Transcription of Spatial Pyramid Pooling in Deep Convolutional Networks …
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Spatial Pyramid Pooling in Deep Convolutional Networks …
1 Spatial Pyramid Pooling in Deep ConvolutionalNetworks for Visual RecognitionKaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian SunAbstract Existing deep Convolutional neural Networks (CNNs) require a fixed-size ( , 224 224) input image. This require-ment is artificial and may reduce the recognition accuracy for the images or sub-images of an arbitrary size/scale. In thiswork, we equip the Networks with another Pooling strategy, Spatial Pyramid Pooling , to eliminate the above requirement. Thenew network structure, called SPP-net, can generate a fixed-length representation regardless of image size/scale. Pyramidpooling is also robust to object deformations. With these advantages, SPP-net should in general improve all CNN-based imageclassification methods. On the ImageNet 2012 dataset, we demonstrate that SPP-net boosts the accuracy of a variety of CNNarchitectures despite their different designs. On the Pascal VOC 2007 and Caltech101 datasets, SPP-net achieves state-of-the-art classification results using a single full-image representation and no power of SPP-net is also significant in object detection.
The convo-lutional layers operate in a sliding-window manner and output feature maps which represent the spatial arrangement of the activations (Figure2). In fact, con- ... networks [3] cannot; 2) SPP uses multi-level spatial bins, while the sliding window pooling uses only a single window size. Multi-level pooling has been
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