# Metric Node

The Tensorleap platform allows you to add metrics to be computed in each model evaluation 

These Metrics are divided into two types:

* Default metrics
  * MeanSquaredError 
  * MeanSquaredLogarithmicError
  * MeanAbsoluteError
  * MeanAbsolutePercentageError
  * Accuracy
  * BinaryAccuracy
  * MeanIOU
* Custom metrics

{% hint style="success" %}
It is highly recommended to use your own custom metrics in the [integration script](/tensorleap-integration/writing-integration-code.md). This would allow you to test results in your[ local development environment](/tensorleap-integration/integration-test.md) before you upload the model to Tensorleap.  This helps to ensure the metrics return the expected values before the integration
{% endhint %}

### Custom Metric Example

Adding the following metric in your code:

```python
@tensorleap_custom_metric("cost")
def cost(pred80,pred40,pred20,gt):
    gt=np.squeeze(gt,axis=0)
    d={}
    d["bboxes"] = torch.from_numpy(gt[...,:4])
    d["cls"] = torch.from_numpy(gt[...,4])
    d["batch_idx"] = torch.zeros_like(d['cls'])
    y_pred_torch = [torch.from_numpy(s) for s in [pred80,pred40,pred20]]
    _,loss_parts= criterion(y_pred_torch, d)
    return {"box":loss_parts[0].unsqueeze(0).numpy(),"cls":loss_parts[1].unsqueeze(0).numpy(),"dfl":loss_parts[2].unsqueeze(0).numpy()}
```

Would result in the following metric in selectable from the menu:

<figure><img src="/files/nENdOASPiFmrWQyjwxVI" alt=""><figcaption><p>Metric Block</p></figcaption></figure>

{% hint style="info" %}
In order to add a **Custom metric** it must be added to the dataset script and parsed first as described in the&#x20;
{% endhint %}

## Setup

The **Metric** node have several properties:

* **Selected Metric**: a dropdown from which a custom or default metric can be selected. The list would include all registered [custom metrics](/tensorleap-integration/writing-integration-code/custom-metrics.md) from your [integration script](/tensorleap-integration/writing-integration-code.md).
* **Name**: The name of the selected visualizer.
* **Labels**: The expected arguments for your visualizer.


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