FYI on Semantic Segmentation


#1
  1. 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 model-accuracy as defined by the unweighted mean intersection-over union score over all classes for the test-set, and S>0, measures model-complexity, as defined by the number of model parameters, measured in unit of model-size in MB. More precisely, model-size in MB will be computed as follows: S = number of learned model parameters x 4 (floating point) / (1024*1024)

  2. 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 re-submit your results for evaluation.
  1. Challenge participation deadline: September 15, 2019
  2. Clarifications on metrics below-

GA: Global Pixel Accuracy
CA: Mean Class Accuracy for different classes

  • Back: Background (non-eye part of peri-ocular 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

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Submission issue