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Keyword: compact modeling ×Clear all

2 claims from 1 paper

1. Computer Science › Advanced Neural Network Applications

   Object Detection from Scratch with Deep Supervision

   Shen, Liu, Li, Jiang, Chen and Xue · IEEE Transactions on Pattern Analysis and Machine Intelligence · 2019

   The paper proposes DSOD, an object detector trained from scratch using deep supervision, and reports better results than state-of-the-art methods with much more compact models on PASCAL VOC and COCO.

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   1. UncheckedDSOD, an object detector trained from scratch, is reported to beat the SSD baseline on three benchmarks while using about half as many parameters.“Specifically, DSOD outperforms baseline method SSD on all three benchmarks, while requiring only 1/2 parameters.”
   2. UncheckedDSOD reaches comparable or slightly better results than Mask RCNN + FPN at similar input size, with about a third of the parameters and no pre-training.“We also observe that DSOD can achieve comparable/slightly better results than Mask RCNN + FPN (under similar input size) with only 1/3 parameters, using no extra data or pre-trained models.”