Official implementation of:
Forest R-CNN: Large-Vocabulary Long-Tailed Object Detection and Instance Segmentation
Jialian Wu, Liangchen Song, Tiancai Wang, Qian Zhang and Junsong Yuan
In ACM International Conference on Multimedia , Seattle WA, October 12-16, 2020.
Many thanks to mmdetection authors for their great framework!
Please refer to INSTALL.md for installation and dataset preparation.
The Forest R-CNN config is in configs/lvis.
# Examples
# single-gpu testing
python tools/test.py configs/lvis/forest_rcnn_r50_fpn.py forest_rcnn_res50.pth --out out.pkl --eval bbox segm
# multi-gpu testing
./tools/dist_test.sh configs/lvis/forest_rcnn_r50_fpn.py forest_rcnn_res50.pth ${GPU_NUM} --out out.pkl --eval bbox segm
# Examples
# single-gpu training
python tools/train.py configs/lvis/forest_rcnn_r50_fpn.py --validate
# multi-gpu training
./tools/dist_train.sh configs/lvis/forest_rcnn_r50_fpn.py ${GPU_NUM} --validate
(Note that we found in our experiments the best result comes up around the 20-th epoch instead of the end of training.)
AP and AP.b denote the mask AP and box AP. r, c, f represent the rare, common, frequent contegoires.
| Method | Backbone | AP | AP.r | AP.c | AP.f | AP.b | AP.b.r | AP.b.c | AP.b.f | download |
|---|---|---|---|---|---|---|---|---|---|---|
| MaskRCNN | R50-FPN | 21.7 | 6.8 | 22.6 | 26.4 | 21.8 | 6.5 | 21.6 | 28.0 | model |
| Forest R-CNN | R50-FPN | 25.6 | 18.3 | 26.4 | 27.6 | 25.9 | 16.9 | 26.1 | 29.2 | model |
| MaskRCNN | R101-FPN | 23.6 | 10.0 | 24.8 | 27.6 | 23.5 | 8.7 | 23.1 | 29.8 | model |
| Forest R-CNN | R101-FPN | 26.9 | 20.1 | 27.9 | 28.3 | 27.5 | 20.0 | 27.5 | 30.4 | model |
| MaskRCNN | X-101-32x4d-FPN | 24.8 | 10.0 | 26.4 | 28.6 | 24.8 | 8.6 | 25.0 | 30.9 | model |
| Forest R-CNN | X-101-32x4d-FPN | 28.5 | 21.6 | 29.7 | 29.7 | 28.8 | 20.6 | 29.2 | 31.7 | model |
If you find it useful in your research, please consider citing our paper as follows:
@article{Wu2020ForestRCNN,
title = {Forest R-CNN: Large-Vocabulary Long-Tailed Object Detection and Instance Segmentation},
author = {Jialian Wu, Liangchen Song, Tiancai Wang, Qian Zhang and Junsong Yuan},
booktitle = {ACM International Conference on Multimedia},
year = {2020}
}