YOLO9000: Better, Faster, Stronger YOLO9000: Better, Faster, Stronger
Paper summary _Objective:_ Train on both classification and detection image to make a better faster and stronger detector. _Dataset:_ [ImageNet](http://www.image-net.org/), [COCO](http://mscoco.org/) and [WordNet](https://wordnet.princeton.edu/). ## Architecture: Apart from amelioration such as batch norm or other general tweaking the real improvements come from: 1. Using both a classification dataset and a detection dataset at the same time. 2. Replacing the usual final soft-max layer (which assumes that all labels are mutually exclusive) with a WordTree label hierarchy base on WordNet which enables the network to predict `dog` even if it doesn't know if it's a `Fox Terrier`. [![screen shot 2017-04-12 at 7 24 28 pm](https://cloud.githubusercontent.com/assets/17261080/24970727/b7abaf02-1fb5-11e7-8b78-2a430a861cbd.png)](https://cloud.githubusercontent.com/assets/17261080/24970727/b7abaf02-1fb5-11e7-8b78-2a430a861cbd.png) ## Results: State of the art results at full resolution and possibility to lower performance to gain in computation time. [![screen shot 2017-04-12 at 7 31 26 pm](https://cloud.githubusercontent.com/assets/17261080/24971010/a51556f8-1fb6-11e7-9289-fc277b182686.png)](https://cloud.githubusercontent.com/assets/17261080/24971010/a51556f8-1fb6-11e7-9289-fc277b182686.png)
YOLO9000: Better, Faster, Stronger
Joseph Redmon and Ali Farhadi
arXiv e-Print archive - 2016 via Local arXiv
Keywords: cs.CV


Summary by Alexander Jung 2 years ago
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