
Please find the evaluation metrics for semantic segmentation.
The winning entry will be evaluated using the performance metric 0<M<=100, defined as follows: where 0<P<=1, measures modelaccuracy as defined by the unweighted mean intersectionover union score over all classes for the testset, and S>0, measures modelcomplexity, as defined by the number of model parameters, measured in unit of modelsize in MB. More precisely, modelsize in MB will be computed as follows: S = number of learned model parameters x 4 (floating point) / (1024*1024) 
Please find common submission errors and corresponding solutions below.

assert pred.shape == gt.shape
This happens for cases where #masks / segmentation of images is different from #masks from ground truth. This usually means that the #masks you generated doesn’t equal to ground truth. Please go back and check your results. 
raise ValueError(“Invalid submission zip!”) ValueError: Invalid submission zip!
This has been fixed now and please resubmit your results for evaluation.
 Challenge participation deadline: September 15, 2019
 Clarifications on metrics below
GA: Global Pixel Accuracy
CA: Mean Class Accuracy for different classes
 Back: Background (noneye part of periocular region)
 Sclera: Sclera
 Iris: Iris
 Pupil: Pupil
Precision: Computed using sklearn.metrics.precision_score(pred, gt, ‘weighted’)
Recall: Computed using sklearn.metrics.recall_score(pred, gt, ‘weighted’)
F1: Computed using sklearn.metrics.f1_score(pred, gt, ‘weighted’)
IoU: Computed using the function below
def compute_mean_iou(flat_pred, flat_label):
'''
compute mean intersection over union (IOU) over all classes
:param flat_pred: flattened prediction matrix
:param flat_label: flattened label matrix
:return: mean IOU
'''
unique_labels = np.unique(flat_label)
num_unique_labels = len(unique_labels)
Intersect = np.zeros(num_unique_labels)
Union = np.zeros(num_unique_labels)
for index, val in enumerate(unique_labels):
pred_i = flat_pred == val
label_i = flat_label == val
Intersect[index] = float(np.sum(np.logical_and(label_i, pred_i)))
Union[index] = float(np.sum(np.logical_or(label_i, pred_i)))
mean_iou = np.mean(Intersect / Union)
return mean_iou