CSP Bottleneck with 3 convolutions

CSP (Cross Stage Partial) bottleneck with 3 convolutions is a type of bottleneck block used in convolutional neural networks (CNNs) for object detection and image classification tasks. It was introduced in the YOLOv4 object detection architecture.

The CSP bottleneck block consists of two parallel convolutional branches, where each branch contains a sequence of convolutional layers. The output feature maps of the two branches are concatenated and passed through a set of additional convolutional layers, which act as a fusion layer. The purpose of this fusion layer is to combine the features learned from the two branches and improve the overall feature representation.

The CSP bottleneck block with 3 convolutions refers to a specific configuration of this block, where each convolutional branch contains three convolutional layers. This configuration has been shown to improve the accuracy and efficiency of object detection models, compared to other bottleneck block configurations.

Overall, the CSP bottleneck block with 3 convolutions is a powerful tool for improving the performance of object detection and image classification models, and has been widely adopted in state-of-the-art architectures.