diff --git a/poetry.lock b/poetry.lock
index 816305c49..b18b2626e 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,4 +1,4 @@
-# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
+# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand.
 
 [[package]]
 name = "apipkg"
@@ -698,6 +698,40 @@ files = [
 [package.dependencies]
 colorama = ">=0.4"
 
+[[package]]
+name = "huggingface-hub"
+version = "0.23.0"
+description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub"
+optional = false
+python-versions = ">=3.8.0"
+files = [
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+    {file = "huggingface_hub-0.23.0.tar.gz", hash = "sha256:7126dedd10a4c6fac796ced4d87a8cf004efc722a5125c2c09299017fa366fa9"},
+]
+
+[package.dependencies]
+filelock = "*"
+fsspec = ">=2023.5.0"
+packaging = ">=20.9"
+pyyaml = ">=5.1"
+requests = "*"
+tqdm = ">=4.42.1"
+typing-extensions = ">=3.7.4.3"
+
+[package.extras]
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+cli = ["InquirerPy (==0.3.4)"]
+dev = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "fastapi", "gradio", "jedi", "minijinja (>=1.0)", "mypy (==1.5.1)", "numpy", "pytest", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "ruff (>=0.3.0)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)", "urllib3 (<2.0)"]
+fastai = ["fastai (>=2.4)", "fastcore (>=1.3.27)", "toml"]
+hf-transfer = ["hf-transfer (>=0.1.4)"]
+inference = ["aiohttp", "minijinja (>=1.0)"]
+quality = ["mypy (==1.5.1)", "ruff (>=0.3.0)"]
+tensorflow = ["graphviz", "pydot", "tensorflow"]
+tensorflow-testing = ["keras (<3.0)", "tensorflow"]
+testing = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "fastapi", "gradio", "jedi", "minijinja (>=1.0)", "numpy", "pytest", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-rerunfailures", "pytest-vcr", "pytest-xdist", "soundfile", "urllib3 (<2.0)"]
+torch = ["safetensors", "torch"]
+typing = ["types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)"]
+
 [[package]]
 name = "idna"
 version = "3.7"
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@@ -2847,6 +2880,128 @@ files = [
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 ]
 
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+dev-tensorflow = ["GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "hf-doc-builder", "hf-doc-builder (>=0.3.0)", "isort (>=5.5.4)", "kenlm", "keras-nlp (>=0.3.1)", "librosa", "nltk", "onnxconverter-common", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.1.5)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "tensorflow (>=2.6,<2.16)", "tensorflow-text (<2.16)", "tf2onnx", "timeout-decorator", "tokenizers (>=0.19,<0.20)", "urllib3 (<2.0.0)"]
+dev-torch = ["GitPython (<3.1.19)", "Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.21.0)", "beautifulsoup4", "codecarbon (==1.2.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "fugashi (>=1.0)", "hf-doc-builder", "hf-doc-builder (>=0.3.0)", "ipadic (>=1.0.0,<2.0)", "isort (>=5.5.4)", "kenlm", "librosa", "nltk", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "optuna", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-timeout", "pytest-xdist", "ray[tune] (>=2.7.0)", "rhoknp (>=1.1.0,<1.3.1)", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.1.5)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "tensorboard", "timeout-decorator", "timm", "tokenizers (>=0.19,<0.20)", "torch", "torchaudio", "torchvision", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)", "urllib3 (<2.0.0)"]
+docs = ["Pillow (>=10.0.1,<=15.0)", "accelerate (>=0.21.0)", "av (==9.2.0)", "codecarbon (==1.2.0)", "decord (==0.6.0)", "flax (>=0.4.1,<=0.7.0)", "hf-doc-builder", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "kenlm", "keras-nlp (>=0.3.1)", "librosa", "onnxconverter-common", "optax (>=0.0.8,<=0.1.4)", "optuna", "phonemizer", "protobuf", "pyctcdecode (>=0.4.0)", "ray[tune] (>=2.7.0)", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "tensorflow (>=2.6,<2.16)", "tensorflow-text (<2.16)", "tf2onnx", "timm", "tokenizers (>=0.19,<0.20)", "torch", "torchaudio", "torchvision"]
+docs-specific = ["hf-doc-builder"]
+flax = ["flax (>=0.4.1,<=0.7.0)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "optax (>=0.0.8,<=0.1.4)"]
+flax-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
+ftfy = ["ftfy"]
+integrations = ["optuna", "ray[tune] (>=2.7.0)", "sigopt"]
+ja = ["fugashi (>=1.0)", "ipadic (>=1.0.0,<2.0)", "rhoknp (>=1.1.0,<1.3.1)", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)"]
+modelcreation = ["cookiecutter (==1.7.3)"]
+natten = ["natten (>=0.14.6,<0.15.0)"]
+onnx = ["onnxconverter-common", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "tf2onnx"]
+onnxruntime = ["onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)"]
+optuna = ["optuna"]
+quality = ["GitPython (<3.1.19)", "datasets (!=2.5.0)", "hf-doc-builder (>=0.3.0)", "isort (>=5.5.4)", "ruff (==0.1.5)", "urllib3 (<2.0.0)"]
+ray = ["ray[tune] (>=2.7.0)"]
+retrieval = ["datasets (!=2.5.0)", "faiss-cpu"]
+sagemaker = ["sagemaker (>=2.31.0)"]
+sentencepiece = ["protobuf", "sentencepiece (>=0.1.91,!=0.1.92)"]
+serving = ["fastapi", "pydantic", "starlette", "uvicorn"]
+sigopt = ["sigopt"]
+sklearn = ["scikit-learn"]
+speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
+testing = ["GitPython (<3.1.19)", "beautifulsoup4", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "hf-doc-builder (>=0.3.0)", "nltk", "parameterized", "protobuf", "psutil", "pydantic", "pytest (>=7.2.0,<8.0.0)", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (==0.1.5)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "timeout-decorator"]
+tf = ["keras-nlp (>=0.3.1)", "onnxconverter-common", "tensorflow (>=2.6,<2.16)", "tensorflow-text (<2.16)", "tf2onnx"]
+tf-cpu = ["keras-nlp (>=0.3.1)", "onnxconverter-common", "tensorflow-cpu (>=2.6,<2.16)", "tensorflow-text (<2.16)", "tf2onnx"]
+tf-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
+timm = ["timm"]
+tokenizers = ["tokenizers (>=0.19,<0.20)"]
+torch = ["accelerate (>=0.21.0)", "torch"]
+torch-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
+torch-vision = ["Pillow (>=10.0.1,<=15.0)", "torchvision"]
+torchhub = ["filelock", "huggingface-hub (>=0.19.3,<1.0)", "importlib-metadata", "numpy (>=1.17)", "packaging (>=20.0)", "protobuf", "regex (!=2019.12.17)", "requests", "sentencepiece (>=0.1.91,!=0.1.92)", "tokenizers (>=0.19,<0.20)", "torch", "tqdm (>=4.27)"]
+video = ["av (==9.2.0)", "decord (==0.6.0)"]
+vision = ["Pillow (>=10.0.1,<=15.0)"]
+
 [[package]]
 name = "triton"
 version = "2.3.0"
@@ -3560,4 +3920,4 @@ files = [
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.11,<3.13"
-content-hash = "b0b59af0288ab5fe6287fbd4af9311c6f3825971b73410d7337506aeac831b37"
+content-hash = "8b37477ecadb4f68cb1ab4f350b5bcc2b0f5addbe244d6bad4f255dfb4db0b55"
diff --git a/pyproject.toml b/pyproject.toml
index 56f210843..75a9d1c73 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -24,6 +24,7 @@ statsmodels = "^0.14.1"
 torch = "^2.3.0"
 torchvision = "^0.18.0"
 xxhash = "^3.4.1"
+transformers = "^4.40.2"
 
 [tool.poetry.group.dev.dependencies]
 pytest = ">=7.2.1,<9.0.0"
diff --git a/src/safeds/data/image/typing/_image_size.py b/src/safeds/data/image/typing/_image_size.py
index b23854503..4f20774bf 100644
--- a/src/safeds/data/image/typing/_image_size.py
+++ b/src/safeds/data/image/typing/_image_size.py
@@ -1,16 +1,7 @@
-from __future__ import annotations
+from safeds.ml.nn.typing import ConstantImageSize
 
-import sys
-from typing import TYPE_CHECKING
 
-from safeds._utils import _structural_hash
-from safeds._validation import _check_bounds, _ClosedBound
-
-if TYPE_CHECKING:
-    from safeds.data.image.containers import Image
-
-
-class ImageSize:
+class ImageSize(ConstantImageSize):
     """
     A container for image size data.
 
@@ -31,82 +22,5 @@ class ImageSize:
         if an invalid channel is given
     """
 
-    def __init__(self, width: int, height: int, channel: int, *, _ignore_invalid_channel: bool = False) -> None:
-        _check_bounds("width", width, lower_bound=_ClosedBound(1))
-        _check_bounds("height", height, lower_bound=_ClosedBound(1))
-        if not _ignore_invalid_channel and channel not in (1, 3, 4):
-            raise ValueError(f"Channel {channel} is not a valid channel option. Use either 1, 3 or 4")
-        _check_bounds("channel", channel, lower_bound=_ClosedBound(1))
-
-        self._width = width
-        self._height = height
-        self._channel = channel
-
-    @staticmethod
-    def from_image(image: Image) -> ImageSize:
-        """
-        Create a `ImageSize` of a given image.
-
-        Parameters
-        ----------
-        image:
-            the given image for the `ImageSize`
-
-        Returns
-        -------
-        image_size:
-            the calculated `ImageSize`
-        """
-        return ImageSize(image.width, image.height, image.channel)
-
-    def __eq__(self, other: object) -> bool:
-        if not isinstance(other, ImageSize):
-            return NotImplemented
-        return (self is other) or (
-            self._width == other._width and self._height == other._height and self._channel == other._channel
-        )
-
-    def __hash__(self) -> int:
-        return _structural_hash(self._width, self._height, self._channel)
-
-    def __sizeof__(self) -> int:
-        return sys.getsizeof(self._width) + sys.getsizeof(self._height) + sys.getsizeof(self._channel)
-
     def __str__(self) -> str:
         return f"{self._width}x{self._height}x{self._channel} (WxHxC)"
-
-    @property
-    def width(self) -> int:
-        """
-        Get the width of this `ImageSize` in pixels.
-
-        Returns
-        -------
-        width:
-            The width of this `ImageSize`.
-        """
-        return self._width
-
-    @property
-    def height(self) -> int:
-        """
-        Get the height of this `ImageSize` in pixels.
-
-        Returns
-        -------
-        height:
-            The height of this `ImageSize`.
-        """
-        return self._height
-
-    @property
-    def channel(self) -> int:
-        """
-        Get the channel of this `ImageSize` in pixels.
-
-        Returns
-        -------
-        channel:
-            The channel of this `ImageSize`.
-        """
-        return self._channel
diff --git a/src/safeds/data/tabular/containers/_column.py b/src/safeds/data/tabular/containers/_column.py
index f047a8741..e94c1e2e0 100644
--- a/src/safeds/data/tabular/containers/_column.py
+++ b/src/safeds/data/tabular/containers/_column.py
@@ -219,7 +219,7 @@ def get_distinct_values(
         else:
             series = self._series
 
-        return series.unique().sort().to_list()
+        return series.unique(maintain_order=True).to_list()
 
     def get_value(self, index: int) -> T_co:
         """
diff --git a/src/safeds/exceptions/_ml.py b/src/safeds/exceptions/_ml.py
index d443216f7..fcb8460dd 100644
--- a/src/safeds/exceptions/_ml.py
+++ b/src/safeds/exceptions/_ml.py
@@ -1,4 +1,4 @@
-from safeds.data.image.typing import ImageSize
+from safeds.ml.nn.typing import ModelImageSize
 
 
 class DatasetMissesFeaturesError(ValueError):
@@ -76,7 +76,7 @@ def __init__(self) -> None:
 class InputSizeError(Exception):
     """Raised when the amount of features being passed to a network does not match with its input size."""
 
-    def __init__(self, data_size: int | ImageSize, input_layer_size: int | ImageSize) -> None:
+    def __init__(self, data_size: int | ModelImageSize, input_layer_size: int | ModelImageSize) -> None:
         super().__init__(
             f"The data size being passed to the network({data_size}) does not match with its input size({input_layer_size}). Consider changing the data size of the model or reformatting the data.",
         )
diff --git a/src/safeds/ml/nn/_model.py b/src/safeds/ml/nn/_model.py
index 3954606a3..b5941feb4 100644
--- a/src/safeds/ml/nn/_model.py
+++ b/src/safeds/ml/nn/_model.py
@@ -7,7 +7,9 @@
 from safeds._validation import _check_bounds, _ClosedBound
 from safeds.data.image.containers import ImageList
 from safeds.data.labeled.containers import ImageDataset, TabularDataset, TimeSeriesDataset
+from safeds.data.labeled.containers._image_dataset import _ColumnAsTensor
 from safeds.data.tabular.containers import Table
+from safeds.data.tabular.transformation import OneHotEncoder
 from safeds.exceptions import (
     FeatureDataMismatchError,
     InputSizeError,
@@ -27,17 +29,18 @@
     ForwardLayer,
 )
 from safeds.ml.nn.layers._pooling2d_layer import _Pooling2DLayer
+from safeds.ml.nn.typing import ConstantImageSize, ModelImageSize, VariableImageSize
 
 if TYPE_CHECKING:
     from collections.abc import Callable
 
     from torch import Tensor, nn
+    from torch.nn import Module
+    from transformers.image_processing_utils import BaseImageProcessor
 
-    from safeds.data.image.typing import ImageSize
     from safeds.ml.nn.converters import InputConversion, OutputConversion
     from safeds.ml.nn.layers import Layer
 
-
 IFT = TypeVar("IFT", TabularDataset, TimeSeriesDataset, ImageDataset)  # InputFitType
 IPT = TypeVar("IPT", Table, TimeSeriesDataset, ImageList)  # InputPredictType
 OT = TypeVar("OT", TabularDataset, TimeSeriesDataset, ImageDataset)  # OutputType
@@ -117,6 +120,61 @@ def __init__(
         self._total_number_of_batches_done = 0
         self._total_number_of_epochs_done = 0
 
+    @staticmethod
+    def load_pretrained_model(huggingface_repo: str) -> NeuralNetworkRegressor:  # pragma: no cover
+        """
+        Load a pretrained model from a [Huggingface repository](https://huggingface.co/models/).
+
+        Parameters
+        ----------
+        huggingface_repo:
+            the name of the huggingface repository
+
+        Returns
+        -------
+        pretrained_model:
+            the pretrained model as a NeuralNetworkRegressor
+        """
+        from transformers import (
+            AutoConfig,
+            AutoImageProcessor,
+            AutoModelForImageToImage,
+            PretrainedConfig,
+            Swin2SRForImageSuperResolution,
+            Swin2SRImageProcessor,
+        )
+
+        _init_default_device()
+
+        config: PretrainedConfig = AutoConfig.from_pretrained(huggingface_repo)
+
+        if config.model_type != "swin2sr":
+            raise ValueError("This model is not supported")
+
+        model: Swin2SRForImageSuperResolution = AutoModelForImageToImage.from_pretrained(huggingface_repo)
+
+        image_processor: Swin2SRImageProcessor = AutoImageProcessor.from_pretrained(huggingface_repo)
+
+        if hasattr(config, "num_channels"):
+            input_size = VariableImageSize(image_processor.pad_size, image_processor.pad_size, config.num_channels)
+        else:  # Should never happen due to model check
+            raise ValueError("This model is not supported")  # pragma: no cover
+
+        in_conversion = InputConversionImage(input_size)
+        out_conversion = OutputConversionImageToImage()
+
+        network = NeuralNetworkRegressor.__new__(NeuralNetworkRegressor)
+        network._input_conversion = in_conversion
+        network._model = model
+        network._output_conversion = out_conversion
+        network._input_size = input_size
+        network._batch_size = 1
+        network._is_fitted = True
+        network._total_number_of_epochs_done = 0
+        network._total_number_of_batches_done = 0
+
+        return network
+
     def fit(
         self,
         train_data: IFT,
@@ -243,6 +301,10 @@ def predict(self, test_data: IPT) -> OT:
         with torch.no_grad():
             for x in dataloader:
                 elem = self._model(x)
+                if not isinstance(elem, torch.Tensor) and hasattr(elem, "reconstruction"):
+                    elem = elem.reconstruction  # pragma: no cover
+                elif not isinstance(elem, torch.Tensor):
+                    raise ValueError(f"Output of model has unsupported type: {type(elem)}")  # pragma: no cover
                 predictions.append(elem.squeeze(dim=1))
         return self._output_conversion._data_conversion(
             test_data,
@@ -255,6 +317,11 @@ def is_fitted(self) -> bool:
         """Whether the model is fitted."""
         return self._is_fitted
 
+    @property
+    def input_size(self) -> int | ModelImageSize:
+        """The input size of the model."""
+        return self._input_size
+
 
 class NeuralNetworkClassifier(Generic[IFT, IPT, OT]):
     """
@@ -285,6 +352,13 @@ def __init__(
             raise InvalidModelStructureError("You need to provide at least one layer to a neural network.")
         if isinstance(output_conversion, OutputConversionImageToImage):
             raise InvalidModelStructureError("A NeuralNetworkClassifier cannot be used with images as output.")
+        if isinstance(input_conversion, InputConversionImage) and isinstance(
+            input_conversion._input_size,
+            VariableImageSize,
+        ):
+            raise InvalidModelStructureError(
+                "A NeuralNetworkClassifier cannot be used with a InputConversionImage that uses a VariableImageSize.",
+            )
         elif isinstance(input_conversion, InputConversionImage):
             if not isinstance(output_conversion, _OutputConversionImage):
                 raise InvalidModelStructureError(
@@ -324,7 +398,7 @@ def __init__(
         self._input_conversion: InputConversion[IFT, IPT] = input_conversion
         self._model = _create_internal_model(input_conversion, layers, is_for_classification=True)
         self._output_conversion: OutputConversion[IPT, OT] = output_conversion
-        self._input_size = self._model.input_size
+        self._input_size: int | ModelImageSize = self._model.input_size
         self._batch_size = 1
         self._is_fitted = False
         self._num_of_classes = (
@@ -333,6 +407,77 @@ def __init__(
         self._total_number_of_batches_done = 0
         self._total_number_of_epochs_done = 0
 
+    @staticmethod
+    def load_pretrained_model(huggingface_repo: str) -> NeuralNetworkClassifier:  # pragma: no cover
+        """
+        Load a pretrained model from a [Huggingface repository](https://huggingface.co/models/).
+
+        Parameters
+        ----------
+        huggingface_repo:
+            the name of the huggingface repository
+
+        Returns
+        -------
+        pretrained_model:
+            the pretrained model as a NeuralNetworkClassifier
+        """
+        from transformers import AutoConfig, AutoImageProcessor, AutoModelForImageClassification, PretrainedConfig
+        from transformers.models.auto.modeling_auto import MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES
+
+        _init_default_device()
+
+        config: PretrainedConfig = AutoConfig.from_pretrained(huggingface_repo)
+
+        if config.model_type not in MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES:
+            raise ValueError("This model is not supported")
+
+        model: Module = AutoModelForImageClassification.from_pretrained(huggingface_repo)
+
+        image_processor: BaseImageProcessor = AutoImageProcessor.from_pretrained(huggingface_repo)
+        if hasattr(image_processor, "size") and hasattr(config, "num_channels"):
+            if "shortest_edge" in image_processor.size:
+                input_size = ConstantImageSize(
+                    image_processor.size.get("shortest_edge"),
+                    image_processor.size.get("shortest_edge"),
+                    config.num_channels,
+                )
+            else:
+                input_size = ConstantImageSize(
+                    image_processor.size.get("width"),
+                    image_processor.size.get("height"),
+                    config.num_channels,
+                )
+        else:  # Should never happen due to model check
+            raise ValueError("This model is not supported")  # pragma: no cover
+
+        label_dict: dict[str, str] = config.id2label
+        column_name = "label"
+        labels_table = Table({column_name: [label for _, label in label_dict.items()]})
+        one_hot_encoder = OneHotEncoder().fit(labels_table, [column_name])
+
+        in_conversion = InputConversionImage(input_size)
+        out_conversion = OutputConversionImageToColumn()
+
+        in_conversion._column_name = column_name
+        in_conversion._one_hot_encoder = one_hot_encoder
+        in_conversion._input_size = input_size
+        in_conversion._output_type = _ColumnAsTensor
+        num_of_classes = labels_table.row_count
+
+        network = NeuralNetworkClassifier.__new__(NeuralNetworkClassifier)
+        network._input_conversion = in_conversion
+        network._model = model
+        network._output_conversion = out_conversion
+        network._input_size = input_size
+        network._batch_size = 1
+        network._is_fitted = True
+        network._num_of_classes = num_of_classes
+        network._total_number_of_epochs_done = 0
+        network._total_number_of_batches_done = 0
+
+        return network
+
     def fit(
         self,
         train_data: IFT,
@@ -466,6 +611,10 @@ def predict(self, test_data: IPT) -> OT:
         with torch.no_grad():
             for x in dataloader:
                 elem = self._model(x)
+                if not isinstance(elem, torch.Tensor) and hasattr(elem, "logits"):
+                    elem = elem.logits  # pragma: no cover
+                elif not isinstance(elem, torch.Tensor):
+                    raise ValueError(f"Output of model has unsupported type: {type(elem)}")  # pragma: no cover
                 if self._num_of_classes > 1:
                     predictions.append(torch.argmax(elem, dim=1))
                 else:
@@ -481,6 +630,11 @@ def is_fitted(self) -> bool:
         """Whether the model is fitted."""
         return self._is_fitted
 
+    @property
+    def input_size(self) -> int | ModelImageSize:
+        """The input size of the model."""
+        return self._input_size
+
 
 def _create_internal_model(
     input_conversion: InputConversion[IFT, IPT],
@@ -518,7 +672,7 @@ def __init__(self, layers: list[Layer], is_for_classification: bool) -> None:
             self._pytorch_layers = nn.Sequential(*internal_layers)
 
         @property
-        def input_size(self) -> int | ImageSize:
+        def input_size(self) -> int | ModelImageSize:
             return self._layer_list[0].input_size
 
         def forward(self, x: Tensor) -> Tensor:
diff --git a/src/safeds/ml/nn/converters/_input_converter.py b/src/safeds/ml/nn/converters/_input_converter.py
index 1ac505c2d..54d45b881 100644
--- a/src/safeds/ml/nn/converters/_input_converter.py
+++ b/src/safeds/ml/nn/converters/_input_converter.py
@@ -11,7 +11,7 @@
     from torch.utils.data import DataLoader
 
     from safeds.data.image.containers._single_size_image_list import _SingleSizeImageList
-    from safeds.data.image.typing import ImageSize
+    from safeds.ml.nn.typing import ModelImageSize
 
 FT = TypeVar("FT", TabularDataset, TimeSeriesDataset, ImageDataset)
 PT = TypeVar("PT", Table, TimeSeriesDataset, ImageList)
@@ -22,7 +22,7 @@ class InputConversion(Generic[FT, PT], ABC):
 
     @property
     @abstractmethod
-    def _data_size(self) -> int | ImageSize:
+    def _data_size(self) -> int | ModelImageSize:
         pass  # pragma: no cover
 
     @abstractmethod
diff --git a/src/safeds/ml/nn/converters/_input_converter_image.py b/src/safeds/ml/nn/converters/_input_converter_image.py
index 31a7c3534..bb94fde7c 100644
--- a/src/safeds/ml/nn/converters/_input_converter_image.py
+++ b/src/safeds/ml/nn/converters/_input_converter_image.py
@@ -12,14 +12,14 @@
 from ._input_converter import InputConversion
 
 if TYPE_CHECKING:
-    from safeds.data.image.typing import ImageSize
     from safeds.data.tabular.transformation import OneHotEncoder
+    from safeds.ml.nn.typing import ModelImageSize
 
 
 class InputConversionImage(InputConversion[ImageDataset, ImageList]):
     """The input conversion for a neural network, defines the input parameters for the neural network."""
 
-    def __init__(self, image_size: ImageSize) -> None:
+    def __init__(self, image_size: ModelImageSize) -> None:
         """
         Define the input parameters for the neural network in the input conversion.
 
@@ -29,14 +29,14 @@ def __init__(self, image_size: ImageSize) -> None:
             the size of the input images
         """
         self._input_size = image_size
-        self._output_size: ImageSize | int | None = None
+        self._output_size: ModelImageSize | int | None = None
         self._one_hot_encoder: OneHotEncoder | None = None
         self._column_name: str | None = None
         self._column_names: list[str] | None = None
         self._output_type: type | None = None
 
     @property
-    def _data_size(self) -> ImageSize:
+    def _data_size(self) -> ModelImageSize:
         return self._input_size
 
     def _data_conversion_fit(
diff --git a/src/safeds/ml/nn/layers/_convolutional2d_layer.py b/src/safeds/ml/nn/layers/_convolutional2d_layer.py
index 8c507c933..db03b22b6 100644
--- a/src/safeds/ml/nn/layers/_convolutional2d_layer.py
+++ b/src/safeds/ml/nn/layers/_convolutional2d_layer.py
@@ -6,13 +6,14 @@
 
 from safeds._config import _init_default_device
 from safeds._utils import _structural_hash
-from safeds.data.image.typing import ImageSize
 
 from ._layer import Layer
 
 if TYPE_CHECKING:
     from torch import Tensor, nn
 
+    from safeds.ml.nn.typing import ModelImageSize
+
 
 def _create_internal_model(
     input_size: int,
@@ -102,8 +103,8 @@ def __init__(self, output_channel: int, kernel_size: int, *, stride: int = 1, pa
         self._kernel_size = kernel_size
         self._stride = stride
         self._padding = padding
-        self._input_size: ImageSize | None = None
-        self._output_size: ImageSize | None = None
+        self._input_size: ModelImageSize | None = None
+        self._output_size: ModelImageSize | None = None
 
     def _get_internal_layer(self, **kwargs: Any) -> nn.Module:
         if self._input_size is None:
@@ -131,7 +132,7 @@ def _get_internal_layer(self, **kwargs: Any) -> nn.Module:
         )
 
     @property
-    def input_size(self) -> ImageSize:
+    def input_size(self) -> ModelImageSize:
         """
         Get the input_size of this layer.
 
@@ -150,7 +151,7 @@ def input_size(self) -> ImageSize:
         return self._input_size
 
     @property
-    def output_size(self) -> ImageSize:
+    def output_size(self) -> ModelImageSize:
         """
         Get the output_size of this layer.
 
@@ -175,10 +176,15 @@ def output_size(self) -> ImageSize:
             new_height = math.ceil(
                 (self._input_size.height + self._padding * 2 - self._kernel_size + 1) / (1.0 * self._stride),
             )
-            self._output_size = ImageSize(new_width, new_height, self._output_channel, _ignore_invalid_channel=True)
+            self._output_size = self._input_size.__class__(
+                new_width,
+                new_height,
+                self._output_channel,
+                _ignore_invalid_channel=True,
+            )
         return self._output_size
 
-    def _set_input_size(self, input_size: int | ImageSize) -> None:
+    def _set_input_size(self, input_size: int | ModelImageSize) -> None:
         if isinstance(input_size, int):
             raise TypeError("The input_size of a convolution layer has to be of type ImageSize.")
         self._input_size = input_size
@@ -303,7 +309,7 @@ def _get_internal_layer(self, **kwargs: Any) -> nn.Module:
         )
 
     @property
-    def output_size(self) -> ImageSize:
+    def output_size(self) -> ModelImageSize:
         if self._input_size is None:
             raise ValueError(
                 "The input_size is not yet set. The layer cannot compute the output_size if the input_size is not set.",
@@ -321,7 +327,12 @@ def output_size(self) -> ImageSize:
                 + self._kernel_size
                 + self._output_padding
             )
-            self._output_size = ImageSize(new_width, new_height, self._output_channel, _ignore_invalid_channel=True)
+            self._output_size = self._input_size.__class__(
+                new_width,
+                new_height,
+                self._output_channel,
+                _ignore_invalid_channel=True,
+            )
         return self._output_size
 
     def __hash__(self) -> int:
diff --git a/src/safeds/ml/nn/layers/_flatten_layer.py b/src/safeds/ml/nn/layers/_flatten_layer.py
index 17f72388f..4bbacc995 100644
--- a/src/safeds/ml/nn/layers/_flatten_layer.py
+++ b/src/safeds/ml/nn/layers/_flatten_layer.py
@@ -5,13 +5,14 @@
 
 from safeds._config import _init_default_device
 from safeds._utils import _structural_hash
+from safeds.ml.nn.typing import ConstantImageSize
 
 from ._layer import Layer
 
 if TYPE_CHECKING:
     from torch import Tensor, nn
 
-    from safeds.data.image.typing import ImageSize
+    from safeds.ml.nn.typing import ModelImageSize
 
 
 def _create_internal_model() -> nn.Module:
@@ -34,14 +35,14 @@ class FlattenLayer(Layer):
     """A flatten layer."""
 
     def __init__(self) -> None:
-        self._input_size: ImageSize | None = None
+        self._input_size: ModelImageSize | None = None
         self._output_size: int | None = None
 
     def _get_internal_layer(self, **kwargs: Any) -> nn.Module:  # noqa: ARG002
         return _create_internal_model()
 
     @property
-    def input_size(self) -> ImageSize:
+    def input_size(self) -> ModelImageSize:
         """
         Get the input_size of this layer.
 
@@ -82,9 +83,11 @@ def output_size(self) -> int:
             self._output_size = self._input_size.width * self._input_size.height * self._input_size.channel
         return self._output_size
 
-    def _set_input_size(self, input_size: int | ImageSize) -> None:
+    def _set_input_size(self, input_size: int | ModelImageSize) -> None:
         if isinstance(input_size, int):
             raise TypeError("The input_size of a flatten layer has to be of type ImageSize.")
+        if not isinstance(input_size, ConstantImageSize):
+            raise TypeError("The input_size of a flatten layer has to be a ConstantImageSize.")
         self._input_size = input_size
         self._output_size = None
 
diff --git a/src/safeds/ml/nn/layers/_forward_layer.py b/src/safeds/ml/nn/layers/_forward_layer.py
index 741668b69..7b50f85e3 100644
--- a/src/safeds/ml/nn/layers/_forward_layer.py
+++ b/src/safeds/ml/nn/layers/_forward_layer.py
@@ -5,7 +5,7 @@
 from safeds._config import _init_default_device
 from safeds._utils import _structural_hash
 from safeds._validation import _check_bounds, _ClosedBound
-from safeds.data.image.typing import ImageSize
+from safeds.ml.nn.typing import ModelImageSize
 
 from ._layer import Layer
 
@@ -72,8 +72,8 @@ def output_size(self) -> int:
         """
         return self._output_size
 
-    def _set_input_size(self, input_size: int | ImageSize) -> None:
-        if isinstance(input_size, ImageSize):
+    def _set_input_size(self, input_size: int | ModelImageSize) -> None:
+        if isinstance(input_size, ModelImageSize):
             raise TypeError("The input_size of a forward layer has to be of type int.")
 
         _check_bounds("input_size", input_size, lower_bound=_ClosedBound(1))
diff --git a/src/safeds/ml/nn/layers/_layer.py b/src/safeds/ml/nn/layers/_layer.py
index c1c305c21..058036688 100644
--- a/src/safeds/ml/nn/layers/_layer.py
+++ b/src/safeds/ml/nn/layers/_layer.py
@@ -6,7 +6,7 @@
 if TYPE_CHECKING:
     from torch import nn
 
-    from safeds.data.image.typing import ImageSize
+    from safeds.ml.nn.typing import ModelImageSize
 
 
 class Layer(ABC):
@@ -20,16 +20,16 @@ def _get_internal_layer(self, **kwargs: Any) -> nn.Module:
 
     @property
     @abstractmethod
-    def input_size(self) -> int | ImageSize:
+    def input_size(self) -> int | ModelImageSize:
         pass  # pragma: no cover
 
     @property
     @abstractmethod
-    def output_size(self) -> int | ImageSize:
+    def output_size(self) -> int | ModelImageSize:
         pass  # pragma: no cover
 
     @abstractmethod
-    def _set_input_size(self, input_size: int | ImageSize) -> None:
+    def _set_input_size(self, input_size: int | ModelImageSize) -> None:
         pass  # pragma: no cover
 
     @abstractmethod
diff --git a/src/safeds/ml/nn/layers/_lstm_layer.py b/src/safeds/ml/nn/layers/_lstm_layer.py
index 0e15149cb..0637db453 100644
--- a/src/safeds/ml/nn/layers/_lstm_layer.py
+++ b/src/safeds/ml/nn/layers/_lstm_layer.py
@@ -6,7 +6,7 @@
 from safeds._config import _init_default_device
 from safeds._utils import _structural_hash
 from safeds._validation import _check_bounds, _ClosedBound
-from safeds.data.image.typing import ImageSize
+from safeds.ml.nn.typing import ModelImageSize
 
 from ._layer import Layer
 
@@ -73,8 +73,8 @@ def output_size(self) -> int:
         """
         return self._output_size
 
-    def _set_input_size(self, input_size: int | ImageSize) -> None:
-        if isinstance(input_size, ImageSize):
+    def _set_input_size(self, input_size: int | ModelImageSize) -> None:
+        if isinstance(input_size, ModelImageSize):
             raise TypeError("The input_size of a forward layer has to be of type int.")
 
         _check_bounds("input_size", input_size, lower_bound=_ClosedBound(1))
diff --git a/src/safeds/ml/nn/layers/_pooling2d_layer.py b/src/safeds/ml/nn/layers/_pooling2d_layer.py
index 1e2170b14..3b49c1356 100644
--- a/src/safeds/ml/nn/layers/_pooling2d_layer.py
+++ b/src/safeds/ml/nn/layers/_pooling2d_layer.py
@@ -6,13 +6,14 @@
 
 from safeds._config import _init_default_device
 from safeds._utils import _structural_hash
-from safeds.data.image.typing import ImageSize
 
 from ._layer import Layer
 
 if TYPE_CHECKING:
     from torch import Tensor, nn
 
+    from safeds.ml.nn.typing import ModelImageSize
+
 
 class _Pooling2DLayer(Layer):
     """
@@ -35,14 +36,14 @@ def __init__(self, strategy: Literal["max", "avg"], kernel_size: int, *, stride:
         self._kernel_size = kernel_size
         self._stride = stride if stride != -1 else kernel_size
         self._padding = padding
-        self._input_size: ImageSize | None = None
-        self._output_size: ImageSize | None = None
+        self._input_size: ModelImageSize | None = None
+        self._output_size: ModelImageSize | None = None
 
     def _get_internal_layer(self, **kwargs: Any) -> nn.Module:  # noqa: ARG002
         return _create_internal_model(self._strategy, self._kernel_size, self._padding, self._stride)
 
     @property
-    def input_size(self) -> ImageSize:
+    def input_size(self) -> ModelImageSize:
         """
         Get the input_size of this layer.
 
@@ -61,7 +62,7 @@ def input_size(self) -> ImageSize:
         return self._input_size
 
     @property
-    def output_size(self) -> ImageSize:
+    def output_size(self) -> ModelImageSize:
         """
         Get the output_size of this layer.
 
@@ -86,10 +87,15 @@ def output_size(self) -> ImageSize:
             new_height = math.ceil(
                 (self.input_size.height + self._padding * 2 - self._kernel_size + 1) / (1.0 * self._stride),
             )
-            self._output_size = ImageSize(new_width, new_height, self._input_size.channel, _ignore_invalid_channel=True)
+            self._output_size = self._input_size.__class__(
+                new_width,
+                new_height,
+                self._input_size.channel,
+                _ignore_invalid_channel=True,
+            )
         return self._output_size
 
-    def _set_input_size(self, input_size: int | ImageSize) -> None:
+    def _set_input_size(self, input_size: int | ModelImageSize) -> None:
         if isinstance(input_size, int):
             raise TypeError("The input_size of a pooling layer has to be of type ImageSize.")
         self._input_size = input_size
diff --git a/src/safeds/ml/nn/typing/__init__.py b/src/safeds/ml/nn/typing/__init__.py
new file mode 100644
index 000000000..913ad6f65
--- /dev/null
+++ b/src/safeds/ml/nn/typing/__init__.py
@@ -0,0 +1,23 @@
+"""Types used to define neural networks and related attributes."""
+
+from typing import TYPE_CHECKING
+
+import apipkg
+
+if TYPE_CHECKING:
+    from ._model_image_size import ConstantImageSize, ModelImageSize, VariableImageSize
+
+apipkg.initpkg(
+    __name__,
+    {
+        "ConstantImageSize": "._model_image_size:ConstantImageSize",
+        "ModelImageSize": "._model_image_size:ModelImageSize",
+        "VariableImageSize": "._model_image_size:VariableImageSize",
+    },
+)
+
+__all__ = [
+    "ConstantImageSize",
+    "ModelImageSize",
+    "VariableImageSize",
+]
diff --git a/src/safeds/ml/nn/typing/_model_image_size.py b/src/safeds/ml/nn/typing/_model_image_size.py
new file mode 100644
index 000000000..b28ea5ed1
--- /dev/null
+++ b/src/safeds/ml/nn/typing/_model_image_size.py
@@ -0,0 +1,205 @@
+from __future__ import annotations
+
+import sys
+from abc import ABC, abstractmethod
+from typing import TYPE_CHECKING, Self
+
+from safeds._utils import _structural_hash
+from safeds._validation import _check_bounds, _ClosedBound
+
+if TYPE_CHECKING:
+    from safeds.data.image.containers import Image
+
+
+class ModelImageSize(ABC):
+    """
+    A container for image size in neural networks.
+
+    Parameters
+    ----------
+    width:
+        the width of the image
+    height:
+        the height of the image
+    channel:
+        the channel of the image
+
+    Raises
+    ------
+    OutOfBoundsError:
+        if width or height are below 1
+    ValueError
+        if an invalid channel is given
+    """
+
+    def __init__(self, width: int, height: int, channel: int, *, _ignore_invalid_channel: bool = False) -> None:
+        _check_bounds("width", width, lower_bound=_ClosedBound(1))
+        _check_bounds("height", height, lower_bound=_ClosedBound(1))
+        if not _ignore_invalid_channel and channel not in (1, 3, 4):
+            raise ValueError(f"Channel {channel} is not a valid channel option. Use either 1, 3 or 4")
+        _check_bounds("channel", channel, lower_bound=_ClosedBound(1))
+
+        self._width = width
+        self._height = height
+        self._channel = channel
+
+    @classmethod
+    def from_image(cls: type[Self], image: Image) -> Self:
+        """
+        Create a `ModelImageSize` of a given image.
+
+        Parameters
+        ----------
+        image:
+            the given image for the `ModelImageSize`
+
+        Returns
+        -------
+        image_size:
+            the calculated `ModelImageSize`
+        """
+        return cls(image.width, image.height, image.channel)
+
+    @classmethod
+    def from_image_size(cls: type[Self], image_size: ModelImageSize) -> Self:
+        """
+        Create a `ModelImageSize` of a given image size.
+
+        Parameters
+        ----------
+        image_size:
+            the given image size for the `ModelImageSize`
+
+        Returns
+        -------
+        image_size:
+            the new `ModelImageSize`
+        """
+        return cls(image_size.width, image_size.height, image_size.channel)
+
+    @abstractmethod
+    def __eq__(self, other: object) -> bool:
+        if not isinstance(other, ModelImageSize):
+            return NotImplemented
+        return (self is other) or (
+            self._width == other._width and self._height == other._height and self._channel == other._channel
+        )
+
+    @abstractmethod
+    def __hash__(self) -> int:
+        return _structural_hash(self.__class__.__name__, self._width, self._height, self._channel)
+
+    def __sizeof__(self) -> int:
+        return sys.getsizeof(self._width) + sys.getsizeof(self._height) + sys.getsizeof(self._channel)
+
+    def __str__(self) -> str:
+        return f"{self._width}x{self._height}x{self._channel} (WxHxC)"
+
+    @property
+    def width(self) -> int:
+        """
+        Get the width of this `ImageSize` in pixels.
+
+        Returns
+        -------
+        width:
+            The width of this `ImageSize`.
+        """
+        return self._width
+
+    @property
+    def height(self) -> int:
+        """
+        Get the height of this `ImageSize` in pixels.
+
+        Returns
+        -------
+        height:
+            The height of this `ImageSize`.
+        """
+        return self._height
+
+    @property
+    def channel(self) -> int:
+        """
+        Get the channel of this `ImageSize` in pixels.
+
+        Returns
+        -------
+        channel:
+            The channel of this `ImageSize`.
+        """
+        return self._channel
+
+
+class ConstantImageSize(ModelImageSize):
+    """
+    A container for constant image size in neural networks.
+
+    Parameters
+    ----------
+    width:
+        the width of the image
+    height:
+        the height of the image
+    channel:
+        the channel of the image
+
+    Raises
+    ------
+    OutOfBoundsError:
+        if width or height are below 1
+    ValueError
+        if an invalid channel is given
+    """
+
+    def __eq__(self, other: object) -> bool:
+        if isinstance(other, VariableImageSize):
+            return other.__eq__(self)
+        else:
+            return super().__eq__(other)
+
+    def __hash__(self) -> int:
+        return super().__hash__()
+
+    def __str__(self) -> str:
+        return f"ConstantImageSize | {super().__str__()}"
+
+
+class VariableImageSize(ModelImageSize):
+    """
+    A container for variable image size in neural networks.
+
+    With a `VariableImageSize`, all image sizes that are a multiple of `width` and `height` are valid.
+
+    Parameters
+    ----------
+    width:
+        the width of the image
+    height:
+        the height of the image
+    channel:
+        the channel of the image
+
+    Raises
+    ------
+    OutOfBoundsError:
+        if width or height are below 1
+    ValueError
+        if an invalid channel is given
+    """
+
+    def __eq__(self, other: object) -> bool:
+        if not isinstance(other, ModelImageSize):
+            return NotImplemented
+        return (self is other) or (
+            not (self._width % other._width and other._width % self._width)
+            and not (self._height % other._height and other._height % self._height)
+            and self._channel == other._channel
+        )
+
+    def __hash__(self) -> int:
+        return super().__hash__()
+
+    def __str__(self) -> str:
+        return f"VariableImageSize | {super().__str__()}"
diff --git a/tests/safeds/data/tabular/containers/_column/test_get_distinct_values.py b/tests/safeds/data/tabular/containers/_column/test_get_distinct_values.py
index 07bc9bdbd..a2d70cdb4 100644
--- a/tests/safeds/data/tabular/containers/_column/test_get_distinct_values.py
+++ b/tests/safeds/data/tabular/containers/_column/test_get_distinct_values.py
@@ -36,7 +36,7 @@ def test_should_get_unique_values_ignoring_missing_values(values: list[Any], exp
 @pytest.mark.parametrize(
     ("values", "expected"),
     [
-        ([1, None, None], [None, 1]),
+        ([1, None, None], [1, None]),
         ([None, None, None], [None]),
     ],
     ids=[
diff --git a/tests/safeds/ml/nn/layers/test_flatten_layer.py b/tests/safeds/ml/nn/layers/test_flatten_layer.py
index b034a7620..64db6127c 100644
--- a/tests/safeds/ml/nn/layers/test_flatten_layer.py
+++ b/tests/safeds/ml/nn/layers/test_flatten_layer.py
@@ -4,6 +4,7 @@
 from safeds.data.image.typing import ImageSize
 from safeds.data.tabular.containers import Table
 from safeds.ml.nn.layers import FlattenLayer
+from safeds.ml.nn.typing import VariableImageSize
 from torch import nn
 
 
@@ -31,6 +32,11 @@ def test_should_raise_if_input_size_is_set_with_int(self) -> None:
         with pytest.raises(TypeError, match=r"The input_size of a flatten layer has to be of type ImageSize."):
             layer._set_input_size(1)
 
+    def test_should_raise_if_input_size_is_set_with_variable_image_size(self) -> None:
+        layer = FlattenLayer()
+        with pytest.raises(TypeError, match=r"The input_size of a flatten layer has to be a ConstantImageSize."):
+            layer._set_input_size(VariableImageSize(1, 2, 3))
+
     class TestEq:
         def test_should_be_equal(self) -> None:
             assert FlattenLayer() == FlattenLayer()
diff --git a/tests/safeds/ml/nn/test_cnn_workflow.py b/tests/safeds/ml/nn/test_cnn_workflow.py
index f927cd88b..f42adad37 100644
--- a/tests/safeds/ml/nn/test_cnn_workflow.py
+++ b/tests/safeds/ml/nn/test_cnn_workflow.py
@@ -5,6 +5,7 @@
 import torch
 from safeds._config import _get_device
 from safeds.data.image.containers import ImageList
+from safeds.data.image.containers._single_size_image_list import _SingleSizeImageList
 from safeds.data.labeled.containers import ImageDataset
 from safeds.data.tabular.containers import Column, Table
 from safeds.data.tabular.transformation import OneHotEncoder
@@ -26,7 +27,7 @@
     ForwardLayer,
     MaxPooling2DLayer,
 )
-from syrupy import SnapshotAssertion
+from safeds.ml.nn.typing import VariableImageSize
 from torch.types import Device
 
 from tests.helpers import configure_test_with_device, device_cpu, device_cuda, images_all, resolve_resource_path
@@ -42,22 +43,22 @@ class TestImageToTableClassifier:
             (
                 1234,
                 device_cuda,
-                ["white_square"] * 7,
+                ["grayscale"] * 7,
             ),
             (
                 4711,
                 device_cuda,
-                ["rgba"] * 7,
+                ["white_square"] * 7,
             ),
             (
                 1234,
                 device_cpu,
-                ["white_square"] * 7,
+                ["grayscale"] * 7,
             ),
             (
                 4711,
                 device_cpu,
-                ["rgba"] * 7,
+                ["white_square"] * 7,
             ),
         ],
         ids=["seed-1234-cuda", "seed-4711-cuda", "seed-1234-cpu", "seed-4711-cpu"],
@@ -109,22 +110,22 @@ class TestImageToColumnClassifier:
             (
                 1234,
                 device_cuda,
-                ["white_square"] * 7,
+                ["grayscale"] * 7,
             ),
             (
                 4711,
                 device_cuda,
-                ["rgba"] * 7,
+                ["white_square"] * 7,
             ),
             (
                 1234,
                 device_cpu,
-                ["white_square"] * 7,
+                ["grayscale"] * 7,
             ),
             (
                 4711,
                 device_cpu,
-                ["rgba"] * 7,
+                ["white_square"] * 7,
             ),
         ],
         ids=["seed-1234-cuda", "seed-4711-cuda", "seed-1234-cpu", "seed-4711-cpu"],
@@ -182,7 +183,6 @@ class TestImageToImageRegressor:
     def test_should_train_and_predict_model(
         self,
         seed: int,
-        snapshot_png_image_list: SnapshotAssertion,
         device: Device,
     ) -> None:
         configure_test_with_device(device)
@@ -215,3 +215,61 @@ def test_should_train_and_predict_model(
         prediction = nn.predict(image_dataset.get_input())
         assert isinstance(prediction.get_output(), ImageList)
         assert prediction._output._tensor.device == _get_device()
+
+    @pytest.mark.parametrize(
+        ("seed", "device"),
+        [
+            (4711, device_cuda),
+            (4711, device_cpu),
+        ],
+        ids=["seed-4711-cuda", "seed-4711-cpu"],
+    )
+    @pytest.mark.parametrize("multi_width", [1, 2, 3])
+    @pytest.mark.parametrize("multi_height", [1, 2, 3])
+    def test_should_train_and_predict_model_variable_image_size(
+        self,
+        seed: int,
+        device: Device,
+        multi_width: int,
+        multi_height: int,
+    ) -> None:
+        configure_test_with_device(device)
+        torch.manual_seed(seed)
+
+        image_list = ImageList.from_files(resolve_resource_path(images_all()))
+        image_list = image_list.resize(20, 20)
+        image_list_grayscale = image_list.convert_to_grayscale()
+        image_dataset = ImageDataset(image_list, image_list_grayscale)
+
+        layers: list[Layer] = [
+            Convolutional2DLayer(6, 2),
+            Convolutional2DLayer(12, 2),
+            ConvolutionalTranspose2DLayer(6, 2),
+            ConvolutionalTranspose2DLayer(4, 2),
+        ]
+        nn_original = NeuralNetworkRegressor(
+            InputConversionImage(VariableImageSize.from_image_size(image_dataset.input_size)),
+            layers,
+            OutputConversionImageToImage(),
+        )
+        nn = nn_original.fit(image_dataset, epoch_size=20)
+        assert str(nn_original._model.state_dict().values()) != str(nn._model.state_dict().values())
+        assert not torch.all(
+            torch.eq(
+                nn_original._model.state_dict()["_pytorch_layers.3._layer.bias"],
+                nn._model.state_dict()["_pytorch_layers.3._layer.bias"],
+            ),
+        ).item()
+        prediction = nn.predict(
+            image_dataset.get_input().resize(
+                image_dataset.input_size.width * multi_width,
+                image_dataset.input_size.height * multi_height,
+            ),
+        )
+        pred_output = prediction.get_output()
+        assert isinstance(pred_output, ImageList)
+        if isinstance(pred_output, _SingleSizeImageList):
+            assert pred_output.widths[0] == image_dataset.input_size.width * multi_width
+            assert pred_output.heights[0] == image_dataset.input_size.height * multi_height
+        assert prediction.input_size.height == image_dataset.input_size.height * multi_height
+        assert prediction._output._tensor.device == _get_device()
diff --git a/tests/safeds/ml/nn/test_model.py b/tests/safeds/ml/nn/test_model.py
index c83172c09..394e34dad 100644
--- a/tests/safeds/ml/nn/test_model.py
+++ b/tests/safeds/ml/nn/test_model.py
@@ -33,6 +33,7 @@
     LSTMLayer,
     MaxPooling2DLayer,
 )
+from safeds.ml.nn.typing import VariableImageSize
 from torch.types import Device
 
 from tests.helpers import configure_test_with_device, get_devices, get_devices_ids
@@ -40,6 +41,24 @@
 
 @pytest.mark.parametrize("device", get_devices(), ids=get_devices_ids())
 class TestClassificationModel:
+
+    @pytest.mark.parametrize(
+        "input_size",
+        [
+            1,
+        ],
+    )
+    def test_should_return_input_size(self, input_size: int, device: Device) -> None:
+        configure_test_with_device(device)
+        assert (
+            NeuralNetworkClassifier(
+                InputConversionTable(),
+                [ForwardLayer(1, input_size)],
+                OutputConversionTable(),
+            ).input_size
+            == input_size
+        )
+
     @pytest.mark.parametrize(
         "epoch_size",
         [
@@ -225,13 +244,13 @@ def test_should_raise_if_train_features_mismatch(self, device: Device) -> None:
             [ForwardLayer(input_size=1, output_size=1), ForwardLayer(output_size=1)],
             OutputConversionTable(),
         )
+        learned_model = model.fit(
+            Table.from_dict({"a": [0.1, 0, 0.2], "b": [0, 0.15, 0.5]}).to_tabular_dataset("b"),
+        )
         with pytest.raises(
             FeatureDataMismatchError,
             match="The features in the given table do not match with the specified feature columns names of the neural network.",
         ):
-            learned_model = model.fit(
-                Table.from_dict({"a": [0.1, 0, 0.2], "b": [0, 0.15, 0.5]}).to_tabular_dataset("b"),
-            )
             learned_model.fit(Table.from_dict({"k": [0.1, 0, 0.2], "l": [0, 0.15, 0.5]}).to_tabular_dataset("k"))
 
     def test_should_raise_if_table_size_and_input_size_mismatch(self, device: Device) -> None:
@@ -483,6 +502,12 @@ def callback_was_called(self) -> bool:
                 OutputConversionImageToColumn(),
                 r"You need to provide at least one layer to a neural network.",
             ),
+            (
+                InputConversionImage(VariableImageSize(1, 1, 1)),
+                [FlattenLayer()],
+                OutputConversionImageToColumn(),
+                r"A NeuralNetworkClassifier cannot be used with a InputConversionImage that uses a VariableImageSize.",
+            ),
         ],
     )
     def test_should_raise_if_model_has_invalid_structure(
@@ -500,6 +525,24 @@ def test_should_raise_if_model_has_invalid_structure(
 
 @pytest.mark.parametrize("device", get_devices(), ids=get_devices_ids())
 class TestRegressionModel:
+
+    @pytest.mark.parametrize(
+        "input_size",
+        [
+            1,
+        ],
+    )
+    def test_should_return_input_size(self, input_size: int, device: Device) -> None:
+        configure_test_with_device(device)
+        assert (
+            NeuralNetworkRegressor(
+                InputConversionTable(),
+                [ForwardLayer(1, input_size)],
+                OutputConversionTable(),
+            ).input_size
+            == input_size
+        )
+
     @pytest.mark.parametrize(
         "epoch_size",
         [
@@ -628,13 +671,13 @@ def test_should_raise_if_train_features_mismatch(self, device: Device) -> None:
             [ForwardLayer(input_size=1, output_size=1)],
             OutputConversionTable(),
         )
+        trained_model = model.fit(
+            Table.from_dict({"a": [1, 0, 2], "b": [0, 15, 5]}).to_tabular_dataset("b"),
+        )
         with pytest.raises(
             FeatureDataMismatchError,
             match="The features in the given table do not match with the specified feature columns names of the neural network.",
         ):
-            trained_model = model.fit(
-                Table.from_dict({"a": [1, 0, 2], "b": [0, 15, 5]}).to_tabular_dataset("b"),
-            )
             trained_model.fit(
                 Table.from_dict({"k": [1, 0, 2], "l": [0, 15, 5]}).to_tabular_dataset("l"),
             )
diff --git a/tests/safeds/ml/nn/typing/__init__.py b/tests/safeds/ml/nn/typing/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/tests/safeds/ml/nn/typing/test_model_image_size.py b/tests/safeds/ml/nn/typing/test_model_image_size.py
new file mode 100644
index 000000000..eb3a6b0ea
--- /dev/null
+++ b/tests/safeds/ml/nn/typing/test_model_image_size.py
@@ -0,0 +1,259 @@
+import sys
+from typing import Any
+
+import pytest
+from safeds.data.image.containers import Image
+from safeds.exceptions import OutOfBoundsError
+from safeds.ml.nn.typing import ConstantImageSize, ModelImageSize, VariableImageSize
+from torch.types import Device
+
+from tests.helpers import (
+    configure_test_with_device,
+    get_devices,
+    get_devices_ids,
+    images_all,
+    images_all_ids,
+    plane_png_path,
+    resolve_resource_path,
+)
+
+
+class TestFromImage:
+    @pytest.mark.parametrize("device", get_devices(), ids=get_devices_ids())
+    @pytest.mark.parametrize("resource_path", images_all(), ids=images_all_ids())
+    @pytest.mark.parametrize(
+        "image_size_class",
+        [
+            ConstantImageSize,
+            VariableImageSize,
+        ],
+        ids=["ConstantImageSize", "VariableImageSize"],
+    )
+    def test_should_create(self, resource_path: str, device: Device, image_size_class: type[ModelImageSize]) -> None:
+        configure_test_with_device(device)
+        image = Image.from_file(resolve_resource_path(resource_path))
+        expected_image_size = image_size_class(image.width, image.height, image.channel)
+        assert image_size_class.from_image(image) == expected_image_size
+
+
+class TestFromImageSize:
+
+    def test_should_create(self) -> None:
+        constant_image_size = ConstantImageSize(1, 2, 3)
+        variable_image_size = VariableImageSize(1, 2, 3)
+        assert isinstance(VariableImageSize.from_image_size(constant_image_size), VariableImageSize)
+        assert isinstance(ConstantImageSize.from_image_size(variable_image_size), ConstantImageSize)
+
+
+class TestEq:
+    @pytest.mark.parametrize(
+        ("image_size", "width", "height", "channel"),
+        [
+            (ConstantImageSize(1, 2, 3), 1, 2, 3),
+            (VariableImageSize(1, 2, 3), 1, 2, 3),
+        ],
+    )
+    @pytest.mark.parametrize(
+        "image_size_class",
+        [
+            ConstantImageSize,
+            VariableImageSize,
+        ],
+        ids=["ConstantImageSize", "VariableImageSize"],
+    )
+    def test_should_be_equal(
+        self,
+        image_size: ModelImageSize,
+        width: int,
+        height: int,
+        channel: int,
+        image_size_class: type[ModelImageSize],
+    ) -> None:
+        assert image_size == image_size_class(width, height, channel)
+
+    @pytest.mark.parametrize(
+        ("image_size", "width", "height", "channel"),
+        [
+            (ConstantImageSize(1, 2, 3), 3, 2, 1),
+            (VariableImageSize(1, 2, 3), 3, 2, 1),
+        ],
+    )
+    @pytest.mark.parametrize(
+        "image_size_class",
+        [
+            ConstantImageSize,
+            VariableImageSize,
+        ],
+        ids=["ConstantImageSize", "VariableImageSize"],
+    )
+    def test_should_not_be_equal(
+        self,
+        image_size: ModelImageSize,
+        width: int,
+        height: int,
+        channel: int,
+        image_size_class: type[ModelImageSize],
+    ) -> None:
+        assert image_size != image_size_class(width, height, channel)
+
+    @pytest.mark.parametrize(
+        ("image_size", "other"),
+        [
+            (ConstantImageSize(1, 2, 3), None),
+            (ConstantImageSize(1, 2, 3), Image.from_file(resolve_resource_path(plane_png_path))),
+            (VariableImageSize(1, 2, 3), None),
+            (VariableImageSize(1, 2, 3), Image.from_file(resolve_resource_path(plane_png_path))),
+        ],
+        ids=["ConstantImageSize-None", "ConstantImageSize-Image", "VariableImageSize-None", "VariableImageSize-Image"],
+    )
+    def test_should_be_not_implemented(self, image_size: ModelImageSize, other: Any) -> None:
+        assert image_size.__eq__(other) is NotImplemented
+
+
+class TestHash:
+    @pytest.mark.parametrize(
+        "resource_path",
+        images_all(),
+        ids=images_all_ids(),
+    )
+    @pytest.mark.parametrize(
+        "image_size_class",
+        [
+            ConstantImageSize,
+            VariableImageSize,
+        ],
+        ids=["ConstantImageSize", "VariableImageSize"],
+    )
+    def test_hash_should_be_equal(self, resource_path: str, image_size_class: type[ModelImageSize]) -> None:
+        image = Image.from_file(resolve_resource_path(resource_path))
+        image2 = Image.from_file(resolve_resource_path(resource_path))
+        assert hash(image_size_class.from_image(image)) == hash(image_size_class.from_image(image2))
+
+    @pytest.mark.parametrize(
+        "image_size_class1",
+        [
+            ConstantImageSize,
+            VariableImageSize,
+        ],
+        ids=["ConstantImageSize", "VariableImageSize"],
+    )
+    @pytest.mark.parametrize(
+        "image_size_class2",
+        [
+            ConstantImageSize,
+            VariableImageSize,
+        ],
+        ids=["ConstantImageSize", "VariableImageSize"],
+    )
+    def test_hash_should_not_be_equal(
+        self,
+        image_size_class1: type[ModelImageSize],
+        image_size_class2: type[ModelImageSize],
+    ) -> None:
+        assert hash(image_size_class1(1, 2, 3)) != hash(image_size_class2(3, 2, 1))
+
+    def test_hash_should_not_be_equal_different_model_image_sizes(self) -> None:
+        assert hash(ConstantImageSize(1, 2, 3)) != hash(VariableImageSize(1, 2, 3))
+
+
+class TestSizeOf:
+
+    @pytest.mark.parametrize(
+        "image_size_class",
+        [
+            ConstantImageSize,
+            VariableImageSize,
+        ],
+        ids=["ConstantImageSize", "VariableImageSize"],
+    )
+    def test_should_size_be_greater_than_normal_object(self, image_size_class: type[ModelImageSize]) -> None:
+        assert sys.getsizeof(image_size_class(1, 2, 3)) >= sys.getsizeof(0) * 3
+
+
+class TestStr:
+
+    @pytest.mark.parametrize(
+        "image_size_class",
+        [
+            ConstantImageSize,
+            VariableImageSize,
+        ],
+        ids=["ConstantImageSize", "VariableImageSize"],
+    )
+    def test_string(self, image_size_class: type[ModelImageSize]) -> None:
+        image_size = image_size_class(1, 2, 3)
+        assert (
+            str(image_size)
+            == f"{image_size_class.__name__} | {image_size.width}x{image_size.height}x{image_size.channel} (WxHxC)"
+        )
+
+
+class TestProperties:
+    @pytest.mark.parametrize("width", list(range(1, 5)))
+    @pytest.mark.parametrize("height", list(range(1, 5)))
+    @pytest.mark.parametrize("channel", [1, 3, 4])
+    @pytest.mark.parametrize(
+        "image_size_class",
+        [
+            ConstantImageSize,
+            VariableImageSize,
+        ],
+        ids=["ConstantImageSize", "VariableImageSize"],
+    )
+    def test_width_height_channel(
+        self,
+        width: int,
+        height: int,
+        channel: int,
+        image_size_class: type[ModelImageSize],
+    ) -> None:
+        image_size = image_size_class(width, height, channel)
+        assert image_size.width == width
+        assert image_size.height == height
+        assert image_size.channel == channel
+
+    @pytest.mark.parametrize("channel", [2, 5, 6])
+    @pytest.mark.parametrize(
+        "image_size_class",
+        [
+            ConstantImageSize,
+            VariableImageSize,
+        ],
+        ids=["ConstantImageSize", "VariableImageSize"],
+    )
+    def test_should_ignore_invalid_channel(self, channel: int, image_size_class: type[ModelImageSize]) -> None:
+        assert image_size_class(1, 1, channel, _ignore_invalid_channel=True).channel == channel
+
+
+@pytest.mark.parametrize(
+    "image_size_class",
+    [
+        ConstantImageSize,
+        VariableImageSize,
+    ],
+    ids=["ConstantImageSize", "VariableImageSize"],
+)
+class TestErrors:
+    @pytest.mark.parametrize("width", [-1, 0])
+    def test_should_raise_invalid_width(self, width: int, image_size_class: type[ModelImageSize]) -> None:
+        with pytest.raises(OutOfBoundsError):
+            image_size_class(width, 1, 1)
+
+    @pytest.mark.parametrize("height", [-1, 0])
+    def test_should_raise_invalid_height(self, height: int, image_size_class: type[ModelImageSize]) -> None:
+        with pytest.raises(OutOfBoundsError):
+            image_size_class(1, height, 1)
+
+    @pytest.mark.parametrize("channel", [-1, 0, 2, 5])
+    def test_should_raise_invalid_channel(self, channel: int, image_size_class: type[ModelImageSize]) -> None:
+        with pytest.raises(ValueError, match=rf"Channel {channel} is not a valid channel option. Use either 1, 3 or 4"):
+            image_size_class(1, 1, channel)
+
+    @pytest.mark.parametrize("channel", [-1, 0])
+    def test_should_raise_negative_channel_ignore_invalid_channel(
+        self,
+        channel: int,
+        image_size_class: type[ModelImageSize],
+    ) -> None:
+        with pytest.raises(OutOfBoundsError):
+            image_size_class(1, 1, channel, _ignore_invalid_channel=True)