Tensorflow OD API - Use of dropout when fine-tuning a model - tensorflow

Question

I'm trying to fine-tune the SSD MobileNet v2 (from the model zoo) with my own data. In pipeline.config I see that use_dropout is set to false. Why is that? I thought dropout should be used to prevent overfitting.
box_predictor {
convolutional_box_predictor {
conv_hyperparams {
regularizer {
l2_regularizer {
weight: 3.99999989895e-05
}
}
initializer {
truncated_normal_initializer {
mean: 0.0
stddev: 0.0299999993294
}
}
activation: RELU_6
batch_norm {
decay: 0.999700009823
center: true
scale: true
epsilon: 0.0010000000475
train: true
}
}
min_depth: 0
max_depth: 0
num_layers_before_predictor: 0
use_dropout: false
dropout_keep_probability: 0.800000011921
kernel_size: 3
box_code_size: 4
apply_sigmoid_to_scores: false
}
}
Is it because of batch normalization? In this paper, it says that:
3.4 Batch Normalization regularizes the
model
When training with Batch Normalization, a training example is seen in conjunction with other examples in the
mini-batch, and the training network no longer producing deterministic values for a given training example. In
our experiments, we found this effect to be advantageous
to the generalization of the network. Whereas Dropout
(Srivastava et al., 2014) is typically used to reduce overfitting, in a batch-normalized network we found that it can
be either removed or reduced in strength.