(self)
return {'Overall Acc': acc,
                'Mean Acc': acc_cls,
                'FreqW Acc': fwavacc,
                'Mean IoU': mean_iu, }, cls_iu
Returns accuracy score evaluation result.
            - overall accuracy
            - mean accuracy
            - mean IU
            - fwavacc
Returns accuracy score evaluation result.
            - overall accuracy
            - mean accuracy
            - mean IU
            - fwavacc
Returns accuracy score evaluation result . - overall accuracy - mean accuracy - mean IU - fwavacc
def get_scores(self):
        """Returns accuracy score evaluation result.
            - overall accuracy
            - mean accuracy
            - mean IU
            - fwavacc
        """
        hist = self.confusion_matrix
        acc = np.diag(hist).sum() / (hist.sum() + 0.0001)
        acc_cls = np.diag(hist) / (hist.sum(axis=1) + 0.0001)
        acc_cls = np.nanmean(acc_cls)
        iu = np.diag(hist) / (hist.sum(axis=1) + hist.sum(axis=0) - np.diag(hist) + 0.0001)
        mean_iu = np.nanmean(iu)
        freq = hist.sum(axis=1) / (hist.sum() + 0.0001)
        fwavacc = (freq[freq > 0] * iu[freq > 0]).sum()
        cls_iu = dict(zip(range(self.n_classes), iu))

        return {'Overall Acc': acc,
                'Mean Acc': acc_cls,
                'FreqW Acc': fwavacc,
                'Mean IoU': mean_iu, }, cls_iu
def get_scores ( self ) : hist = self . confusion_matrix acc = np . diag ( hist ) . sum ( ) / ( hist . sum ( ) + 0.0001 ) acc_cls = np . diag ( hist ) / ( hist . sum ( axis = 1 ) + 0.0001 ) acc_cls = np . nanmean ( acc_cls ) iu = np . diag ( hist ) / ( hist . sum ( axis = 1 ) + hist . sum ( axis = 0 ) - np . diag ( hist ) + 0.0001 ) mean_iu = np . nanmean ( iu ) freq = hist . sum ( axis = 1 ) / ( hist . sum ( ) + 0.0001 ) fwavacc = ( freq [ freq > 0 ] * iu [ freq > 0 ] ) . sum ( ) cls_iu = dict ( zip ( range ( self . n_classes ) , iu ) ) return { 'Overall Acc' : acc , 'Mean Acc' : acc_cls , 'FreqW Acc' : fwavacc , 'Mean IoU' : mean_iu , } , cls_iu