An Analysis of Scale Invariance in Object Detection - SNIP

by   Bharat Singh, et al.

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 obtains an mAP of 48.3 with bounding box supervision. Our submission won the Best Student Entry in the COCO 2017 challenge. Code will be made available at



There are no comments yet.


page 3

page 5


MFPN: A Novel Mixture Feature Pyramid Network of Multiple Architectures for Object Detection

Feature pyramids are widely exploited in many detectors to solve the sca...

USB: Universal-Scale Object Detection Benchmark

Benchmarks, such as COCO, play a crucial role in object detection. Howev...

Scale Normalized Image Pyramids with AutoFocus for Object Detection

We present an efficient foveal framework to perform object detection. A ...

Detector With Focus: Normalizing Gradient In Image Pyramid

An image pyramid can extend many object detection algorithms to solve de...

Scale-Aware Trident Networks for Object Detection

Scale variation is one of the key challenges in object detection. In thi...

Finding Tiny Faces

Though tremendous strides have been made in object recognition, one of t...

Learning Fixation Point Strategy for Object Detection and Classification

We propose a novel recurrent attentional structure to localize and recog...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.