<p>If you refer to the <a href="https://arxiv.org/pdf/1506.02640.pdf" rel="nofollow noreferrer">original paper</a>, they use linear activation for the final layer. In section &quot;2.2. Training&quot; you can find:</p>
<blockquote>
<p>We use a linear activation function for the final layer and
all other layers use the following leaky rectified linear activation...</p>
</blockquote>
Which activation function do we use in the output layer of YOLO?
<p>I have built a simple YOLO localization model in Keras like,</p>

<pre><code>model_layers = [
    keras.layers.Conv2D( 32 , input_shape=( input_dim , input_dim , 3 ) , kernel_size=( 3 , 3 ) , strides=1 , activation='relu' ),
    keras.layers.Conv2D( 32 , kernel_size=( 3 , 3 ) , strides=1 , activation='relu' ),
    keras.layers.MaxPooling2D( pool_size=( 2 , 2 ) , strides=2 ),
    keras.layers.Conv2D( 64 , kernel_size=( 3 , 3 ) , strides=1 , activation='relu' ),
    keras.layers.Conv2D( 64 , kernel_size=( 3 , 3 ) , strides=1 , activation='relu' ),
    keras.layers.MaxPooling2D( pool_size=( 2 , 2 ) , strides=2 ),
    keras.layers.Conv2D( 64 , kernel_size=( 3 , 3 ) , strides=1 , activation='relu' ),
    keras.layers.Conv2D( 64 , kernel_size=( 3 , 3 ) , strides=1 , activation='relu' ),
    keras.layers.MaxPooling2D( pool_size=( 2 , 2 ) , strides=2 ),
    keras.layers.Conv2D( 128 , kernel_size=( 3 , 3 ) , strides=1 , activation='relu' ),
    keras.layers.Conv2D( 128 , kernel_size=( 3 , 3 ) , strides=1 , activation='relu' ),
    keras.layers.Conv2D( 64 , kernel_size=( 3 , 3 ) , strides=1 , activation='relu' ),
    keras.layers.Conv2D( 64 , kernel_size=( 3 , 3 ) , strides=1 , activation='relu' ),
    keras.layers.Conv2D( 32 , kernel_size=( 3 , 3 ) , strides=1 , activation='relu' ),
    keras.layers.Conv2D( 8 , kernel_size=( 3 , 3 ) , strides=1 ),
]

model = keras.models.Sequential( model_layers ) 
model.compile( loss=yolo_keras_loss , optimizer=keras.optimizers.Adam( lr=0.0001 ) )
model.summary()
</code></pre>

<p>As observed, the last layer's activation function is 'linear'. </p>

<blockquote>
  <p>But with regards to YOLO's output, all the values ( confidence score,
  bounding box coordinates and class probabilities ) are normalized. So
  should I use a sigmoid activation function or a linear activation
  function?</p>
</blockquote>

<p>I cannot find the output layer's activation function in any of resources concerning YOLO.</p>