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이미지 분할 지표

MeanIoU class

tf.keras.metrics.MeanIoU(num_classes, name=None, dtype=None)

Computes the mean Intersection-Over-Union metric.

Mean Intersection-Over-Union is a common evaluation metric for semantic image segmentation, which first computes the IOU for each semantic class and then computes the average over classes. IOU is defined as follows: IOU = true_positive / (true_positive + false_positive + false_negative). The predictions are accumulated in a confusion matrix, weighted by sample_weight and the metric is then calculated from it.

If sample_weight is None, weights default to 1. Use sample_weight of 0 to mask values.

Arguments

  • num_classes: The possible number of labels the prediction task can have. This value must be provided, since a confusion matrix of dimension = [num_classes, num_classes] will be allocated.
  • name: (Optional) string name of the metric instance.
  • dtype: (Optional) data type of the metric result.

Standalone usage:

>>> # cm = [[1, 1],  
>>> #        [1, 1]]  
>>> # sum_row = [2, 2], sum_col = [2, 2], true_positives = [1, 1]  
>>> # iou = true_positives / (sum_row + sum_col - true_positives))  
>>> # result = (1 / (2 + 2 - 1) + 1 / (2 + 2 - 1)) / 2 = 0.33  
>>> m = tf.keras.metrics.MeanIoU(num_classes=2)
>>> m.update_state([0, 0, 1, 1], [0, 1, 0, 1])
>>> m.result().numpy()
0.33333334

>>> m.reset_states()
>>> m.update_state([0, 0, 1, 1], [0, 1, 0, 1],
...                sample_weight=[0.3, 0.3, 0.3, 0.1])
>>> m.result().numpy()
0.23809525

Usage with compile() API:

model.compile(
  optimizer='sgd',
  loss='mse',
  metrics=[tf.keras.metrics.MeanIoU(num_classes=2)])