datasets(
-    max_length: int = 34,
-    min_overlap: float = 0,
-    max_overlap: float = 0.33,
-    num_train: int = 10000,
-    num_test: int = 1000,
-) -> None:
-    """Creates a training an validation dataset of Emnist lines."""
-    num_samples = [num_train, num_test]
-    for num, train in zip(num_samples, [True, False]):
-        emnist_lines = EmnistLinesDataset(
-            train=train,
-            max_length=max_length,
-            min_overlap=min_overlap,
-            max_overlap=max_overlap,
-            num_samples=num,
-        )
-        emnist_lines.load_or_generate_data()
-
-
-if __name__ == "__main__":
-    create_datasets()
diff --git a/src/text_recognizer/datasets/iam_dataset.py b/src/text_recognizer/datasets/iam_dataset.py
deleted file mode 100644
index f4a869d..0000000
--- a/src/text_recognizer/datasets/iam_dataset.py
+++ /dev/null
@@ -1,132 +0,0 @@
-"""Class for loading the IAM dataset, which encompasses both paragraphs and lines, with associated utilities."""
-import os
-from typing import Any, Dict, List
-import zipfile
-
-from boltons.cacheutils import cachedproperty
-import defusedxml.ElementTree as ET
-from loguru import logger
-import toml
-
-from text_recognizer.datasets.util import _download_raw_dataset, DATA_DIRNAME
-
-RAW_DATA_DIRNAME = DATA_DIRNAME / "raw" / "iam"
-METADATA_FILENAME = RAW_DATA_DIRNAME / "metadata.toml"
-EXTRACTED_DATASET_DIRNAME = RAW_DATA_DIRNAME / "iamdb"
-
-DOWNSAMPLE_FACTOR = 2  # If images were downsampled, the regions must also be.
-LINE_REGION_PADDING = 0  # Add this many pixels around the exact coordinates.
-
-
-class IamDataset:
-    """IAM dataset.
-
-    "The IAM Lines dataset, first published at the ICDAR 1999, contains forms of unconstrained handwritten text,
-    which were scanned at a resolution of 300dpi and saved as PNG images with 256 gray levels."
-    From http://www.fki.inf.unibe.ch/databases/iam-handwriting-database
-
-    The data split we will use is
-    IAM lines Large Writer Independent Text Line Recognition Task (lwitlrt): 9,862 text lines.
-        The validation set has been merged into the train set.
-        The train set has 7,101 lines from 326 writers.
-        The test set has 1,861 lines from 128 writers.
-        The text lines of all data sets are mutually exclusive, thus each writer has contributed to one set only.
-
-    """
-
-    def __init__(self) -> None:
-        self.metadata = toml.load(METADATA_FILENAME)
-
-    def load_or_generate_data(self) -> None:
-        """Downloads IAM dataset if xml files does not exist."""
-        if not self.xml_filenames:
-            self._download_iam()
-
-    @property
-    def xml_filenames(self) -> List:
-        """List of xml filenames."""
-        return list((EXTRACTED_DATASET_DIRNAME / "xml").glob("*.xml"))
-
-    @property
-    def form_filenames(self) -> List:
-        """List of forms filenames."""
-        return list((EXTRACTED_DATASET_DIRNAME / "forms").glob("*.jpg"))
-
-    def _download_iam(self) -> None:
-        curdir = os.getcwd()
-        os.chdir(RAW_DATA_DIRNAME)
-        _download_raw_dataset(self.metadata)
-        _extract_raw_dataset(self.metadata)
-        os.chdir(curdir)
-
-    @property
-    def form_filenames_by_id(self) -> Dict:
-        """Creates a dictionary with filenames as keys and forms as values."""
-        return {filename.stem: filename for filename in self.form_filenames}
-
-    @cachedproperty
-    def line_strings_by_id(self) -> Dict:
-        """Return a dict from name of IAM form to a list of line texts in it."""
-        return {
-            filename.stem: _get_line_strings_from_xml_file(filename)
-            for filename in self.xml_filenames
-        }
-
-    @cachedproperty
-    def line_regions_by_id(self) -> Dict:
-        """Return a dict from name of IAM form to a list of (x1, x2, y1, y2) coordinates of all lines in it."""
-        return {
-            filename.stem: _get_line_regions_from_xml_file(filename)
-            for filename in self.xml_filenames
-        }
-
-    def __repr__(self) -> str:
-        """Print info about dataset."""
-        return "IAM Dataset\n" f"Number of forms: {len(self.xml_filenames)}\n"
-
-
-def _extract_raw_dataset(metadata: Dict) -> None:
-    logger.info("Extracting IAM data.")
-    with zipfile.ZipFile(metadata["filename"], "r") as zip_file:
-        zip_file.extractall()
-
-
-def _get_line_strings_from_xml_file(filename: str) -> List[str]:
-    """Get the text content of each line. Note that we replace &quot; with "."""
-    xml_root_element = ET.parse(filename).getroot()  # nosec
-    xml_line_elements = xml_root_element.findall("handwritten-part/line")
-    return [el.attrib["text"].replace("&quot;", '"') for el in xml_line_elements]
-
-
-def _get_line_regions_from_xml_file(filename: str) -> List[Dict[str, int]]:
-    """Get the line region dict for each line."""
-    xml_root_element = ET.parse(filename).getroot()  # nosec
-    xml_line_elements = xml_root_element.findall("handwritten-part/line")
-    return [_get_line_region_from_xml_element(el) for el in xml_line_elements]
-
-
-def _get_line_region_from_xml_element(xml_line: Any) -> Dict[str, int]:
-    """Extracts coordinates for each line of text."""
-    # TODO: fix input!
-    word_elements = xml_line.findall("word/cmp")
-    x1s = [int(el.attrib["x"]) for el in word_elements]
-    y1s = [int(el.attrib["y"]) for el in word_elements]
-    x2s = [int(el.attrib["x"]) + int(el.attrib["width"]) for el in word_elements]
-    y2s = [int(el.attrib["y"]) + int(el.attrib["height"]) for el in word_elements]
-    return {
-        "x1": min(x1s) // DOWNSAMPLE_FACTOR - LINE_REGION_PADDING,
-        "y1": min(y1s) // DOWNSAMPLE_FACTOR - LINE_REGION_PADDING,
-        "x2": max(x2s) // DOWNSAMPLE_FACTOR + LINE_REGION_PADDING,
-        "y2": max(y2s) // DOWNSAMPLE_FACTOR + LINE_REGION_PADDING,
-    }
-
-
-def main() -> None:
-    """Initializes the dataset and print info about the dataset."""
-    dataset = IamDataset()
-    dataset.load_or_generate_data()
-    print(dataset)
-
-
-if __name__ == "__main__":
-    main()
diff --git a/src/text_recognizer/datasets/iam_lines_dataset.py b/src/text_recognizer/datasets/iam_lines_dataset.py
deleted file mode 100644
index 1cb84bd..0000000
--- a/src/text_recognizer/datasets/iam_lines_dataset.py
+++ /dev/null
@@ -1,110 +0,0 @@
-"""IamLinesDataset class."""
-from typing import Callable, Dict, List, Optional, Tuple, Union
-
-import h5py
-from loguru import logger
-import torch
-from torch import Tensor
-from torchvision.transforms import ToTensor
-
-from text_recognizer.datasets.dataset import Dataset
-from text_recognizer.datasets.util import (
-    compute_sha256,
-    DATA_DIRNAME,
-    download_url,
-    EmnistMapper,
-)
-
-
-PROCESSED_DATA_DIRNAME = DATA_DIRNAME / "processed" / "iam_lines"
-PROCESSED_DATA_FILENAME = PROCESSED_DATA_DIRNAME / "iam_lines.h5"
-PROCESSED_DATA_URL = (
-    "https://s3-us-west-2.amazonaws.com/fsdl-public-assets/iam_lines.h5"
-)
-
-
-class IamLinesDataset(Dataset):
-    """IAM lines datasets for handwritten text lines."""
-
-    def __init__(
-        self,
-        train: bool = False,
-        subsample_fraction: float = None,
-        transform: Optional[Callable] = None,
-        target_transform: Optional[Callable] = None,
-        init_token: Optional[str] = None,
-        pad_token: Optional[str] = None,
-        eos_token: Optional[str] = None,
-        lower: bool = False,
-    ) -> None:
-        self.pad_token = "_" if pad_token is None else pad_token
-
-        super().__init__(
-            train=train,
-            subsample_fraction=subsample_fraction,
-            transform=transform,
-            target_transform=target_transform,
-            init_token=init_token,
-            pad_token=pad_token,
-            eos_token=eos_token,
-            lower=lower,
-        )
-
-    @property
-    def input_shape(self) -> Tuple:
-        """Input shape of the data."""
-        return self.data.shape[1:] if self.data is not None else None
-
-    @property
-    def output_shape(self) -> Tuple:
-        """Output shape of the data."""
-        return (
-            self.targets.shape[1:] + (self.num_classes,)
-            if self.targets is not None
-            else None
-        )
-
-    def load_or_generate_data(self) -> None:
-        """Load or generate dataset data."""
-        if not PROCESSED_DATA_FILENAME.exists():
-            PROCESSED_DATA_DIRNAME.mkdir(parents=True, exist_ok=True)
-            logger.info("Downloading IAM lines...")
-            download_url(PROCESSED_DATA_URL, PROCESSED_DATA_FILENAME)
-        with h5py.File(PROCESSED_DATA_FILENAME, "r") as f:
-            self._data = f[f"x_{self.split}"][:]
-            self._targets = f[f"y_{self.split}"][:]
-        self._subsample()
-
-    def __repr__(self) -> str:
-        """Print info about the dataset."""
-        return (
-            "IAM Lines Dataset\n"  # pylint: disable=no-member
-            f"Number classes: {self.num_classes}\n"
-            f"Mapping: {self.mapper.mapping}\n"
-            f"Data: {self.data.shape}\n"
-            f"Targets: {self.targets.shape}\n"
-        )
-
-    def __getitem__(self, index: Union[Tensor, int]) -> Tuple[Tensor, Tensor]:
-        """Fetches data, target pair of the dataset for a given and index or indices.
-
-        Args:
-            index (Union[int, Tensor]): Either a list or int of indices/index.
-
-        Returns:
-            Tuple[Tensor, Tensor]: Data target pair.
-
-        """
-        if torch.is_tensor(index):
-            index = index.tolist()
-
-        data = self.data[index]
-        targets = self.targets[index]
-
-        if self.transform:
-            data = self.transform(data)
-
-        if self.target_transform:
-            targets = self.target_transform(targets)
-
-        return data, targets
diff --git a/src/text_recognizer/datasets/iam_paragraphs_dataset.py b/src/text_recognizer/datasets/iam_paragraphs_dataset.py
deleted file mode 100644
index 8ba5142..0000000
--- a/src/text_recognizer/datasets/iam_paragraphs_dataset.py
+++ /dev/null
@@ -1,291 +0,0 @@
-"""IamParagraphsDataset class and functions for data processing."""
-import random
-from typing import Callable, Dict, List, Optional, Tuple, Union
-
-import click
-import cv2
-import h5py
-from loguru import logger
-import numpy as np
-import torch
-from torch import Tensor
-from torchvision.transforms import ToTensor
-
-from text_recognizer import util
-from text_recognizer.datasets.dataset import Dataset
-from text_recognizer.datasets.iam_dataset import IamDataset
-from text_recognizer.datasets.util import (
-    compute_sha256,
-    DATA_DIRNAME,
-    download_url,
-    EmnistMapper,
-)
-
-INTERIM_DATA_DIRNAME = DATA_DIRNAME / "interim" / "iam_paragraphs"
-DEBUG_CROPS_DIRNAME = INTERIM_DATA_DIRNAME / "debug_crops"
-PROCESSED_DATA_DIRNAME = DATA_DIRNAME / "processed" / "iam_paragraphs"
-CROPS_DIRNAME = PROCESSED_DATA_DIRNAME / "crops"
-GT_DIRNAME = PROCESSED_DATA_DIRNAME / "gt"
-
-PARAGRAPH_BUFFER = 50  # Pixels in the IAM form images to leave around the lines.
-TEST_FRACTION = 0.2
-SEED = 4711
-
-
-class IamParagraphsDataset(Dataset):
-    """IAM Paragraphs dataset for paragraphs of handwritten text."""
-
-    def __init__(
-        self,
-        train: bool = False,
-        subsample_fraction: float = None,
-        transform: Optional[Callable] = None,
-        target_transform: Optional[Callable] = None,
-    ) -> None:
-        super().__init__(
-            train=train,
-            subsample_fraction=subsample_fraction,
-            transform=transform,
-            target_transform=target_transform,
-        )
-        # Load Iam dataset.
-        self.iam_dataset = IamDataset()
-
-        self.num_classes = 3
-        self._input_shape = (256, 256)
-        self._output_shape = self._input_shape + (self.num_classes,)
-        self._ids = None
-
-    def __getitem__(self, index: Union[Tensor, int]) -> Tuple[Tensor, Tensor]:
-        """Fetches data, target pair of the dataset for a given and index or indices.
-
-        Args:
-            index (Union[int, Tensor]): Either a list or int of indices/index.
-
-        Returns:
-            Tuple[Tensor, Tensor]: Data target pair.
-
-        """
-        if torch.is_tensor(index):
-            index = index.tolist()
-
-        data = self.data[index]
-        targets = self.targets[index]
-
-        seed = np.random.randint(SEED)
-        random.seed(seed)  # apply this seed to target tranfsorms
-        torch.manual_seed(seed)  # needed for torchvision 0.7
-        if self.transform:
-            data = self.transform(data)
-
-        random.seed(seed)  # apply this seed to target tranfsorms
-        torch.manual_seed(seed)  # needed for torchvision 0.7
-        if self.target_transform:
-            targets = self.target_transform(targets)
-
-        return data, targets.long()
-
-    @property
-    def ids(self) -> Tensor:
-        """Ids of the dataset."""
-        return self._ids
-
-    def get_data_and_target_from_id(self, id_: str) -> Tuple[Tensor, Tensor]:
-        """Get data target pair from id."""
-        ind = self.ids.index(id_)
-        return self.data[ind], self.targets[ind]
-
-    def load_or_generate_data(self) -> None:
-        """Load or generate dataset data."""
-        num_actual = len(list(CROPS_DIRNAME.glob("*.jpg")))
-        num_targets = len(self.iam_dataset.line_regions_by_id)
-
-        if num_actual < num_targets - 2:
-            self._process_iam_paragraphs()
-
-        self._data, self._targets, self._ids = _load_iam_paragraphs()
-        self._get_random_split()
-        self._subsample()
-
-    def _get_random_split(self) -> None:
-        np.random.seed(SEED)
-        num_train = int((1 - TEST_FRACTION) * self.data.shape[0])
-        indices = np.random.permutation(self.data.shape[0])
-        train_indices, test_indices = indices[:num_train], indices[num_train:]
-        if self.train:
-            self._data = self.data[train_indices]
-            self._targets = self.targets[train_indices]
-        else:
-            self._data = self.data[test_indices]
-            self._targets = self.targets[test_indices]
-
-    def _process_iam_paragraphs(self) -> None:
-        """Crop the part with the text.
-
-        For each page, crop out the part of it that correspond to the paragraph of text, and make sure all crops are
-        self.input_shape. The ground truth data is the same size, with a one-hot vector at each pixel
-        corresponding to labels 0=background, 1=odd-numbered line, 2=even-numbered line
-        """
-        crop_dims = self._decide_on_crop_dims()
-        CROPS_DIRNAME.mkdir(parents=True, exist_ok=True)
-        DEBUG_CROPS_DIRNAME.mkdir(parents=True, exist_ok=True)
-        GT_DIRNAME.mkdir(parents=True, exist_ok=True)
-        logger.info(
-            f"Cropping paragraphs, generating ground truth, and saving debugging images to {DEBUG_CROPS_DIRNAME}"
-        )
-        for filename in self.iam_dataset.form_filenames:
-            id_ = filename.stem
-            line_region = self.iam_dataset.line_regions_by_id[id_]
-            _crop_paragraph_image(filename, line_region, crop_dims, self.input_shape)
-
-    def _decide_on_crop_dims(self) -> Tuple[int, int]:
-        """Decide on the dimensions to crop out of the form image.
-
-        Since image width is larger than a comfortable crop around the longest paragraph,
-        we will make the crop a square form factor.
-        And since the found dimensions 610x610 are pretty close to 512x512,
-        we might as well resize crops and make it exactly that, which lets us
-        do all kinds of power-of-2 pooling and upsampling should we choose to.
-
-        Returns:
-            Tuple[int, int]: A tuple of crop dimensions.
-
-        Raises:
-            RuntimeError: When max crop height is larger than max crop width.
-
-        """
-
-        sample_form_filename = self.iam_dataset.form_filenames[0]
-        sample_image = util.read_image(sample_form_filename, grayscale=True)
-        max_crop_width = sample_image.shape[1]
-        max_crop_height = _get_max_paragraph_crop_height(
-            self.iam_dataset.line_regions_by_id
-        )
-        if not max_crop_height <= max_crop_width:
-            raise RuntimeError(
-                f"Max crop height is larger then max crop width: {max_crop_height} >= {max_crop_width}"
-            )
-
-        crop_dims = (max_crop_width, max_crop_width)
-        logger.info(
-            f"Max crop width and height were found to be {max_crop_width}x{max_crop_height}."
-        )
-        logger.info(f"Setting them to {max_crop_width}x{max_crop_width}")
-        return crop_dims
-
-    def __repr__(self) -> str:
-        """Return info about the dataset."""
-        return (
-            "IAM Paragraph Dataset\n"  # pylint: disable=no-member
-            f"Num classes: {self.num_classes}\n"
-            f"Data: {self.data.shape}\n"
-            f"Targets: {self.targets.shape}\n"
-        )
-
-
-def _get_max_paragraph_crop_height(line_regions_by_id: Dict) -> int:
-    heights = []
-    for regions in line_regions_by_id.values():
-        min_y1 = min(region["y1"] for region in regions) - PARAGRAPH_BUFFER
-        max_y2 = max(region["y2"] for region in regions) + PARAGRAPH_BUFFER
-        height = max_y2 - min_y1
-        heights.append(height)
-    return max(heights)
-
-
-def _crop_paragraph_image(
-    filename: str, line_regions: Dict, crop_dims: Tuple[int, int], final_dims: Tuple
-) -> None:
-    image = util.read_image(filename, grayscale=True)
-
-    min_y1 = min(region["y1"] for region in line_regions) - PARAGRAPH_BUFFER
-    max_y2 = max(region["y2"] for region in line_regions) + PARAGRAPH_BUFFER
-    height = max_y2 - min_y1
-    crop_height = crop_dims[0]
-    buffer = (crop_height - height) // 2
-
-    # Generate image crop.
-    image_crop = 255 * np.ones(crop_dims, dtype=np.uint8)
-    try:
-        image_crop[buffer : buffer + height] = image[min_y1:max_y2]
-    except Exception as e:  # pylint: disable=broad-except
-        logger.error(f"Rescued {filename}: {e}")
-        return
-
-    # Generate ground truth.
-    gt_image = np.zeros_like(image_crop, dtype=np.uint8)
-    for index, region in enumerate(line_regions):
-        gt_image[
-            (region["y1"] - min_y1 + buffer) : (region["y2"] - min_y1 + buffer),
-            region["x1"] : region["x2"],
-        ] = (index % 2 + 1)
-
-    # Generate image for debugging.
-    import matplotlib.pyplot as plt
-
-    cmap = plt.get_cmap("Set1")
-    image_crop_for_debug = np.dstack([image_crop, image_crop, image_crop])
-    for index, region in enumerate(line_regions):
-        color = [255 * _ for _ in cmap(index)[:-1]]
-        cv2.rectangle(
-            image_crop_for_debug,
-            (region["x1"], region["y1"] - min_y1 + buffer),
-            (region["x2"], region["y2"] - min_y1 + buffer),
-            color,
-            3,
-        )
-    image_crop_for_debug = cv2.resize(
-        image_crop_for_debug, final_dims, interpolation=cv2.INTER_AREA
-    )
-    util.write_image(image_crop_for_debug, DEBUG_CROPS_DIRNAME / f"{filename.stem}.jpg")
-
-    image_crop = cv2.resize(image_crop, final_dims, interpolation=cv2.INTER_AREA)
-    util.write_image(image_crop, CROPS_DIRNAME / f"{filename.stem}.jpg")
-
-    gt_image = cv2.resize(gt_image, final_dims, interpolation=cv2.INTER_NEAREST)
-    util.write_image(gt_image, GT_DIRNAME / f"{filename.stem}.png")
-
-
-def _load_iam_paragraphs() -> None:
-    logger.info("Loading IAM paragraph crops and ground truth from image files...")
-    images = []
-    gt_images = []
-    ids = []
-    for filename in CROPS_DIRNAME.glob("*.jpg"):
-        id_ = filename.stem
-        image = util.read_image(filename, grayscale=True)
-        image = 1.0 - image / 255
-
-        gt_filename = GT_DIRNAME / f"{id_}.png"
-        gt_image = util.read_image(gt_filename, grayscale=True)
-
-        images.append(image)
-        gt_images.append(gt_image)
-        ids.append(id_)
-    images = np.array(images).astype(np.float32)
-    gt_images = np.array(gt_images).astype(np.uint8)
-    ids = np.array(ids)
-    return images, gt_images, ids
-
-
-@click.command()
-@click.option(
-    "--subsample_fraction",
-    type=float,
-    default=None,
-    help="The subsampling factor of the dataset.",
-)
-def main(subsample_fraction: float) -> None:
-    """Load dataset and print info."""
-    logger.info("Creating train set...")
-    dataset = IamParagraphsDataset(train=True, subsample_fraction=subsample_fraction)
-    dataset.load_or_generate_data()
-    print(dataset)
-    logger.info("Creating test set...")
-    dataset = IamParagraphsDataset(subsample_fraction=subsample_fraction)
-    dataset.load_or_generate_data()
-    print(dataset)
-
-
-if __name__ == "__main__":
-    main()
diff --git a/src/text_recognizer/datasets/iam_preprocessor.py b/src/text_recognizer/datasets/iam_preprocessor.py
deleted file mode 100644
index a93eb00..0000000
--- a/src/text_recognizer/datasets/iam_preprocessor.py
+++ /dev/null
@@ -1,196 +0,0 @@
-"""Preprocessor for extracting word letters from the IAM dataset.
-
-The code is mostly stolen from:
-
-    https://github.com/facebookresearch/gtn_applications/blob/master/datasets/iamdb.py
-
-"""
-
-import collections
-import itertools
-from pathlib import Path
-import re
-from typing import List, Optional, Union
-
-import click
-from loguru import logger
-import torch
-
-
-def load_metadata(
-    data_dir: Path, wordsep: str, use_words: bool = False
-) -> collections.defaultdict:
-    """Loads IAM metadata and returns it as a dictionary."""
-    forms = collections.defaultdict(list)
-    filename = "words.txt" if use_words else "lines.txt"
-
-    with open(data_dir / "ascii" / filename, "r") as f:
-        lines = (line.strip().split() for line in f if line[0] != "#")
-        for line in lines:
-            # Skip word segmentation errors.
-            if use_words and line[1] == "err":
-                continue
-            text = " ".join(line[8:])
-
-            # Remove garbage tokens:
-            text = text.replace("#", "")
-
-            # Swap word sep form | to wordsep
-            text = re.sub(r"\|+|\s", wordsep, text).strip(wordsep)
-            form_key = "-".join(line[0].split("-")[:2])
-            line_key = "-".join(line[0].split("-")[:3])
-            box_idx = 4 - use_words
-            box = tuple(int(val) for val in line[box_idx : box_idx + 4])
-            forms[form_key].append({"key": line_key, "box": box, "text": text})
-    return forms
-
-
-class Preprocessor:
-    """A preprocessor for the IAM dataset."""
-
-    # TODO: add lower case only to when generating...
-
-    def __init__(
-        self,
-        data_dir: Union[str, Path],
-        num_features: int,
-        tokens_path: Optional[Union[str, Path]] = None,
-        lexicon_path: Optional[Union[str, Path]] = None,
-        use_words: bool = False,
-        prepend_wordsep: bool = False,
-    ) -> None:
-        self.wordsep = "▁"
-        self._use_word = use_words
-        self._prepend_wordsep = prepend_wordsep
-
-        self.data_dir = Path(data_dir)
-
-        self.forms = load_metadata(self.data_dir, self.wordsep, use_words=use_words)
-
-        # Load the set of graphemes:
-        graphemes = set()
-        for _, form in self.forms.items():
-            for line in form:
-                graphemes.update(line["text"].lower())
-        self.graphemes = sorted(graphemes)
-
-        # Build the token-to-index and index-to-token maps.
-        if tokens_path is not None:
-            with open(tokens_path, "r") as f:
-                self.tokens = [line.strip() for line in f]
-        else:
-            self.tokens = self.graphemes
-
-        if lexicon_path is not None:
-            with open(lexicon_path, "r") as f:
-                lexicon = (line.strip().split() for line in f)
-                lexicon = {line[0]: line[1:] for line in lexicon}
-                self.lexicon = lexicon
-        else:
-            self.lexicon = None
-
-        self.graphemes_to_index = {t: i for i, t in enumerate(self.graphemes)}
-        self.tokens_to_index = {t: i for i, t in enumerate(self.tokens)}
-        self.num_features = num_features
-        self.text = []
-
-    @property
-    def num_tokens(self) -> int:
-        """Returns the number or tokens."""
-        return len(self.tokens)
-
-    @property
-    def use_words(self) -> bool:
-        """If words are used."""
-        return self._use_word
-
-    def extract_train_text(self) -> None:
-        """Extracts training text."""
-        keys = []
-        with open(self.data_dir / "task" / "trainset.txt") as f:
-            keys.extend((line.strip() for line in f))
-
-        for _, examples in self.forms.items():
-            for example in examples:
-                if example["key"] not in keys:
-                    continue
-                self.text.append(example["text"].lower())
-
-    def to_index(self, line: str) -> torch.LongTensor:
-        """Converts text to a tensor of indices."""
-        token_to_index = self.graphemes_to_index
-        if self.lexicon is not None:
-            if len(line) > 0:
-                # If the word is not found in the lexicon, fall back to letters.
-                line = [
-                    t
-                    for w in line.split(self.wordsep)
-                    for t in self.lexicon.get(w, self.wordsep + w)
-                ]
-            token_to_index = self.tokens_to_index
-        if self._prepend_wordsep:
-            line = itertools.chain([self.wordsep], line)
-        return torch.LongTensor([token_to_index[t] for t in line])
-
-    def to_text(self, indices: List[int]) -> str:
-        """Converts indices to text."""
-        # Roughly the inverse of `to_index`
-        encoding = self.graphemes
-        if self.lexicon is not None:
-            encoding = self.tokens
-        return self._post_process(encoding[i] for i in indices)
-
-    def tokens_to_text(self, indices: List[int]) -> str:
-        """Converts tokens to text."""
-        return self._post_process(self.tokens[i] for i in indices)
-
-    def _post_process(self, indices: List[int]) -> str:
-        """A list join."""
-        return "".join(indices).strip(self.wordsep)
-
-
-@click.command()
-@click.option("--data_dir", type=str, default=None, help="Path to iam dataset")
-@click.option(
-    "--use_words", is_flag=True, help="Load word segmented dataset instead of lines"
-)
-@click.option(
-    "--save_text", type=str, default=None, help="Path to save parsed train text"
-)
-@click.option("--save_tokens", type=str, default=None, help="Path to save tokens")
-def cli(
-    data_dir: Optional[str],
-    use_words: bool,
-    save_text: Optional[str],
-    save_tokens: Optional[str],
-) -> None:
-    """CLI for extracting text data from the iam dataset."""
-    if data_dir is None:
-        data_dir = (
-            Path(__file__).resolve().parents[3] / "data" / "raw" / "iam" / "iamdb"
-        )
-        logger.debug(f"Using data dir: {data_dir}")
-        if not data_dir.exists():
-            raise RuntimeError(f"Could not locate iamdb directory at {data_dir}")
-    else:
-        data_dir = Path(data_dir)
-
-    preprocessor = Preprocessor(data_dir, 64, use_words=use_words)
-    preprocessor.extract_train_text()
-
-    processed_dir = data_dir.parents[2] / "processed" / "iam_lines"
-    logger.debug(f"Saving processed files at: {processed_dir}")
-
-    if save_text is not None:
-        logger.info("Saving training text")
-        with open(processed_dir / save_text, "w") as f:
-            f.write("\n".join(t for t in preprocessor.text))
-
-    if save_tokens is not None:
-        logger.info("Saving tokens")
-        with open(processed_dir / save_tokens, "w") as f:
-            f.write("\n".join(preprocessor.tokens))
-
-
-if __name__ == "__main__":
-    cli()
diff --git a/src/text_recognizer/datasets/sentence_generator.py b/src/text_recognizer/datasets/sentence_generator.py
deleted file mode 100644
index dd76652..0000000
--- a/src/text_recognizer/datasets/sentence_generator.py
+++ /dev/null
@@ -1,81 +0,0 @@
-"""Downloading the Brown corpus with NLTK for sentence generating."""
-
-import itertools
-import re
-import string
-from typing import Optional
-
-import nltk
-from nltk.corpus.reader.util import ConcatenatedCorpusView
-import numpy as np
-
-from text_recognizer.datasets.util import DATA_DIRNAME
-
-NLTK_DATA_DIRNAME = DATA_DIRNAME / "raw" / "nltk"
-
-
-class SentenceGenerator:
-    """Generates text sentences using the Brown corpus."""
-
-    def __init__(self, max_length: Optional[int] = None) -> None:
-        """Loads the corpus and sets word start indices."""
-        self.corpus = brown_corpus()
-        self.word_start_indices = [0] + [
-            _.start(0) + 1 for _ in re.finditer(" ", self.corpus)
-        ]
-        self.max_length = max_length
-
-    def generate(self, max_length: Optional[int] = None) -> str:
-        """Generates a word or sentences from the Brown corpus.
-
-        Sample a string from the Brown corpus of length at least one word and at most max_length, padding to
-        max_length with the '_' characters if sentence is shorter.
-
-        Args:
-            max_length (Optional[int]): The maximum number of characters in the sentence. Defaults to None.
-
-        Returns:
-            str: A sentence from the Brown corpus.
-
-        Raises:
-            ValueError: If max_length was not specified at initialization and not given as an argument.
-
-        """
-        if max_length is None:
-            max_length = self.max_length
-        if max_length is None:
-            raise ValueError(
-                "Must provide max_length to this method or when making this object."
-            )
-
-        index = np.random.randint(0, len(self.word_start_indices) - 1)
-        start_index = self.word_start_indices[index]
-        end_index_candidates = []
-        for index in range(index + 1, len(self.word_start_indices)):
-            if self.word_start_indices[index] - start_index > max_length:
-                break
-            end_index_candidates.append(self.word_start_indices[index])
-        end_index = np.random.choice(end_index_candidates)
-        sampled_text = self.corpus[start_index:end_index].strip()
-        padding = "_" * (max_length - len(sampled_text))
-        return sampled_text + padding
-
-
-def brown_corpus() -> str:
-    """Returns a single string with the Brown corpus with all punctuations stripped."""
-    sentences = load_nltk_brown_corpus()
-    corpus = " ".join(itertools.chain.from_iterable(sentences))
-    corpus = corpus.translate({ord(c): None for c in string.punctuation})
-    corpus = re.sub(" +", " ", corpus)
-    return corpus
-
-
-def load_nltk_brown_corpus() -> ConcatenatedCorpusView:
-    """Load the Brown corpus using the NLTK library."""
-    nltk.data.path.append(NLTK_DATA_DIRNAME)
-    try:
-        nltk.corpus.brown.sents()
-    except LookupError:
-        NLTK_DATA_DIRNAME.mkdir(parents=True, exist_ok=True)
-        nltk.download("brown", download_dir=NLTK_DATA_DIRNAME)
-    return nltk.corpus.brown.sents()
diff --git a/src/text_recognizer/datasets/transforms.py b/src/text_recognizer/datasets/transforms.py
deleted file mode 100644
index b6a48f5..0000000
--- a/src/text_recognizer/datasets/transforms.py
+++ /dev/null
@@ -1,266 +0,0 @@
-"""Transforms for PyTorch datasets."""
-from abc import abstractmethod
-from pathlib import Path
-import random
-from typing import Any, Optional, Union
-
-from loguru import logger
-import numpy as np
-from PIL import Image
-import torch
-from torch import Tensor
-import torch.nn.functional as F
-from torchvision import transforms
-from torchvision.transforms import (
-    ColorJitter,
-    Compose,
-    Normalize,
-    RandomAffine,
-    RandomHorizontalFlip,
-    RandomRotation,
-    ToPILImage,
-    ToTensor,
-)
-
-from text_recognizer.datasets.iam_preprocessor import Preprocessor
-from text_recognizer.datasets.util import EmnistMapper
-
-
-class RandomResizeCrop:
-    """Image transform with random resize and crop applied.
-
-    Stolen from
-
-    https://github.com/facebookresearch/gtn_applications/blob/master/datasets/iamdb.py
-
-    """
-
-    def __init__(self, jitter: int = 10, ratio: float = 0.5) -> None:
-        self.jitter = jitter
-        self.ratio = ratio
-
-    def __call__(self, img: np.ndarray) -> np.ndarray:
-        """Applies random crop and rotation to an image."""
-        w, h = img.size
-
-        # pad with white:
-        img = transforms.functional.pad(img, self.jitter, fill=255)
-
-        # crop at random (x, y):
-        x = self.jitter + random.randint(-self.jitter, self.jitter)
-        y = self.jitter + random.randint(-self.jitter, self.jitter)
-
-        # randomize aspect ratio:
-        size_w = w * random.uniform(1 - self.ratio, 1 + self.ratio)
-        size = (h, int(size_w))
-        img = transforms.functional.resized_crop(img, y, x, h, w, size)
-        return img
-
-
-class Transpose:
-    """Transposes the EMNIST image to the correct orientation."""
-
-    def __call__(self, image: Image) -> np.ndarray:
-        """Swaps axis."""
-        return np.array(image).swapaxes(0, 1)
-
-
-class Resize:
-    """Resizes a tensor to a specified width."""
-
-    def __init__(self, width: int = 952) -> None:
-        # The default is 952 because of the IAM dataset.
-        self.width = width
-
-    def __call__(self, image: Tensor) -> Tensor:
-        """Resize tensor in the last dimension."""
-        return F.interpolate(image, size=self.width, mode="nearest")
-
-
-class AddTokens:
-    """Adds start of sequence and end of sequence tokens to target tensor."""
-
-    def __init__(self, pad_token: str, eos_token: str, init_token: str = None) -> None:
-        self.init_token = init_token
-        self.pad_token = pad_token
-        self.eos_token = eos_token
-        if self.init_token is not None:
-            self.emnist_mapper = EmnistMapper(
-                init_token=self.init_token,
-                pad_token=self.pad_token,
-                eos_token=self.eos_token,
-            )
-        else:
-            self.emnist_mapper = EmnistMapper(
-                pad_token=self.pad_token, eos_token=self.eos_token,
-            )
-        self.pad_value = self.emnist_mapper(self.pad_token)
-        self.eos_value = self.emnist_mapper(self.eos_token)
-
-    def __call__(self, target: Tensor) -> Tensor:
-        """Adds a sos token to the begining and a eos token to the end of a target sequence."""
-        dtype, device = target.dtype, target.device
-
-        # Find the where padding starts.
-        pad_index = torch.nonzero(target == self.pad_value, as_tuple=False)[0].item()
-
-        target[pad_index] = self.eos_value
-
-        if self.init_token is not None:
-            self.sos_value = self.emnist_mapper(self.init_token)
-            sos = torch.tensor([self.sos_value], dtype=dtype, device=device)
-            target = torch.cat([sos, target], dim=0)
-
-        return target
-
-
-class ApplyContrast:
-    """Sets everything below a threshold to zero, i.e. increase contrast."""
-
-    def __init__(self, low: float = 0.0, high: float = 0.25) -> None:
-        self.low = low
-        self.high = high
-
-    def __call__(self, x: Tensor) -> Tensor:
-        """Apply mask binary mask to input tensor."""
-        mask = x > np.random.RandomState().uniform(low=self.low, high=self.high)
-        return x * mask
-
-
-class Unsqueeze:
-    """Add a dimension to the tensor."""
-
-    def __call__(self, x: Tensor) -> Tensor:
-        """Adds dim."""
-        return x.unsqueeze(0)
-
-
-class Squeeze:
-    """Removes the first dimension of a tensor."""
-
-    def __call__(self, x: Tensor) -> Tensor:
-        """Removes first dim."""
-        return x.squeeze(0)
-
-
-class ToLower:
-    """Converts target to lower case."""
-
-    def __call__(self, target: Tensor) -> Tensor:
-        """Corrects index value in target tensor."""
-        device = target.device
-        return torch.stack([x - 26 if x > 35 else x for x in target]).to(device)
-
-
-class ToCharcters:
-    """Converts integers to characters."""
-
-    def __init__(
-        self, pad_token: str, eos_token: str, init_token: str = None, lower: bool = True
-    ) -> None:
-        self.init_token = init_token
-        self.pad_token = pad_token
-        self.eos_token = eos_token
-        if self.init_token is not None:
-            self.emnist_mapper = EmnistMapper(
-                init_token=self.init_token,
-                pad_token=self.pad_token,
-                eos_token=self.eos_token,
-                lower=lower,
-            )
-        else:
-            self.emnist_mapper = EmnistMapper(
-                pad_token=self.pad_token, eos_token=self.eos_token, lower=lower
-            )
-
-    def __call__(self, y: Tensor) -> str:
-        """Converts a Tensor to a str."""
-        return (
-            "".join([self.emnist_mapper(int(i)) for i in y])
-            .strip("_")
-            .replace(" ", "▁")
-        )
-
-
-class WordPieces:
-    """Abstract transform for word pieces."""
-
-    def __init__(
-        self,
-        num_features: int,
-        data_dir: Optional[Union[str, Path]] = None,
-        tokens: Optional[Union[str, Path]] = None,
-        lexicon: Optional[Union[str, Path]] = None,
-        use_words: bool = False,
-        prepend_wordsep: bool = False,
-    ) -> None:
-        if data_dir is None:
-            data_dir = (
-                Path(__file__).resolve().parents[3] / "data" / "raw" / "iam" / "iamdb"
-            )
-            logger.debug(f"Using data dir: {data_dir}")
-            if not data_dir.exists():
-                raise RuntimeError(f"Could not locate iamdb directory at {data_dir}")
-        else:
-            data_dir = Path(data_dir)
-        processed_path = (
-            Path(__file__).resolve().parents[3] / "data" / "processed" / "iam_lines"
-        )
-        tokens_path = processed_path / tokens
-        lexicon_path = processed_path / lexicon
-
-        self.preprocessor = Preprocessor(
-            data_dir,
-            num_features,
-            tokens_path,
-            lexicon_path,
-            use_words,
-            prepend_wordsep,
-        )
-
-    @abstractmethod
-    def __call__(self, *args, **kwargs) -> Any:
-        """Transforms input."""
-        ...
-
-
-class ToWordPieces(WordPieces):
-    """Transforms str to word pieces."""
-
-    def __init__(
-        self,
-        num_features: int,
-        data_dir: Optional[Union[str, Path]] = None,
-        tokens: Optional[Union[str, Path]] = None,
-        lexicon: Optional[Union[str, Path]] = None,
-        use_words: bool = False,
-        prepend_wordsep: bool = False,
-    ) -> None:
-        super().__init__(
-            num_features, data_dir, tokens, lexicon, use_words, prepend_wordsep
-        )
-
-    def __call__(self, line: str) -> Tensor:
-        """Transforms str to word pieces."""
-        return self.preprocessor.to_index(line)
-
-
-class ToText(WordPieces):
-    """Takes word pieces and converts them to text."""
-
-    def __init__(
-        self,
-        num_features: int,
-        data_dir: Optional[Union[str, Path]] = None,
-        tokens: Optional[Union[str, Path]] = None,
-        lexicon: Optional[Union[str, Path]] = None,
-        use_words: bool = False,
-        prepend_wordsep: bool = False,
-    ) -> None:
-        super().__init__(
-            num_features, data_dir, tokens, lexicon, use_words, prepend_wordsep
-        )
-
-    def __call__(self, x: Tensor) -> str:
-        """Converts tensor to text."""
-        return self.preprocessor.to_text(x.tolist())
diff --git a/src/text_recognizer/datasets/util.py b/src/text_recognizer/datasets/util.py
deleted file mode 100644
index da87756..0000000
--- a/src/text_recognizer/datasets/util.py
+++ /dev/null
@@ -1,209 +0,0 @@
-"""Util functions for datasets."""
-import hashlib
-import json
-import os
-from pathlib import Path
-import string
-from typing import Dict, List, Optional, Union
-from urllib.request import urlretrieve
-
-from loguru import logger
-import numpy as np
-import torch
-from torch import Tensor
-from torchvision.datasets import EMNIST
-from tqdm import tqdm
-
-DATA_DIRNAME = Path(__file__).resolve().parents[3] / "data"
-ESSENTIALS_FILENAME = Path(__file__).resolve().parents[0] / "emnist_essentials.json"
-
-
-def save_emnist_essentials(emnsit_dataset: EMNIST = EMNIST) -> None:
-    """Extract and saves EMNIST essentials."""
-    labels = emnsit_dataset.classes
-    labels.sort()
-    mapping = [(i, str(label)) for i, label in enumerate(labels)]
-    essentials = {
-        "mapping": mapping,
-        "input_shape": tuple(np.array(emnsit_dataset[0][0]).shape[:]),
-    }
-    logger.info("Saving emnist essentials...")
-    with open(ESSENTIALS_FILENAME, "w") as f:
-        json.dump(essentials, f)
-
-
-def download_emnist() -> None:
-    """Download the EMNIST dataset via the PyTorch class."""
-    logger.info(f"Data directory is: {DATA_DIRNAME}")
-    dataset = EMNIST(root=DATA_DIRNAME, split="byclass", download=True)
-    save_emnist_essentials(dataset)
-
-
-class EmnistMapper:
-    """Mapper between network output to Emnist character."""
-
-    def __init__(
-        self,
-        pad_token: str,
-        init_token: Optional[str] = None,
-        eos_token: Optional[str] = None,
-        lower: bool = False,
-    ) -> None:
-        """Loads the emnist essentials file with the mapping and input shape."""
-        self.init_token = init_token
-        self.pad_token = pad_token
-        self.eos_token = eos_token
-        self.lower = lower
-
-        self.essentials = self._load_emnist_essentials()
-        # Load dataset information.
-        self._mapping = dict(self.essentials["mapping"])
-        self._augment_emnist_mapping()
-        self._inverse_mapping = {v: k for k, v in self.mapping.items()}
-        self._num_classes = len(self.mapping)
-        self._input_shape = self.essentials["input_shape"]
-
-    def __call__(self, token: Union[str, int, np.uint8, Tensor]) -> Union[str, int]:
-        """Maps the token to emnist character or character index.
-
-        If the token is an integer (index), the method will return the Emnist character corresponding to that index.
-        If the token is a str (Emnist character), the method will return the corresponding index for that character.
-
-        Args:
-            token (Union[str, int, np.uint8, Tensor]): Either a string or index (integer).
-
-        Returns:
-            Union[str, int]: The mapping result.
-
-        Raises:
-            KeyError: If the index or string does not exist in the mapping.
-
-        """
-        if (
-            (isinstance(token, np.uint8) or isinstance(token, int))
-            or torch.is_tensor(token)
-            and int(token) in self.mapping
-        ):
-            return self.mapping[int(token)]
-        elif isinstance(token, str) and token in self._inverse_mapping:
-            return self._inverse_mapping[token]
-        else:
-            raise KeyError(f"Token {token} does not exist in the mappings.")
-
-    @property
-    def mapping(self) -> Dict:
-        """Returns the mapping between index and character."""
-        return self._mapping
-
-    @property
-    def inverse_mapping(self) -> Dict:
-        """Returns the mapping between character and index."""
-        return self._inverse_mapping
-
-    @property
-    def num_classes(self) -> int:
-        """Returns the number of classes in the dataset."""
-        return self._num_classes
-
-    @property
-    def input_shape(self) -> List[int]:
-        """Returns the input shape of the Emnist characters."""
-        return self._input_shape
-
-    def _load_emnist_essentials(self) -> Dict:
-        """Load the EMNIST mapping."""
-        with open(str(ESSENTIALS_FILENAME)) as f:
-            essentials = json.load(f)
-        return essentials
-
-    def _augment_emnist_mapping(self) -> None:
-        """Augment the mapping with extra symbols."""
-        # Extra symbols in IAM dataset
-        if self.lower:
-            self._mapping = {
-                k: str(v)
-                for k, v in enumerate(list(range(10)) + list(string.ascii_lowercase))
-            }
-
-        extra_symbols = [
-            " ",
-            "!",
-            '"',
-            "#",
-            "&",
-            "'",
-            "(",
-            ")",
-            "*",
-            "+",
-            ",",
-            "-",
-            ".",
-            "/",
-            ":",
-            ";",
-            "?",
-        ]
-
-        # padding symbol, and acts as blank symbol as well.
-        extra_symbols.append(self.pad_token)
-
-        if self.init_token is not None:
-            extra_symbols.append(self.init_token)
-
-        if self.eos_token is not None:
-            extra_symbols.append(self.eos_token)
-
-        max_key = max(self.mapping.keys())
-        extra_mapping = {}
-        for i, symbol in enumerate(extra_symbols):
-            extra_mapping[max_key + 1 + i] = symbol
-
-        self._mapping = {**self.mapping, **extra_mapping}
-
-
-def compute_sha256(filename: Union[Path, str]) -> str:
-    """Returns the SHA256 checksum of a file."""
-    with open(filename, "rb") as f:
-        return hashlib.sha256(f.read()).hexdigest()
-
-
-class TqdmUpTo(tqdm):
-    """TQDM progress bar when downloading files.
-
-    From https://github.com/tqdm/tqdm/blob/master/examples/tqdm_wget.py
-
-    """
-
-    def update_to(
-        self, blocks: int = 1, block_size: int = 1, total_size: Optional[int] = None
-    ) -> None:
-        """Updates the progress bar.
-
-        Args:
-            blocks (int): Number of blocks transferred so far. Defaults to 1.
-            block_size (int): Size of each block, in tqdm units. Defaults to 1.
-            total_size (Optional[int]): Total size in tqdm units. Defaults to None.
-        """
-        if total_size is not None:
-            self.total = total_size  # pylint: disable=attribute-defined-outside-init
-        self.update(blocks * block_size - self.n)
-
-
-def download_url(url: str, filename: str) -> None:
-    """Downloads a file from url to filename, with a progress bar."""
-    with TqdmUpTo(unit="B", unit_scale=True, unit_divisor=1024, miniters=1) as t:
-        urlretrieve(url, filename, reporthook=t.update_to, data=None)  # nosec
-
-
-def _download_raw_dataset(metadata: Dict) -> None:
-    if os.path.exists(metadata["filename"]):
-        return
-    logger.info(f"Downloading raw dataset from {metadata['url']}...")
-    download_url(metadata["url"], metadata["filename"])
-    logger.info("Computing SHA-256...")
-    sha256 = compute_sha256(metadata["filename"])
-    if sha256 != metadata["sha256"]:
-        raise ValueError(
-            "Downloaded data file SHA-256 does not match that listed in metadata document."
-        )
diff --git a/src/text_recognizer/line_predictor.py b/src/text_recognizer/line_predictor.py
deleted file mode 100644
index 8e348fe..0000000
--- a/src/text_recognizer/line_predictor.py
+++ /dev/null
@@ -1,28 +0,0 @@
-"""LinePredictor class."""
-import importlib
-from typing import Tuple, Union
-
-import numpy as np
-from torch import nn
-
-from text_recognizer import datasets, networks
-from text_recognizer.models import TransformerModel
-from text_recognizer.util import read_image
-
-
-class LinePredictor:
-    """Given an image of a line of handwritten text, recognizes the text content."""
-
-    def __init__(self, dataset: str, network_fn: str) -> None:
-        network_fn = getattr(networks, network_fn)
-        dataset = getattr(datasets, dataset)
-        self.model = TransformerModel(network_fn=network_fn, dataset=dataset)
-        self.model.eval()
-
-    def predict(self, image_or_filename: Union[np.ndarray, str]) -> Tuple[str, float]:
-        """Predict on a single images contianing a handwritten character."""
-        if isinstance(image_or_filename, str):
-            image = read_image(image_or_filename, grayscale=True)
-        else:
-            image = image_or_filename
-        return self.model.predict_on_image(image)
diff --git a/src/text_recognizer/models/__init__.py b/src/text_recognizer/models/__init__.py
deleted file mode 100644
index 7647d7e..0000000
--- a/src/text_recognizer/models/__init__.py
+++ /dev/null
@@ -1,18 +0,0 @@
-"""Model modules."""
-from .base import Model
-from .character_model import CharacterModel
-from .crnn_model import CRNNModel
-from .ctc_transformer_model import CTCTransformerModel
-from .segmentation_model import SegmentationModel
-from .transformer_model import TransformerModel
-from .vqvae_model import VQVAEModel
-
-__all__ = [
-    "CharacterModel",
-    "CRNNModel",
-    "CTCTransformerModel",
-    "Model",
-    "SegmentationModel",
-    "TransformerModel",
-    "VQVAEModel",
-]
diff --git a/src/text_recognizer/models/base.py b/src/text_recognizer/models/base.py
deleted file mode 100644
index 70f4cdb..0000000
--- a/src/text_recognizer/models/base.py
+++ /dev/null
@@ -1,455 +0,0 @@
-"""Abstract Model class for PyTorch neural networks."""
-
-from abc import ABC, abstractmethod
-from glob import glob
-import importlib
-from pathlib import Path
-import re
-import shutil
-from typing import Callable, Dict, List, Optional, Tuple, Type, Union
-
-from loguru import logger
-import torch
-from torch import nn
-from torch import Tensor
-from torch.optim.swa_utils import AveragedModel, SWALR
-from torch.utils.data import DataLoader, Dataset, random_split
-from torchsummary import summary
-
-from text_recognizer import datasets
-from text_recognizer import networks
-from text_recognizer.datasets import EmnistMapper
-
-WEIGHT_DIRNAME = Path(__file__).parents[1].resolve() / "weights"
-
-
-class Model(ABC):
-    """Abstract Model class with composition of different parts defining a PyTorch neural network."""
-
-    def __init__(
-        self,
-        network_fn: str,
-        dataset: str,
-        network_args: Optional[Dict] = None,
-        dataset_args: Optional[Dict] = None,
-        metrics: Optional[Dict] = None,
-        criterion: Optional[Callable] = None,
-        criterion_args: Optional[Dict] = None,
-        optimizer: Optional[Callable] = None,
-        optimizer_args: Optional[Dict] = None,
-        lr_scheduler: Optional[Callable] = None,
-        lr_scheduler_args: Optional[Dict] = None,
-        swa_args: Optional[Dict] = None,
-        device: Optional[str] = None,
-    ) -> None:
-        """Base class, to be inherited by model for specific type of data.
-
-        Args:
-            network_fn (str): The name of network.
-            dataset (str): The name dataset class.
-            network_args (Optional[Dict]): Arguments for the network. Defaults to None.
-            dataset_args (Optional[Dict]):  Arguments for the dataset.
-            metrics (Optional[Dict]): Metrics to evaluate the performance with. Defaults to None.
-            criterion (Optional[Callable]): The criterion to evaluate the performance of the network.
-                Defaults to None.
-            criterion_args (Optional[Dict]): Dict of arguments for criterion. Defaults to None.
-            optimizer (Optional[Callable]): The optimizer for updating the weights. Defaults to None.
-            optimizer_args (Optional[Dict]): Dict of arguments for optimizer. Defaults to None.
-            lr_scheduler (Optional[Callable]): A PyTorch learning rate scheduler. Defaults to None.
-            lr_scheduler_args (Optional[Dict]): Dict of arguments for learning rate scheduler. Defaults to
-                None.
-            swa_args (Optional[Dict]): Dict of arguments for stochastic weight averaging. Defaults to
-                None.
-            device (Optional[str]): Name of the device to train on. Defaults to None.
-
-        """
-        self._name = f"{self.__class__.__name__}_{dataset}_{network_fn}"
-        # Has to be set in subclass.
-        self._mapper = None
-
-        # Placeholder.
-        self._input_shape = None
-
-        self.dataset_name = dataset
-        self.dataset = None
-        self.dataset_args = dataset_args
-
-        # Placeholders for datasets.
-        self.train_dataset = None
-        self.val_dataset = None
-        self.test_dataset = None
-
-        # Stochastic Weight Averaging placeholders.
-        self.swa_args = swa_args
-        self._swa_scheduler = None
-        self._swa_network = None
-        self._use_swa_model = False
-
-        # Experiment directory.
-        self.model_dir = None
-
-        # Flag for configured model.
-        self.is_configured = False
-        self.data_prepared = False
-
-        # Flag for stopping training.
-        self.stop_training = False
-
-        self._metrics = metrics if metrics is not None else None
-
-        # Set the device.
-        self._device = (
-            torch.device("cuda" if torch.cuda.is_available() else "cpu")
-            if device is None
-            else device
-        )
-
-        # Configure network.
-        self._network = None
-        self._network_args = network_args
-        self._configure_network(network_fn)
-
-        # Place network on device (GPU).
-        self.to_device()
-
-        # Loss and Optimizer placeholders for before loading.
-        self._criterion = criterion
-        self.criterion_args = criterion_args
-
-        self._optimizer = optimizer
-        self.optimizer_args = optimizer_args
-
-        self._lr_scheduler = lr_scheduler
-        self.lr_scheduler_args = lr_scheduler_args
-
-    def configure_model(self) -> None:
-        """Configures criterion and optimizers."""
-        if not self.is_configured:
-            self._configure_criterion()
-            self._configure_optimizers()
-
-            # Set this flag to true to prevent the model from configuring again.
-            self.is_configured = True
-
-    def prepare_data(self) -> None:
-        """Prepare data for training."""
-        # TODO add downloading.
-        if not self.data_prepared:
-            # Load dataset module.
-            self.dataset = getattr(datasets, self.dataset_name)
-
-            # Load train dataset.
-            train_dataset = self.dataset(train=True, **self.dataset_args["args"])
-            train_dataset.load_or_generate_data()
-
-            # Set input shape.
-            self._input_shape = train_dataset.input_shape
-
-            # Split train dataset into a training and validation partition.
-            dataset_len = len(train_dataset)
-            train_len = int(
-                self.dataset_args["train_args"]["train_fraction"] * dataset_len
-            )
-            val_len = dataset_len - train_len
-            self.train_dataset, self.val_dataset = random_split(
-                train_dataset, lengths=[train_len, val_len]
-            )
-
-            # Load test dataset.
-            self.test_dataset = self.dataset(train=False, **self.dataset_args["args"])
-            self.test_dataset.load_or_generate_data()
-
-            # Set the flag to true to disable ability to load data again.
-            self.data_prepared = True
-
-    def train_dataloader(self) -> DataLoader:
-        """Returns data loader for training set."""
-        return DataLoader(
-            self.train_dataset,
-            batch_size=self.dataset_args["train_args"]["batch_size"],
-            num_workers=self.dataset_args["train_args"]["num_workers"],
-            shuffle=True,
-            pin_memory=True,
-        )
-
-    def val_dataloader(self) -> DataLoader:
-        """Returns data loader for validation set."""
-        return DataLoader(
-            self.val_dataset,
-            batch_size=self.dataset_args["train_args"]["batch_size"],
-            num_workers=self.dataset_args["train_args"]["num_workers"],
-            shuffle=True,
-            pin_memory=True,
-        )
-
-    def test_dataloader(self) -> DataLoader:
-        """Returns data loader for test set."""
-        return DataLoader(
-            self.test_dataset,
-            batch_size=self.dataset_args["train_args"]["batch_size"],
-            num_workers=self.dataset_args["train_args"]["num_workers"],
-            shuffle=False,
-            pin_memory=True,
-        )
-
-    def _configure_network(self, network_fn: Type[nn.Module]) -> None:
-        """Loads the network."""
-        # If no network arguments are given, load pretrained weights if they exist.
-        # Load network module.
-        network_fn = getattr(networks, network_fn)
-        if self._network_args is None:
-            self.load_weights(network_fn)
-        else:
-            self._network = network_fn(**self._network_args)
-
-    def _configure_criterion(self) -> None:
-        """Loads the criterion."""
-        self._criterion = (
-            self._criterion(**self.criterion_args)
-            if self._criterion is not None
-            else None
-        )
-
-    def _configure_optimizers(self,) -> None:
-        """Loads the optimizers."""
-        if self._optimizer is not None:
-            self._optimizer = self._optimizer(
-                self._network.parameters(), **self.optimizer_args
-            )
-        else:
-            self._optimizer = None
-
-        if self._optimizer and self._lr_scheduler is not None:
-            if "steps_per_epoch" in self.lr_scheduler_args:
-                self.lr_scheduler_args["steps_per_epoch"] = len(self.train_dataloader())
-
-            # Assume lr scheduler should update at each epoch if not specified.
-            if "interval" not in self.lr_scheduler_args:
-                interval = "epoch"
-            else:
-                interval = self.lr_scheduler_args.pop("interval")
-            self._lr_scheduler = {
-                "lr_scheduler": self._lr_scheduler(
-                    self._optimizer, **self.lr_scheduler_args
-                ),
-                "interval": interval,
-            }
-
-        if self.swa_args is not None:
-            self._swa_scheduler = {
-                "swa_scheduler": SWALR(self._optimizer, swa_lr=self.swa_args["lr"]),
-                "swa_start": self.swa_args["start"],
-            }
-            self._swa_network = AveragedModel(self._network).to(self.device)
-
-    @property
-    def name(self) -> str:
-        """Returns the name of the model."""
-        return self._name
-
-    @property
-    def input_shape(self) -> Tuple[int, ...]:
-        """The input shape."""
-        return self._input_shape
-
-    @property
-    def mapper(self) -> EmnistMapper:
-        """Returns the mapper that maps between ints and chars."""
-        return self._mapper
-
-    @property
-    def mapping(self) -> Dict:
-        """Returns the mapping between network output and Emnist character."""
-        return self._mapper.mapping if self._mapper is not None else None
-
-    def eval(self) -> None:
-        """Sets the network to evaluation mode."""
-        self._network.eval()
-
-    def train(self) -> None:
-        """Sets the network to train mode."""
-        self._network.train()
-
-    @property
-    def device(self) -> str:
-        """Device where the weights are stored, i.e. cpu or cuda."""
-        return self._device
-
-    @property
-    def metrics(self) -> Optional[Dict]:
-        """Metrics."""
-        return self._metrics
-
-    @property
-    def criterion(self) -> Optional[Callable]:
-        """Criterion."""
-        return self._criterion
-
-    @property
-    def optimizer(self) -> Optional[Callable]:
-        """Optimizer."""
-        return self._optimizer
-
-    @property
-    def lr_scheduler(self) -> Optional[Dict]:
-        """Returns a directory with the learning rate scheduler."""
-        return self._lr_scheduler
-
-    @property
-    def swa_scheduler(self) -> Optional[Dict]:
-        """Returns a directory with the stochastic weight averaging scheduler."""
-        return self._swa_scheduler
-
-    @property
-    def swa_network(self) -> Optional[Callable]:
-        """Returns the stochastic weight averaging network."""
-        return self._swa_network
-
-    @property
-    def network(self) -> Type[nn.Module]:
-        """Neural network."""
-        # Returns the SWA network if available.
-        return self._network
-
-    @property
-    def weights_filename(self) -> str:
-        """Filepath to the network weights."""
-        WEIGHT_DIRNAME.mkdir(parents=True, exist_ok=True)
-        return str(WEIGHT_DIRNAME / f"{self._name}_weights.pt")
-
-    def use_swa_model(self) -> None:
-        """Set to use predictions from SWA model."""
-        if self.swa_network is not None:
-            self._use_swa_model = True
-
-    def forward(self, x: Tensor) -> Tensor:
-        """Feedforward pass with the network."""
-        if self._use_swa_model:
-            return self.swa_network(x)
-        else:
-            return self.network(x)
-
-    def summary(
-        self,
-        input_shape: Optional[Union[List, Tuple]] = None,
-        depth: int = 3,
-        device: Optional[str] = None,
-    ) -> None:
-        """Prints a summary of the network architecture."""
-        device = self.device if device is None else device
-
-        if input_shape is not None:
-            summary(self.network, input_shape, depth=depth, device=device)
-        elif self._input_shape is not None:
-            input_shape = tuple(self._input_shape)
-            summary(self.network, input_shape, depth=depth, device=device)
-        else:
-            logger.warning("Could not print summary as input shape is not set.")
-
-    def to_device(self) -> None:
-        """Places the network on the device (GPU)."""
-        self._network.to(self._device)
-
-    def _get_state_dict(self) -> Dict:
-        """Get the state dict of the model."""
-        state = {"model_state": self._network.state_dict()}
-
-        if self._optimizer is not None:
-            state["optimizer_state"] = self._optimizer.state_dict()
-
-        if self._lr_scheduler is not None:
-            state["scheduler_state"] = self._lr_scheduler["lr_scheduler"].state_dict()
-            state["scheduler_interval"] = self._lr_scheduler["interval"]
-
-        if self._swa_network is not None:
-            state["swa_network"] = self._swa_network.state_dict()
-
-        return state
-
-    def load_from_checkpoint(self, checkpoint_path: Union[str, Path]) -> None:
-        """Load a previously saved checkpoint.
-
-        Args:
-            checkpoint_path (Path): Path to the experiment with the checkpoint.
-
-        """
-        checkpoint_path = Path(checkpoint_path)
-        self.prepare_data()
-        self.configure_model()
-        logger.debug("Loading checkpoint...")
-        if not checkpoint_path.exists():
-            logger.debug("File does not exist {str(checkpoint_path)}")
-
-        checkpoint = torch.load(str(checkpoint_path), map_location=self.device)
-        self._network.load_state_dict(checkpoint["model_state"])
-
-        if self._optimizer is not None:
-            self._optimizer.load_state_dict(checkpoint["optimizer_state"])
-
-        if self._lr_scheduler is not None:
-            # Does not work when loading from previous checkpoint and trying to train beyond the last max epochs
-            # with OneCycleLR.
-            if self._lr_scheduler["lr_scheduler"].__class__.__name__ != "OneCycleLR":
-                self._lr_scheduler["lr_scheduler"].load_state_dict(
-                    checkpoint["scheduler_state"]
-                )
-                self._lr_scheduler["interval"] = checkpoint["scheduler_interval"]
-
-        if self._swa_network is not None:
-            self._swa_network.load_state_dict(checkpoint["swa_network"])
-
-    def save_checkpoint(
-        self, checkpoint_path: Path, is_best: bool, epoch: int, val_metric: str
-    ) -> None:
-        """Saves a checkpoint of the model.
-
-        Args:
-            checkpoint_path (Path): Path to the experiment with the checkpoint.
-            is_best (bool): If it is the currently best model.
-            epoch (int): The epoch of the checkpoint.
-            val_metric (str): Validation metric.
-
-        """
-        state = self._get_state_dict()
-        state["is_best"] = is_best
-        state["epoch"] = epoch
-        state["network_args"] = self._network_args
-
-        checkpoint_path.mkdir(parents=True, exist_ok=True)
-
-        logger.debug("Saving checkpoint...")
-        filepath = str(checkpoint_path / "last.pt")
-        torch.save(state, filepath)
-
-        if is_best:
-            logger.debug(
-                f"Found a new best {val_metric}. Saving best checkpoint and weights."
-            )
-            shutil.copyfile(filepath, str(checkpoint_path / "best.pt"))
-
-    def load_weights(self, network_fn: Optional[Type[nn.Module]] = None) -> None:
-        """Load the network weights."""
-        logger.debug("Loading network with pretrained weights.")
-        filename = glob(self.weights_filename)[0]
-        if not filename:
-            raise FileNotFoundError(
-                f"Could not find any pretrained weights at {self.weights_filename}"
-            )
-        # Loading state directory.
-        state_dict = torch.load(filename, map_location=torch.device(self._device))
-        self._network_args = state_dict["network_args"]
-        weights = state_dict["model_state"]
-
-        # Initializes the network with trained weights.
-        if network_fn is not None:
-            self._network = network_fn(**self._network_args)
-        self._network.load_state_dict(weights)
-
-        if "swa_network" in state_dict:
-            self._swa_network = AveragedModel(self._network).to(self.device)
-            self._swa_network.load_state_dict(state_dict["swa_network"])
-
-    def save_weights(self, path: Path) -> None:
-        """Save the network weights."""
-        logger.debug("Saving the best network weights.")
-        shutil.copyfile(str(path / "best.pt"), self.weights_filename)
diff --git a/src/text_recognizer/models/character_model.py b/src/text_recognizer/models/character_model.py
deleted file mode 100644
index f9944f3..0000000
--- a/src/text_recognizer/models/character_model.py
+++ /dev/null
@@ -1,88 +0,0 @@
-"""Defines the CharacterModel class."""
-from typing import Callable, Dict, Optional, Tuple, Type, Union
-
-import numpy as np
-import torch
-from torch import nn
-from torch.utils.data import Dataset
-from torchvision.transforms import ToTensor
-
-from text_recognizer.datasets import EmnistMapper
-from text_recognizer.models.base import Model
-
-
-class CharacterModel(Model):
-    """Model for predicting characters from images."""
-
-    def __init__(
-        self,
-        network_fn: Type[nn.Module],
-        dataset: Type[Dataset],
-        network_args: Optional[Dict] = None,
-        dataset_args: Optional[Dict] = None,
-        metrics: Optional[Dict] = None,
-        criterion: Optional[Callable] = None,
-        criterion_args: Optional[Dict] = None,
-        optimizer: Optional[Callable] = None,
-        optimizer_args: Optional[Dict] = None,
-        lr_scheduler: Optional[Callable] = None,
-        lr_scheduler_args: Optional[Dict] = None,
-        swa_args: Optional[Dict] = None,
-        device: Optional[str] = None,
-    ) -> None:
-        """Initializes the CharacterModel."""
-
-        super().__init__(
-            network_fn,
-            dataset,
-            network_args,
-            dataset_args,
-            metrics,
-            criterion,
-            criterion_args,
-            optimizer,
-            optimizer_args,
-            lr_scheduler,
-            lr_scheduler_args,
-            swa_args,
-            device,
-        )
-        self.pad_token = dataset_args["args"]["pad_token"]
-        if self._mapper is None:
-            self._mapper = EmnistMapper(pad_token=self.pad_token,)
-        self.tensor_transform = ToTensor()
-        self.softmax = nn.Softmax(dim=0)
-
-    @torch.no_grad()
-    def predict_on_image(
-        self, image: Union[np.ndarray, torch.Tensor]
-    ) -> Tuple[str, float]:
-        """Character prediction on an image.
-
-        Args:
-            image (Union[np.ndarray, torch.Tensor]): An image containing a character.
-
-        Returns:
-            Tuple[str, float]: The predicted character and the confidence in the prediction.
-
-        """
-        self.eval()
-
-        if image.dtype == np.uint8:
-            # Converts an image with range [0, 255] with to Pytorch Tensor with range [0, 1].
-            image = self.tensor_transform(image)
-        if image.dtype == torch.uint8:
-            # If the image is an unscaled tensor.
-            image = image.type("torch.FloatTensor") / 255
-
-        # Put the image tensor on the device the model weights are on.
-        image = image.to(self.device)
-        logits = self.forward(image)
-
-        prediction = self.softmax(logits.squeeze(0))
-
-        index = int(torch.argmax(prediction, dim=0))
-        confidence_of_prediction = prediction[index]
-        predicted_character = self.mapper(index)
-
-        return predicted_character, confidence_of_prediction
diff --git a/src/text_recognizer/models/crnn_model.py b/src/text_recognizer/models/crnn_model.py
deleted file mode 100644
index 1e01a83..0000000
--- a/src/text_recognizer/models/crnn_model.py
+++ /dev/null
@@ -1,119 +0,0 @@
-"""Defines the CRNNModel class."""
-from typing import Callable, Dict, Optional, Tuple, Type, Union
-
-import numpy as np
-import torch
-from torch import nn
-from torch import Tensor
-from torch.utils.data import Dataset
-from torchvision.transforms import ToTensor
-
-from text_recognizer.datasets import EmnistMapper
-from text_recognizer.models.base import Model
-from text_recognizer.networks import greedy_decoder
-
-
-class CRNNModel(Model):
-    """Model for predicting a sequence of characters from an image of a text line."""
-
-    def __init__(
-        self,
-        network_fn: Type[nn.Module],
-        dataset: Type[Dataset],
-        network_args: Optional[Dict] = None,
-        dataset_args: Optional[Dict] = None,
-        metrics: Optional[Dict] = None,
-        criterion: Optional[Callable] = None,
-        criterion_args: Optional[Dict] = None,
-        optimizer: Optional[Callable] = None,
-        optimizer_args: Optional[Dict] = None,
-        lr_scheduler: Optional[Callable] = None,
-        lr_scheduler_args: Optional[Dict] = None,
-        swa_args: Optional[Dict] = None,
-        device: Optional[str] = None,
-    ) -> None:
-        super().__init__(
-            network_fn,
-            dataset,
-            network_args,
-            dataset_args,
-            metrics,
-            criterion,
-            criterion_args,
-            optimizer,
-            optimizer_args,
-            lr_scheduler,
-            lr_scheduler_args,
-            swa_args,
-            device,
-        )
-
-        self.pad_token = dataset_args["args"]["pad_token"]
-        if self._mapper is None:
-            self._mapper = EmnistMapper(pad_token=self.pad_token,)
-        self.tensor_transform = ToTensor()
-
-    def criterion(self, output: Tensor, targets: Tensor) -> Tensor:
-        """Computes the CTC loss.
-
-        Args:
-            output (Tensor): Model predictions.
-            targets (Tensor): Correct output sequence.
-
-        Returns:
-            Tensor: The CTC loss.
-
-        """
-
-        # Input lengths on the form [T, B]
-        input_lengths = torch.full(
-            size=(output.shape[1],), fill_value=output.shape[0], dtype=torch.long,
-        )
-
-        # Configure target tensors for ctc loss.
-        targets_ = Tensor([]).to(self.device)
-        target_lengths = []
-        for t in targets:
-            # Remove padding symbol as it acts as the blank symbol.
-            t = t[t < 79]
-            targets_ = torch.cat([targets_, t])
-            target_lengths.append(len(t))
-
-        targets = targets_.type(dtype=torch.long)
-        target_lengths = (
-            torch.Tensor(target_lengths).type(dtype=torch.long).to(self.device)
-        )
-
-        return self._criterion(output, targets, input_lengths, target_lengths)
-
-    @torch.no_grad()
-    def predict_on_image(self, image: Union[np.ndarray, Tensor]) -> Tuple[str, float]:
-        """Predict on a single input."""
-        self.eval()
-
-        if image.dtype == np.uint8:
-            # Converts an image with range [0, 255] with to Pytorch Tensor with range [0, 1].
-            image = self.tensor_transform(image)
-
-        # Rescale image between 0 and 1.
-        if image.dtype == torch.uint8:
-            # If the image is an unscaled tensor.
-            image = image.type("torch.FloatTensor") / 255
-
-        # Put the image tensor on the device the model weights are on.
-        image = image.to(self.device)
-        log_probs = self.forward(image)
-
-        raw_pred, _ = greedy_decoder(
-            predictions=log_probs,
-            character_mapper=self.mapper,
-            blank_label=79,
-            collapse_repeated=True,
-        )
-
-        log_probs, _ = log_probs.max(dim=2)
-
-        predicted_characters = "".join(raw_pred[0])
-        confidence_of_prediction = log_probs.cumprod(dim=0)[-1].item()
-
-        return predicted_characters, confidence_of_prediction
diff --git a/src/text_recognizer/models/ctc_transformer_model.py b/src/text_recognizer/models/ctc_transformer_model.py
deleted file mode 100644
index 25925f2..0000000
--- a/src/text_recognizer/models/ctc_transformer_model.py
+++ /dev/null
@@ -1,120 +0,0 @@
-"""Defines the CTC Transformer Model class."""
-from typing import Callable, Dict, Optional, Tuple, Type, Union
-
-import numpy as np
-import torch
-from torch import nn
-from torch import Tensor
-from torch.utils.data import Dataset
-from torchvision.transforms import ToTensor
-
-from text_recognizer.datasets import EmnistMapper
-from text_recognizer.models.base import Model
-from text_recognizer.networks import greedy_decoder
-
-
-class CTCTransformerModel(Model):
-    """Model for predicting a sequence of characters from an image of a text line."""
-
-    def __init__(
-        self,
-        network_fn: Type[nn.Module],
-        dataset: Type[Dataset],
-        network_args: Optional[Dict] = None,
-        dataset_args: Optional[Dict] = None,
-        metrics: Optional[Dict] = None,
-        criterion: Optional[Callable] = None,
-        criterion_args: Optional[Dict] = None,
-        optimizer: Optional[Callable] = None,
-        optimizer_args: Optional[Dict] = None,
-        lr_scheduler: Optional[Callable] = None,
-        lr_scheduler_args: Optional[Dict] = None,
-        swa_args: Optional[Dict] = None,
-        device: Optional[str] = None,
-    ) -> None:
-        super().__init__(
-            network_fn,
-            dataset,
-            network_args,
-            dataset_args,
-            metrics,
-            criterion,
-            criterion_args,
-            optimizer,
-            optimizer_args,
-            lr_scheduler,
-            lr_scheduler_args,
-            swa_args,
-            device,
-        )
-        self.pad_token = dataset_args["args"]["pad_token"]
-        self.lower = dataset_args["args"]["lower"]
-
-        if self._mapper is None:
-            self._mapper = EmnistMapper(pad_token=self.pad_token, lower=self.lower,)
-
-        self.tensor_transform = ToTensor()
-
-    def criterion(self, output: Tensor, targets: Tensor) -> Tensor:
-        """Computes the CTC loss.
-
-        Args:
-            output (Tensor): Model predictions.
-            targets (Tensor): Correct output sequence.
-
-        Returns:
-            Tensor: The CTC loss.
-
-        """
-        # Input lengths on the form [T, B]
-        input_lengths = torch.full(
-            size=(output.shape[1],), fill_value=output.shape[0], dtype=torch.long,
-        )
-
-        # Configure target tensors for ctc loss.
-        targets_ = Tensor([]).to(self.device)
-        target_lengths = []
-        for t in targets:
-            # Remove padding symbol as it acts as the blank symbol.
-            t = t[t < 53]
-            targets_ = torch.cat([targets_, t])
-            target_lengths.append(len(t))
-
-        targets = targets_.type(dtype=torch.long)
-        target_lengths = (
-            torch.Tensor(target_lengths).type(dtype=torch.long).to(self.device)
-        )
-
-        return self._criterion(output, targets, input_lengths, target_lengths)
-
-    @torch.no_grad()
-    def predict_on_image(self, image: Union[np.ndarray, Tensor]) -> Tuple[str, float]:
-        """Predict on a single input."""
-        self.eval()
-
-        if image.dtype == np.uint8:
-            # Converts an image with range [0, 255] with to Pytorch Tensor with range [0, 1].
-            image = self.tensor_transform(image)
-
-        # Rescale image between 0 and 1.
-        if image.dtype == torch.uint8:
-            # If the image is an unscaled tensor.
-            image = image.type("torch.FloatTensor") / 255
-
-        # Put the image tensor on the device the model weights are on.
-        image = image.to(self.device)
-        log_probs = self.forward(image)
-
-        raw_pred, _ = greedy_decoder(
-            predictions=log_probs,
-            character_mapper=self.mapper,
-            blank_label=53,
-            collapse_repeated=True,
-        )
-
-        log_probs, _ = log_probs.max(dim=2)
-
-        predicted_characters = "".join(raw_pred[0])
-        confidence_of_prediction = log_probs.cumprod(dim=0)[-1].item()
-
-        return predicted_characters, confidence_of_prediction
diff --git a/src/text_recognizer/models/segmentation_model.py b/src/text_recognizer/models/segmentation_model.py
deleted file mode 100644
index 613108a..0000000
--- a/src/text_recognizer/models/segmentation_model.py
+++ /dev/null
@@ -1,75 +0,0 @@
-"""Segmentation model for detecting and segmenting lines."""
-from typing import Callable, Dict, Optional, Type, Union
-
-import numpy as np
-import torch
-from torch import nn
-from torch import Tensor
-from torch.utils.data import Dataset
-from torchvision.transforms import ToTensor
-
-from text_recognizer.models.base import Model
-
-
-class SegmentationModel(Model):
-    """Model for segmenting lines in an image."""
-
-    def __init__(
-        self,
-        network_fn: str,
-        dataset: str,
-        network_args: Optional[Dict] = None,
-        dataset_args: Optional[Dict] = None,
-        metrics: Optional[Dict] = None,
-        criterion: Optional[Callable] = None,
-        criterion_args: Optional[Dict] = None,
-        optimizer: Optional[Callable] = None,
-        optimizer_args: Optional[Dict] = None,
-        lr_scheduler: Optional[Callable] = None,
-        lr_scheduler_args: Optional[Dict] = None,
-        swa_args: Optional[Dict] = None,
-        device: Optional[str] = None,
-    ) -> None:
-        super().__init__(
-            network_fn,
-            dataset,
-            network_args,
-            dataset_args,
-            metrics,
-            criterion,
-            criterion_args,
-            optimizer,
-            optimizer_args,
-            lr_scheduler,
-            lr_scheduler_args,
-            swa_args,
-            device,
-        )
-        self.tensor_transform = ToTensor()
-        self.softmax = nn.Softmax(dim=2)
-
-    @torch.no_grad()
-    def predict_on_image(self, image: Union[np.ndarray, Tensor]) -> Tensor:
-        """Predict on a single input."""
-        self.eval()
-
-        if image.dtype is np.uint8:
-            # Converts an image with range [0, 255] with to PyTorch Tensor with range [0, 1].
-            image = self.tensor_transform(image)
-
-        # Rescale image between 0 and 1.
-        if image.dtype is torch.uint8 or image.dtype is torch.int64:
-            # If the image is an unscaled tensor.
-            image = image.type("torch.FloatTensor") / 255
-
-        if not torch.is_tensor(image):
-            image = Tensor(image)
-
-        # Put the image tensor on the device the model weights are on.
-        image = image.to(self.device)
-
-        logits = self.forward(image)
-
-        segmentation_mask = torch.argmax(logits, dim=1)
-
-        return segmentation_mask
diff --git a/src/text_recognizer/models/transformer_model.py b/src/text_recognizer/models/transformer_model.py
deleted file mode 100644
index 3f63053..0000000
--- a/src/text_recognizer/models/transformer_model.py
+++ /dev/null
@@ -1,124 +0,0 @@
-"""Defines the CNN-Transformer class."""
-from typing import Callable, Dict, List, Optional, Tuple, Type, Union
-
-import numpy as np
-import torch
-from torch import nn
-from torch import Tensor
-from torch.utils.data import Dataset
-
-from text_recognizer.datasets import EmnistMapper
-import text_recognizer.datasets.transforms as transforms
-from text_recognizer.models.base import Model
-from text_recognizer.networks import greedy_decoder
-
-
-class TransformerModel(Model):
-    """Model for predicting a sequence of characters from an image of a text line with a cnn-transformer."""
-
-    def __init__(
-        self,
-        network_fn: str,
-        dataset: str,
-        network_args: Optional[Dict] = None,
-        dataset_args: Optional[Dict] = None,
-        metrics: Optional[Dict] = None,
-        criterion: Optional[Callable] = None,
-        criterion_args: Optional[Dict] = None,
-        optimizer: Optional[Callable] = None,
-        optimizer_args: Optional[Dict] = None,
-        lr_scheduler: Optional[Callable] = None,
-        lr_scheduler_args: Optional[Dict] = None,
-        swa_args: Optional[Dict] = None,
-        device: Optional[str] = None,
-    ) -> None:
-        super().__init__(
-            network_fn,
-            dataset,
-            network_args,
-            dataset_args,
-            metrics,
-            criterion,
-            criterion_args,
-            optimizer,
-            optimizer_args,
-            lr_scheduler,
-            lr_scheduler_args,
-            swa_args,
-            device,
-        )
-        self.init_token = dataset_args["args"]["init_token"]
-        self.pad_token = dataset_args["args"]["pad_token"]
-        self.eos_token = dataset_args["args"]["eos_token"]
-        self.lower = dataset_args["args"]["lower"]
-        self.max_len = 100
-
-        if self._mapper is None:
-            self._mapper = EmnistMapper(
-                init_token=self.init_token,
-                pad_token=self.pad_token,
-                eos_token=self.eos_token,
-                lower=self.lower,
-            )
-        self.tensor_transform = transforms.Compose(
-            [transforms.ToTensor(), transforms.Normalize(mean=[0.912], std=[0.168])]
-        )
-        self.softmax = nn.Softmax(dim=2)
-
-    @torch.no_grad()
-    def _generate_sentence(self, image: Tensor) -> Tuple[List, float]:
-        src = self.network.extract_image_features(image)
-
-        # Added for vqvae transformer.
-        if isinstance(src, Tuple):
-            src = src[0]
-
-        memory = self.network.encoder(src)
-
-        confidence_of_predictions = []
-        trg_indices = [self.mapper(self.init_token)]
-
-        for _ in range(self.max_len - 1):
-            trg = torch.tensor(trg_indices, device=self.device)[None, :].long()
-            trg = self.network.target_embedding(trg)
-            logits = self.network.decoder(trg=trg, memory=memory, trg_mask=None)
-
-            # Convert logits to probabilities.
-            probs = self.softmax(logits)
-
-            pred_token = probs.argmax(2)[:, -1].item()
-            confidence = probs.max(2).values[:, -1].item()
-
-            trg_indices.append(pred_token)
-            confidence_of_predictions.append(confidence)
-
-            if pred_token == self.mapper(self.eos_token):
-                break
-
-        confidence = np.min(confidence_of_predictions)
-        predicted_characters = "".join([self.mapper(x) for x in trg_indices[1:]])
-
-        return predicted_characters, confidence
-
-    @torch.no_grad()
-    def predict_on_image(self, image: Union[np.ndarray, Tensor]) -> Tuple[str, float]:
-        """Predict on a single input."""
-        self.eval()
-
-        if image.dtype == np.uint8:
-            # Converts an image with range [0, 255] with to PyTorch Tensor with range [0, 1].
-            image = self.tensor_transform(image)
-
-        # Rescale image between 0 and 1.
-        if image.dtype == torch.uint8:
-            # If the image is an unscaled tensor.
-            image = image.type("torch.FloatTensor") / 255
-
-        # Put the image tensor on the device the model weights are on.
-        image = image.to(self.device)
-
-        (predicted_characters, confidence_of_prediction,) = self._generate_sentence(
-            image
-        )
-
-        return predicted_characters, confidence_of_prediction
diff --git a/src/text_recognizer/models/vqvae_model.py b/src/text_recognizer/models/vqvae_model.py
deleted file mode 100644
index 70f6f1f..0000000
--- a/src/text_recognizer/models/vqvae_model.py
+++ /dev/null
@@ -1,80 +0,0 @@
-"""Defines the VQVAEModel class."""
-from typing import Callable, Dict, Optional, Tuple, Type, Union
-
-import numpy as np
-import torch
-from torch import nn
-from torch.utils.data import Dataset
-from torchvision.transforms import ToTensor
-
-from text_recognizer.datasets import EmnistMapper
-from text_recognizer.models.base import Model
-
-
-class VQVAEModel(Model):
-    """Model for reconstructing images from codebook."""
-
-    def __init__(
-        self,
-        network_fn: Type[nn.Module],
-        dataset: Type[Dataset],
-        network_args: Optional[Dict] = None,
-        dataset_args: Optional[Dict] = None,
-        metrics: Optional[Dict] = None,
-        criterion: Optional[Callable] = None,
-        criterion_args: Optional[Dict] = None,
-        optimizer: Optional[Callable] = None,
-        optimizer_args: Optional[Dict] = None,
-        lr_scheduler: Optional[Callable] = None,
-        lr_scheduler_args: Optional[Dict] = None,
-        swa_args: Optional[Dict] = None,
-        device: Optional[str] = None,
-    ) -> None:
-        """Initializes the CharacterModel."""
-
-        super().__init__(
-            network_fn,
-            dataset,
-            network_args,
-            dataset_args,
-            metrics,
-            criterion,
-            criterion_args,
-            optimizer,
-            optimizer_args,
-            lr_scheduler,
-            lr_scheduler_args,
-            swa_args,
-            device,
-        )
-        self.pad_token = dataset_args["args"]["pad_token"]
-        if self._mapper is None:
-            self._mapper = EmnistMapper(pad_token=self.pad_token,)
-        self.tensor_transform = ToTensor()
-        self.softmax = nn.Softmax(dim=0)
-
-    @torch.no_grad()
-    def predict_on_image(self, image: Union[np.ndarray, torch.Tensor]) -> torch.Tensor:
-        """Reconstruction of image.
-
-        Args:
-            image (Union[np.ndarray, torch.Tensor]): An image containing a character.
-
-        Returns:
-            Tuple[str, float]: The predicted character and the confidence in the prediction.
-
-        """
-        self.eval()
-
-        if image.dtype == np.uint8:
-            # Converts an image with range [0, 255] with to Pytorch Tensor with range [0, 1].
-            image = self.tensor_transform(image)
-        if image.dtype == torch.uint8:
-            # If the image is an unscaled tensor.
-            image = image.type("torch.FloatTensor") / 255
-
-        # Put the image tensor on the device the model weights are on.
-        image = image.to(self.device)
-        image_reconstructed, _ = self.forward(image)
-
-        return image_reconstructed
diff --git a/src/text_recognizer/networks/__init__.py b/src/text_recognizer/networks/__init__.py
deleted file mode 100644
index 1521355..0000000
--- a/src/text_recognizer/networks/__init__.py
+++ /dev/null
@@ -1,43 +0,0 @@
-"""Network modules."""
-from .cnn import CNN
-from .cnn_transformer import CNNTransformer
-from .crnn import ConvolutionalRecurrentNetwork
-from .ctc import greedy_decoder
-from .densenet import DenseNet
-from .lenet import LeNet
-from .metrics import accuracy, cer, wer
-from .mlp import MLP
-from .residual_network import ResidualNetwork, ResidualNetworkEncoder
-from .transducer import load_transducer_loss, TDS2d
-from .transformer import Transformer
-from .unet import UNet
-from .util import sliding_window
-from .vit import ViT
-from .vq_transformer import VQTransformer
-from .vqvae import VQVAE
-from .wide_resnet import WideResidualNetwork
-
-__all__ = [
-    "accuracy",
-    "cer",
-    "CNN",
-    "CNNTransformer",
-    "ConvolutionalRecurrentNetwork",
-    "DenseNet",
-    "FCN",
-    "greedy_decoder",
-    "MLP",
-    "LeNet",
-    "load_transducer_loss",
-    "ResidualNetwork",
-    "ResidualNetworkEncoder",
-    "sliding_window",
-    "UNet",
-    "TDS2d",
-    "Transformer",
-    "ViT",
-    "VQTransformer",
-    "VQVAE",
-    "wer",
-    "WideResidualNetwork",
-]
diff --git a/src/text_recognizer/networks/beam.py b/src/text_recognizer/networks/beam.py
deleted file mode 100644
index dccccdb..0000000
--- a/src/text_recognizer/networks/beam.py
+++ /dev/null
@@ -1,83 +0,0 @@
-"""Implementation of beam search decoder for a sequence to sequence network.
-
-Stolen from: https://github.com/budzianowski/PyTorch-Beam-Search-Decoding/blob/master/decode_beam.py
-
-"""
-# from typing import List
-# from Queue import PriorityQueue
-
-# from loguru import logger
-# import torch
-# from torch import nn
-# from torch import Tensor
-# import torch.nn.functional as F
-
-
-# class Node:
-#     def __init__(
-#         self, parent: Node, target_index: int, log_prob: Tensor, length: int
-#     ) -> None:
-#         self.parent = parent
-#         self.target_index = target_index
-#         self.log_prob = log_prob
-#         self.length = length
-#         self.reward = 0.0
-
-#     def eval(self, alpha: float = 1.0) -> Tensor:
-#         return self.log_prob / (self.length - 1 + 1e-6) + alpha * self.reward
-
-
-# @torch.no_grad()
-# def beam_decoder(
-#     network, mapper, device, memory: Tensor = None, max_len: int = 97,
-# ) -> Tensor:
-#     beam_width = 10
-#     topk = 1  # How many sentences to generate.
-
-#     trg_indices = [mapper(mapper.init_token)]
-
-#     end_nodes = []
-
-#     node = Node(None, trg_indices, 0, 1)
-#     nodes = PriorityQueue()
-
-#     nodes.put((node.eval(), node))
-#     q_size = 1
-
-#     # Beam search
-#     for _ in range(max_len):
-#         if q_size > 2000:
-#             logger.warning("Could not decoder input")
-#             break
-
-#         # Fetch the best node.
-#         score, n = nodes.get()
-#         decoder_input = n.target_index
-
-#         if n.target_index == mapper(mapper.eos_token) and n.parent is not None:
-#             end_nodes.append((score, n))
-
-#             # If we reached the maximum number of sentences required.
-#             if len(end_nodes) >= 1:
-#                 break
-#             else:
-#                 continue
-
-#         # Forward pass with transformer.
-#         trg = torch.tensor(trg_indices, device=device)[None, :].long()
-#         trg = network.target_embedding(trg)
-#         logits = network.decoder(trg=trg, memory=memory, trg_mask=None)
-#         log_prob = F.log_softmax(logits, dim=2)
-
-#         log_prob, indices = torch.topk(log_prob, beam_width)
-
-#         for new_k in range(beam_width):
-#             # TODO: continue from here
-#             token_index = indices[0][new_k].view(1, -1)
-#             log_p = log_prob[0][new_k].item()
-
-#             node = Node()
-
-#             pass
-
-#     pass
diff --git a/src/text_recognizer/networks/cnn.py b/src/text_recognizer/networks/cnn.py
deleted file mode 100644
index 1807bb9..0000000
--- a/src/text_recognizer/networks/cnn.py
+++ /dev/null
@@ -1,101 +0,0 @@
-"""Implementation of a simple backbone cnn network."""
-from typing import Callable, Dict, Optional, Tuple
-
-from einops.layers.torch import Rearrange
-import torch
-from torch import nn
-
-from text_recognizer.networks.util import activation_function
-
-
-class CNN(nn.Module):
-    """LeNet network for character prediction."""
-
-    def __init__(
-        self,
-        channels: Tuple[int, ...] = (1, 32, 64, 128),
-        kernel_sizes: Tuple[int, ...] = (4, 4, 4),
-        strides: Tuple[int, ...] = (2, 2, 2),
-        max_pool_kernel: int = 2,
-        dropout_rate: float = 0.2,
-        activation: Optional[str] = "relu",
-    ) -> None:
-        """Initialization of the LeNet network.
-
-        Args:
-            channels (Tuple[int, ...]): Channels in the convolutional layers. Defaults to (1, 32, 64).
-            kernel_sizes (Tuple[int, ...]): Kernel sizes in the convolutional layers. Defaults to (3, 3, 2).
-            strides (Tuple[int, ...]): Stride length of the convolutional filter. Defaults to (2, 2, 2).
-            max_pool_kernel (int): 2D max pooling kernel. Defaults to 2.
-            dropout_rate (float): The dropout rate. Defaults to 0.2.
-            activation (Optional[str]): The name of non-linear activation function. Defaults to relu.
-
-        Raises:
-            RuntimeError: if the number of hyperparameters does not match in length.
-
-        """
-        super().__init__()
-
-        if len(channels) - 1 != len(kernel_sizes) and len(kernel_sizes) != len(strides):
-            raise RuntimeError("The number of the hyperparameters does not match.")
-
-        self.cnn = self._build_network(
-            channels, kernel_sizes, strides, max_pool_kernel, dropout_rate, activation,
-        )
-
-    def _build_network(
-        self,
-        channels: Tuple[int, ...],
-        kernel_sizes: Tuple[int, ...],
-        strides: Tuple[int, ...],
-        max_pool_kernel: int,
-        dropout_rate: float,
-        activation: str,
-    ) -> nn.Sequential:
-        # Load activation function.
-        activation_fn = activation_function(activation)
-
-        channels = list(channels)
-        in_channels = channels.pop(0)
-        configuration = zip(channels, kernel_sizes, strides)
-
-        modules = nn.ModuleList([])
-
-        for i, (out_channels, kernel_size, stride) in enumerate(configuration):
-            # Add max pool to reduce output size.
-            if i == len(channels) // 2:
-                modules.append(nn.MaxPool2d(max_pool_kernel))
-            if i == 0:
-                modules.append(
-                    nn.Conv2d(
-                        in_channels, out_channels, kernel_size, stride=stride, padding=1
-                    )
-                )
-            else:
-                modules.append(
-                    nn.Sequential(
-                        activation_fn,
-                        nn.BatchNorm2d(in_channels),
-                        nn.Conv2d(
-                            in_channels,
-                            out_channels,
-                            kernel_size,
-                            stride=stride,
-                            padding=1,
-                        ),
-                    )
-                )
-
-            if dropout_rate:
-                modules.append(nn.Dropout2d(p=dropout_rate))
-
-            in_channels = out_channels
-
-        return nn.Sequential(*modules)
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        """The feedforward pass."""
-        # If batch dimenstion is missing, it needs to be added.
-        if len(x.shape) < 4:
-            x = x[(None,) * (4 - len(x.shape))]
-        return self.cnn(x)
diff --git a/src/text_recognizer/networks/cnn_transformer.py b/src/text_recognizer/networks/cnn_transformer.py
deleted file mode 100644
index a2d7926..0000000
--- a/src/text_recognizer/networks/cnn_transformer.py
+++ /dev/null
@@ -1,158 +0,0 @@
-"""A CNN-Transformer for image to text recognition."""
-from typing import Dict, Optional, Tuple
-
-from einops import rearrange, repeat
-import torch
-from torch import nn
-from torch import Tensor
-
-from text_recognizer.networks.transformer import PositionalEncoding, Transformer
-from text_recognizer.networks.util import activation_function
-from text_recognizer.networks.util import configure_backbone
-
-
-class CNNTransformer(nn.Module):
-    """CNN+Transfomer for image to sequence prediction."""
-
-    def __init__(
-        self,
-        num_encoder_layers: int,
-        num_decoder_layers: int,
-        hidden_dim: int,
-        vocab_size: int,
-        num_heads: int,
-        adaptive_pool_dim: Tuple,
-        expansion_dim: int,
-        dropout_rate: float,
-        trg_pad_index: int,
-        max_len: int,
-        backbone: str,
-        backbone_args: Optional[Dict] = None,
-        activation: str = "gelu",
-        pool_kernel: Optional[Tuple[int, int]] = None,
-    ) -> None:
-        super().__init__()
-        self.trg_pad_index = trg_pad_index
-        self.vocab_size = vocab_size
-        self.backbone = configure_backbone(backbone, backbone_args)
-
-        if pool_kernel is not None:
-            self.max_pool = nn.MaxPool2d(pool_kernel, stride=2)
-        else:
-            self.max_pool = None
-
-        self.character_embedding = nn.Embedding(self.vocab_size, hidden_dim)
-
-        self.src_position_embedding = nn.Parameter(torch.randn(1, max_len, hidden_dim))
-        self.pos_dropout = nn.Dropout(p=dropout_rate)
-        self.trg_position_encoding = PositionalEncoding(hidden_dim, dropout_rate)
-
-        nn.init.normal_(self.character_embedding.weight, std=0.02)
-
-        self.adaptive_pool = (
-            nn.AdaptiveAvgPool2d((adaptive_pool_dim)) if adaptive_pool_dim else None
-        )
-
-        self.transformer = Transformer(
-            num_encoder_layers,
-            num_decoder_layers,
-            hidden_dim,
-            num_heads,
-            expansion_dim,
-            dropout_rate,
-            activation,
-        )
-
-        self.head = nn.Sequential(
-            # nn.Linear(hidden_dim, hidden_dim * 2),
-            # activation_function(activation),
-            nn.Linear(hidden_dim, vocab_size),
-        )
-
-    def _create_trg_mask(self, trg: Tensor) -> Tensor:
-        # Move this outside the transformer.
-        trg_pad_mask = (trg != self.trg_pad_index)[:, None, None]
-        trg_len = trg.shape[1]
-        trg_sub_mask = torch.tril(
-            torch.ones((trg_len, trg_len), device=trg.device)
-        ).bool()
-        trg_mask = trg_pad_mask & trg_sub_mask
-        return trg_mask
-
-    def encoder(self, src: Tensor) -> Tensor:
-        """Forward pass with the encoder of the transformer."""
-        return self.transformer.encoder(src)
-
-    def decoder(self, trg: Tensor, memory: Tensor, trg_mask: Tensor) -> Tensor:
-        """Forward pass with the decoder of the transformer + classification head."""
-        return self.head(
-            self.transformer.decoder(trg=trg, memory=memory, trg_mask=trg_mask)
-        )
-
-    def extract_image_features(self, src: Tensor) -> Tensor:
-        """Extracts image features with a backbone neural network.
-
-        It seem like the winning idea was to swap channels and width dimension and collapse
-        the height dimension. The transformer is learning like a baby with this implementation!!! :D
-        Ohhhh, the joy I am experiencing right now!! Bring in the beers! :D :D :D
-
-        Args:
-            src (Tensor): Input tensor.
-
-        Returns:
-            Tensor: A input src to the transformer.
-
-        """
-        # If batch dimension is missing, it needs to be added.
-        if len(src.shape) < 4:
-            src = src[(None,) * (4 - len(src.shape))]
-
-        src = self.backbone(src)
-
-        if self.max_pool is not None:
-            src = self.max_pool(src)
-
-        if self.adaptive_pool is not None and len(src.shape) == 4:
-            src = rearrange(src, "b c h w -> b w c h")
-            src = self.adaptive_pool(src)
-            src = src.squeeze(3)
-        elif len(src.shape) == 4:
-            src = rearrange(src, "b c h w -> b (h w) c")
-
-        b, t, _ = src.shape
-
-        src += self.src_position_embedding[:, :t]
-        src = self.pos_dropout(src)
-
-        return src
-
-    def target_embedding(self, trg: Tensor) -> Tuple[Tensor, Tensor]:
-        """Encodes target tensor with embedding and postion.
-
-        Args:
-            trg (Tensor): Target tensor.
-
-        Returns:
-            Tuple[Tensor, Tensor]: Encoded target tensor and target mask.
-
-        """
-        trg = self.character_embedding(trg.long())
-        trg = self.trg_position_encoding(trg)
-        return trg
-
-    def decode_image_features(
-        self, image_features: Tensor, trg: Optional[Tensor] = None
-    ) -> Tensor:
-        """Takes images features from the backbone and decodes them with the transformer."""
-        trg_mask = self._create_trg_mask(trg)
-        trg = self.target_embedding(trg)
-        out = self.transformer(image_features, trg, trg_mask=trg_mask)
-
-        logits = self.head(out)
-        return logits
-
-    def forward(self, x: Tensor, trg: Optional[Tensor] = None) -> Tensor:
-        """Forward pass with CNN transfomer."""
-        image_features = self.extract_image_features(x)
-        logits = self.decode_image_features(image_features, trg)
-        return logits
diff --git a/src/text_recognizer/networks/crnn.py b/src/text_recognizer/networks/crnn.py
deleted file mode 100644
index 778e232..0000000
--- a/src/text_recognizer/networks/crnn.py
+++ /dev/null
@@ -1,110 +0,0 @@
-"""CRNN for handwritten text recognition."""
-from typing import Dict, Tuple
-
-from einops import rearrange, reduce
-from einops.layers.torch import Rearrange
-from loguru import logger
-from torch import nn
-from torch import Tensor
-
-from text_recognizer.networks.util import configure_backbone
-
-
-class ConvolutionalRecurrentNetwork(nn.Module):
-    """Network that takes a image of a text line and predicts tokens that are in the image."""
-
-    def __init__(
-        self,
-        backbone: str,
-        backbone_args: Dict = None,
-        input_size: int = 128,
-        hidden_size: int = 128,
-        bidirectional: bool = False,
-        num_layers: int = 1,
-        num_classes: int = 80,
-        patch_size: Tuple[int, int] = (28, 28),
-        stride: Tuple[int, int] = (1, 14),
-        recurrent_cell: str = "lstm",
-        avg_pool: bool = False,
-        use_sliding_window: bool = True,
-    ) -> None:
-        super().__init__()
-        self.backbone_args = backbone_args or {}
-        self.patch_size = patch_size
-        self.stride = stride
-        self.sliding_window = (
-            self._configure_sliding_window() if use_sliding_window else None
-        )
-        self.input_size = input_size
-        self.hidden_size = hidden_size
-        self.backbone = configure_backbone(backbone, backbone_args)
-        self.bidirectional = bidirectional
-        self.avg_pool = avg_pool
-
-        if recurrent_cell.upper() in ["LSTM", "GRU"]:
-            recurrent_cell = getattr(nn, recurrent_cell)
-        else:
-            logger.warning(
-                f"Option {recurrent_cell} not valid, defaulting to LSTM cell."
-            )
-            recurrent_cell = nn.LSTM
-
-        self.rnn = recurrent_cell(
-            input_size=self.input_size,
-            hidden_size=self.hidden_size,
-            bidirectional=bidirectional,
-            num_layers=num_layers,
-        )
-
-        decoder_size = self.hidden_size * 2 if self.bidirectional else self.hidden_size
-
-        self.decoder = nn.Sequential(
-            nn.Linear(in_features=decoder_size, out_features=num_classes),
-            nn.LogSoftmax(dim=2),
-        )
-
-    def _configure_sliding_window(self) -> nn.Sequential:
-        return nn.Sequential(
-            nn.Unfold(kernel_size=self.patch_size, stride=self.stride),
-            Rearrange(
-                "b (c h w) t -> b t c h w",
-                h=self.patch_size[0],
-                w=self.patch_size[1],
-                c=1,
-            ),
-        )
-
-    def forward(self, x: Tensor) -> Tensor:
-        """Converts images to sequence of patches, feeds them to a CNN, then predictions are made with an LSTM."""
-        if len(x.shape) < 4:
-            x = x[(None,) * (4 - len(x.shape))]
-
-        if self.sliding_window is not None:
-            # Create image patches with a sliding window kernel.
-            x = self.sliding_window(x)
-
-            # Rearrange from a sequence of patches for feedforward network.
-            b, t = x.shape[:2]
-            x = rearrange(x, "b t c h w -> (b t) c h w", b=b, t=t)
-
-            x = self.backbone(x)
-
-            # Average pooling.
-            if self.avg_pool:
-                x = reduce(x, "(b t) c h w -> t b c", "mean", b=b, t=t)
-            else:
-                x = rearrange(x, "(b t) h -> t b h", b=b, t=t)
-        else:
-            # Encode the entire image with a CNN, and use the channels as temporal dimension.
-            x = self.backbone(x)
-            x = rearrange(x, "b c h w -> b w c h")
-            if self.adaptive_pool is not None:
-                x = self.adaptive_pool(x)
-            x = x.squeeze(3)
-
-        # Sequence predictions.
-        x, _ = self.rnn(x)
-
-        # Sequence to classification layer.
-        x = self.decoder(x)
-        return x
diff --git a/src/text_recognizer/networks/ctc.py b/src/text_recognizer/networks/ctc.py
deleted file mode 100644
index af9b700..0000000
--- a/src/text_recognizer/networks/ctc.py
+++ /dev/null
@@ -1,58 +0,0 @@
-"""Decodes the CTC output."""
-from typing import Callable, List, Optional, Tuple
-
-from einops import rearrange
-import torch
-from torch import Tensor
-
-from text_recognizer.datasets.util import EmnistMapper
-
-
-def greedy_decoder(
-    predictions: Tensor,
-    targets: Optional[Tensor] = None,
-    target_lengths: Optional[Tensor] = None,
-    character_mapper: Optional[Callable] = None,
-    blank_label: int = 79,
-    collapse_repeated: bool = True,
-) -> Tuple[List[str], List[str]]:
-    """Greedy CTC decoder.
-
-    Args:
-        predictions (Tensor): Tenor of network predictions, shape [time, batch, classes].
-        targets (Optional[Tensor]): Target tensor, shape is [batch, targets]. Defaults to None.
-        target_lengths (Optional[Tensor]): Length of each target tensor. Defaults to None.
-        character_mapper (Optional[Callable]): A emnist/character mapper for mapping integers to characters.  Defaults
-            to None.
-        blank_label (int): The blank character to be ignored. Defaults to 80.
-        collapse_repeated (bool): Collapase consecutive predictions of the same character. Defaults to True.
-
-    Returns:
-        Tuple[List[str], List[str]]: Tuple of decoded predictions and decoded targets.
-
-    """
-
-    if character_mapper is None:
-        character_mapper = EmnistMapper(pad_token="_")  # noqa: S106
-
-    predictions = rearrange(torch.argmax(predictions, dim=2), "t b -> b t")
-    decoded_predictions = []
-    decoded_targets = []
-    for i, prediction in enumerate(predictions):
-        decoded_prediction = []
-        decoded_target = []
-        if targets is not None and target_lengths is not None:
-            for target_index in targets[i][: target_lengths[i]]:
-                if target_index == blank_label:
-                    continue
-                decoded_target.append(character_mapper(int(target_index)))
-            decoded_targets.append(decoded_target)
-        for j, index in enumerate(prediction):
-            if index != blank_label:
-                if collapse_repeated and j != 0 and index == prediction[j - 1]:
-                    continue
-                decoded_prediction.append(index.item())
-        decoded_predictions.append(
-            [character_mapper(int(pred_index)) for pred_index in decoded_prediction]
-        )
-    return decoded_predictions, decoded_targets
diff --git a/src/text_recognizer/networks/densenet.py b/src/text_recognizer/networks/densenet.py
deleted file mode 100644
index 7dc58d9..0000000
--- a/src/text_recognizer/networks/densenet.py
+++ /dev/null
@@ -1,225 +0,0 @@
-"""Defines a Densely Connected Convolutional Networks in PyTorch.
-
-Sources:
-https://arxiv.org/abs/1608.06993
-https://github.com/pytorch/vision/blob/master/torchvision/models/densenet.py
-
-"""
-from typing import List, Optional, Union
-
-from einops.layers.torch import Rearrange
-import torch
-from torch import nn
-from torch import Tensor
-
-from text_recognizer.networks.util import activation_function
-
-
-class _DenseLayer(nn.Module):
-    """A dense layer with pre-batch norm -> activation function -> Conv-layer x 2."""
-
-    def __init__(
-        self,
-        in_channels: int,
-        growth_rate: int,
-        bn_size: int,
-        dropout_rate: float,
-        activation: str = "relu",
-    ) -> None:
-        super().__init__()
-        activation_fn = activation_function(activation)
-        self.dense_layer = [
-            nn.BatchNorm2d(in_channels),
-            activation_fn,
-            nn.Conv2d(
-                in_channels=in_channels,
-                out_channels=bn_size * growth_rate,
-                kernel_size=1,
-                stride=1,
-                bias=False,
-            ),
-            nn.BatchNorm2d(bn_size * growth_rate),
-            activation_fn,
-            nn.Conv2d(
-                in_channels=bn_size * growth_rate,
-                out_channels=growth_rate,
-                kernel_size=3,
-                stride=1,
-                padding=1,
-                bias=False,
-            ),
-        ]
-        if dropout_rate:
-            self.dense_layer.append(nn.Dropout(p=dropout_rate))
-
-        self.dense_layer = nn.Sequential(*self.dense_layer)
-
-    def forward(self, x: Union[Tensor, List[Tensor]]) -> Tensor:
-        if isinstance(x, list):
-            x = torch.cat(x, 1)
-        return self.dense_layer(x)
-
-
-class _DenseBlock(nn.Module):
-    def __init__(
-        self,
-        num_layers: int,
-        in_channels: int,
-        bn_size: int,
-        growth_rate: int,
-        dropout_rate: float,
-        activation: str = "relu",
-    ) -> None:
-        super().__init__()
-        self.dense_block = self._build_dense_blocks(
-            num_layers, in_channels, bn_size, growth_rate, dropout_rate, activation,
-        )
-
-    def _build_dense_blocks(
-        self,
-        num_layers: int,
-        in_channels: int,
-        bn_size: int,
-        growth_rate: int,
-        dropout_rate: float,
-        activation: str = "relu",
-    ) -> nn.ModuleList:
-        dense_block = []
-        for i in range(num_layers):
-            dense_block.append(
-                _DenseLayer(
-                    in_channels=in_channels + i * growth_rate,
-                    growth_rate=growth_rate,
-                    bn_size=bn_size,
-                    dropout_rate=dropout_rate,
-                    activation=activation,
-                )
-            )
-        return nn.ModuleList(dense_block)
-
-    def forward(self, x: Tensor) -> Tensor:
-        feature_maps = [x]
-        for layer in self.dense_block:
-            x = layer(feature_maps)
-            feature_maps.append(x)
-        return torch.cat(feature_maps, 1)
-
-
-class _Transition(nn.Module):
-    def __init__(
-        self, in_channels: int, out_channels: int, activation: str = "relu",
-    ) -> None:
-        super().__init__()
-        activation_fn = activation_function(activation)
-        self.transition = nn.Sequential(
-            nn.BatchNorm2d(in_channels),
-            activation_fn,
-            nn.Conv2d(
-                in_channels=in_channels,
-                out_channels=out_channels,
-                kernel_size=1,
-                stride=1,
-                bias=False,
-            ),
-            nn.AvgPool2d(kernel_size=2, stride=2),
-        )
-
-    def forward(self, x: Tensor) -> Tensor:
-        return self.transition(x)
-
-
-class DenseNet(nn.Module):
-    """Implementation of Densenet, a network archtecture that concats previous layers for maximum infomation flow."""
-
-    def __init__(
-        self,
-        growth_rate: int = 32,
-        block_config: List[int] = (6, 12, 24, 16),
-        in_channels: int = 1,
-        base_channels: int = 64,
-        num_classes: int = 80,
-        bn_size: int = 4,
-        dropout_rate: float = 0,
-        classifier: bool = True,
-        activation: str = "relu",
-    ) -> None:
-        super().__init__()
-        self.densenet = self._configure_densenet(
-            in_channels,
-            base_channels,
-            num_classes,
-            growth_rate,
-            block_config,
-            bn_size,
-            dropout_rate,
-            classifier,
-            activation,
-        )
-
-    def _configure_densenet(
-        self,
-        in_channels: int,
-        base_channels: int,
-        num_classes: int,
-        growth_rate: int,
-        block_config: List[int],
-        bn_size: int,
-        dropout_rate: float,
-        classifier: bool,
-        activation: str,
-    ) -> nn.Sequential:
-        activation_fn = activation_function(activation)
-        densenet = [
-            nn.Conv2d(
-                in_channels=in_channels,
-                out_channels=base_channels,
-                kernel_size=3,
-                stride=1,
-                padding=1,
-                bias=False,
-            ),
-            nn.BatchNorm2d(base_channels),
-            activation_fn,
-        ]
-
-        num_features = base_channels
-
-        for i, num_layers in enumerate(block_config):
-            densenet.append(
-                _DenseBlock(
-                    num_layers=num_layers,
-                    in_channels=num_features,
-                    bn_size=bn_size,
-                    growth_rate=growth_rate,
-                    dropout_rate=dropout_rate,
-                    activation=activation,
-                )
-            )
-            num_features = num_features + num_layers * growth_rate
-            if i != len(block_config) - 1:
-                densenet.append(
-                    _Transition(
-                        in_channels=num_features,
-                        out_channels=num_features // 2,
-                        activation=activation,
-                    )
-                )
-                num_features = num_features // 2
-
-        densenet.append(activation_fn)
-
-        if classifier:
-            densenet.append(nn.AdaptiveAvgPool2d((1, 1)))
-            densenet.append(Rearrange("b c h w -> b (c h w)"))
-            densenet.append(
-                nn.Linear(in_features=num_features, out_features=num_classes)
-            )
-
-        return nn.Sequential(*densenet)
-
-    def forward(self, x: Tensor) -> Tensor:
-        """Forward pass of Densenet."""
-        # If batch dimenstion is missing, it will be added.
-        if len(x.shape) < 4:
-            x = x[(None,) * (4 - len(x.shape))]
-        return self.densenet(x)
diff --git a/src/text_recognizer/networks/lenet.py b/src/text_recognizer/networks/lenet.py
deleted file mode 100644
index 527e1a0..0000000
--- a/src/text_recognizer/networks/lenet.py
+++ /dev/null
@@ -1,68 +0,0 @@
-"""Implementation of the LeNet network."""
-from typing import Callable, Dict, Optional, Tuple
-
-from einops.layers.torch import Rearrange
-import torch
-from torch import nn
-
-from text_recognizer.networks.util import activation_function
-
-
-class LeNet(nn.Module):
-    """LeNet network for character prediction."""
-
-    def __init__(
-        self,
-        channels: Tuple[int, ...] = (1, 32, 64),
-        kernel_sizes: Tuple[int, ...] = (3, 3, 2),
-        hidden_size: Tuple[int, ...] = (9216, 128),
-        dropout_rate: float = 0.2,
-        num_classes: int = 10,
-        activation_fn: Optional[str] = "relu",
-    ) -> None:
-        """Initialization of the LeNet network.
-
-        Args:
-            channels (Tuple[int, ...]): Channels in the convolutional layers. Defaults to (1, 32, 64).
-            kernel_sizes (Tuple[int, ...]): Kernel sizes in the convolutional layers. Defaults to (3, 3, 2).
-            hidden_size (Tuple[int, ...]): Size of the flattend output form the convolutional layers.
-                Defaults to (9216, 128).
-            dropout_rate (float): The dropout rate. Defaults to 0.2.
-            num_classes (int): Number of classes. Defaults to 10.
-            activation_fn (Optional[str]): The name of non-linear activation function. Defaults to relu.
-
-        """
-        super().__init__()
-
-        activation_fn = activation_function(activation_fn)
-
-        self.layers = [
-            nn.Conv2d(
-                in_channels=channels[0],
-                out_channels=channels[1],
-                kernel_size=kernel_sizes[0],
-            ),
-            activation_fn,
-            nn.Conv2d(
-                in_channels=channels[1],
-                out_channels=channels[2],
-                kernel_size=kernel_sizes[1],
-            ),
-            activation_fn,
-            nn.MaxPool2d(kernel_sizes[2]),
-            nn.Dropout(p=dropout_rate),
-            Rearrange("b c h w -> b (c h w)"),
-            nn.Linear(in_features=hidden_size[0], out_features=hidden_size[1]),
-            activation_fn,
-            nn.Dropout(p=dropout_rate),
-            nn.Linear(in_features=hidden_size[1], out_features=num_classes),
-        ]
-
-        self.layers = nn.Sequential(*self.layers)
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        """The feedforward pass."""
-        # If batch dimenstion is missing, it needs to be added.
-        if len(x.shape) < 4:
-            x = x[(None,) * (4 - len(x.shape))]
-        return self.layers(x)
diff --git a/src/text_recognizer/networks/loss/__init__.py b/src/text_recognizer/networks/loss/__init__.py
deleted file mode 100644
index b489264..0000000
--- a/src/text_recognizer/networks/loss/__init__.py
+++ /dev/null
@@ -1,2 +0,0 @@
-"""Loss module."""
-from .loss import EmbeddingLoss, LabelSmoothingCrossEntropy
diff --git a/src/text_recognizer/networks/loss/loss.py b/src/text_recognizer/networks/loss/loss.py
deleted file mode 100644
index cf9fa0d..0000000
--- a/src/text_recognizer/networks/loss/loss.py
+++ /dev/null
@@ -1,69 +0,0 @@
-"""Implementations of custom loss functions."""
-from pytorch_metric_learning import distances, losses, miners, reducers
-import torch
-from torch import nn
-from torch import Tensor
-from torch.autograd import Variable
-import torch.nn.functional as F
-
-__all__ = ["EmbeddingLoss", "LabelSmoothingCrossEntropy"]
-
-
-class EmbeddingLoss:
-    """Metric loss for training encoders to produce information-rich latent embeddings."""
-
-    def __init__(self, margin: float = 0.2, type_of_triplets: str = "semihard") -> None:
-        self.distance = distances.CosineSimilarity()
-        self.reducer = reducers.ThresholdReducer(low=0)
-        self.loss_fn = losses.TripletMarginLoss(
-            margin=margin, distance=self.distance, reducer=self.reducer
-        )
-        self.miner = miners.MultiSimilarityMiner(epsilon=margin, distance=self.distance)
-
-    def __call__(self, embeddings: Tensor, labels: Tensor) -> Tensor:
-        """Computes the metric loss for the embeddings based on their labels.
-
-        Args:
-            embeddings (Tensor): The laten vectors encoded by the network.
-            labels (Tensor): Labels of the embeddings.
-
-        Returns:
-            Tensor: The metric loss for the embeddings.
-
-        """
-        hard_pairs = self.miner(embeddings, labels)
-        loss = self.loss_fn(embeddings, labels, hard_pairs)
-        return loss
-
-
-class LabelSmoothingCrossEntropy(nn.Module):
-    """Label smoothing loss function."""
-
-    def __init__(
-        self,
-        classes: int,
-        smoothing: float = 0.0,
-        ignore_index: int = None,
-        dim: int = -1,
-    ) -> None:
-        super().__init__()
-        self.confidence = 1.0 - smoothing
-        self.smoothing = smoothing
-        self.ignore_index = ignore_index
-        self.cls = classes
-        self.dim = dim
-
-    def forward(self, pred: Tensor, target: Tensor) -> Tensor:
-        """Calculates the loss."""
-        pred = pred.log_softmax(dim=self.dim)
-        with torch.no_grad():
-            # true_dist = pred.data.clone()
-            true_dist = torch.zeros_like(pred)
-            true_dist.fill_(self.smoothing / (self.cls - 1))
-            true_dist.scatter_(1, target.data.unsqueeze(1), self.confidence)
-            if self.ignore_index is not None:
-                true_dist[:, self.ignore_index] = 0
-                mask = torch.nonzero(target == self.ignore_index, as_tuple=False)
-                if mask.dim() > 0:
-                    true_dist.index_fill_(0, mask.squeeze(), 0.0)
-        return torch.mean(torch.sum(-true_dist * pred, dim=self.dim))
diff --git a/src/text_recognizer/networks/metrics.py b/src/text_recognizer/networks/metrics.py
deleted file mode 100644
index 2605731..0000000
--- a/src/text_recognizer/networks/metrics.py
+++ /dev/null
@@ -1,123 +0,0 @@
-"""Utility functions for models."""
-from typing import Optional
-
-from einops import rearrange
-import Levenshtein as Lev
-import torch
-from torch import Tensor
-
-from text_recognizer.networks import greedy_decoder
-
-
-def accuracy(outputs: Tensor, labels: Tensor, pad_index: int = 53) -> float:
-    """Computes the accuracy.
-
-    Args:
-        outputs (Tensor): The output from the network.
-        labels (Tensor): Ground truth labels.
-        pad_index (int): Padding index.
-
-    Returns:
-        float: The accuracy for the batch.
-
-    """
-
-    _, predicted = torch.max(outputs, dim=-1)
-
-    # Mask out the pad tokens
-    mask = labels != pad_index
-
-    predicted *= mask
-    labels *= mask
-
-    acc = (predicted == labels).sum().float() / labels.shape[0]
-    acc = acc.item()
-    return acc
-
-
-def cer(
-    outputs: Tensor,
-    targets: Tensor,
-    batch_size: Optional[int] = None,
-    blank_label: Optional[int] = int,
-) -> float:
-    """Computes the character error rate.
-
-    Args:
-        outputs (Tensor): The output from the network.
-        targets (Tensor): Ground truth labels.
-        batch_size (Optional[int]): Batch size if target and output has been flattend.
-        blank_label (Optional[int]): The blank character to be ignored. Defaults to 79.
-
-    Returns:
-        float: The cer for the batch.
-
-    """
-    if len(outputs.shape) == 2 and len(targets.shape) == 1 and batch_size is not None:
-        targets = rearrange(targets, "(b t) -> b t", b=batch_size)
-        outputs = rearrange(outputs, "(b t) v -> t b v", b=batch_size)
-
-    target_lengths = torch.full(
-        size=(outputs.shape[1],), fill_value=targets.shape[1], dtype=torch.long,
-    )
-    decoded_predictions, decoded_targets = greedy_decoder(
-        outputs, targets, target_lengths, blank_label=blank_label,
-    )
-
-    lev_dist = 0
-
-    for prediction, target in zip(decoded_predictions, decoded_targets):
-        prediction = "".join(prediction)
-        target = "".join(target)
-        prediction, target = (
-            prediction.replace(" ", ""),
-            target.replace(" ", ""),
-        )
-        lev_dist += Lev.distance(prediction, target)
-    return lev_dist / len(decoded_predictions)
-
-
-def wer(
-    outputs: Tensor,
-    targets: Tensor,
-    batch_size: Optional[int] = None,
-    blank_label: Optional[int] = int,
-) -> float:
-    """Computes the Word error rate.
-
-    Args:
-        outputs (Tensor): The output from the network.
-        targets (Tensor): Ground truth labels.
-        batch_size (optional[int]): Batch size if target and output has been flattend.
-        blank_label (Optional[int]): The blank character to be ignored. Defaults to 79.
-
-    Returns:
-        float: The wer for the batch.
-
-    """
-    if len(outputs.shape) == 2 and len(targets.shape) == 1 and batch_size is not None:
-        targets = rearrange(targets, "(b t) -> b t", b=batch_size)
-        outputs = rearrange(outputs, "(b t) v -> t b v", b=batch_size)
-
-    target_lengths = torch.full(
-        size=(outputs.shape[1],), fill_value=targets.shape[1], dtype=torch.long,
-    )
-    decoded_predictions, decoded_targets = greedy_decoder(
-        outputs, targets, target_lengths, blank_label=blank_label,
-    )
-
-    lev_dist = 0
-
-    for prediction, target in zip(decoded_predictions, decoded_targets):
-        prediction = "".join(prediction)
-        target = "".join(target)
-
-        b = set(prediction.split() + target.split())
-        word2char = dict(zip(b, range(len(b))))
-
-        w1 = [chr(word2char[w]) for w in prediction.split()]
-        w2 = [chr(word2char[w]) for w in target.split()]
-
-        lev_dist += Lev.distance("".join(w1), "".join(w2))
-
-    return lev_dist / len(decoded_predictions)
diff --git a/src/text_recognizer/networks/mlp.py b/src/text_recognizer/networks/mlp.py
deleted file mode 100644
index 1101912..0000000
--- a/src/text_recognizer/networks/mlp.py
+++ /dev/null
@@ -1,73 +0,0 @@
-"""Defines the MLP network."""
-from typing import Callable, Dict, List, Optional, Union
-
-from einops.layers.torch import Rearrange
-import torch
-from torch import nn
-
-from text_recognizer.networks.util import activation_function
-
-
-class MLP(nn.Module):
-    """Multi layered perceptron network."""
-
-    def __init__(
-        self,
-        input_size: int = 784,
-        num_classes: int = 10,
-        hidden_size: Union[int, List] = 128,
-        num_layers: int = 3,
-        dropout_rate: float = 0.2,
-        activation_fn: str = "relu",
-    ) -> None:
-        """Initialization of the MLP network.
-
-        Args:
-            input_size (int): The input shape of the network. Defaults to 784.
-            num_classes (int): Number of classes in the dataset. Defaults to 10.
-            hidden_size (Union[int, List]): The number of `neurons` in each hidden layer. Defaults to 128.
-            num_layers (int): The number of hidden layers. Defaults to 3.
-            dropout_rate (float): The dropout rate at each layer. Defaults to 0.2.
-            activation_fn (str): Name of the activation function in the hidden layers. Defaults to
-                relu.
-
-        """
-        super().__init__()
-
-        activation_fn = activation_function(activation_fn)
-
-        if isinstance(hidden_size, int):
-            hidden_size = [hidden_size] * num_layers
-
-        self.layers = [
-            Rearrange("b c h w -> b (c h w)"),
-            nn.Linear(in_features=input_size, out_features=hidden_size[0]),
-            activation_fn,
-        ]
-
-        for i in range(num_layers - 1):
-            self.layers += [
-                nn.Linear(in_features=hidden_size[i], out_features=hidden_size[i + 1]),
-                activation_fn,
-            ]
-
-            if dropout_rate:
-                self.layers.append(nn.Dropout(p=dropout_rate))
-
-        self.layers.append(
-            nn.Linear(in_features=hidden_size[-1], out_features=num_classes)
-        )
-
-        self.layers = nn.Sequential(*self.layers)
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        """The feedforward pass."""
-        # If batch dimenstion is missing, it needs to be added.
-        if len(x.shape) < 4:
-            x = x[(None,) * (4 - len(x.shape))]
-        return self.layers(x)
-
-    @property
-    def __name__(self) -> str:
-        """Returns the name of the network."""
-        return "mlp"
diff --git a/src/text_recognizer/networks/residual_network.py b/src/text_recognizer/networks/residual_network.py
deleted file mode 100644
index c33f419..0000000
--- a/src/text_recognizer/networks/residual_network.py
+++ /dev/null
@@ -1,310 +0,0 @@
-"""Residual CNN."""
-from functools import partial
-from typing import Callable, Dict, List, Optional, Type, Union
-
-from einops.layers.torch import Rearrange, Reduce
-import torch
-from torch import nn
-from torch import Tensor
-
-from text_recognizer.networks.util import activation_function
-
-
-class Conv2dAuto(nn.Conv2d):
-    """Convolution with auto padding based on kernel size."""
-
-    def __init__(self, *args, **kwargs) -> None:
-        super().__init__(*args, **kwargs)
-        self.padding = (self.kernel_size[0] // 2, self.kernel_size[1] // 2)
-
-
-def conv_bn(in_channels: int, out_channels: int, *args, **kwargs) -> nn.Sequential:
-    """3x3 convolution with batch norm."""
-    conv3x3 = partial(Conv2dAuto, kernel_size=3, bias=False,)
-    return nn.Sequential(
-        conv3x3(in_channels, out_channels, *args, **kwargs),
-        nn.BatchNorm2d(out_channels),
-    )
-
-
-class IdentityBlock(nn.Module):
-    """Residual with identity block."""
-
-    def __init__(
-        self, in_channels: int, out_channels: int, activation: str = "relu"
-    ) -> None:
-        super().__init__()
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.blocks = nn.Identity()
-        self.activation_fn = activation_function(activation)
-        self.shortcut = nn.Identity()
-
-    def forward(self, x: Tensor) -> Tensor:
-        """Forward pass."""
-        residual = x
-        if self.apply_shortcut:
-            residual = self.shortcut(x)
-        x = self.blocks(x)
-        x += residual
-        x = self.activation_fn(x)
-        return x
-
-    @property
-    def apply_shortcut(self) -> bool:
-        """Check if shortcut should be applied."""
-        return self.in_channels != self.out_channels
-
-
-class ResidualBlock(IdentityBlock):
-    """Residual with nonlinear shortcut."""
-
-    def __init__(
-        self,
-        in_channels: int,
-        out_channels: int,
-        expansion: int = 1,
-        downsampling: int = 1,
-        *args,
-        **kwargs
-    ) -> None:
-        """Short summary.
-
-        Args:
-            in_channels (int): Number of in channels.
-            out_channels (int): umber of out channels.
-            expansion (int): Expansion factor of the out channels. Defaults to 1.
-            downsampling (int): Downsampling factor used in stride. Defaults to 1.
-            *args (type): Extra arguments.
-            **kwargs (type): Extra key value arguments.
-
-        """
-        super().__init__(in_channels, out_channels, *args, **kwargs)
-        self.expansion = expansion
-        self.downsampling = downsampling
-
-        self.shortcut = (
-            nn.Sequential(
-                nn.Conv2d(
-                    in_channels=self.in_channels,
-                    out_channels=self.expanded_channels,
-                    kernel_size=1,
-                    stride=self.downsampling,
-                    bias=False,
-                ),
-                nn.BatchNorm2d(self.expanded_channels),
-            )
-            if self.apply_shortcut
-            else None
-        )
-
-    @property
-    def expanded_channels(self) -> int:
-        """Computes the expanded output channels."""
-        return self.out_channels * self.expansion
-
-    @property
-    def apply_shortcut(self) -> bool:
-        """Check if shortcut should be applied."""
-        return self.in_channels != self.expanded_channels
-
-
-class BasicBlock(ResidualBlock):
-    """Basic ResNet block."""
-
-    expansion = 1
-
-    def __init__(self, in_channels: int, out_channels: int, *args, **kwargs) -> None:
-        super().__init__(in_channels, out_channels, *args, **kwargs)
-        self.blocks = nn.Sequential(
-            conv_bn(
-                in_channels=self.in_channels,
-                out_channels=self.out_channels,
-                bias=False,
-                stride=self.downsampling,
-            ),
-            self.activation_fn,
-            conv_bn(
-                in_channels=self.out_channels,
-                out_channels=self.expanded_channels,
-                bias=False,
-            ),
-        )
-
-
-class BottleNeckBlock(ResidualBlock):
-    """Bottleneck block to increase depth while minimizing parameter size."""
-
-    expansion = 4
-
-    def __init__(self, in_channels: int, out_channels: int, *args, **kwargs) -> None:
-        super().__init__(in_channels, out_channels, *args, **kwargs)
-        self.blocks = nn.Sequential(
-            conv_bn(
-                in_channels=self.in_channels,
-                out_channels=self.out_channels,
-                kernel_size=1,
-            ),
-            self.activation_fn,
-            conv_bn(
-                in_channels=self.out_channels,
-                out_channels=self.out_channels,
-                kernel_size=3,
-                stride=self.downsampling,
-            ),
-            self.activation_fn,
-            conv_bn(
-                in_channels=self.out_channels,
-                out_channels=self.expanded_channels,
-                kernel_size=1,
-            ),
-        )
-
-
-class ResidualLayer(nn.Module):
-    """ResNet layer."""
-
-    def __init__(
-        self,
-        in_channels: int,
-        out_channels: int,
-        block: BasicBlock = BasicBlock,
-        num_blocks: int = 1,
-        *args,
-        **kwargs
-    ) -> None:
-        super().__init__()
-        downsampling = 2 if in_channels != out_channels else 1
-        self.blocks = nn.Sequential(
-            block(
-                in_channels, out_channels, *args, **kwargs, downsampling=downsampling
-            ),
-            *[
-                block(
-                    out_channels * block.expansion,
-                    out_channels,
-                    downsampling=1,
-                    *args,
-                    **kwargs
-                )
-                for _ in range(num_blocks - 1)
-            ]
-        )
-
-    def forward(self, x: Tensor) -> Tensor:
-        """Forward pass."""
-        x = self.blocks(x)
-        return x
-
-
-class ResidualNetworkEncoder(nn.Module):
-    """Encoder network."""
-
-    def __init__(
-        self,
-        in_channels: int = 1,
-        block_sizes: Union[int, List[int]] = (32, 64),
-        depths: Union[int, List[int]] = (2, 2),
-        activation: str = "relu",
-        block: Type[nn.Module] = BasicBlock,
-        levels: int = 1,
-        *args,
-        **kwargs
-    ) -> None:
-        super().__init__()
-        self.block_sizes = (
-            block_sizes if isinstance(block_sizes, list) else [block_sizes] * levels
-        )
-        self.depths = depths if isinstance(depths, list) else [depths] * levels
-        self.activation = activation
-        self.gate = nn.Sequential(
-            nn.Conv2d(
-                in_channels=in_channels,
-                out_channels=self.block_sizes[0],
-                kernel_size=7,
-                stride=2,
-                padding=1,
-                bias=False,
-            ),
-            nn.BatchNorm2d(self.block_sizes[0]),
-            activation_function(self.activation),
-            # nn.MaxPool2d(kernel_size=2, stride=2, padding=1),
-        )
-
-        self.blocks = self._configure_blocks(block)
-
-    def _configure_blocks(
-        self, block: Type[nn.Module], *args, **kwargs
-    ) -> nn.Sequential:
-        channels = [self.block_sizes[0]] + list(
-            zip(self.block_sizes, self.block_sizes[1:])
-        )
-        blocks = [
-            ResidualLayer(
-                in_channels=channels[0],
-                out_channels=channels[0],
-                num_blocks=self.depths[0],
-                block=block,
-                activation=self.activation,
-                *args,
-                **kwargs
-            )
-        ]
-        blocks += [
-            ResidualLayer(
-                in_channels=in_channels * block.expansion,
-                out_channels=out_channels,
-                num_blocks=num_blocks,
-                block=block,
-                activation=self.activation,
-                *args,
-                **kwargs
-            )
-            for (in_channels, out_channels), num_blocks in zip(
-                channels[1:], self.depths[1:]
-            )
-        ]
-
-        return nn.Sequential(*blocks)
-
-    def forward(self, x: Tensor) -> Tensor:
-        """Forward pass."""
-        # If batch dimenstion is missing, it needs to be added.
-        if len(x.shape) == 3:
-            x = x.unsqueeze(0)
-        x = self.gate(x)
-        x = self.blocks(x)
-        return x
-
-
-class ResidualNetworkDecoder(nn.Module):
-    """Classification head."""
-
-    def __init__(self, in_features: int, num_classes: int = 80) -> None:
-        super().__init__()
-        self.decoder = nn.Sequential(
-            Reduce("b c h w -> b c", "mean"),
-            nn.Linear(in_features=in_features, out_features=num_classes),
-        )
-
-    def forward(self, x: Tensor) -> Tensor:
-        """Forward pass."""
-        return self.decoder(x)
-
-
-class ResidualNetwork(nn.Module):
-    """Full residual network."""
-
-    def __init__(self, in_channels: int, num_classes: int, *args, **kwargs) -> None:
-        super().__init__()
-        self.encoder = ResidualNetworkEncoder(in_channels, *args, **kwargs)
-        self.decoder = ResidualNetworkDecoder(
-            in_features=self.encoder.blocks[-1].blocks[-1].expanded_channels,
-            num_classes=num_classes,
-        )
-
-    def forward(self, x: Tensor) -> Tensor:
-        """Forward pass."""
-        x = self.encoder(x)
-        x = self.decoder(x)
-        return x
diff --git a/src/text_recognizer/networks/stn.py b/src/text_recognizer/networks/stn.py
deleted file mode 100644
index e9d216f..0000000
--- a/src/text_recognizer/networks/stn.py
+++ /dev/null
@@ -1,44 +0,0 @@
-"""Spatial Transformer Network."""
-
-from einops.layers.torch import Rearrange
-import torch
-from torch import nn
-from torch import Tensor
-import torch.nn.functional as F
-
-
-class SpatialTransformerNetwork(nn.Module):
-    """A network with differentiable attention.
-
-    Network that learns how to perform spatial transformations on the input image in order to enhance the
-    geometric invariance of the model.
-
-    # TODO: add arguments to make it more general.
-
-    """
-
-    def __init__(self) -> None:
-        super().__init__()
-        # Initialize the identity transformation and its weights and biases.
-        linear = nn.Linear(32, 3 * 2)
-        linear.weight.data.zero_()
-        linear.bias.data.copy_(torch.tensor([1, 0, 0, 0, 1, 0], dtype=torch.float))
-
-        self.theta = nn.Sequential(
-            nn.Conv2d(in_channels=1, out_channels=8, kernel_size=7),
-            nn.MaxPool2d(kernel_size=2, stride=2),
-            nn.ReLU(inplace=True),
-            nn.Conv2d(in_channels=8, out_channels=10, kernel_size=5),
-            nn.MaxPool2d(kernel_size=2, stride=2),
-            nn.ReLU(inplace=True),
-            Rearrange("b c h w -> b (c h w)", h=3, w=3),
-            nn.Linear(in_features=10 * 3 * 3, out_features=32),
-            nn.ReLU(inplace=True),
-            linear,
-            Rearrange("b (row col) -> b row col", row=2, col=3),
-        )
-
-    def forward(self, x: Tensor) -> Tensor:
-        """The spatial transformation."""
-        grid = F.affine_grid(self.theta(x), x.shape)
-        return F.grid_sample(x, grid, align_corners=False)
diff --git a/src/text_recognizer/networks/transducer/__init__.py b/src/text_recognizer/networks/transducer/__init__.py
deleted file mode 100644
index 8c19a01..0000000
--- a/src/text_recognizer/networks/transducer/__init__.py
+++ /dev/null
@@ -1,3 +0,0 @@
-"""Transducer modules."""
-from .tds_conv import TDS2d
-from .transducer import load_transducer_loss, Transducer
diff --git a/src/text_recognizer/networks/transducer/tds_conv.py b/src/text_recognizer/networks/transducer/tds_conv.py
deleted file mode 100644
index 5fb8ba9..0000000
--- a/src/text_recognizer/networks/transducer/tds_conv.py
+++ /dev/null
@@ -1,208 +0,0 @@
-"""Time-Depth Separable Convolutions.
-
-References:
-    https://arxiv.org/abs/1904.02619
-    https://arxiv.org/pdf/2010.01003.pdf
-
-Code stolen from:
-    https://github.com/facebookresearch/gtn_applications
-
-
-"""
-from typing import List, Tuple
-
-from einops import rearrange
-import gtn
-import numpy as np
-import torch
-from torch import nn
-from torch import Tensor
-
-
-class TDSBlock2d(nn.Module):
-    """Internal block of a 2D TDSC network."""
-
-    def __init__(
-        self,
-        in_channels: int,
-        img_depth: int,
-        kernel_size: Tuple[int],
-        dropout_rate: float,
-    ) -> None:
-        super().__init__()
-
-        self.in_channels = in_channels
-        self.img_depth = img_depth
-        self.kernel_size = kernel_size
-        self.dropout_rate = dropout_rate
-        self.fc_dim = in_channels * img_depth
-
-        # Network placeholders.
-        self.conv = None
-        self.mlp = None
-        self.instance_norm = None
-
-        self._build_block()
-
-    def _build_block(self) -> None:
-        # Convolutional block.
-        self.conv = nn.Sequential(
-            nn.Conv3d(
-                in_channels=self.in_channels,
-                out_channels=self.in_channels,
-                kernel_size=(1, self.kernel_size[0], self.kernel_size[1]),
-                padding=(0, self.kernel_size[0] // 2, self.kernel_size[1] // 2),
-            ),
-            nn.ReLU(inplace=True),
-            nn.Dropout(self.dropout_rate),
-        )
-
-        # MLP block.
-        self.mlp = nn.Sequential(
-            nn.Linear(self.fc_dim, self.fc_dim),
-            nn.ReLU(inplace=True),
-            nn.Dropout(self.dropout_rate),
-            nn.Linear(self.fc_dim, self.fc_dim),
-            nn.Dropout(self.dropout_rate),
-        )
-
-        # Instance norm.
-        self.instance_norm = nn.ModuleList(
-            [
-                nn.InstanceNorm2d(self.fc_dim, affine=True),
-                nn.InstanceNorm2d(self.fc_dim, affine=True),
-            ]
-        )
-
-    def forward(self, x: Tensor) -> Tensor:
-        """Forward pass.
-
-        Args:
-            x (Tensor): Input tensor.
-
-        Shape:
-            - x: :math: `(B, CD, H, W)`
-
-        Returns:
-            Tensor: Output tensor.
-
-        """
-        B, CD, H, W = x.shape
-        C, D = self.in_channels, self.img_depth
-        residual = x
-        x = rearrange(x, "b (c d) h w -> b c d h w", c=C, d=D)
-        x = self.conv(x)
-        x = rearrange(x, "b c d h w -> b (c d) h w")
-        x += residual
-
-        x = self.instance_norm[0](x)
-
-        x = self.mlp(x.transpose(1, 3)).transpose(1, 3) + x
-        x + self.instance_norm[1](x)
-
-        # Output shape: [B, CD, H, W]
-        return x
-
-
-class TDS2d(nn.Module):
-    """TDS Netowrk.
-
-    Structure is the following:
-        Downsample layer -> TDS2d group -> ... -> Linear output layer
-
-
-    """
-
-    def __init__(
-        self,
-        input_dim: int,
-        output_dim: int,
-        depth: int,
-        tds_groups: Tuple[int],
-        kernel_size: Tuple[int],
-        dropout_rate: float,
-        in_channels: int = 1,
-    ) -> None:
-        super().__init__()
-
-        self.in_channels = in_channels
-        self.input_dim = input_dim
-        self.output_dim = output_dim
-        self.depth = depth
-        self.tds_groups = tds_groups
-        self.kernel_size = kernel_size
-        self.dropout_rate = dropout_rate
-
-        self.tds = None
-        self.fc = None
-
-        self._build_network()
-
-    def _build_network(self) -> None:
-        in_channels = self.in_channels
-        modules = []
-        stride_h = np.prod([grp["stride"][0] for grp in self.tds_groups])
-        if self.input_dim % stride_h:
-            raise RuntimeError(
-                f"Image height not divisible by total stride {stride_h}."
-            )
-
-        for tds_group in self.tds_groups:
-            # Add downsample layer.
-            out_channels = self.depth * tds_group["channels"]
-            modules.extend(
-                [
-                    nn.Conv2d(
-                        in_channels=in_channels,
-                        out_channels=out_channels,
-                        kernel_size=self.kernel_size,
-                        padding=(self.kernel_size[0] // 2, self.kernel_size[1] // 2),
-                        stride=tds_group["stride"],
-                    ),
-                    nn.ReLU(inplace=True),
-                    nn.Dropout(self.dropout_rate),
-                    nn.InstanceNorm2d(out_channels, affine=True),
-                ]
-            )
-
-            for _ in range(tds_group["num_blocks"]):
-                modules.append(
-                    TDSBlock2d(
-                        tds_group["channels"],
-                        self.depth,
-                        self.kernel_size,
-                        self.dropout_rate,
-                    )
-                )
-
-            in_channels = out_channels
-
-        self.tds = nn.Sequential(*modules)
-        self.fc = nn.Linear(in_channels * self.input_dim // stride_h, self.output_dim)
-
-    def forward(self, x: Tensor) -> Tensor:
-        """Forward pass.
-
-        Args:
-            x (Tensor): Input tensor.
-
-        Shape:
-            - x: :math: `(B, H, W)`
-
-        Returns:
-            Tensor: Output tensor.
-
-        """
-        if len(x.shape) == 4:
-            x = x.squeeze(1)  # Squeeze the channel dim away.
-
-        B, H, W = x.shape
-        x = rearrange(
-            x, "b (h1 h2) w -> b h1 h2 w", h1=self.in_channels, h2=H // self.in_channels
-        )
-        x = self.tds(x)
-
-        # x shape: [B, C, H, W]
-        x = rearrange(x, "b c h w -> b w (c h)")
-
-        return self.fc(x)
diff --git a/src/text_recognizer/networks/transducer/test.py b/src/text_recognizer/networks/transducer/test.py
deleted file mode 100644
index cadcecc..0000000
--- a/src/text_recognizer/networks/transducer/test.py
+++ /dev/null
@@ -1,60 +0,0 @@
-import torch
-from torch import nn
-
-from text_recognizer.networks.transducer import load_transducer_loss, Transducer
-import unittest
-
-
-class TestTransducer(unittest.TestCase):
-    def test_viterbi(self):
-        T = 5
-        N = 4
-        B = 2
-
-        # fmt: off
-        emissions1 = torch.tensor((
-            0, 4, 0, 1,
-            0, 2, 1, 1,
-            0, 0, 0, 2,
-            0, 0, 0, 2,
-            8, 0, 0, 2,
-            ),
-            dtype=torch.float,
-        ).view(T, N)
-        emissions2 = torch.tensor((
-            0, 2, 1, 7,
-            0, 2, 9, 1,
-            0, 0, 0, 2,
-            0, 0, 5, 2,
-            1, 0, 0, 2,
-            ),
-            dtype=torch.float,
-        ).view(T, N)
-        # fmt: on
-
-        # Test without blank:
-        labels = [[1, 3, 0], [3, 2, 3, 2, 3]]
-        transducer = Transducer(
-            tokens=["a", "b", "c", "d"],
-            graphemes_to_idx={"a": 0, "b": 1, "c": 2, "d": 3},
-            blank="none",
-        )
-        emissions = torch.stack([emissions1, emissions2], dim=0)
-        predictions = transducer.viterbi(emissions)
-        self.assertEqual([p.tolist() for p in predictions], labels)
-
-        # Test with blank without repeats:
-        labels = [[1, 0], [2, 2]]
-        transducer = Transducer(
-            tokens=["a", "b", "c"],
-            graphemes_to_idx={"a": 0, "b": 1, "c": 2},
-            blank="optional",
-            allow_repeats=False,
-        )
-        emissions = torch.stack([emissions1, emissions2], dim=0)
-        predictions = transducer.viterbi(emissions)
-        self.assertEqual([p.tolist() for p in predictions], labels)
-
-
-if __name__ == "__main__":
-    unittest.main()
diff --git a/src/text_recognizer/networks/transducer/transducer.py b/src/text_recognizer/networks/transducer/transducer.py
deleted file mode 100644
index d7e3d08..0000000
--- a/src/text_recognizer/networks/transducer/transducer.py
+++ /dev/null
@@ -1,410 +0,0 @@
-"""Transducer and the transducer loss function.py
-
-Stolen from:
-    https://github.com/facebookresearch/gtn_applications/blob/master/transducer.py
-
-"""
-from pathlib import Path
-import itertools
-from typing import Dict, List, Optional, Union, Tuple
-
-from loguru import logger
-import gtn
-import torch
-from torch import nn
-from torch import Tensor
-
-from text_recognizer.datasets.iam_preprocessor import Preprocessor
-
-
-def make_scalar_graph(weight) -> gtn.Graph:
-    scalar = gtn.Graph()
-    scalar.add_node(True)
-    scalar.add_node(False, True)
-    scalar.add_arc(0, 1, 0, 0, weight)
-    return scalar
-
-
-def make_chain_graph(sequence) -> gtn.Graph:
-    graph = gtn.Graph(False)
-    graph.add_node(True)
-    for i, s in enumerate(sequence):
-        graph.add_node(False, i == (len(sequence) - 1))
-        graph.add_arc(i, i + 1, s)
-    return graph
-
-
-def make_transitions_graph(
-    ngram: int, num_tokens: int, calc_grad: bool = False
-) -> gtn.Graph:
-    transitions = gtn.Graph(calc_grad)
-    transitions.add_node(True, ngram == 1)
-
-    state_map = {(): 0}
-
-    # First build transitions which include <s>:
-    for n in range(1, ngram):
-        for state in itertools.product(range(num_tokens), repeat=n):
-            in_idx = state_map[state[:-1]]
-            out_idx = transitions.add_node(False, ngram == 1)
-            state_map[state] = out_idx
-            transitions.add_arc(in_idx, out_idx, state[-1])
-
-    for state in itertools.product(range(num_tokens), repeat=ngram):
-        state_idx = state_map[state[:-1]]
-        new_state_idx = state_map[state[1:]]
-        # p(state[-1] | state[:-1])
-        transitions.add_arc(state_idx, new_state_idx, state[-1])
-
-    if ngram > 1:
-        # Build transitions which include </s>:
-        end_idx = transitions.add_node(False, True)
-        for in_idx in range(end_idx):
-            transitions.add_arc(in_idx, end_idx, gtn.epsilon)
-
-    return transitions
-
-
-def make_lexicon_graph(word_pieces: List, graphemes_to_idx: Dict) -> gtn.Graph:
-    """Constructs a graph which transduces letters to word pieces."""
-    graph = gtn.Graph(False)
-    graph.add_node(True, True)
-    for i, wp in enumerate(word_pieces):
-        prev = 0
-        for l in wp[:-1]:
-            n = graph.add_node()
-            graph.add_arc(prev, n, graphemes_to_idx[l], gtn.epsilon)
-            prev = n
-        graph.add_arc(prev, 0, graphemes_to_idx[wp[-1]], i)
-    graph.arc_sort()
-    return graph
-
-
-def make_token_graph(
-    token_list: List, blank: str = "none", allow_repeats: bool = True
-) -> gtn.Graph:
-    """Constructs a graph with all the individual token transition models."""
-    if not allow_repeats and blank != "optional":
-        raise ValueError("Must use blank='optional' if disallowing repeats.")
-
-    ntoks = len(token_list)
-    graph = gtn.Graph(False)
-
-    # Creating nodes
-    graph.add_node(True, True)
-    for i in range(ntoks):
-        # We can consume one or more consecutive word
-        # pieces for each emission:
-        # E.g. [ab, ab, ab] transduces to [ab]
-        graph.add_node(False, blank != "forced")
-
-    if blank != "none":
-        graph.add_node()
-
-    # Creating arcs
-    if blank != "none":
-        # Blank index is assumed to be last (ntoks)
-        graph.add_arc(0, ntoks + 1, ntoks, gtn.epsilon)
-        graph.add_arc(ntoks + 1, 0, gtn.epsilon)
-
-    for i in range(ntoks):
-        graph.add_arc((ntoks + 1) if blank == "forced" else 0, i + 1, i)
-        graph.add_arc(i + 1, i + 1, i, gtn.epsilon)
-
-        if allow_repeats:
-            if blank == "forced":
-                # Allow transitions from token to blank only
-                graph.add_arc(i + 1, ntoks + 1, ntoks, gtn.epsilon)
-            else:
-                # Allow transition from token to blank and all other tokens
-                graph.add_arc(i + 1, 0, gtn.epsilon)
-
-        else:
-            # allow transitions to blank and all other tokens except the same token
-            graph.add_arc(i + 1, ntoks + 1, ntoks, gtn.epsilon)
-            for j in range(ntoks):
-                if i != j:
-                    graph.add_arc(i + 1, j + 1, j, j)
-
-    return graph
-
-
-class TransducerLossFunction(torch.autograd.Function):
-    @staticmethod
-    def forward(
-        ctx,
-        inputs,
-        targets,
-        tokens,
-        lexicon,
-        transition_params=None,
-        transitions=None,
-        reduction="none",
-    ) -> Tensor:
-        B, T, C = inputs.shape
-
-        losses = [None] * B
-        emissions_graphs = [None] * B
-
-        if transitions is not None:
-            if transition_params is None:
-                raise ValueError("Specified transitions, but not transition params.")
-
-            cpu_data = transition_params.cpu().contiguous()
-            transitions.set_weights(cpu_data.data_ptr())
-            transitions.calc_grad = transition_params.requires_grad
-            transitions.zero_grad()
-
-        def process(b: int) -> None:
-            # Create emission graph:
-            emissions = gtn.linear_graph(T, C, inputs.requires_grad)
-            cpu_data = inputs[b].cpu().contiguous()
-            emissions.set_weights(cpu_data.data_ptr())
-            target = make_chain_graph(targets[b])
-            target.arc_sort(True)
-
-            # Create token tot grapheme decomposition graph
-            tokens_target = gtn.remove(gtn.project_output(gtn.compose(target, lexicon)))
-            tokens_target.arc_sort()
-
-            # Create alignment graph:
-            aligments = gtn.project_input(
-                gtn.remove(gtn.compose(tokens, tokens_target))
-            )
-            aligments.arc_sort()
-
-            # Add transitions scores:
-            if transitions is not None:
-                aligments = gtn.intersect(transitions, aligments)
-                aligments.arc_sort()
-
-            loss = gtn.forward_score(gtn.intersect(emissions, aligments))
-
-            # Normalize if needed:
-            if transitions is not None:
-                norm = gtn.forward_score(gtn.intersect(emissions, transitions))
-                loss = gtn.subtract(loss, norm)
-
-            losses[b] = gtn.negate(loss)
-
-            # Save for backward:
-            if emissions.calc_grad:
-                emissions_graphs[b] = emissions
-
-        gtn.parallel_for(process, range(B))
-
-        ctx.graphs = (losses, emissions_graphs, transitions)
-        ctx.input_shape = inputs.shape
-
-        # Optionally reduce by target length
-        if reduction == "mean":
-            scales = [(1 / len(t) if len(t) > 0 else 1.0) for t in targets]
-        else:
-            scales = [1.0] * B
-
-        ctx.scales = scales
-
-        loss = torch.tensor([l.item() * s for l, s in zip(losses, scales)])
-        return torch.mean(loss.to(inputs.device))
-
-    @staticmethod
-    def backward(ctx, grad_output) -> Tuple:
-        losses, emissions_graphs, transitions = ctx.graphs
-        scales = ctx.scales
-
-        B, T, C = ctx.input_shape
-        calc_emissions = ctx.needs_input_grad[0]
-        input_grad = torch.empty((B, T, C)) if calc_emissions else None
-
-        def process(b: int) -> None:
-            scale = make_scalar_graph(scales[b])
-            gtn.backward(losses[b], scale)
-            emissions = emissions_graphs[b]
-            if calc_emissions:
-                grad = emissions.grad().weights_to_numpy()
-                input_grad[b] = torch.tensor(grad).view(1, T, C)
-
-        gtn.parallel_for(process, range(B))
-
-        if calc_emissions:
-            input_grad = input_grad.to(grad_output.device)
-            input_grad *= grad_output / B
-
-        if ctx.needs_input_grad[4]:
-            grad = transitions.grad().weights_to_numpy()
-            transition_grad = torch.tensor(grad).to(grad_output.device)
-            transition_grad *= grad_output / B
-        else:
-            transition_grad = None
-
-        return (
-            input_grad,
-            None,  # target
-            None,  # tokens
-            None,  # lexicon
-            transition_grad,  # transition params
-            None,  # transitions graph
-            None,
-        )
-
-
-TransducerLoss = TransducerLossFunction.apply
-
-
-class Transducer(nn.Module):
-    def __init__(
-        self,
-        tokens: List,
-        graphemes_to_idx: Dict,
-        ngram: int = 0,
-        transitions: str = None,
-        blank: str = "none",
-        allow_repeats: bool = True,
-        reduction: str = "none",
-    ) -> None:
-        """A generic transducer loss function.
-
-        Args:
-            tokens (List) : A list of iterable objects (e.g. strings, tuples, etc)
-                representing the output tokens of the model (e.g. letters,
-                word-pieces, words). For example ["a", "b", "ab", "ba", "aba"]
-                could be a list of sub-word tokens.
-            graphemes_to_idx (dict) : A dictionary mapping grapheme units (e.g.
-                "a", "b", ..) to their corresponding integer index.
-            ngram (int) : Order of the token-level transition model. If `ngram=0`
-                then no transition model is used.
-            blank (string) : Specifies the usage of blank token
-                'none' - do not use blank token
-                'optional' - allow an optional blank inbetween tokens
-                'forced' - force a blank inbetween tokens (also referred to as garbage token)
-            allow_repeats (boolean) : If false, then we don't allow paths with
-                consecutive tokens in the alignment graph. This keeps the graph
-                unambiguous in the sense that the same input cannot transduce to
-                different outputs.
-        """
-        super().__init__()
-        if blank not in ["optional", "forced", "none"]:
-            raise ValueError(
-                "Invalid value specified for blank. Must be in ['optional', 'forced', 'none']"
-            )
-        self.tokens = make_token_graph(tokens, blank=blank, allow_repeats=allow_repeats)
-        self.lexicon = make_lexicon_graph(tokens, graphemes_to_idx)
-        self.ngram = ngram
-        if ngram > 0 and transitions is not None:
-            raise ValueError("Only one of ngram and transitions may be specified")
-
-        if ngram > 0:
-            transitions = make_transitions_graph(
-                ngram, len(tokens) + int(blank != "none"), True
-            )
-
-        if transitions is not None:
-            self.transitions = transitions
-            self.transitions.arc_sort()
-            self.transitions_params = nn.Parameter(
-                torch.zeros(self.transitions.num_arcs())
-            )
-        else:
-            self.transitions = None
-            self.transitions_params = None
-        self.reduction = reduction
-
-    def forward(self, inputs: Tensor, targets: Tensor) -> TransducerLoss:
-        TransducerLoss(
-            inputs,
-            targets,
-            self.tokens,
-            self.lexicon,
-            self.transitions_params,
-            self.transitions,
-            self.reduction,
-        )
-
-    def viterbi(self, outputs: Tensor) -> List[Tensor]:
-        B, T, C = outputs.shape
-
-        if self.transitions is not None:
-            cpu_data = self.transition_params.cpu().contiguous()
-            self.transitions.set_weights(cpu_data.data_ptr())
-            self.transitions.calc_grad = False
-
-        self.tokens.arc_sort()
-
-        paths = [None] * B
-
-        def process(b: int) -> None:
-            emissions = gtn.linear_graph(T, C, False)
-            cpu_data = outputs[b].cpu().contiguous()
-            emissions.set_weights(cpu_data.data_ptr())
-
-            if self.transitions is not None:
-                full_graph = gtn.intersect(emissions, self.transitions)
-            else:
-                full_graph = emissions
-
-            # Find the best path and remove back-off arcs:
-            path = gtn.remove(gtn.viterbi_path(full_graph))
-
-            # Left compose the viterbi path with the "aligment to token"
-            # transducer to get the outputs:
-            path = gtn.compose(path, self.tokens)
-
-            # When there are ambiguous paths (allow_repeats is true), we take
-            # the shortest:
-            path = gtn.viterbi_path(path)
-            path = gtn.remove(gtn.project_output(path))
-            paths[b] = path.labels_to_list()
-
-        gtn.parallel_for(process, range(B))
-        predictions = [torch.IntTensor(path) for path in paths]
-        return predictions
-
-
-def load_transducer_loss(
-    num_features: int,
-    ngram: int,
-    tokens: str,
-    lexicon: str,
-    transitions: str,
-    blank: str,
-    allow_repeats: bool,
-    prepend_wordsep: bool = False,
-    use_words: bool = False,
-    data_dir: Optional[Union[str, Path]] = None,
-    reduction: str = "mean",
-) -> Tuple[Transducer, int]:
-    if data_dir is None:
-        data_dir = (
-            Path(__file__).resolve().parents[4] / "data" / "raw" / "iam" / "iamdb"
-        )
-        logger.debug(f"Using data dir: {data_dir}")
-        if not data_dir.exists():
-            raise RuntimeError(f"Could not locate iamdb directory at {data_dir}")
-    else:
-        data_dir = Path(data_dir)
-    processed_path = (
-        Path(__file__).resolve().parents[4] / "data" / "processed" / "iam_lines"
-    )
-    tokens_path = processed_path / tokens
-    lexicon_path = processed_path / lexicon
-
-    if transitions is not None:
-        transitions = gtn.load(str(processed_path / transitions))
-
-    preprocessor = Preprocessor(
-        data_dir, num_features, tokens_path, lexicon_path, use_words, prepend_wordsep,
-    )
-
-    num_tokens = preprocessor.num_tokens
-
-    criterion = Transducer(
-        preprocessor.tokens,
-        preprocessor.graphemes_to_index,
-        ngram=ngram,
-        transitions=transitions,
-        blank=blank,
-        allow_repeats=allow_repeats,
-        reduction=reduction,
-    )
-
-    return criterion, num_tokens + int(blank != "none")
diff --git a/src/text_recognizer/networks/transformer/__init__.py b/src/text_recognizer/networks/transformer/__init__.py
deleted file mode 100644
index 9febc88..0000000
--- a/src/text_recognizer/networks/transformer/__init__.py
+++ /dev/null
@@ -1,3 +0,0 @@
-"""Transformer modules."""
-from .positional_encoding import PositionalEncoding
-from .transformer import Decoder, Encoder, EncoderLayer, Transformer
diff --git a/src/text_recognizer/networks/transformer/attention.py b/src/text_recognizer/networks/transformer/attention.py
deleted file mode 100644
index cce1ecc..0000000
--- a/src/text_recognizer/networks/transformer/attention.py
+++ /dev/null
@@ -1,93 +0,0 @@
-"""Implementes the attention module for the transformer."""
-from typing import Optional, Tuple
-
-from einops import rearrange
-import numpy as np
-import torch
-from torch import nn
-from torch import Tensor
-
-
-class MultiHeadAttention(nn.Module):
-    """Implementation of multihead attention."""
-
-    def __init__(
-        self, hidden_dim: int, num_heads: int = 8, dropout_rate: float = 0.0
-    ) -> None:
-        super().__init__()
-        self.hidden_dim = hidden_dim
-        self.num_heads = num_heads
-        self.fc_q = nn.Linear(
-            in_features=hidden_dim, out_features=hidden_dim, bias=False
-        )
-        self.fc_k = nn.Linear(
-            in_features=hidden_dim, out_features=hidden_dim, bias=False
-        )
-        self.fc_v = nn.Linear(
-            in_features=hidden_dim, out_features=hidden_dim, bias=False
-        )
-        self.fc_out = nn.Linear(in_features=hidden_dim, out_features=hidden_dim)
-
-        self._init_weights()
-
-        self.dropout = nn.Dropout(p=dropout_rate)
-
-    def _init_weights(self) -> None:
-        nn.init.normal_(
-            self.fc_q.weight,
-            mean=0,
-            std=np.sqrt(self.hidden_dim + int(self.hidden_dim / self.num_heads)),
-        )
-        nn.init.normal_(
-            self.fc_k.weight,
-            mean=0,
-            std=np.sqrt(self.hidden_dim + int(self.hidden_dim / self.num_heads)),
-        )
-        nn.init.normal_(
-            self.fc_v.weight,
-            mean=0,
-            std=np.sqrt(self.hidden_dim + int(self.hidden_dim / self.num_heads)),
-        )
-        nn.init.xavier_normal_(self.fc_out.weight)
-
-    def scaled_dot_product_attention(
-        self, query: Tensor, key: Tensor, value: Tensor, mask: Optional[Tensor] = None
-    ) -> Tensor:
-        """Calculates the scaled dot product attention."""
-
-        # Compute the energy.
-        energy = torch.einsum("bhlk,bhtk->bhlt", [query, key]) / np.sqrt(
-            query.shape[-1]
-        )
-
-        # If we have a mask for padding some inputs.
-        if mask is not None:
-            energy = energy.masked_fill(mask == 0, -np.inf)
-
-        # Compute the attention from the energy.
-        attention = torch.softmax(energy, dim=3)
-
-        out = torch.einsum("bhlt,bhtv->bhlv", [attention, value])
-        out = rearrange(out, "b head l v -> b l (head v)")
-        return out, attention
-
-    def forward(
-        self, query: Tensor, key: Tensor, value: Tensor, mask: Optional[Tensor] = None
-    ) -> Tuple[Tensor, Tensor]:
-        """Forward pass for computing the multihead attention."""
-        # Get the query, key, and value tensor.
-        query = rearrange(
-            self.fc_q(query), "b l (head k) -> b head l k", head=self.num_heads
-        )
-        key = rearrange(
-            self.fc_k(key), "b t (head k) -> b head t k", head=self.num_heads
-        )
-        value = rearrange(
-            self.fc_v(value), "b t (head v) -> b head t v", head=self.num_heads
-        )
-
-        out, attention = self.scaled_dot_product_attention(query, key, value, mask)
-
-        out = self.fc_out(out)
-        out = self.dropout(out)
-        return out, attention
diff --git a/src/text_recognizer/networks/transformer/positional_encoding.py b/src/text_recognizer/networks/transformer/positional_encoding.py
deleted file mode 100644
index 1ba5537..0000000
--- a/src/text_recognizer/networks/transformer/positional_encoding.py
+++ /dev/null
@@ -1,32 +0,0 @@
-"""A positional encoding for the image features, as the transformer has no notation of the order of the sequence."""
-import numpy as np
-import torch
-from torch import nn
-from torch import Tensor
-
-
-class PositionalEncoding(nn.Module):
-    """Encodes a sense of distance or time for transformer networks."""
-
-    def __init__(
-        self, hidden_dim: int, dropout_rate: float, max_len: int = 1000
-    ) -> None:
-        super().__init__()
-        self.dropout = nn.Dropout(p=dropout_rate)
-        self.max_len = max_len
-
-        pe = torch.zeros(max_len, hidden_dim)
-        position = torch.arange(0, max_len).unsqueeze(1)
-        div_term = torch.exp(
-            torch.arange(0, hidden_dim, 2) * -(np.log(10000.0) / hidden_dim)
-        )
-
-        pe[:, 0::2] = torch.sin(position * div_term)
-        pe[:, 1::2] = torch.cos(position * div_term)
-        pe = pe.unsqueeze(0)
-        self.register_buffer("pe", pe)
-
-    def forward(self, x: Tensor) -> Tensor:
-        """Encodes the tensor with a postional embedding."""
-        x = x + self.pe[:, : x.shape[1]]
-        return self.dropout(x)
diff --git a/src/text_recognizer/networks/transformer/transformer.py b/src/text_recognizer/networks/transformer/transformer.py
deleted file mode 100644
index dd180c4..0000000
--- a/src/text_recognizer/networks/transformer/transformer.py
+++ /dev/null
@@ -1,264 +0,0 @@
-"""Transfomer module."""
-import copy
-from typing import Dict, Optional, Type, Union
-
-import numpy as np
-import torch
-from torch import nn
-from torch import Tensor
-import torch.nn.functional as F
-
-from text_recognizer.networks.transformer.attention import MultiHeadAttention
-from text_recognizer.networks.util import activation_function
-
-
-class GEGLU(nn.Module):
-    """GLU activation for improving feedforward activations."""
-
-    def __init__(self, dim_in: int, dim_out: int) -> None:
-        super().__init__()
-        self.proj = nn.Linear(dim_in, dim_out * 2)
-
-    def forward(self, x: Tensor) -> Tensor:
-        """Forward propagation."""
-        x, gate = self.proj(x).chunk(2, dim=-1)
-        return x * F.gelu(gate)
-
-
-def _get_clones(module: Type[nn.Module], num_layers: int) -> nn.ModuleList:
-    return nn.ModuleList([copy.deepcopy(module) for _ in range(num_layers)])
-
-
-class _IntraLayerConnection(nn.Module):
-    """Preforms the residual connection inside the transfomer blocks and applies layernorm."""
-
-    def __init__(self, dropout_rate: float, hidden_dim: int) -> None:
-        super().__init__()
-        self.norm = nn.LayerNorm(normalized_shape=hidden_dim)
-        self.dropout = nn.Dropout(p=dropout_rate)
-
-    def forward(self, src: Tensor, residual: Tensor) -> Tensor:
-        return self.norm(self.dropout(src) + residual)
-
-
-class _ConvolutionalLayer(nn.Module):
-    def __init__(
-        self,
-        hidden_dim: int,
-        expansion_dim: int,
-        dropout_rate: float,
-        activation: str = "relu",
-    ) -> None:
-        super().__init__()
-
-        in_projection = (
-            nn.Sequential(
-                nn.Linear(hidden_dim, expansion_dim), activation_function(activation)
-            )
-            if activation != "glu"
-            else GEGLU(hidden_dim, expansion_dim)
-        )
-
-        self.layer = nn.Sequential(
-            in_projection,
-            nn.Dropout(p=dropout_rate),
-            nn.Linear(in_features=expansion_dim, out_features=hidden_dim),
-        )
-
-    def forward(self, x: Tensor) -> Tensor:
-        return self.layer(x)
-
-
-class EncoderLayer(nn.Module):
-    """Transfomer encoding layer."""
-
-    def __init__(
-        self,
-        hidden_dim: int,
-        num_heads: int,
-        expansion_dim: int,
-        dropout_rate: float,
-        activation: str = "relu",
-    ) -> None:
-        super().__init__()
-        self.self_attention = MultiHeadAttention(hidden_dim, num_heads, dropout_rate)
-        self.cnn = _ConvolutionalLayer(
-            hidden_dim, expansion_dim, dropout_rate, activation
-        )
-        self.block1 = _IntraLayerConnection(dropout_rate, hidden_dim)
-        self.block2 = _IntraLayerConnection(dropout_rate, hidden_dim)
-
-    def forward(self, src: Tensor, mask: Optional[Tensor] = None) -> Tensor:
-        """Forward pass through the encoder."""
-        # First block.
-        # Multi head attention.
-        out, _ = self.self_attention(src, src, src, mask)
-
-        # Add & norm.
-        out = self.block1(out, src)
-
-        # Second block.
-        # Apply 1D-convolution.
-        cnn_out = self.cnn(out)
-
-        # Add & norm.
-        out = self.block2(cnn_out, out)
-
-        return out
-
-
-class Encoder(nn.Module):
-    """Transfomer encoder module."""
-
-    def __init__(
-        self,
-        num_layers: int,
-        encoder_layer: Type[nn.Module],
-        norm: Optional[Type[nn.Module]] = None,
-    ) -> None:
-        super().__init__()
-        self.layers = _get_clones(encoder_layer, num_layers)
-        self.norm = norm
-
-    def forward(self, src: Tensor, src_mask: Optional[Tensor] = None) -> Tensor:
-        """Forward pass through all encoder layers."""
-        for layer in self.layers:
-            src = layer(src, src_mask)
-
-        if self.norm is not None:
-            src = self.norm(src)
-
-        return src
-
-
-class DecoderLayer(nn.Module):
-    """Transfomer decoder layer."""
-
-    def __init__(
-        self,
-        hidden_dim: int,
-        num_heads: int,
-        expansion_dim: int,
-        dropout_rate: float = 0.0,
-        activation: str = "relu",
-    ) -> None:
-        super().__init__()
-        self.hidden_dim = hidden_dim
-        self.self_attention = MultiHeadAttention(hidden_dim, num_heads, dropout_rate)
-        self.multihead_attention = MultiHeadAttention(
-            hidden_dim, num_heads, dropout_rate
-        )
-        self.cnn = _ConvolutionalLayer(
-            hidden_dim, expansion_dim, dropout_rate, activation
-        )
-        self.block1 = _IntraLayerConnection(dropout_rate, hidden_dim)
-        self.block2 = _IntraLayerConnection(dropout_rate, hidden_dim)
-        self.block3 = _IntraLayerConnection(dropout_rate, hidden_dim)
-
-    def forward(
-        self,
-        trg: Tensor,
-        memory: Tensor,
-        trg_mask: Optional[Tensor] = None,
-        memory_mask: Optional[Tensor] = None,
-    ) -> Tensor:
-        """Forward pass of the layer."""
-        out, _ = self.self_attention(trg, trg, trg, trg_mask)
-        trg = self.block1(out, trg)
-
-        out, _ = self.multihead_attention(trg, memory, memory, memory_mask)
-        trg = self.block2(out, trg)
-
-        out = self.cnn(trg)
-        out = self.block3(out, trg)
-
-        return out
-
-
-class Decoder(nn.Module):
-    """Transfomer decoder module."""
-
-    def __init__(
-        self,
-        decoder_layer: Type[nn.Module],
-        num_layers: int,
-        norm: Optional[Type[nn.Module]] = None,
-    ) -> None:
-        super().__init__()
-        self.layers = _get_clones(decoder_layer, num_layers)
-        self.num_layers = num_layers
-        self.norm = norm
-
-    def forward(
-        self,
-        trg: Tensor,
-        memory: Tensor,
-        trg_mask: Optional[Tensor] = None,
-        memory_mask: Optional[Tensor] = None,
-    ) -> Tensor:
-        """Forward pass through the decoder."""
-        for layer in self.layers:
-            trg = layer(trg, memory, trg_mask, memory_mask)
-
-        if self.norm is not None:
-            trg = self.norm(trg)
-
-        return trg
-
-
-class Transformer(nn.Module):
-    """Transformer network."""
-
-    def __init__(
-        self,
-        num_encoder_layers: int,
-        num_decoder_layers: int,
-        hidden_dim: int,
-        num_heads: int,
-        expansion_dim: int,
-        dropout_rate: float,
-        activation: str = "relu",
-    ) -> None:
-        super().__init__()
-
-        # Configure encoder.
-        encoder_norm = nn.LayerNorm(hidden_dim)
-        encoder_layer = EncoderLayer(
-            hidden_dim, num_heads, expansion_dim, dropout_rate, activation
-        )
-        self.encoder = Encoder(num_encoder_layers, encoder_layer, encoder_norm)
-
-        # Configure decoder.
-        decoder_norm = nn.LayerNorm(hidden_dim)
-        decoder_layer = DecoderLayer(
-            hidden_dim, num_heads, expansion_dim, dropout_rate, activation
-        )
-        self.decoder = Decoder(decoder_layer, num_decoder_layers, decoder_norm)
-
-        self._reset_parameters()
-
-    def _reset_parameters(self) -> None:
-        for p in self.parameters():
-            if p.dim() > 1:
-                nn.init.xavier_uniform_(p)
-
-    def forward(
-        self,
-        src: Tensor,
-        trg: Tensor,
-        src_mask: Optional[Tensor] = None,
-        trg_mask: Optional[Tensor] = None,
-        memory_mask: Optional[Tensor] = None,
-    ) -> Tensor:
-        """Forward pass through the transformer."""
-        if src.shape[0] != trg.shape[0]:
-            print(trg.shape)
-            raise RuntimeError("The batch size of the src and trg must be the same.")
-        if src.shape[2] != trg.shape[2]:
-            raise RuntimeError(
-                "The number of features for the src and trg must be the same."
-            )
-
-        memory = self.encoder(src, src_mask)
-        output = self.decoder(trg, memory, trg_mask, memory_mask)
-        return output
diff --git a/src/text_recognizer/networks/unet.py b/src/text_recognizer/networks/unet.py
deleted file mode 100644
index 510910f..0000000
--- a/src/text_recognizer/networks/unet.py
+++ /dev/null
@@ -1,255 +0,0 @@
-"""UNet for segmentation."""
-from typing import List, Optional, Tuple, Union
-
-import torch
-from torch import nn
-from torch import Tensor
-
-from text_recognizer.networks.util import activation_function
-
-
-class _ConvBlock(nn.Module):
-    """Modified UNet convolutional block with dilation."""
-
-    def __init__(
-        self,
-        channels: List[int],
-        activation: str,
-        num_groups: int,
-        dropout_rate: float = 0.1,
-        kernel_size: int = 3,
-        dilation: int = 1,
-        padding: int = 0,
-    ) -> None:
-        super().__init__()
-        self.channels = channels
-        self.dropout_rate = dropout_rate
-        self.kernel_size = kernel_size
-        self.dilation = dilation
-        self.padding = padding
-        self.num_groups = num_groups
-        self.activation = activation_function(activation)
-        self.block = self._configure_block()
-        self.residual_conv = nn.Sequential(
-            nn.Conv2d(
-                self.channels[0], self.channels[-1], kernel_size=3, stride=1, padding=1
-            ),
-            self.activation,
-        )
-
-    def _configure_block(self) -> nn.Sequential:
-        block = []
-        for i in range(len(self.channels) - 1):
-            block += [
-                nn.Dropout(p=self.dropout_rate),
-                nn.GroupNorm(self.num_groups, self.channels[i]),
-                self.activation,
-                nn.Conv2d(
-                    self.channels[i],
-                    self.channels[i + 1],
-                    kernel_size=self.kernel_size,
-                    padding=self.padding,
-                    stride=1,
-                    dilation=self.dilation,
-                ),
-            ]
-
-        return nn.Sequential(*block)
-
-    def forward(self, x: Tensor) -> Tensor:
-        """Apply the convolutional block."""
-        residual = self.residual_conv(x)
-        return self.block(x) + residual
-
-
-class _DownSamplingBlock(nn.Module):
-    """Basic down sampling block."""
-
-    def __init__(
-        self,
-        channels: List[int],
-        activation: str,
-        num_groups: int,
-        pooling_kernel: Union[int, bool] = 2,
-        dropout_rate: float = 0.1,
-        kernel_size: int = 3,
-        dilation: int = 1,
-        padding: int = 0,
-    ) -> None:
-        super().__init__()
-        self.conv_block = _ConvBlock(
-            channels,
-            activation,
-            num_groups,
-            dropout_rate,
-            kernel_size,
-            dilation,
-            padding,
-        )
-        self.down_sampling = nn.MaxPool2d(pooling_kernel) if pooling_kernel else None
-
-    def forward(self, x: Tensor) -> Tuple[Tensor, Tensor]:
-        """Return the convolutional block output and a down sampled tensor."""
-        x = self.conv_block(x)
-        x_down = self.down_sampling(x) if self.down_sampling is not None else x
-
-        return x_down, x
-
-
-class _UpSamplingBlock(nn.Module):
-    """The upsampling block of the UNet."""
-
-    def __init__(
-        self,
-        channels: List[int],
-        activation: str,
-        num_groups: int,
-        scale_factor: int = 2,
-        dropout_rate: float = 0.1,
-        kernel_size: int = 3,
-        dilation: int = 1,
-        padding: int = 0,
-    ) -> None:
-        super().__init__()
-        self.conv_block = _ConvBlock(
-            channels,
-            activation,
-            num_groups,
-            dropout_rate,
-            kernel_size,
-            dilation,
-            padding,
-        )
-        self.up_sampling = nn.Upsample(
-            scale_factor=scale_factor, mode="bilinear", align_corners=True
-        )
-
-    def forward(self, x: Tensor, x_skip: Optional[Tensor] = None) -> Tensor:
-        """Apply the up sampling and convolutional block."""
-        x = self.up_sampling(x)
-        if x_skip is not None:
-            x = torch.cat((x, x_skip), dim=1)
-        return self.conv_block(x)
-
-
-class UNet(nn.Module):
-    """UNet architecture."""
-
-    def __init__(
-        self,
-        in_channels: int = 1,
-        base_channels: int = 64,
-        num_classes: int = 3,
-        depth: int = 4,
-        activation: str = "relu",
-        num_groups: int = 8,
-        dropout_rate: float = 0.1,
-        pooling_kernel: int = 2,
-        scale_factor: int = 2,
-        kernel_size: Optional[List[int]] = None,
-        dilation: Optional[List[int]] = None,
-        padding: Optional[List[int]] = None,
-    ) -> None:
-        super().__init__()
-        self.depth = depth
-        self.num_groups = num_groups
-
-        if kernel_size is not None and dilation is not None and padding is not None:
-            if (
-                len(kernel_size) != depth
-                and len(dilation) != depth
-                and len(padding) != depth
-            ):
-                raise RuntimeError(
-                    "Length of convolutional parameters does not match the depth."
-                )
-            self.kernel_size = kernel_size
-            self.padding = padding
-            self.dilation = dilation
-
-        else:
-            self.kernel_size = [3] * depth
-            self.padding = [1] * depth
-            self.dilation = [1] * depth
-
-        self.dropout_rate = dropout_rate
-        self.conv = nn.Conv2d(
-            in_channels, base_channels, kernel_size=3, stride=1, padding=1
-        )
-
-        channels = [base_channels] + [base_channels * 2 ** i for i in range(depth)]
-        self.encoder_blocks = self._configure_down_sampling_blocks(
-            channels, activation, pooling_kernel
-        )
-        self.decoder_blocks = self._configure_up_sampling_blocks(
-            channels, activation, scale_factor
-        )
-
-        self.head = nn.Conv2d(base_channels, num_classes, kernel_size=1)
-
-    def _configure_down_sampling_blocks(
-        self, channels: List[int], activation: str, pooling_kernel: int
-    ) -> nn.ModuleList:
-        blocks = nn.ModuleList([])
-        for i in range(len(channels) - 1):
-            pooling_kernel = pooling_kernel if i < self.depth - 1 else False
-            dropout_rate = self.dropout_rate if i < 0 else 0
-            blocks += [
-                _DownSamplingBlock(
-                    [channels[i], channels[i + 1], channels[i + 1]],
-                    activation,
-                    self.num_groups,
-                    pooling_kernel,
-                    dropout_rate,
-                    self.kernel_size[i],
-                    self.dilation[i],
-                    self.padding[i],
-                )
-            ]
-
-        return blocks
-
-    def _configure_up_sampling_blocks(
-        self, channels: List[int], activation: str, scale_factor: int,
-    ) -> nn.ModuleList:
-        channels.reverse()
-        self.kernel_size.reverse()
-        self.dilation.reverse()
-        self.padding.reverse()
-        return nn.ModuleList(
-            [
-                _UpSamplingBlock(
-                    [channels[i] + channels[i + 1], channels[i + 1], channels[i + 1]],
-                    activation,
-                    self.num_groups,
-                    scale_factor,
-                    self.dropout_rate,
-                    self.kernel_size[i],
-                    self.dilation[i],
-                    self.padding[i],
-                )
-                for i in range(len(channels) - 2)
-            ]
-        )
-
-    def _encode(self, x: Tensor) -> List[Tensor]:
-        x_skips = []
-        for block in self.encoder_blocks:
-            x, x_skip = block(x)
-            x_skips.append(x_skip)
-        return x_skips
-
-    def _decode(self, x_skips: List[Tensor]) -> Tensor:
-        x = x_skips[-1]
-        for i, block in enumerate(self.decoder_blocks):
-            x = block(x, x_skips[-(i + 2)])
-        return x
-
-    def forward(self, x: Tensor) -> Tensor:
-        """Forward pass with the UNet model."""
-        if len(x.shape) < 4:
-            x = x[(None,) * (4 - len(x.shape))]
-        x = self.conv(x)
-        x_skips = self._encode(x)
-        x = self._decode(x_skips)
-        return self.head(x)
diff --git a/src/text_recognizer/networks/util.py b/src/text_recognizer/networks/util.py
deleted file mode 100644
index 131a6b4..0000000
--- a/src/text_recognizer/networks/util.py
+++ /dev/null
@@ -1,89 +0,0 @@
-"""Miscellaneous neural network functionality."""
-import importlib
-from pathlib import Path
-from typing import Dict, Tuple, Type
-
-from einops import rearrange
-from loguru import logger
-import torch
-from torch import nn
-
-
-def sliding_window(
-    images: torch.Tensor, patch_size: Tuple[int, int], stride: Tuple[int, int]
-) -> torch.Tensor:
-    """Creates patches of an image.
-
-    Args:
-        images (torch.Tensor): A Torch tensor of a 4D image(s), i.e. (batch, channel, height, width).
-        patch_size (Tuple[int, int]): The size of the patches to generate, e.g. 28x28 for EMNIST.
-        stride (Tuple[int, int]): The stride of the sliding window.
-
-    Returns:
-        torch.Tensor: A tensor with the shape (batch, patches, height, width).
-
-    """
-    unfold = nn.Unfold(kernel_size=patch_size, stride=stride)
-    # Preform the sliding window, unsqueeze as the channel dimesion is lost.
-    c = images.shape[1]
-    patches = unfold(images)
-    patches = rearrange(
-        patches, "b (c h w) t -> b t c h w", c=c, h=patch_size[0], w=patch_size[1],
-    )
-    return patches
-
-
-def activation_function(activation: str) -> Type[nn.Module]:
-    """Returns the callable activation function."""
-    activation_fns = nn.ModuleDict(
-        [
-            ["elu", nn.ELU(inplace=True)],
-            ["gelu", nn.GELU()],
-            ["glu", nn.GLU()],
-            ["leaky_relu", nn.LeakyReLU(negative_slope=1.0e-2, inplace=True)],
-            ["none", nn.Identity()],
-            ["relu", nn.ReLU(inplace=True)],
-            ["selu", nn.SELU(inplace=True)],
-        ]
-    )
-    return activation_fns[activation.lower()]
-
-
-def configure_backbone(backbone: str, backbone_args: Dict) -> Type[nn.Module]:
-    """Loads a backbone network."""
-    network_module = importlib.import_module("text_recognizer.networks")
-    backbone_ = getattr(network_module, backbone)
-
-    if "pretrained" in backbone_args:
-        logger.info("Loading pretrained backbone.")
-        checkpoint_file = Path(__file__).resolve().parents[2] / backbone_args.pop(
-            "pretrained"
-        )
-
-        # Loading state directory.
-        state_dict = torch.load(checkpoint_file)
-        network_args = state_dict["network_args"]
-        weights = state_dict["model_state"]
-
-        freeze = False
-        if "freeze" in backbone_args and backbone_args["freeze"] is True:
-            backbone_args.pop("freeze")
-            freeze = True
-        network_args = backbone_args
-
-        # Initializes the network with trained weights.
-        backbone = backbone_(**network_args)
-        backbone.load_state_dict(weights)
-        if freeze:
-            for params in backbone.parameters():
-                params.requires_grad = False
-    else:
-        backbone_ = getattr(network_module, backbone)
-        backbone = backbone_(**backbone_args)
-
-    if "remove_layers" in backbone_args and backbone_args["remove_layers"] is not None:
-        backbone = nn.Sequential(
-            *list(backbone.children())[:][: -backbone_args["remove_layers"]]
-        )
-
-    return backbone
diff --git a/src/text_recognizer/networks/vit.py b/src/text_recognizer/networks/vit.py
deleted file mode 100644
index efb3701..0000000
--- a/src/text_recognizer/networks/vit.py
+++ /dev/null
@@ -1,150 +0,0 @@
-"""A Vision Transformer.
-
-Inspired by:
-https://openreview.net/pdf?id=YicbFdNTTy
-
-"""
-from typing import Optional, Tuple
-
-from einops import rearrange, repeat
-import torch
-from torch import nn
-from torch import Tensor
-
-from text_recognizer.networks.transformer import Transformer
-
-
-class ViT(nn.Module):
-    """Transfomer for image to sequence prediction."""
-
-    def __init__(
-        self,
-        num_encoder_layers: int,
-        num_decoder_layers: int,
-        hidden_dim: int,
-        vocab_size: int,
-        num_heads: int,
-        expansion_dim: int,
-        patch_dim: Tuple[int, int],
-        image_size: Tuple[int, int],
-        dropout_rate: float,
-        trg_pad_index: int,
-        max_len: int,
-        activation: str = "gelu",
-    ) -> None:
-        super().__init__()
-
-        self.trg_pad_index = trg_pad_index
-        self.patch_dim = patch_dim
-        self.num_patches = image_size[-1] // self.patch_dim[1]
-
-        # Encoder
-        self.patch_to_embedding = nn.Linear(
-            self.patch_dim[0] * self.patch_dim[1], hidden_dim
-        )
-        self.cls_token = nn.Parameter(torch.randn(1, 1, hidden_dim))
-        self.character_embedding = nn.Embedding(vocab_size, hidden_dim)
-        self.pos_embedding = nn.Parameter(torch.randn(1, max_len, hidden_dim))
-        self.dropout = nn.Dropout(dropout_rate)
-        self._init()
-
-        self.transformer = Transformer(
-            num_encoder_layers,
-            num_decoder_layers,
-            hidden_dim,
-            num_heads,
-            expansion_dim,
-            dropout_rate,
-            activation,
-        )
-
-        self.head = nn.Sequential(nn.Linear(hidden_dim, vocab_size),)
-
-    def _init(self) -> None:
-        nn.init.normal_(self.character_embedding.weight, std=0.02)
-        # nn.init.normal_(self.pos_embedding.weight, std=0.02)
-
-    def _create_trg_mask(self, trg: Tensor) -> Tensor:
-        # Move this outside the transformer.
-        trg_pad_mask = (trg != self.trg_pad_index)[:, None, None]
-        trg_len = trg.shape[1]
-        trg_sub_mask = torch.tril(
-            torch.ones((trg_len, trg_len), device=trg.device)
-        ).bool()
-        trg_mask = trg_pad_mask & trg_sub_mask
-        return trg_mask
-
-    def encoder(self, src: Tensor) -> Tensor:
-        """Forward pass with the encoder of the transformer."""
-        return self.transformer.encoder(src)
-
-    def decoder(self, trg: Tensor, memory: Tensor, trg_mask: Tensor) -> Tensor:
-        """Forward pass with the decoder of the transformer + classification head."""
-        return self.head(
-            self.transformer.decoder(trg=trg, memory=memory, trg_mask=trg_mask)
-        )
-
-    def extract_image_features(self, src: Tensor) -> Tensor:
-        """Extracts image features with a backbone neural network.
-
-        It seem like the winning idea was to swap channels and width dimension and collapse
-        the height dimension. The transformer is learning like a baby with this implementation!!! :D
-        Ohhhh, the joy I am experiencing right now!! Bring in the beers! :D :D :D
-
-        Args:
-            src (Tensor): Input tensor.
-
-        Returns:
-            Tensor: A input src to the transformer.
-
-        """
-        # If batch dimension is missing, it needs to be added.
-        if len(src.shape) < 4:
-            src = src[(None,) * (4 - len(src.shape))]
-
-        patches = rearrange(
-            src,
-            "b c (h p1) (w p2) -> b (h w) (p1 p2 c)",
-            p1=self.patch_dim[0],
-            p2=self.patch_dim[1],
-        )
-
-        # From patches to encoded sequence.
-        x = self.patch_to_embedding(patches)
-        b, n, _ = x.shape
-        cls_tokens = repeat(self.cls_token, "() n d -> b n d", b=b)
-        x = torch.cat((cls_tokens, x), dim=1)
-        x += self.pos_embedding[:, : (n + 1)]
-        x = self.dropout(x)
-
-        return x
-
-    def target_embedding(self, trg: Tensor) -> Tuple[Tensor, Tensor]:
-        """Encodes target tensor with embedding and postion.
-
-        Args:
-            trg (Tensor): Target tensor.
-
-        Returns:
-            Tuple[Tensor, Tensor]: Encoded target tensor and target mask.
-
-        """
-        _, n = trg.shape
-        trg = self.character_embedding(trg.long())
-        trg += self.pos_embedding[:, :n]
-        return trg
-
-    def decode_image_features(self, h: Tensor, trg: Optional[Tensor] = None) -> Tensor:
-        """Takes images features from the backbone and decodes them with the transformer."""
-        trg_mask = self._create_trg_mask(trg)
-        trg = self.target_embedding(trg)
-        out = self.transformer(h, trg, trg_mask=trg_mask)
-
-        logits = self.head(out)
-        return logits
-
-    def forward(self, x: Tensor, trg: Optional[Tensor] = None) -> Tensor:
-        """Forward pass with CNN transfomer."""
-        h = self.extract_image_features(x)
-        logits = self.decode_image_features(h, trg)
-        return logits
diff --git a/src/text_recognizer/networks/vq_transformer.py b/src/text_recognizer/networks/vq_transformer.py
deleted file mode 100644
index c673d96..0000000
--- a/src/text_recognizer/networks/vq_transformer.py
+++ /dev/null
@@ -1,150 +0,0 @@
-"""A VQ-Transformer for image to text recognition."""
-from typing import Dict, Optional, Tuple
-
-from einops import rearrange, repeat
-import torch
-from torch import nn
-from torch import Tensor
-
-from text_recognizer.networks.transformer import PositionalEncoding, Transformer
-from text_recognizer.networks.util import activation_function
-from text_recognizer.networks.util import configure_backbone
-from text_recognizer.networks.vqvae.encoder import _ResidualBlock
-
-
-class VQTransformer(nn.Module):
-    """VQ+Transfomer for image to character sequence prediction."""
-
-    def __init__(
-        self,
-        num_encoder_layers: int,
-        num_decoder_layers: int,
-        hidden_dim: int,
-        vocab_size: int,
-        num_heads: int,
-        adaptive_pool_dim: Tuple,
-        expansion_dim: int,
-        dropout_rate: float,
-        trg_pad_index: int,
-        max_len: int,
-        backbone: str,
-        backbone_args: Optional[Dict] = None,
-        activation: str = "gelu",
-    ) -> None:
-        super().__init__()
-
-        # Configure vector quantized backbone.
-        self.backbone = configure_backbone(backbone, backbone_args)
-        self.conv = nn.Sequential(
-            nn.Conv2d(hidden_dim, hidden_dim, kernel_size=3, stride=2),
-            nn.ReLU(inplace=True),
-        )
-
-        # Configure embeddings for Transformer network.
-        self.trg_pad_index = trg_pad_index
-        self.vocab_size = vocab_size
-        self.character_embedding = nn.Embedding(self.vocab_size, hidden_dim)
-        self.src_position_embedding = nn.Parameter(torch.randn(1, max_len, hidden_dim))
-        self.trg_position_encoding = PositionalEncoding(hidden_dim, dropout_rate)
-        nn.init.normal_(self.character_embedding.weight, std=0.02)
-
-        self.adaptive_pool = (
-            nn.AdaptiveAvgPool2d((adaptive_pool_dim)) if adaptive_pool_dim else None
-        )
-
-        self.transformer = Transformer(
-            num_encoder_layers,
-            num_decoder_layers,
-            hidden_dim,
-            num_heads,
-            expansion_dim,
-            dropout_rate,
-            activation,
-        )
-
-        self.head = nn.Sequential(nn.Linear(hidden_dim, vocab_size),)
-
-    def _create_trg_mask(self, trg: Tensor) -> Tensor:
-        # Move this outside the transformer.
-        trg_pad_mask = (trg != self.trg_pad_index)[:, None, None]
-        trg_len = trg.shape[1]
-        trg_sub_mask = torch.tril(
-            torch.ones((trg_len, trg_len), device=trg.device)
-        ).bool()
-        trg_mask = trg_pad_mask & trg_sub_mask
-        return trg_mask
-
-    def encoder(self, src: Tensor) -> Tensor:
-        """Forward pass with the encoder of the transformer."""
-        return self.transformer.encoder(src)
-
-    def decoder(self, trg: Tensor, memory: Tensor, trg_mask: Tensor) -> Tensor:
-        """Forward pass with the decoder of the transformer + classification head."""
-        return self.head(
-            self.transformer.decoder(trg=trg, memory=memory, trg_mask=trg_mask)
-        )
-
-    def extract_image_features(self, src: Tensor) -> Tuple[Tensor, Tensor]:
-        """Extracts image features with a backbone neural network.
-
-        It seem like the winning idea was to swap channels and width dimension and collapse
-        the height dimension. The transformer is learning like a baby with this implementation!!! :D
-        Ohhhh, the joy I am experiencing right now!! Bring in the beers! :D :D :D
-
-        Args:
-            src (Tensor): Input tensor.
-
-        Returns:
-            Tensor: The input src to the transformer and the vq loss.
-
-        """
-        # If batch dimension is missing, it needs to be added.
-        if len(src.shape) < 4:
-            src = src[(None,) * (4 - len(src.shape))]
-        src, vq_loss = self.backbone.encode(src)
-        # src = self.backbone.decoder.res_block(src)
-        src = self.conv(src)
-
-        if self.adaptive_pool is not None:
-            src = rearrange(src, "b c h w -> b w c h")
-            src = self.adaptive_pool(src)
-            src = src.squeeze(3)
-        else:
-            src = rearrange(src, "b c h w -> b (w h) c")
-
-        b, t, _ = src.shape
-
-        src += self.src_position_embedding[:, :t]
-
-        return src, vq_loss
-
-    def target_embedding(self, trg: Tensor) -> Tensor:
-        """Encodes target tensor with embedding and postion.
-
-        Args:
-            trg (Tensor): Target tensor.
-
-        Returns:
-            Tensor: Encoded target tensor.
-
-        """
-        trg = self.character_embedding(trg.long())
-        trg = self.trg_position_encoding(trg)
-        return trg
-
-    def decode_image_features(
-        self, image_features: Tensor, trg: Optional[Tensor] = None
-    ) -> Tensor:
-        """Takes images features from the backbone and decodes them with the transformer."""
-        trg_mask = self._create_trg_mask(trg)
-        trg = self.target_embedding(trg)
-        out = self.transformer(image_features, trg, trg_mask=trg_mask)
-
-        logits = self.head(out)
-        return logits
-
-    def forward(self, x: Tensor, trg: Optional[Tensor] = None) -> Tensor:
-        """Forward pass with CNN transfomer."""
-        image_features, vq_loss = self.extract_image_features(x)
-        logits = self.decode_image_features(image_features, trg)
-        return logits, vq_loss
diff --git a/src/text_recognizer/networks/vqvae/__init__.py b/src/text_recognizer/networks/vqvae/__init__.py
deleted file mode 100644
index 763953c..0000000
--- a/src/text_recognizer/networks/vqvae/__init__.py
+++ /dev/null
@@ -1,5 +0,0 @@
-"""VQ-VAE module."""
-from .decoder import Decoder
-from .encoder import Encoder
-from .vector_quantizer import VectorQuantizer
-from .vqvae import VQVAE
diff --git a/src/text_recognizer/networks/vqvae/decoder.py b/src/text_recognizer/networks/vqvae/decoder.py
deleted file mode 100644
index 8847aba..0000000
--- a/src/text_recognizer/networks/vqvae/decoder.py
+++ /dev/null
@@ -1,133 +0,0 @@
-"""CNN decoder for the VQ-VAE."""
-
-from typing import List, Optional, Tuple, Type
-
-import torch
-from torch import nn
-from torch import Tensor
-
-from text_recognizer.networks.util import activation_function
-from text_recognizer.networks.vqvae.encoder import _ResidualBlock
-
-
-class Decoder(nn.Module):
-    """A CNN encoder network."""
-
-    def __init__(
-        self,
-        channels: List[int],
-        kernel_sizes: List[int],
-        strides: List[int],
-        num_residual_layers: int,
-        embedding_dim: int,
-        upsampling: Optional[List[List[int]]] = None,
-        activation: str = "leaky_relu",
-        dropout_rate: float = 0.0,
-    ) -> None:
-        super().__init__()
-
-        if dropout_rate:
-            if activation == "selu":
-                dropout = nn.AlphaDropout(p=dropout_rate)
-            else:
-                dropout = nn.Dropout(p=dropout_rate)
-        else:
-            dropout = None
-
-        self.upsampling = upsampling
-
-        self.res_block = nn.ModuleList([])
-        self.upsampling_block = nn.ModuleList([])
-
-        self.embedding_dim = embedding_dim
-        activation = activation_function(activation)
-
-        # Configure encoder.
-        self.decoder = self._build_decoder(
-            channels, kernel_sizes, strides, num_residual_layers, activation, dropout,
-        )
-
-    def _build_decompression_block(
-        self,
-        in_channels: int,
-        channels: int,
-        kernel_sizes: List[int],
-        strides: List[int],
-        activation: Type[nn.Module],
-        dropout: Optional[Type[nn.Module]],
-    ) -> nn.ModuleList:
-        modules = nn.ModuleList([])
-        configuration = zip(channels, kernel_sizes, strides)
-        for i, (out_channels, kernel_size, stride) in enumerate(configuration):
-            modules.append(
-                nn.Sequential(
-                    nn.ConvTranspose2d(
-                        in_channels,
-                        out_channels,
-                        kernel_size,
-                        stride=stride,
-                        padding=1,
-                    ),
-                    activation,
-                )
-            )
-
-            if i < len(self.upsampling):
-                modules.append(nn.Upsample(size=self.upsampling[i]),)
-
-            if dropout is not None:
-                modules.append(dropout)
-
-            in_channels = out_channels
-
-        modules.extend(
-            nn.Sequential(
-                nn.ConvTranspose2d(
-                    in_channels, 1, kernel_size=kernel_size, stride=stride, padding=1
-                ),
-                nn.Tanh(),
-            )
-        )
-
-        return modules
-
-    def _build_decoder(
-        self,
-        channels: int,
-        kernel_sizes: List[int],
-        strides: List[int],
-        num_residual_layers: int,
-        activation: Type[nn.Module],
-        dropout: Optional[Type[nn.Module]],
-    ) -> nn.Sequential:
-
-        self.res_block.append(
-            nn.Conv2d(self.embedding_dim, channels[0], kernel_size=1, stride=1,)
-        )
-
-        # Bottleneck module.
-        self.res_block.extend(
-            nn.ModuleList(
-                [
-                    _ResidualBlock(channels[0], channels[0], dropout)
-                    for i in range(num_residual_layers)
-                ]
-            )
-        )
-
-        # Decompression module
-        self.upsampling_block.extend(
-            self._build_decompression_block(
-                channels[0], channels[1:], kernel_sizes, strides, activation, dropout
-            )
-        )
-
-        self.res_block = nn.Sequential(*self.res_block)
-        self.upsampling_block = nn.Sequential(*self.upsampling_block)
-
-        return nn.Sequential(self.res_block, self.upsampling_block)
-
-    def forward(self, z_q: Tensor) -> Tensor:
-        """Reconstruct input from given codes."""
-        x_reconstruction = self.decoder(z_q)
-        return x_reconstruction
diff --git a/src/text_recognizer/networks/vqvae/encoder.py b/src/text_recognizer/networks/vqvae/encoder.py
deleted file mode 100644
index d3adac5..0000000
--- a/src/text_recognizer/networks/vqvae/encoder.py
+++ /dev/null
@@ -1,147 +0,0 @@
-"""CNN encoder for the VQ-VAE."""
-from typing import List, Optional, Tuple, Type
-
-import torch
-from torch import nn
-from torch import Tensor
-
-from text_recognizer.networks.util import activation_function
-from text_recognizer.networks.vqvae.vector_quantizer import VectorQuantizer
-
-
-class _ResidualBlock(nn.Module):
-    def __init__(
-        self, in_channels: int, out_channels: int, dropout: Optional[Type[nn.Module]],
-    ) -> None:
-        super().__init__()
-        self.block = [
-            nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1, bias=False),
-            nn.ReLU(inplace=True),
-            nn.Conv2d(out_channels, out_channels, kernel_size=1, bias=False),
-        ]
-
-        if dropout is not None:
-            self.block.append(dropout)
-
-        self.block = nn.Sequential(*self.block)
-
-    def forward(self, x: Tensor) -> Tensor:
-        """Apply the residual forward pass."""
-        return x + self.block(x)
-
-
-class Encoder(nn.Module):
-    """A CNN encoder network."""
-
-    def __init__(
-        self,
-        in_channels: int,
-        channels: List[int],
-        kernel_sizes: List[int],
-        strides: List[int],
-        num_residual_layers: int,
-        embedding_dim: int,
-        num_embeddings: int,
-        beta: float = 0.25,
-        activation: str = "leaky_relu",
-        dropout_rate: float = 0.0,
-    ) -> None:
-        super().__init__()
-
-        if dropout_rate:
-            if activation == "selu":
-                dropout = nn.AlphaDropout(p=dropout_rate)
-            else:
-                dropout = nn.Dropout(p=dropout_rate)
-        else:
-            dropout = None
-
-        self.embedding_dim = embedding_dim
-        self.num_embeddings = num_embeddings
-        self.beta = beta
-        activation = activation_function(activation)
-
-        # Configure encoder.
-        self.encoder = self._build_encoder(
-            in_channels,
-            channels,
-            kernel_sizes,
-            strides,
-            num_residual_layers,
-            activation,
-            dropout,
-        )
-
-        # Configure Vector Quantizer.
-        self.vector_quantizer = VectorQuantizer(
-            self.num_embeddings, self.embedding_dim, self.beta
-        )
-
-    def _build_compression_block(
-        self,
-        in_channels: int,
-        channels: int,
-        kernel_sizes: List[int],
-        strides: List[int],
-        activation: Type[nn.Module],
-        dropout: Optional[Type[nn.Module]],
-    ) -> nn.ModuleList:
-        modules = nn.ModuleList([])
-        configuration = zip(channels, kernel_sizes, strides)
-        for out_channels, kernel_size, stride in configuration:
-            modules.append(
-                nn.Sequential(
-                    nn.Conv2d(
-                        in_channels, out_channels, kernel_size, stride=stride, padding=1
-                    ),
-                    activation,
-                )
-            )
-
-            if dropout is not None:
-                modules.append(dropout)
-
-            in_channels = out_channels
-
-        return modules
-
-    def _build_encoder(
-        self,
-        in_channels: int,
-        channels: int,
-        kernel_sizes: List[int],
-        strides: List[int],
-        num_residual_layers: int,
-        activation: Type[nn.Module],
-        dropout: Optional[Type[nn.Module]],
-    ) -> nn.Sequential:
-        encoder = nn.ModuleList([])
-
-        # compression module
-        encoder.extend(
-            self._build_compression_block(
-                in_channels, channels, kernel_sizes, strides, activation, dropout
-            )
-        )
-
-        # Bottleneck module.
-        encoder.extend(
-            nn.ModuleList(
-                [
-                    _ResidualBlock(channels[-1], channels[-1], dropout)
-                    for i in range(num_residual_layers)
-                ]
-            )
-        )
-
-        encoder.append(
-            nn.Conv2d(channels[-1], self.embedding_dim, kernel_size=1, stride=1,)
-        )
-
-        return nn.Sequential(*encoder)
-
-    def forward(self, x: Tensor) -> Tuple[Tensor, Tensor]:
-        """Encodes input into a discrete representation."""
-        z_e = self.encoder(x)
-        z_q, vq_loss = self.vector_quantizer(z_e)
-        return z_q, vq_loss
diff --git a/src/text_recognizer/networks/vqvae/vector_quantizer.py b/src/text_recognizer/networks/vqvae/vector_quantizer.py
deleted file mode 100644
index f92c7ee..0000000
--- a/src/text_recognizer/networks/vqvae/vector_quantizer.py
+++ /dev/null
@@ -1,119 +0,0 @@
-"""Implementation of a Vector Quantized Variational AutoEncoder.
-
-Reference:
-https://github.com/AntixK/PyTorch-VAE/blob/master/models/vq_vae.py
-
-"""
-
-from einops import rearrange
-import torch
-from torch import nn
-from torch import Tensor
-from torch.nn import functional as F
-
-
-class VectorQuantizer(nn.Module):
-    """The codebook that contains quantized vectors."""
-
-    def __init__(
-        self, num_embeddings: int, embedding_dim: int, beta: float = 0.25
-    ) -> None:
-        super().__init__()
-        self.K = num_embeddings
-        self.D = embedding_dim
-        self.beta = beta
-
-        self.embedding = nn.Embedding(self.K, self.D)
-
-        # Initialize the codebook.
-        nn.init.uniform_(self.embedding.weight, -1 / self.K, 1 / self.K)
-
-    def discretization_bottleneck(self, latent: Tensor) -> Tensor:
-        """Computes the code nearest to the latent representation.
-
-        First we compute the posterior categorical distribution, and then map
-        the latent representation to the nearest element of the embedding.
-
-        Args:
-            latent (Tensor): The latent representation.
-
-        Shape:
-            - latent :math:`(B x H x W, D)`
-
-        Returns:
-            Tensor: The quantized embedding vector.
-
-        """
-        # Store latent shape.
-        b, h, w, d = latent.shape
-
-        # Flatten the latent representation to 2D.
-        latent = rearrange(latent, "b h w d -> (b h w) d")
-
-        # Compute the L2 distance between the latents and the embeddings.
-        l2_distance = (
-            torch.sum(latent ** 2, dim=1, keepdim=True)
-            + torch.sum(self.embedding.weight ** 2, dim=1)
-            - 2 * latent @ self.embedding.weight.t()
-        )  # [BHW x K]
-
-        # Find the embedding k nearest to each latent.
-        encoding_indices = torch.argmin(l2_distance, dim=1).unsqueeze(1)  # [BHW, 1]
-
-        # Convert to one-hot encodings, aka discrete bottleneck.
-        one_hot_encoding = torch.zeros(
-            encoding_indices.shape[0], self.K, device=latent.device
-        )
-        one_hot_encoding.scatter_(1, encoding_indices, 1)  # [BHW x K]
-
-        # Embedding quantization.
-        quantized_latent = one_hot_encoding @ self.embedding.weight  # [BHW, D]
-        quantized_latent = rearrange(
-            quantized_latent, "(b h w) d -> b h w d", b=b, h=h, w=w
-        )
-
-        return quantized_latent
-
-    def vq_loss(self, latent: Tensor, quantized_latent: Tensor) -> Tensor:
-        """Vector Quantization loss.
-
-        The vector quantization algorithm allows us to create a codebook. The VQ
-        algorithm works by moving the embedding vectors towards the encoder outputs.
-
-        The embedding loss moves the embedding vector towards the encoder outputs. The
-        .detach() works as the stop gradient (sg) described in the paper.
-
-        Because the volume of the embedding space is dimensionless, it can arbitarily
-        grow if the embeddings are not trained as fast as the encoder parameters. To
-        mitigate this, a commitment loss is added in the second term which makes sure
-        that the encoder commits to an embedding and that its output does not grow.
-
-        Args:
-            latent (Tensor): The encoder output.
-            quantized_latent (Tensor): The quantized latent.
-
-        Returns:
-            Tensor: The combinded VQ loss.
-
-        """
-        embedding_loss = F.mse_loss(quantized_latent, latent.detach())
-        commitment_loss = F.mse_loss(quantized_latent.detach(), latent)
-        return embedding_loss + self.beta * commitment_loss
-
-    def forward(self, latent: Tensor) -> Tensor:
-        """Forward pass that returns the quantized vector and the vq loss."""
-        # Rearrange latent representation s.t. the hidden dim is at the end.
-        latent = rearrange(latent, "b d h w -> b h w d")
-
-        # Maps latent to the nearest code in the codebook.
-        quantized_latent = self.discretization_bottleneck(latent)
-
-        loss = self.vq_loss(latent, quantized_latent)
-
-        # Add residue to the quantized latent.
-        quantized_latent = latent + (quantized_latent - latent).detach()
-
-        # Rearrange the quantized shape back to the original shape.
-        quantized_latent = rearrange(quantized_latent, "b h w d -> b d h w")
-
-        return quantized_latent, loss
diff --git a/src/text_recognizer/networks/vqvae/vqvae.py b/src/text_recognizer/networks/vqvae/vqvae.py
deleted file mode 100644
index 50448b4..0000000
--- a/src/text_recognizer/networks/vqvae/vqvae.py
+++ /dev/null
@@ -1,74 +0,0 @@
-"""The VQ-VAE."""
-
-from typing import List, Optional, Tuple, Type
-
-import torch
-from torch import nn
-from torch import Tensor
-
-from text_recognizer.networks.vqvae import Decoder, Encoder
-
-
-class VQVAE(nn.Module):
-    """Vector Quantized Variational AutoEncoder."""
-
-    def __init__(
-        self,
-        in_channels: int,
-        channels: List[int],
-        kernel_sizes: List[int],
-        strides: List[int],
-        num_residual_layers: int,
-        embedding_dim: int,
-        num_embeddings: int,
-        upsampling: Optional[List[List[int]]] = None,
-        beta: float = 0.25,
-        activation: str = "leaky_relu",
-        dropout_rate: float = 0.0,
-    ) -> None:
-        super().__init__()
-
-        # configure encoder.
-        self.encoder = Encoder(
-            in_channels,
-            channels,
-            kernel_sizes,
-            strides,
-            num_residual_layers,
-            embedding_dim,
-            num_embeddings,
-            beta,
-            activation,
-            dropout_rate,
-        )
-
-        # Configure decoder.
-        channels.reverse()
-        kernel_sizes.reverse()
-        strides.reverse()
-        self.decoder = Decoder(
-            channels,
-            kernel_sizes,
-            strides,
-            num_residual_layers,
-            embedding_dim,
-            upsampling,
-            activation,
-            dropout_rate,
-        )
-
-    def encode(self, x: Tensor) -> Tuple[Tensor, Tensor]:
-        """Encodes input to a latent code."""
-        return self.encoder(x)
-
-    def decode(self, z_q: Tensor) -> Tensor:
-        """Reconstructs input from latent codes."""
-        return self.decoder(z_q)
-
-    def forward(self, x: Tensor) -> Tuple[Tensor, Tensor]:
-        """Compresses and decompresses input."""
-        if len(x.shape) < 4:
-            x = x[(None,) * (4 - len(x.shape))]
-        z_q, vq_loss = self.encode(x)
-        x_reconstruction = self.decode(z_q)
-        return x_reconstruction, vq_loss
diff --git a/src/text_recognizer/networks/wide_resnet.py b/src/text_recognizer/networks/wide_resnet.py
deleted file mode 100644
index b767778..0000000
--- a/src/text_recognizer/networks/wide_resnet.py
+++ /dev/null
@@ -1,221 +0,0 @@
-"""Wide Residual CNN."""
-from functools import partial
-from typing import Callable, Dict, List, Optional, Type, Union
-
-from einops.layers.torch import Reduce
-import numpy as np
-import torch
-from torch import nn
-from torch import Tensor
-
-from text_recognizer.networks.util import activation_function
-
-
-def conv3x3(in_planes: int, out_planes: int, stride: int = 1) -> nn.Conv2d:
-    """Helper function for a 3x3 2d convolution."""
-    return nn.Conv2d(
-        in_channels=in_planes,
-        out_channels=out_planes,
-        kernel_size=3,
-        stride=stride,
-        padding=1,
-        bias=False,
-    )
-
-
-def conv_init(module: Type[nn.Module]) -> None:
-    """Initializes the weights for convolution and batchnorms."""
-    classname = module.__class__.__name__
-    if classname.find("Conv") != -1:
-        nn.init.xavier_uniform_(module.weight, gain=np.sqrt(2))
-        nn.init.constant_(module.bias, 0)
-    elif classname.find("BatchNorm") != -1:
-        nn.init.constant_(module.weight, 1)
-        nn.init.constant_(module.bias, 0)
-
-
-class WideBlock(nn.Module):
-    """Block used in WideResNet."""
-
-    def __init__(
-        self,
-        in_planes: int,
-        out_planes: int,
-        dropout_rate: float,
-        stride: int = 1,
-        activation: str = "relu",
-    ) -> None:
-        super().__init__()
-        self.in_planes = in_planes
-        self.out_planes = out_planes
-        self.dropout_rate = dropout_rate
-        self.stride = stride
-        self.activation = activation_function(activation)
-
-        # Build blocks.
-        self.blocks = nn.Sequential(
-            nn.BatchNorm2d(self.in_planes),
-            self.activation,
-            conv3x3(in_planes=self.in_planes, out_planes=self.out_planes),
-            nn.Dropout(p=self.dropout_rate),
-            nn.BatchNorm2d(self.out_planes),
-            self.activation,
-            conv3x3(
-                in_planes=self.out_planes,
-                out_planes=self.out_planes,
-                stride=self.stride,
-            ),
-        )
-
-        self.shortcut = (
-            nn.Sequential(
-                nn.Conv2d(
-                    in_channels=self.in_planes,
-                    out_channels=self.out_planes,
-                    kernel_size=1,
-                    stride=self.stride,
-                    bias=False,
-                ),
-            )
-            if self._apply_shortcut
-            else None
-        )
-
-    @property
-    def _apply_shortcut(self) -> bool:
-        """If shortcut should be applied or not."""
-        return self.stride != 1 or self.in_planes != self.out_planes
-
-    def forward(self, x: Tensor) -> Tensor:
-        """Forward pass."""
-        residual = x
-        if self._apply_shortcut:
-            residual = self.shortcut(x)
-        x = self.blocks(x)
-        x += residual
-        return x
-
-
-class WideResidualNetwork(nn.Module):
-    """WideResNet for character predictions.
-
-    Can be used for classification or encoding of images to a latent vector.
-
-    """
-
-    def __init__(
-        self,
-        in_channels: int = 1,
-        in_planes: int = 16,
-        num_classes: int = 80,
-        depth: int = 16,
-        width_factor: int = 10,
-        dropout_rate: float = 0.0,
-        num_layers: int = 3,
-        block: Type[nn.Module] = WideBlock,
-        num_stages: Optional[List[int]] = None,
-        activation: str = "relu",
-        use_decoder: bool = True,
-    ) -> None:
-        """The initialization of the WideResNet.
-
-        Args:
-            in_channels (int): Number of input channels. Defaults to 1.
-            in_planes (int): Number of channels to use in the first output kernel. Defaults to 16.
-            num_classes (int): Number of classes. Defaults to 80.
-            depth (int): Set the number of blocks to use. Defaults to 16.
-            width_factor (int): Factor for scaling the number of channels in the network. Defaults to 10.
-            dropout_rate (float): The dropout rate. Defaults to 0.0.
-            num_layers (int): Number of layers of blocks. Defaults to 3.
-            block (Type[nn.Module]): The default block is WideBlock. Defaults to WideBlock.
-            num_stages (List[int]): If given, will use these channel values. Defaults to None.
-            activation (str): Name of the activation to use. Defaults to "relu".
-            use_decoder (bool): If True, the network output character predictions, if False, the network outputs a
-                latent vector. Defaults to True.
-
-        Raises:
-            RuntimeError: If the depth is not of the size `6n+4`.
-
-        """
-
-        super().__init__()
-        if (depth - 4) % 6 != 0:
-            raise RuntimeError("Wide-resnet depth should be 6n+4")
-        self.in_channels = in_channels
-        self.in_planes = in_planes
-        self.num_classes = num_classes
-        self.num_blocks = (depth - 4) // 6
-        self.width_factor = width_factor
-        self.num_layers = num_layers
-        self.block = block
-        self.dropout_rate = dropout_rate
-        self.activation = activation_function(activation)
-
-        if num_stages is None:
-            self.num_stages = [self.in_planes] + [
-                self.in_planes * 2 ** n * self.width_factor
-                for n in range(self.num_layers)
-            ]
-        else:
-            self.num_stages = [self.in_planes] + num_stages
-
-        self.num_stages = list(zip(self.num_stages, self.num_stages[1:]))
-        self.strides = [1] + [2] * (self.num_layers - 1)
-
-        self.encoder = nn.Sequential(
-            conv3x3(in_planes=self.in_channels, out_planes=self.in_planes),
-            *[
-                self._configure_wide_layer(
-                    in_planes=in_planes,
-                    out_planes=out_planes,
-                    stride=stride,
-                    activation=activation,
-                )
-                for (in_planes, out_planes), stride in zip(
-                    self.num_stages, self.strides
-                )
-            ],
-        )
-
-        self.decoder = (
-            nn.Sequential(
-                nn.BatchNorm2d(self.num_stages[-1][-1], momentum=0.8),
-                self.activation,
-                Reduce("b c h w -> b c", "mean"),
-                nn.Linear(
-                    in_features=self.num_stages[-1][-1], out_features=self.num_classes
-                ),
-            )
-            if use_decoder
-            else None
-        )
-
-        # self.apply(conv_init)
-
-    def _configure_wide_layer(
-        self, in_planes: int, out_planes: int, stride: int, activation: str
-    ) -> List:
-        strides = [stride] + [1] * (self.num_blocks - 1)
-        planes = [out_planes] * len(strides)
-        planes = [(in_planes, out_planes)] + list(zip(planes, planes[1:]))
-        return nn.Sequential(
-            *[
-                self.block(
-                    in_planes=in_planes,
-                    out_planes=out_planes,
-                    dropout_rate=self.dropout_rate,
-                    stride=stride,
-                    activation=activation,
-                )
-                for (in_planes, out_planes), stride in zip(planes, strides)
-            ]
-        )
-
-    def forward(self, x: Tensor) -> Tensor:
-        """Feedforward pass."""
-        if len(x.shape) < 4:
-            x = x[(None,) * int(4 - len(x.shape))]
-        x = self.encoder(x)
-        if self.decoder is not None:
-            x = self.decoder(x)
-        return x
diff --git a/src/text_recognizer/paragraph_text_recognizer.py b/src/text_recognizer/paragraph_text_recognizer.py
deleted file mode 100644
index aa39662..0000000
--- a/src/text_recognizer/paragraph_text_recognizer.py
+++ /dev/null
@@ -1,153 +0,0 @@
-"""Full model.
-
-Takes an image and returns the text in the image, by first segmenting the image with a LineDetector, then extracting the
-each crop of the image corresponding to line regions, and feeding them to a LinePredictor model that outputs the text
-in each region.
-"""
-from typing import Dict, List, Tuple, Union
-
-import cv2
-import numpy as np
-import torch
-
-from text_recognizer.models import SegmentationModel, TransformerModel
-from text_recognizer.util import read_image
-
-
-class ParagraphTextRecognizor:
-    """Given an image of a single handwritten character, recognizes it."""
-
-    def __init__(self, line_predictor_args: Dict, line_detector_args: Dict) -> None:
-        self._line_predictor = TransformerModel(**line_predictor_args)
-        self._line_detector = SegmentationModel(**line_detector_args)
-        self._line_detector.eval()
-        self._line_predictor.eval()
-
-    def predict(self, image_or_filename: Union[str, np.ndarray]) -> Tuple:
-        """Takes an image and returns all text within it."""
-        image = (
-            read_image(image_or_filename)
-            if isinstance(image_or_filename, str)
-            else image_or_filename
-        )
-
-        line_region_crops = self._get_line_region_crops(image)
-        processed_line_region_crops = [
-            self._process_image_for_line_predictor(image=crop)
-            for crop in line_region_crops
-        ]
-        line_region_strings = [
-            self.line_predictor_model.predict_on_image(crop)[0]
-            for crop in processed_line_region_crops
-        ]
-
-        return " ".join(line_region_strings), line_region_crops
-
-    def _get_line_region_crops(
-        self, image: np.ndarray, min_crop_len_factor: float = 0.02
-    ) -> List[np.ndarray]:
-        """Returns all the crops of text lines in a square image."""
-        processed_image, scale_down_factor = self._process_image_for_line_detector(
-            image
-        )
-        line_segmentation = self._line_detector.predict_on_image(processed_image)
-        bounding_boxes = _find_line_bounding_boxes(line_segmentation)
-
-        bounding_boxes = (bounding_boxes * scale_down_factor).astype(int)
-
-        min_crop_len = int(min_crop_len_factor * min(image.shape[0], image.shape[1]))
-        line_region_crops = [
-            image[y : y + h, x : x + w]
-            for x, y, w, h in bounding_boxes
-            if w >= min_crop_len and h >= min_crop_len
-        ]
-        return line_region_crops
-
-    def _process_image_for_line_detector(
-        self, image: np.ndarray
-    ) -> Tuple[np.ndarray, float]:
-        """Convert uint8 image to float image with black background with shape self._line_detector.image_shape."""
-        resized_image, scale_down_factor = _resize_image_for_line_detector(
-            image=image, max_shape=self._line_detector.image_shape
-        )
-        resized_image = (1.0 - resized_image / 255).astype("float32")
-        return resized_image, scale_down_factor
-
-    def _process_image_for_line_predictor(self, image: np.ndarray) -> np.ndarray:
-        """Preprocessing of image before feeding it to the LinePrediction model.
-
-        Convert uint8 image to float image with black background with shape
-        self._line_predictor.image_shape while maintaining the image aspect ratio.
-
-        Args:
-            image (np.ndarray): Crop of text line.
-
-        Returns:
-            np.ndarray: Processed crop for feeding line predictor.
-        """
-        expected_shape = self._line_detector.image_shape
-        scale_factor = (np.array(expected_shape) / np.array(image.shape)).min()
-        scaled_image = cv2.resize(
-            image,
-            dsize=None,
-            fx=scale_factor,
-            fy=scale_factor,
-            interpolation=cv2.INTER_AREA,
-        )
-
-        pad_with = (
-            (0, expected_shape[0] - scaled_image.shape[0]),
-            (0, expected_shape[1] - scaled_image.shape[1]),
-        )
-
-        padded_image = np.pad(
-            scaled_image, pad_with=pad_with, mode="constant", constant_values=255
-        )
-        return 1 - padded_image / 255
-
-
-def _find_line_bounding_boxes(line_segmentation: np.ndarray) -> np.ndarray:
-    """Given a line segmentation, find bounding boxes for connected-component regions corresponding to non-0 labels."""
-
-    def _find_line_bounding_boxes_in_channel(
-        line_segmentation_channel: np.ndarray,
-    ) -> np.ndarray:
-        line_segmentation_image = cv2.dilate(
-            line_segmentation_channel, kernel=np.ones((3, 3)), iterations=1
-        )
-        line_activation_image = (line_segmentation_image * 255).astype("uint8")
-        line_activation_image = cv2.threshold(
-            line_activation_image, 0.5, 1, cv2.THRESH_BINARY | cv2.THRESH_OTSU
-        )[1]
-
-        bounding_cnts, _ = cv2.findContours(
-            line_segmentation_image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE
-        )
-        return np.array([cv2.boundingRect(cnt) for cnt in bounding_cnts])
-
-    bounding_boxes = np.concatenate(
-        [
-            _find_line_bounding_boxes_in_channel(line_segmentation[:, :, i])
-            for i in [1, 2]
-        ],
-        axis=0,
-    )
-
-    return bounding_boxes[np.argsort(bounding_boxes[:, 1])]
-
-
-def _resize_image_for_line_detector(
-    image: np.ndarray, max_shape: Tuple[int, int]
-) -> Tuple[np.ndarray, float]:
-    """Resize the image to less than the max_shape while maintaining the aspect ratio."""
-    scale_down_factor = max(np.ndarray(image.shape) / np.ndarray(max_shape))
-    if scale_down_factor == 1:
-        return image.copy(), scale_down_factor
-    resize_image = cv2.resize(
-        image,
-        dsize=None,
-        fx=1 / scale_down_factor,
-        fy=1 / scale_down_factor,
-        interpolation=cv2.INTER_AREA,
-    )
-    return resize_image, scale_down_factor
diff --git a/src/text_recognizer/tests/__init__.py b/src/text_recognizer/tests/__init__.py
deleted file mode 100644
index 18ff212..0000000
--- a/src/text_recognizer/tests/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-"""Test modules for the text text recognizer."""
diff --git a/src/text_recognizer/tests/support/__init__.py b/src/text_recognizer/tests/support/__init__.py
deleted file mode 100644
index a265ede..0000000
--- a/src/text_recognizer/tests/support/__init__.py
+++ /dev/null
@@ -1,2 +0,0 @@
-"""Support file modules."""
-from .create_emnist_support_files import create_emnist_support_files
diff --git a/src/text_recognizer/tests/support/create_emnist_lines_support_files.py b/src/text_recognizer/tests/support/create_emnist_lines_support_files.py
deleted file mode 100644
index 9abe143..0000000
--- a/src/text_recognizer/tests/support/create_emnist_lines_support_files.py
+++ /dev/null
@@ -1,51 +0,0 @@
-"""Module for creating EMNIST Lines test support files."""
-# flake8: noqa: S106
-
-from pathlib import Path
-import shutil
-
-import numpy as np
-
-from text_recognizer.datasets import EmnistLinesDataset
-import text_recognizer.util as util
-
-
-SUPPORT_DIRNAME = Path(__file__).parents[0].resolve() / "emnist_lines"
-
-
-def create_emnist_lines_support_files() -> None:
-    """Create EMNIST Lines test images."""
-    shutil.rmtree(SUPPORT_DIRNAME, ignore_errors=True)
-    SUPPORT_DIRNAME.mkdir()
-
-    # TODO: maybe have to add args to dataset.
-    dataset = EmnistLinesDataset(
-        init_token="<sos>",
-        pad_token="_",
-        eos_token="<eos>",
-        transform=[{"type": "ToTensor", "args": {}}],
-        target_transform=[
-            {
-                "type": "AddTokens",
-                "args": {"init_token": "<sos>", "pad_token": "_", "eos_token": "<eos>"},
-            }
-        ],
-    )  # nosec: S106
-    dataset.load_or_generate_data()
-
-    for index in [5, 7, 9]:
-        image, target = dataset[index]
-        if len(image.shape) == 3:
-            image = image.squeeze(0)
-        print(image.sum(), image.dtype)
-
-        label = "".join(dataset.mapper(label) for label in target[1:]).strip(
-            dataset.mapper.pad_token
-        )
-        print(label)
-        image = image.numpy()
-        util.write_image(image, str(SUPPORT_DIRNAME / f"{label}.png"))
-
-
-if __name__ == "__main__":
-    create_emnist_lines_support_files()
diff --git a/src/text_recognizer/tests/support/create_emnist_support_files.py b/src/text_recognizer/tests/support/create_emnist_support_files.py
deleted file mode 100644
index f9ff030..0000000
--- a/src/text_recognizer/tests/support/create_emnist_support_files.py
+++ /dev/null
@@ -1,30 +0,0 @@
-"""Module for creating EMNIST test support files."""
-from pathlib import Path
-import shutil
-
-from text_recognizer.datasets import EmnistDataset
-from text_recognizer.util import write_image
-
-SUPPORT_DIRNAME = Path(__file__).parents[0].resolve() / "emnist"
-
-
-def create_emnist_support_files() -> None:
-    """Create support images for test of CharacterPredictor class."""
-    shutil.rmtree(SUPPORT_DIRNAME, ignore_errors=True)
-    SUPPORT_DIRNAME.mkdir()
-
-    dataset = EmnistDataset(train=False)
-    dataset.load_or_generate_data()
-
-    for index in [5, 7, 9]:
-        image, label = dataset[index]
-        if len(image.shape) == 3:
-            image = image.squeeze(0)
-        image = image.numpy()
-        label = dataset.mapper(int(label))
-        print(index, label)
-        write_image(image, str(SUPPORT_DIRNAME / f"{label}.png"))
-
-
-if __name__ == "__main__":
-    create_emnist_support_files()
diff --git a/src/text_recognizer/tests/support/create_iam_lines_support_files.py b/src/text_recognizer/tests/support/create_iam_lines_support_files.py
deleted file mode 100644
index 50f9e3d..0000000
--- a/src/text_recognizer/tests/support/create_iam_lines_support_files.py
+++ /dev/null
@@ -1,50 +0,0 @@
-"""Module for creating IAM Lines test support files."""
-# flake8: noqa
-from pathlib import Path
-import shutil
-
-import numpy as np
-
-from text_recognizer.datasets import IamLinesDataset
-import text_recognizer.util as util
-
-
-SUPPORT_DIRNAME = Path(__file__).parents[0].resolve() / "iam_lines"
-
-
-def create_emnist_lines_support_files() -> None:
-    """Create IAM Lines test images."""
-    shutil.rmtree(SUPPORT_DIRNAME, ignore_errors=True)
-    SUPPORT_DIRNAME.mkdir()
-
-    # TODO: maybe have to add args to dataset.
-    dataset = IamLinesDataset(
-        init_token="<sos>",
-        pad_token="_",
-        eos_token="<eos>",
-        transform=[{"type": "ToTensor", "args": {}}],
-        target_transform=[
-            {
-                "type": "AddTokens",
-                "args": {"init_token": "<sos>", "pad_token": "_", "eos_token": "<eos>"},
-            }
-        ],
-    )
-    dataset.load_or_generate_data()
-
-    for index in [0, 1, 3]:
-        image, target = dataset[index]
-        if len(image.shape) == 3:
-            image = image.squeeze(0)
-        print(image.sum(), image.dtype)
-
-        label = "".join(dataset.mapper(label) for label in target[1:]).strip(
-            dataset.mapper.pad_token
-        )
-        print(label)
-        image = image.numpy()
-        util.write_image(image, str(SUPPORT_DIRNAME / f"{label}.png"))
-
-
-if __name__ == "__main__":
-    create_emnist_lines_support_files()
diff --git a/src/text_recognizer/tests/support/emnist/8.png b/src/text_recognizer/tests/support/emnist/8.png
deleted file mode 100644
index faa29aa..0000000
--- a/src/text_recognizer/tests/support/emnist/8.png
+++ /dev/null
diff --git a/src/text_recognizer/tests/support/emnist/U.png b/src/text_recognizer/tests/support/emnist/U.png
deleted file mode 100644
index 304eaec..0000000
--- a/src/text_recognizer/tests/support/emnist/U.png
+++ /dev/null
diff --git a/src/text_recognizer/tests/support/emnist/e.png b/src/text_recognizer/tests/support/emnist/e.png
deleted file mode 100644
index a03ecd4..0000000
--- a/src/text_recognizer/tests/support/emnist/e.png
+++ /dev/null
diff --git a/src/text_recognizer/tests/support/emnist_lines/Knox Ky<eos>.png b/src/text_recognizer/tests/support/emnist_lines/Knox Ky<eos>.png
deleted file mode 100644
index b7d0618..0000000
--- a/src/text_recognizer/tests/support/emnist_lines/Knox Ky<eos>.png
+++ /dev/null
diff --git a/src/text_recognizer/tests/support/emnist_lines/ancillary beliefs and<eos>.png b/src/text_recognizer/tests/support/emnist_lines/ancillary beliefs and<eos>.png
deleted file mode 100644
index 14a8cf3..0000000
--- a/src/text_recognizer/tests/support/emnist_lines/ancillary beliefs and<eos>.png
+++ /dev/null
diff --git a/src/text_recognizer/tests/support/emnist_lines/they<eos>.png b/src/text_recognizer/tests/support/emnist_lines/they<eos>.png
deleted file mode 100644
index 7f05951..0000000
--- a/src/text_recognizer/tests/support/emnist_lines/they<eos>.png
+++ /dev/null
diff --git a/src/text_recognizer/tests/support/iam_lines/He rose from his breakfast-nook bench<eos>.png b/src/text_recognizer/tests/support/iam_lines/He rose from his breakfast-nook bench<eos>.png
deleted file mode 100644
index 6eeb642..0000000
--- a/src/text_recognizer/tests/support/iam_lines/He rose from his breakfast-nook bench<eos>.png
+++ /dev/null
diff --git a/src/text_recognizer/tests/support/iam_lines/and came into the livingroom, where<eos>.png b/src/text_recognizer/tests/support/iam_lines/and came into the livingroom, where<eos>.png
deleted file mode 100644
index 4974cf8..0000000
--- a/src/text_recognizer/tests/support/iam_lines/and came into the livingroom, where<eos>.png
+++ /dev/null
diff --git a/src/text_recognizer/tests/support/iam_lines/his entrance. He came, almost falling<eos>.png b/src/text_recognizer/tests/support/iam_lines/his entrance. He came, almost falling<eos>.png
deleted file mode 100644
index a731245..0000000
--- a/src/text_recognizer/tests/support/iam_lines/his entrance. He came, almost falling<eos>.png
+++ /dev/null
diff --git a/src/text_recognizer/tests/support/iam_paragraphs/a01-000u.jpg b/src/text_recognizer/tests/support/iam_paragraphs/a01-000u.jpg
deleted file mode 100644
index d9753b6..0000000
--- a/src/text_recognizer/tests/support/iam_paragraphs/a01-000u.jpg
+++ /dev/null
diff --git a/src/text_recognizer/tests/test_character_predictor.py b/src/text_recognizer/tests/test_character_predictor.py
deleted file mode 100644
index 01bda78..0000000
--- a/src/text_recognizer/tests/test_character_predictor.py
+++ /dev/null
@@ -1,31 +0,0 @@
-"""Test for CharacterPredictor class."""
-import importlib
-import os
-from pathlib import Path
-import unittest
-
-from loguru import logger
-
-from text_recognizer.character_predictor import CharacterPredictor
-from text_recognizer.networks import MLP
-
-SUPPORT_DIRNAME = Path(__file__).parents[0].resolve() / "support" / "emnist"
-
-os.environ["CUDA_VISIBLE_DEVICES"] = ""
-
-
-class TestCharacterPredictor(unittest.TestCase):
-    """Tests for the CharacterPredictor class."""
-
-    def test_filename(self) -> None:
-        """Test that CharacterPredictor correctly predicts on a single image, for serveral test images."""
-        network_fn_ = MLP
-        predictor = CharacterPredictor(network_fn=network_fn_)
-
-        for filename in SUPPORT_DIRNAME.glob("*.png"):
-            pred, conf = predictor.predict(str(filename))
-            logger.info(
-                f"Prediction: {pred} at confidence: {conf} for image with character {filename.stem}"
-            )
-            self.assertEqual(pred, filename.stem)
-            self.assertGreater(conf, 0.7)
diff --git a/src/text_recognizer/tests/test_line_predictor.py b/src/text_recognizer/tests/test_line_predictor.py
deleted file mode 100644
index eede4d4..0000000
--- a/src/text_recognizer/tests/test_line_predictor.py
+++ /dev/null
@@ -1,35 +0,0 @@
-"""Tests for LinePredictor."""
-import os
-from pathlib import Path
-import unittest
-
-
-import editdistance
-import numpy as np
-
-from text_recognizer.datasets import IamLinesDataset
-from text_recognizer.line_predictor import LinePredictor
-import text_recognizer.util as util
-
-SUPPORT_DIRNAME = Path(__file__).parents[0].resolve() / "support"
-
-os.environ["CUDA_VISIBLE_DEVICES"] = ""
-
-
-class TestEmnistLinePredictor(unittest.TestCase):
-    """Test LinePredictor class on the EmnistLines dataset."""
-
-    def test_filename(self) -> None:
-        """Test that LinePredictor correctly predicts on single images, for several test images."""
-        predictor = LinePredictor(
-            dataset="EmnistLineDataset", network_fn="CNNTransformer"
-        )
-
-        for filename in (SUPPORT_DIRNAME / "emnist_lines").glob("*.png"):
-            pred, conf = predictor.predict(str(filename))
-            true = str(filename.stem)
-            edit_distance = editdistance.eval(pred, true) / len(pred)
-            print(
-                f'Pred: "{pred}" | Confidence: {conf} | True: {true} | Edit distance: {edit_distance}'
-            )
-            self.assertLess(edit_distance, 0.2)
diff --git a/src/text_recognizer/tests/test_paragraph_text_recognizer.py b/src/text_recognizer/tests/test_paragraph_text_recognizer.py
deleted file mode 100644
index 3e280b9..0000000
--- a/src/text_recognizer/tests/test_paragraph_text_recognizer.py
+++ /dev/null
@@ -1,37 +0,0 @@
-"""Test for ParagraphTextRecognizer class."""
-import os
-from pathlib import Path
-import unittest
-
-from text_recognizer.paragraph_text_recognizer import ParagraphTextRecognizor
-import text_recognizer.util as util
-
-
-SUPPORT_DIRNAME = Path(__file__).parents[0].resolve() / "support" / "iam_paragraph"
-
-# Prevent using GPU.
-os.environ["CUDA_VISIBLE_DEVICES"] = ""
-
-
-class TestParagraphTextRecognizor(unittest.TestCase):
-    """Test that it can take non-square images of max dimension larger than 256px."""
-
-    def test_filename(self) -> None:
-        """Test model on support image."""
-        line_predictor_args = {
-            "dataset": "EmnistLineDataset",
-            "network_fn": "CNNTransformer",
-        }
-        line_detector_args = {"dataset": "EmnistLineDataset", "network_fn": "UNet"}
-        model = ParagraphTextRecognizor(
-            line_predictor_args=line_predictor_args,
-            line_detector_args=line_detector_args,
-        )
-        num_text_lines_by_name = {"a01-000u-cropped": 7}
-        for filename in (SUPPORT_DIRNAME).glob("*.jpg"):
-            full_image = util.read_image(str(filename), grayscale=True)
-            predicted_text, line_region_crops = model.predict(full_image)
-            print(predicted_text)
-            self.assertTrue(
-                len(line_region_crops), num_text_lines_by_name[filename.stem]
-            )
diff --git a/src/text_recognizer/util.py b/src/text_recognizer/util.py
deleted file mode 100644
index b431e22..0000000
--- a/src/text_recognizer/util.py
+++ /dev/null
@@ -1,52 +0,0 @@
-"""Utility functions for text_recognizer module."""
-import os
-from pathlib import Path
-from typing import Union
-from urllib.request import urlopen
-
-import cv2
-import numpy as np
-
-
-def read_image(image_uri: Union[Path, str], grayscale: bool = False) -> np.ndarray:
-    """Read image_uri."""
-
-    def read_image_from_filename(image_filename: str, imread_flag: int) -> np.ndarray:
-        return cv2.imread(str(image_filename), imread_flag)
-
-    def read_image_from_url(image_url: str, imread_flag: int) -> np.ndarray:
-        if image_url.lower().startswith("http"):
-            url_response = urlopen(str(image_url))
-            image_array = np.array(bytearray(url_response.read()), dtype=np.uint8)
-            return cv2.imdecode(image_array, imread_flag)
-        else:
-            raise ValueError(
-                "Url does not start with http, therefore not safe to open..."
-            ) from None
-
-    imread_flag = cv2.IMREAD_GRAYSCALE if grayscale else cv2.IMREAD_COLOR
-    local_file = os.path.exists(image_uri)
-    image = None
-
-    if local_file:
-        image = read_image_from_filename(image_uri, imread_flag)
-    else:
-        image = read_image_from_url(image_uri, imread_flag)
-
-    if image is None:
-        raise ValueError(f"Could not load image at {image_uri}")
-
-    return image
-
-
-def rescale_image(image: np.ndarray) -> np.ndarray:
-    """Rescale image from [0, 1] to [0, 255]."""
-    if image.max() <= 1.0:
-        image = 255 * (image - image.min()) / (image.max() - image.min())
-    return image
-
-
-def write_image(image: np.ndarray, filename: Union[Path, str]) -> None:
-    """Write image to file."""
-    image = rescale_image(image)
-    cv2.imwrite(str(filename), image)
diff --git a/src/text_recognizer/weights/CRNNModel_IamLinesDataset_ConvolutionalRecurrentNetwork_weights.pt b/src/text_recognizer/weights/CRNNModel_IamLinesDataset_ConvolutionalRecurrentNetwork_weights.pt
deleted file mode 100644
index 344e0a3..0000000
--- a/src/text_recognizer/weights/CRNNModel_IamLinesDataset_ConvolutionalRecurrentNetwork_weights.pt
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:46d483950ef0876ba072d06cd94021e08d99c4fa14eeccf22aeae1cbb2066b4f
-size 5628749
diff --git a/src/text_recognizer/weights/CharacterModel_EmnistDataset_DenseNet_weights.pt b/src/text_recognizer/weights/CharacterModel_EmnistDataset_DenseNet_weights.pt
deleted file mode 100644
index f2dfd84..0000000
--- a/src/text_recognizer/weights/CharacterModel_EmnistDataset_DenseNet_weights.pt
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:8a69e5efedea70c4c5cb8ccdcc8cd480400f6c73e3313423f4dbbfe615644f0a
-size 4500617
diff --git a/src/text_recognizer/weights/CharacterModel_EmnistDataset_WideResidualNetwork_weights.pt b/src/text_recognizer/weights/CharacterModel_EmnistDataset_WideResidualNetwork_weights.pt
deleted file mode 100644
index e1add8d..0000000
--- a/src/text_recognizer/weights/CharacterModel_EmnistDataset_WideResidualNetwork_weights.pt
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:68dd5c98eedc8753546f88b4e6fd5fc38725dc0079b837c30fb3d48069ec412b
-size 15002754
diff --git a/src/text_recognizer/weights/SegmentationModel_IamParagraphsDataset_FCN_weights.pt b/src/text_recognizer/weights/SegmentationModel_IamParagraphsDataset_FCN_weights.pt
deleted file mode 100644
index d9ca01d..0000000
--- a/src/text_recognizer/weights/SegmentationModel_IamParagraphsDataset_FCN_weights.pt
+++ /dev/null
diff --git a/src/text_recognizer/weights/SegmentationModel_IamParagraphsDataset_UNet_weights.pt b/src/text_recognizer/weights/SegmentationModel_IamParagraphsDataset_UNet_weights.pt
deleted file mode 100644
index 0af0e57..0000000
--- a/src/text_recognizer/weights/SegmentationModel_IamParagraphsDataset_UNet_weights.pt
+++ /dev/null
diff --git a/src/text_recognizer/weights/VQVAEModel_IamLinesDataset_VQVAE_weights.pt b/src/text_recognizer/weights/VQVAEModel_IamLinesDataset_VQVAE_weights.pt
deleted file mode 100644
index b5295c2..0000000
--- a/src/text_recognizer/weights/VQVAEModel_IamLinesDataset_VQVAE_weights.pt
+++ /dev/null