Skip to content

Latest commit

 

History

History
161 lines (132 loc) · 4.98 KB

README.md

File metadata and controls

161 lines (132 loc) · 4.98 KB

Forest R-CNN: Large-Vocabulary Long-Tailed Object Detection and Instance Segmentation (ACM MM 2020)

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!

Installation

Please refer to INSTALL.md for installation and dataset preparation.

Train and inference

The Forest R-CNN config is in configs/lvis.

Inference

# 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

Training

# 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.)

Main Results

Instance Segmentation on LVIS

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 

Visualized Examples

Citation

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}
}