# ChainerCV: a Library for Deep Learning in Computer Vision

arXiv: https://arxiv.org/abs/1708.08169 · HTML: https://mlsanity.com/paper/1708.08169/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Aug 2017.

## Built on by (most cited first, 2 of 2)

- [Multi defect detection and analysis of electron microscopy images with deep learning](https://mlsanity.com/paper/2108.08883/) (2021)
- [Performance, Successes and Limitations of Deep Learning Semantic Segmentation of Multiple Defects in Transmission Electron Micrographs](https://mlsanity.com/paper/2110.08244/) (2021)

## Abstract (arXiv)

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. We introduce ChainerCV, a software library that is intended to fill this gap. ChainerCV supports numerous neural network models as well as software components needed to conduct research in computer vision. These implementations emphasize simplicity, flexibility and good software engineering practices. The library is designed to perform on par with the results reported in published papers and its tools can be used as a baseline for future research in computer vision. Our implementation includes sophisticated models like Faster R-CNN and SSD, and covers tasks such as object detection and semantic segmentation.