Compute the absolute error This function computes the elementwise absolute error for a vector.

Accuracy = (TP + TN) / (everything).

Bhattacharyya computes the distance between the probability distributions p and q given by: -\ln ( \sum_i \sqrt{p_i q_i} ) The lengths of p and q must be equal.

https://stackoverflow.com/questions/28723670/intersection-over-union-between-two-detections https://resources.wolframcloud.com/NeuralNetRepository/resources/SSD-VGG-300-Trained-on-PASCAL-VOC-Data.

CDF calculates an empirical cumulative distribution function.

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Correlation returns the weighted correlation between the samples of x and y with the given WeightedMeans.

Covariance returns the weighted covariance between the samples of x and y.

Edges returns sorted unique elements of a number of data sets, ensuring that the first and last elements are -∞ and +∞, respectively.

Expectation computes an estimate of the population mean from a finite sample.

F1Score = 2 * [(precision*recall) / (precision + recall)].

FPR = FP / non-monitored elements = (FPP + FNP) / (TN + FNP).

Hellinger computes the distance between the probability distributions p and q given by: \sqrt{ 1 - \sum_i \sqrt{p_i q_i} } The lengths of p and q must be equal.

Histogram counts the number of points that fall into each of the bins specified by a set of edges.

JensenShannon computes the JensenShannon divergence between the distributions p and q.

KolmogorovSmirnov computes the Kolmogorov–Smirnov statistic for two samples.

KullbackLeibler computes the Kullback-Leibler distance between the distributions p and q.

L2 computes the Euclidean distance between two vectors.

Mean computes the weighted Mean of the data set.

MSPE computes the mean-square-percentage error.

NRMSE computes the normalized root-mean-square error.

PDF calculates an empirical probability density function.

Suppose pixel values in [0,1] refer https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio */.

precision = TP / (TP + FPP + FNP).

recall = TPR = TP / (TP + FN + FPP).

RMSE computes the root-mean-square error.

RMSPE computes the root-mean-square-percentage error.

Compute the squared error This function computes the elementwise squared error for a vector.

Compute the squared log error This function computes the elementwise squared log error for a vector.

Sum returns the sum of the elements of the slice.

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Uniform computes the uniform distance between two vectors.

Variance computes an estimate of the population variance from a finite sample.

WeightedMean computes the weighted WeightedMean of the data set.