Convolutional Neural Networks

SPP-Net is a convolutional neural architecture that employs spatial pyramid pooling to remove the fixed-size constraint of the network. Specifically, we add an SPP layer on top of the last convolutional layer. The SPP layer pools the features and generates fixed-length outputs, which are then fed into the fully-connected layers (or other classifiers). In other words, we perform some information aggregation at a deeper stage of the network hierarchy (between convolutional layers and fully-connected layers) to avoid the need for cropping or warping at the beginning.

Source: Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition


Paper Code Results Date Stars


Task Papers Share
General Classification 1 25.00%
Image Classification 1 25.00%
Object Detection 1 25.00%
Object Recognition 1 25.00%