diff --git a/src/torchmetrics/image/inception.py b/src/torchmetrics/image/inception.py index 80444c911ee..4913a5fe50b 100644 --- a/src/torchmetrics/image/inception.py +++ b/src/torchmetrics/image/inception.py @@ -38,7 +38,7 @@ class InceptionScore(Metric): IS = exp(\mathbb{E}_x KL(p(y | x ) || p(y))) where :math:`KL(p(y | x) || p(y))` is the KL divergence between the conditional distribution :math:`p(y|x)` - and the margianl distribution :math:`p(y)`. Both the conditional and marginal distribution is calculated + and the marginal distribution :math:`p(y)`. Both the conditional and marginal distribution is calculated from features extracted from the images. The score is calculated on random splits of the images such that both a mean and standard deviation of the score are returned. The metric was originally proposed in `inception ref1`_. @@ -59,7 +59,9 @@ class InceptionScore(Metric): As output of `forward` and `compute` the metric returns the following output - - ``fid`` (:class:`~torch.Tensor`): float scalar tensor with mean FID value over samples + - ``inception_mean`` (:class:`~torch.Tensor`): float scalar tensor with mean inception score over subsets + - ``inception_std`` (:class:`~torch.Tensor`): float scalar tensor with standard deviation of inception score + over subsets Args: feature: