The Hourglass architecture is a deep learning structure designed for computer vision tasks. It is characterized by its hourglass shape, which consists of a series of convolutional layers that successively downsample and then upsample the input data. This architecture allows the network to capture and process features at multiple scales, making it particularly effective for tasks that involve complex spatial relationships. Additionally, the Hourglass architecture employs shortcut connections between mirroring layers to mitigate the vanishing gradient problem and enable the model to combine feature maps from earlier and later layers.