  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. By evaluating the performance of different network architectures
for classifying small objects on ImageNet, we show that CNNs are not robust to
changes in scale. Based on this analysis, we propose to train and test
detectors on the same scales of an image-pyramid. 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 off-the-shelf 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
\url{http://bit.ly/2yXVg4c}.
