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 RecognitionPaper | Code | Results | Date | Stars |
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Task | Papers | Share |
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General Classification | 1 | 25.00% |
Image Classification | 1 | 25.00% |
Object Detection | 1 | 25.00% |
Object Recognition | 1 | 25.00% |