An analysis of different techniques for recognizing and detecting objects under extreme scale variation is presented. Scale specific and scale invariant design of detectors are compared by training them with different configurations of input data. To examine if upsampling images is necessary for detecting small objects, we evaluate the performance of different network architectures for classifying small objects on ImageNet. Based on this analysis, we propose a deep end-to-end trainable Image Pyramid Network for object detection which operates on the same image scales during training and inference. Since small and large objects are difficult to recognize at smaller and larger scales respectively, we present a novel training scheme called Scale Normalization for Image Pyramids (SNIP) which selectively back-propagates the gradients of object instances of different sizes as a function of the image scale. On the COCO dataset, our single model performance is 45.7% and an ensemble of 3 networks obtains an mAP of 48.3%. We use ImageNet-1000 pre-trained models and only train with bounding box supervision. Our submission won the Best Student Entry in the COCO 2017 challenge. Code will be made available at http://bit.ly/2yXVg4c.

本文分析了不同的目标识别和检测技术，针对极端尺度变化进行了比较，比较了尺度特定和尺度不变的设计，提出了一种新的训练方案：图像金字塔尺度归一化检测器（SNIP），并在COCO数据集上进行了评估。

目标检测中尺度不变性的分析 - SNIP