# How does the classification head of EfficientDet work?

EfficientDet outputs classes and bounding boxes. My question is about both but specifically I am interested in the class prediction net part. In the paper's diagram it shows 2 conv layers. I don't understand the code and how it works. And what's the difference between the 2 conv layers of classification and box prediction?

After the stack of BiFPN we have a feature map of size B x C x H x W.
For EfficientDet H and W are 1/8 of the input image size.
Then for each pixel in this feature map one applies one convolution to get the bounding boxes. The model predicts n_anchors - rescaled and shifted versions of reference boxes. The number of output convolution channels is n_anchors x 4. 4 channels are for the location and scale of each box.