Team PFDet's Methods for Open Images Challenge 2019

no code implementations • 25 Oct 2019 • Yusuke Niitani, Toru Ogawa, Shuji Suzuki, Takuya Akiba, Tommi Kerola, Kohei Ozaki, Shotaro Sano

Using this method, the team PFDet achieved 3rd and 4th place in the instance segmentation and the object detection track, respectively.

Instance Segmentation Object +4

Chainer: A Deep Learning Framework for Accelerating the Research Cycle

no code implementations • 1 Aug 2019 • Seiya Tokui, Ryosuke Okuta, Takuya Akiba, Yusuke Niitani, Toru Ogawa, Shunta Saito, Shuji Suzuki, Kota Uenishi, Brian Vogel, Hiroyuki Yamazaki Vincent

Software frameworks for neural networks play a key role in the development and application of deep learning methods.

Sampling Techniques for Large-Scale Object Detection from Sparsely Annotated Objects

no code implementations • CVPR 2019 • Yusuke Niitani, Takuya Akiba, Tommi Kerola, Toru Ogawa, Shotaro Sano, Shuji Suzuki

However, large datasets like Open Images Dataset v4 (OID) are sparsely annotated, and some measure must be taken in order to ensure the training of a reliable detector.

object-detection Object Detection

PFDet: 2nd Place Solution to Open Images Challenge 2018 Object Detection Track

no code implementations • 4 Sep 2018 • Takuya Akiba, Tommi Kerola, Yusuke Niitani, Toru Ogawa, Shotaro Sano, Shuji Suzuki

We present a large-scale object detection system by team PFDet.

Object object-detection +1

ChainerCV: a Library for Deep Learning in Computer Vision

2 code implementations • 28 Aug 2017 • Yusuke Niitani, Toru Ogawa, Shunta Saito, Masaki Saito

Despite significant progress of deep learning in the field of computer vision, there has not been a software library that covers these methods in a unifying manner.

object-detection Object Detection +1