# Visualizing Performance Metrics

Encord Active enables you to visualize the important performance metrics, such as mean Average-Precision (mAP), for your model. Performance metrics can be visualized based on different classes and intersection-over-Union (IoU) thresholds. Performance metrics are supported for bounding-boxes (object detection) and polygons (segmentation).

Prerequisites: Dataset, Labels, Predictions

### Steps​

1. Navigate to the Model Quality > Metrics tab on the left sidebar.
2. Under the Subset selection scores, you will see the average precision (AP) and average recall (AR) for each class in the graph to the left and Precision-Recall curves for each class on the graph to the right.
3. You can select classes of interest and change IoU threshold on the upper sidebar to customize plots.
4. On the Mean scores plot, you can observe in which classes the model has difficulty and in which classes it does well.
5. According to insights you get here, you can, e.g., prioritize from which classes you need to collect more data.

### Example​

Comparing person and clock objects.

From the above figure, it is apparent that clock class degrades overall performance considerably. So, when collecting and labelling more data, prioritizing it over person class will make more sense for overall performance.