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[第45会コンピュータービジョン勉強会@関東] ChainerCV


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ChainerCV: a library for deep learning in computer vision

Published in: Engineering
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[第45会コンピュータービジョン勉強会@関東] ChainerCV

  1. 1. Preferred Networks, inc. Yusuke Niitani ChainerCV: a Library for Deep Learning in Computer Vision 第45会 コンピュータビジョン勉強会 @関東
  2. 2. Why we developed ChainerCV Make running and training deep-learning easier in CV
  3. 3. General information CV extension of Chainer Open source and MIT Licensed! core frameworks extensions there is also ChainerUI and ChainerChemistry
  4. 4. ChainerCV compared to others Chainer examples Target Code reuse Faithful reimplementation ~User implementations ChainerCV DL beginner & General Researchers Researchers & Non-CVers
  5. 5. Guiding principles of ChainerCV 1. Ease of Use 2. Reproduciblity 3. Compositionality
  6. 6. Ease of use
  7. 7. ChainerCV is easy-to-use • Easy to install • Comprehensive documentation and tests • Unified interface for models (next slide)
  8. 8. Unified interface for models
  9. 9. Inside predict for detection models 1. Preprocess images 2. Forward images through network 3. Post-process outputs
  10. 10. Reproducibility
  11. 11. ChainerCV training code Training scripts for SSD, Faster R-CNN and SegNet
  12. 12. Reproducing original results
  13. 13. Compositionality
  14. 14. Abstraction for utilities
  15. 15. Dataset classes • ADE20K • CamVid • Cityscapes • CUB • Online Products • SBD • VOC
  16. 16. Research with ChainerCV ACM Multimedia 2018 (OSSC) Projects using ChainerCV Hatori et al., Interactively Picking Real-World Objects with Unconstrained Spoken Language Instructions Ogawa et al., Object Detection for Comics using Manga109 Annotations
  17. 17. demo
  18. 18. Conclusion • easy-to-use interfce • faithful re-implementations • compositional utilities Useful for researchers and non-experts! source code: doc: