As the number of layers grows, the last layers have a lesser and lesser context of the features … See more

I assume that all of you know about Batch Normalisation, so cross-batch normalization is normalizing the data with 4 batches in training. So, during the training data is passed as … See more

YOLOv4 uses an SPP block after CSPDarknet53 to increase the receptive field and separate out the most important features from the backbone. Spatial pyramid po… See more

In order to understand the Special attention Module, we need to understand SENet (Squeeze and Excitation Network). What SENet basically does is find which channel is more imp… See more

YOLOv4 chooses PANetfor the feature aggregation of the network. They don’t write much on the rationale for this decision, and since NAS-FPN and BiFPN were written concurrentl… See more