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. 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.
| Task | Dataset | Metric | Value | Model |
|---|---|---|---|---|
| Object Detection | COCO test-dev | box mAP | 39.5 | FPN (ResNet101 backbone) |
| 3D | COCO test-dev | box mAP | 39.5 | FPN (ResNet101 backbone) |
| 2D Classification | COCO test-dev | box mAP | 39.5 | FPN (ResNet101 backbone) |
| 2D Object Detection | COCO test-dev | box mAP | 39.5 | FPN (ResNet101 backbone) |
| 16k | COCO test-dev | box mAP | 39.5 | FPN (ResNet101 backbone) |