deepinv.metric#

Metrics are generally used to evaluate the performance of a model, or as the distance function inside a loss function. Please refer to the user guide for more information.

Base class#

User Guide: refer to Metrics for more information.

deepinv.loss.metric.Metric

Base class for metrics.

Full Reference Metrics#

User Guide: refer to Full Reference Metrics for more information.

`deepinv.loss.metric.MSE`	Mean Squared Error metric.
`deepinv.loss.metric.NMSE`	Normalised Mean Squared Error metric.
`deepinv.loss.metric.MAE`	Mean Absolute Error metric.
`deepinv.loss.metric.PSNR`	Peak Signal-to-Noise Ratio (PSNR) metric.
`deepinv.loss.metric.SSIM`	Structural Similarity Index (SSIM) metric using torchmetrics.
`deepinv.loss.metric.QNR`	Quality with No Reference (QNR) metric for pansharpening.
`deepinv.loss.metric.L1L2`	Combined L2 and L1 metric.
`deepinv.loss.metric.LpNorm`	\(\ell_p\) metric for \(p>0\).
`deepinv.loss.metric.LPIPS`	Learned Perceptual Image Patch Similarity (LPIPS) metric.

No Reference Metrics#

User Guide: refer to No Reference Metrics for more information.

deepinv.loss.metric.NIQE

Natural Image Quality Evaluator (NIQE) metric.