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Status: Unchecked ×Keyword: small object recognition ×Clear all

2 claims from 1 paper

1. Computer Science › Advanced Neural Network Applications

   An Analysis of Scale Invariance in Object Detection - SNIP

   Singh and Davis · arXiv (Cornell University) · 2017

   The paper compares scale-specific and scale-invariant detector designs, proposes training and testing on the same image-pyramid scales, and introduces SNIP, a training scheme reported to reach 45.7% single-model performance on COCO.

   Unchecked2 claims
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   1. UncheckedTesting network designs on small-object classification in ImageNet, the authors report that CNNs are not robust to changes in object scale.“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.”
   2. UncheckedOn the COCO dataset, the authors' single detector scores 45.7% and an ensemble of three networks reaches 48.3% mAP.“On the COCO dataset, our single model performance is 45.7% and an ensemble of 3 networks obtains an mAP of 48.3%.”