We present a large-scale object detection system by team PFDet. Our system
enables training with huge datasets using 512 GPUs, handles sparsely verified
classes, and massive class imbalance. Using our method, we achieved 2nd place
in the Google AI Open Images Object Detection Track 2018 on Kaggle.
%0 Generic
%1 citeulike:14643360
%A xxx,
%D 2018
%K detection finegrained loss rcnn
%T PFDet: 2nd Place Solution to Open Images Challenge 2018 Object Detection Track
%U http://arxiv.org/abs/1809.00778
%X We present a large-scale object detection system by team PFDet. Our system
enables training with huge datasets using 512 GPUs, handles sparsely verified
classes, and massive class imbalance. Using our method, we achieved 2nd place
in the Google AI Open Images Object Detection Track 2018 on Kaggle.
@misc{citeulike:14643360,
abstract = {{We present a large-scale object detection system by team PFDet. Our system
enables training with huge datasets using 512 GPUs, handles sparsely verified
classes, and massive class imbalance. Using our method, we achieved 2nd place
in the Google AI Open Images Object Detection Track 2018 on Kaggle.}},
added-at = {2019-02-27T22:23:29.000+0100},
archiveprefix = {arXiv},
author = {xxx},
biburl = {https://www.bibsonomy.org/bibtex/22eee03b837ab26fe9790201baff13187/nmatsuk},
citeulike-article-id = {14643360},
citeulike-linkout-0 = {http://arxiv.org/abs/1809.00778},
citeulike-linkout-1 = {http://arxiv.org/pdf/1809.00778},
day = 4,
eprint = {1809.00778},
interhash = {c0047fffd5c29ec3f1c06a6b540b27af},
intrahash = {2eee03b837ab26fe9790201baff13187},
keywords = {detection finegrained loss rcnn},
month = sep,
posted-at = {2018-10-05 12:31:30},
priority = {2},
timestamp = {2019-02-27T22:23:29.000+0100},
title = {{PFDet: 2nd Place Solution to Open Images Challenge 2018 Object Detection Track}},
url = {http://arxiv.org/abs/1809.00778},
year = 2018
}