# Focal Loss

[source]

`FocalLoss`

class

```
keras_cv.losses.FocalLoss(
alpha=0.25, gamma=2, from_logits=False, label_smoothing=0, **kwargs
)
```

Implements Focal loss

Focal loss is a modified cross-entropy designed to perform better with
class imbalance. For this reason, it's commonly used with object detectors.

**Arguments**

**alpha**: a float value between 0 and 1 representing a weighting factor
used to deal with class imbalance. Positive classes and negative
classes have alpha and (1 - alpha) as their weighting factors
respectively. Defaults to 0.25.
**gamma**: a positive float value representing the tunable focusing
parameter, defaults to 2.
**from_logits**: Whether `y_pred`

is expected to be a logits tensor. By
default, `y_pred`

is assumed to encode a probability distribution.
Default to `False`

.
**label_smoothing**: Float in `[0, 1]`

. If higher than 0 then smooth the
labels by squeezing them towards `0.5`

, i.e., using
`1. - 0.5 * label_smoothing`

for the target class and
`0.5 * label_smoothing`

for the non-target class.

**References**

**Example**

```
y_true = np.random.uniform(size=[10], low=0, high=4)
y_pred = np.random.uniform(size=[10], low=0, high=4)
loss = FocalLoss()
loss(y_true, y_pred)
```

Usage with the `compile()`

API:

```
model.compile(optimizer='adam', loss=keras_cv.losses.FocalLoss())
```