We provide config files to reproduce the object detection & instance segmentation results in the ICCV 2019 Oral paper for CARAFE: Content-Aware ReAssembly of FEatures.
@inproceedings{Wang_2019_ICCV,
title = {CARAFE: Content-Aware ReAssembly of FEatures},
author = {Wang, Jiaqi and Chen, Kai and Xu, Rui and Liu, Ziwei and Loy, Chen Change and Lin, Dahua},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {October},
year = {2019}
}
The results on COCO 2017 val is shown in the below table.
Method | Backbone | Style | Lr schd | Test Proposal Num | Inf time (fps) | Box AP | Mask AP | Download |
---|---|---|---|---|---|---|---|---|
Faster R-CNN w/ CARAFE | R-50-FPN | pytorch | 1x | 1000 | 16.5 | 38.6 | 38.6 | model | log |
- | - | - | - | 2000 | ||||
Mask R-CNN w/ CARAFE | R-50-FPN | pytorch | 1x | 1000 | 14.0 | 39.3 | 35.8 | model | log |
- | - | - | - | 2000 |
The CUDA implementation of CARAFE can be find at https://github.com/myownskyW7/CARAFE.