Sept. 7, 2018, Tokyo Japan – Preferred Networks, Inc. (PFN, Headquarters: Chiyoda-ku, Tokyo, President and CEO: Toru Nishikawa) participated in the Google AI Open Images – Object Detection Track, an object detection challenge hosted by Kaggle^*1, and won second place in the competition among 454 teams from around the world.

Object detection, which is one of the major research subjects in computer vision, is a basic technology that is critical for autonomous driving and robotics. Challenges in using large-scale datasets, such as ImageNet and MS COCO, to achieve better accuracy in object detection have been the unifying force of the research community, contributing to the rapid improvement of detection techniques and algorithms.

The Google AI Open Images – Object Detection Track held between July 3, 2018 and August 30, 2018 was a competition of an unprecedented scale that used Open Images V4^*2, a large and complex dataset released by Google this year. As a result, the event attracted the attention of many researchers. A total of 454 teams from around the world participated in the competition.

PFN entered the competition as team “PFDet”, comprising interested members, mainly developers of ChainerMN and ChainerCV, PFN’s distributed deep learning library and computer vision library based on deep learning, respectively, as well as specialists in the fields of autonomous driving and robotics. During the competition, PFN’s large-scale cluster MN-1b that has 512 NVIDIA ^(R) Tesla^(R) V100 32GB GPUs was in full operation for the first time since its launch in July this year. In addition, the team utilized a parallel deep learning technique to speed up training with a large-scale dataset and made full use of research results PFN had accumulated over the years in the fields of autonomous driving and robotics.  These efforts resulted in the team finishing in a close second place by a narrow margin of 0.023% behind the team who won first place.

We have published a paper, entitled “PFDet: 2nd Place Solution to Open Images Challenge 2018 Object Detection Track,” regarding our solution method in this competition, at https://arxiv.org/abs/1809.00778

We also plan to present the content of the paper at a workshop at the European Conference on Computer Vision （ECCV）2018.

A part of the techniques developed for this competition will be released as additional functionality to ChainerMN and ChainerCV.

• Distributed deep learning framework ChainerMN  https://github.com/chainer/chainermn
• Deep learning-based computer vision library ChainerCV  https://github.com/chainer/chainercv

PFN will continue to work on research and development of image analysis and object detection technologies, and promote their practical applications in our three primary business domains, namely, transportation, manufacturing, and bio/healthcare.

*１：A platform for machine learning competitions

*２：A very large training dataset comprised of 1.7 million images (including 12 million objects of 500 classes)