--- title: Structural Similarity Index Measure (SSIM) description: Compute Structural Similarity Index Measure (SSIM). canonical: 'https://lightning.ai/docs/torchmetrics/stable/image/structural_similarity' --- # Structural Similarity Index Measure (SSIM) {#structural-similarity-index-measure-ssim} ## Module Interface {#module-interface} ### `class torchmetrics.image.StructuralSimilarityIndexMeasure(gaussian_kernel=True, sigma=1.5, kernel_size=11, reduction='elementwise_mean', data_range=None, k1=0.01, k2=0.03, return_full_image=False, return_contrast_sensitivity=False, **kwargs)` {#torchmetrics.image.StructuralSimilarityIndexMeasure} [\[source\]](https://github.com/Lightning-AI/torchmetrics/blob/v1.9.0/src/torchmetrics/image/ssim.py#L31-L239) Compute Structural Similarity Index Measure ([SSIM](https://en.wikipedia.org/wiki/Structural_similarity)). As input to `forward` and `update` the metric accepts the following input * `preds` ([`Tensor`](https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor "(in PyTorch v2.15)")): Predictions from model * `target` ([`Tensor`](https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor "(in PyTorch v2.15)")): Ground truth values As output of forward and compute the metric returns the following output * `ssim` ([`Tensor`](https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor "(in PyTorch v2.15)")): if `reduction!='none'` returns float scalar tensor with average SSIM value over sample else returns tensor of shape `(N,)` with SSIM values per sample Parameters: * **preds** – estimated image * **target** – ground truth image * **gaussian\_kernel** ([`bool`](https://docs.python.org/3/builtins/functions.html#bool "(in Python v3.15)")) – If `True` (default), a gaussian kernel is used, if `False` a uniform kernel is used * **sigma** (`Union`\[[`float`](https://docs.python.org/3/builtins/functions.html#float "(in Python v3.15)"), [`Sequence`](https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence "(in Python v3.15)")\[[`float`](https://docs.python.org/3/builtins/functions.html#float "(in Python v3.15)")\]\]) – Standard deviation of the gaussian kernel, anisotropic kernels are possible. Ignored if a uniform kernel is used * **kernel\_size** (`Union`\[[`int`](https://docs.python.org/3/builtins/functions.html#int "(in Python v3.15)"), [`Sequence`](https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence "(in Python v3.15)")\[[`int`](https://docs.python.org/3/builtins/functions.html#int "(in Python v3.15)")\]\]) – the size of the uniform kernel, anisotropic kernels are possible. Ignored if a Gaussian kernel is used * **reduction** ([`Literal`](https://docs.python.org/3/library/typing.html#typing.Literal "(in Python v3.15)")\[`'elementwise_mean'`, `'sum'`, `'none'`, `None`\]) – a method to reduce metric score over individual batch scores * `'elementwise_mean'`: takes the mean * `'sum'`: takes the sum * `'none'` or `None`: no reduction will be applied * **data\_range** (`Union`\[[`float`](https://docs.python.org/3/builtins/functions.html#float "(in Python v3.15)"), [`tuple`](https://docs.python.org/3/builtins/stdtypes.html#tuple "(in Python v3.15)")\[[`float`](https://docs.python.org/3/builtins/functions.html#float "(in Python v3.15)"), [`float`](https://docs.python.org/3/builtins/functions.html#float "(in Python v3.15)")\], [`None`](https://docs.python.org/3/builtins/constants.html#None "(in Python v3.15)")\]) – the range of the data. If None, it is determined from the data (max - min). If a tuple is provided then the range is calculated as the difference and input is clamped between the values. * **k1** ([`float`](https://docs.python.org/3/builtins/functions.html#float "(in Python v3.15)")) – Parameter of SSIM. * **k2** ([`float`](https://docs.python.org/3/builtins/functions.html#float "(in Python v3.15)")) – Parameter of SSIM. * **return\_full\_image** ([`bool`](https://docs.python.org/3/builtins/functions.html#bool "(in Python v3.15)")) – If true, the full `ssim` image is returned as a second argument. Mutually exclusive with `return_contrast_sensitivity` * **return\_contrast\_sensitivity** ([`bool`](https://docs.python.org/3/builtins/functions.html#bool "(in Python v3.15)")) – If true, the constant term is returned as a second argument. The luminance term can be obtained with luminance=ssim/contrast Mutually exclusive with `return_full_image` * **kwargs** ([`Any`](https://docs.python.org/3/library/typing.html#typing.Any "(in Python v3.15)")) – Additional keyword arguments, see [Advanced metric settings](/docs/torchmetrics/1.9.0/user-guide/overview.md#metric-kwargs) for more info. Example ```python >>> import torch >>> from torchmetrics.image import StructuralSimilarityIndexMeasure >>> preds = torch.rand([3, 3, 256, 256]) >>> target = preds * 0.75 >>> ssim = StructuralSimilarityIndexMeasure(data_range=1.0) >>> ssim(preds, target) tensor(0.9219) ``` #### `plot(val=None, ax=None)` {#torchmetrics.image.StructuralSimilarityIndexMeasure.plot} [\[source\]](https://github.com/Lightning-AI/torchmetrics/blob/v1.9.0/src/torchmetrics/image/ssim.py#L195-L239) Plot a single or multiple values from the metric. Parameters: * **val** (`Union`\[[`Tensor`](https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor "(in PyTorch v2.15)"), [`Sequence`](https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence "(in Python v3.15)")\[[`Tensor`](https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor "(in PyTorch v2.15)")\], [`None`](https://docs.python.org/3/builtins/constants.html#None "(in Python v3.15)")\]) – Either a single result from calling metric.forward or metric.compute or a list of these results. If no value is provided, will automatically call metric.compute and plot that result. * **ax** ([`Optional`](https://docs.python.org/3/library/typing.html#typing.Optional "(in Python v3.15)")\[[`Axes`](https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes "(in Matplotlib v3.11.2)")\]) – An matplotlib axis object. If provided will add plot to that axis Return type: [`tuple`](https://docs.python.org/3/builtins/stdtypes.html#tuple "(in Python v3.15)")\[[`Figure`](https://matplotlib.org/stable/api/_as_gen/matplotlib.figure.Figure.html#matplotlib.figure.Figure "(in Matplotlib v3.11.2)"), `Union`\[[`Axes`](https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.html#matplotlib.axes.Axes "(in Matplotlib v3.11.2)"), [`ndarray`](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray "(in NumPy v2.5)")\]\] Returns: Figure and Axes object Raises: [**ModuleNotFoundError**](https://docs.python.org/3/builtins/exceptions.html#ModuleNotFoundError "(in Python v3.15)") – If matplotlib is not installed ```python >>> # Example plotting a single value >>> import torch >>> from torchmetrics.image import StructuralSimilarityIndexMeasure >>> preds = torch.rand([3, 3, 256, 256]) >>> target = preds * 0.75 >>> metric = StructuralSimilarityIndexMeasure(data_range=1.0) >>> metric.update(preds, target) >>> fig_, ax_ = metric.plot() ``` [![../../\_images/structural\_similarity-1.png](/docs/torchmetrics/1.9.0/static/_images/structural_similarity-1.png)](/docs/torchmetrics/1.9.0/static/_images/structural_similarity-1.png) ```python >>> # Example plotting multiple values >>> import torch >>> from torchmetrics.image import StructuralSimilarityIndexMeasure >>> preds = torch.rand([3, 3, 256, 256]) >>> target = preds * 0.75 >>> metric = StructuralSimilarityIndexMeasure(data_range=1.0) >>> values = [ ] >>> for _ in range(10): ... values.append(metric(preds, target)) >>> fig_, ax_ = metric.plot(values) ``` [![../../\_images/structural\_similarity-2.png](/docs/torchmetrics/1.9.0/static/_images/structural_similarity-2.png)](/docs/torchmetrics/1.9.0/static/_images/structural_similarity-2.png) ## Functional Interface {#functional-interface} ### `torchmetrics.functional.image.structural_similarity_index_measure(preds, target, gaussian_kernel=True, sigma=1.5, kernel_size=11, reduction='elementwise_mean', data_range=None, k1=0.01, k2=0.03, return_full_image=False, return_contrast_sensitivity=False)` {#torchmetrics.functional.image.structural_similarity_index_measure} [\[source\]](https://github.com/Lightning-AI/torchmetrics/blob/v1.9.0/src/torchmetrics/functional/image/ssim.py#L210-L292) Compute Structural Similarity Index Measure. Parameters: * **preds** ([`Tensor`](https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor "(in PyTorch v2.15)")) – estimated image * **target** ([`Tensor`](https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor "(in PyTorch v2.15)")) – ground truth image * **gaussian\_kernel** ([`bool`](https://docs.python.org/3/builtins/functions.html#bool "(in Python v3.15)")) – If true (default), a gaussian kernel is used, if false a uniform kernel is used * **sigma** (`Union`\[[`float`](https://docs.python.org/3/builtins/functions.html#float "(in Python v3.15)"), [`Sequence`](https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence "(in Python v3.15)")\[[`float`](https://docs.python.org/3/builtins/functions.html#float "(in Python v3.15)")\]\]) – Standard deviation of the gaussian kernel, anisotropic kernels are possible. Ignored if a uniform kernel is used * **kernel\_size** (`Union`\[[`int`](https://docs.python.org/3/builtins/functions.html#int "(in Python v3.15)"), [`Sequence`](https://docs.python.org/3/library/collections.abc.html#collections.abc.Sequence "(in Python v3.15)")\[[`int`](https://docs.python.org/3/builtins/functions.html#int "(in Python v3.15)")\]\]) – the size of the uniform kernel, anisotropic kernels are possible. Ignored if a Gaussian kernel is used * **reduction** ([`Literal`](https://docs.python.org/3/library/typing.html#typing.Literal "(in Python v3.15)")\[`'elementwise_mean'`, `'sum'`, `'none'`, `None`\]) – a method to reduce metric score over labels. * `'elementwise_mean'`: takes the mean * `'sum'`: takes the sum * `'none'` or `None`: no reduction will be applied * **data\_range** (`Union`\[[`float`](https://docs.python.org/3/builtins/functions.html#float "(in Python v3.15)"), [`tuple`](https://docs.python.org/3/builtins/stdtypes.html#tuple "(in Python v3.15)")\[[`float`](https://docs.python.org/3/builtins/functions.html#float "(in Python v3.15)"), [`float`](https://docs.python.org/3/builtins/functions.html#float "(in Python v3.15)")\], [`None`](https://docs.python.org/3/builtins/constants.html#None "(in Python v3.15)")\]) – the range of the data. If None, it is determined from the data (max - min). If a tuple is provided then the range is calculated as the difference and input is clamped between the values. * **k1** ([`float`](https://docs.python.org/3/builtins/functions.html#float "(in Python v3.15)")) – Parameter of SSIM. * **k2** ([`float`](https://docs.python.org/3/builtins/functions.html#float "(in Python v3.15)")) – Parameter of SSIM. * **return\_full\_image** ([`bool`](https://docs.python.org/3/builtins/functions.html#bool "(in Python v3.15)")) – If true, the full `ssim` image is returned as a second argument. Mutually exclusive with `return_contrast_sensitivity` * **return\_contrast\_sensitivity** ([`bool`](https://docs.python.org/3/builtins/functions.html#bool "(in Python v3.15)")) – If true, the constant term is returned as a second argument. The luminance term can be obtained with luminance=ssim/contrast Mutually exclusive with `return_full_image` Return type: `Union`\[[`Tensor`](https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor "(in PyTorch v2.15)"), [`tuple`](https://docs.python.org/3/builtins/stdtypes.html#tuple "(in Python v3.15)")\[[`Tensor`](https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor "(in PyTorch v2.15)"), [`Tensor`](https://docs.pytorch.org/docs/stable/tensors.html#torch.Tensor "(in PyTorch v2.15)")\]\] Returns: Tensor with SSIM score Raises: * [**TypeError**](https://docs.python.org/3/builtins/exceptions.html#TypeError "(in Python v3.15)") – If `preds` and `target` don’t have the same data type. * [**ValueError**](https://docs.python.org/3/builtins/exceptions.html#ValueError "(in Python v3.15)") – If `preds` and `target` don’t have `BxCxHxW shape`. * [**ValueError**](https://docs.python.org/3/builtins/exceptions.html#ValueError "(in Python v3.15)") – If the length of `kernel_size` or `sigma` is not `2`. * [**ValueError**](https://docs.python.org/3/builtins/exceptions.html#ValueError "(in Python v3.15)") – If one of the elements of `kernel_size` is not an `odd positive number`. * [**ValueError**](https://docs.python.org/3/builtins/exceptions.html#ValueError "(in Python v3.15)") – If one of the elements of `sigma` is not a `positive number`. Example ```python >>> from torchmetrics.functional.image import structural_similarity_index_measure >>> preds = torch.rand([3, 3, 256, 256]) >>> target = preds * 0.75 >>> structural_similarity_index_measure(preds, target) tensor(0.9219) ``` --- **Navigation** - Parent: [Image](/docs/torchmetrics/1.9.0/image.md)