Spatial Pyramid Pooling (SPP) is a pooling layer that removes the fixed-size constraint of the network, i.e. a CNN does not require a fixed-size input image. 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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Semantic Segmentation | 86 | 15.87% |
Object Detection | 75 | 13.84% |
Image Segmentation | 30 | 5.54% |
Image Classification | 18 | 3.32% |
Instance Segmentation | 13 | 2.40% |
Medical Image Segmentation | 12 | 2.21% |
Autonomous Driving | 12 | 2.21% |
Real-Time Object Detection | 11 | 2.03% |
Classification | 10 | 1.85% |
Component | Type |
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🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |