ECCV 2014 - LNCS 8689-8695

Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition

Kaiming He¹, Xiangyu Zhang², Shaoqing Ren³, and Jian Sun¹

¹Microsoft Research, China

²Xi’an Jiaotong University, China

³University of Science and Technology, China

Abstract. Existing deep convolutional neural networks (CNNs) require a fixed-size (e.g. 224×224) input image. This requirement is “artificial” and may hurt the recognition accuracy for the images or sub-images of an arbitrary size/scale. In this work, we equip the networks with a more principled pooling strategy, “spatial pyramid pooling”, to eliminate the above requirement. The new network structure, called SPP-net, can generate a fixed-length representation regardless of image size/scale. By removing the fixed-size limitation, we can improve all CNN-based image classification methods in general. Our SPP-net achieves state-of-the-art accuracy on the datasets of ImageNet 2012, Pascal VOC 2007, and Caltech101.

The power of SPP-net is more significant in object detection. Using SPP-net, we compute the feature maps from the entire image only once, and then pool features in arbitrary regions (sub-images) to generate fixed-length representations for training the detectors. This method avoids repeatedly computing the convolutional features. In processing test images, our method computes convolutional features 30-170× faster than the recent leading method R-CNN (and 24-64× faster overall), while achieving better or comparable accuracy on Pascal VOC 2007.

LNCS 8691, p. 346 ff.

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