# Metrics¶

## Classification & Segmentation¶

### Accuracy¶

tllib.utils.metric.accuracy(output, target, topk=(1, ))[source]

Computes the accuracy over the k top predictions for the specified values of k

Parameters
• output (tensor) – Classification outputs, $$(N, C)$$ where C = number of classes

• target (tensor) – $$(N)$$ where each value is $$0 \leq \text{targets}[i] \leq C-1$$

• topk (sequence[int]) – A list of top-N number.

Returns

Top-N accuracies (N $$\in$$ topK).

### ConfusionMatrix¶

class tllib.utils.metric.ConfusionMatrix(num_classes)[source]
compute()[source]

compute global accuracy, per-class accuracy and per-class IoU

format(classes)[source]

Get the accuracy and IoU for each class in the table format

update(target, output)[source]

Update confusion matrix.

Parameters
• target – ground truth

• output – predictions of models

Shape:
• target: $$(minibatch, C)$$ where C means the number of classes.

• output: $$(minibatch, C)$$ where C means the number of classes.