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BlendMask: Top-Down Meets Bottom-Up for Instance Segmentation

BlendMask: Top-Down Meets Bottom-Up for Instance Segmentation;
Hao Chen, Kunyang Sun, Zhi Tian, Chunhua Shen, Yongming Huang, and Youliang Yan;
In: Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2020.

[Paper] [BibTeX]

This project contains training BlendMask for instance segmentation and panoptic segmentation on COCO and configs for segmenting persons on PIC.

Quick Start

Demo

wget -O blendmask_r101_dcni3_5x.pth https://cloudstor.aarnet.edu.au/plus/s/vbnKnQtaGlw8TKv/download
python demo/demo.py \
    --config-file configs/BlendMask/R_101_dcni3_5x.yaml \
    --input datasets/coco/val2017/000000005992.jpg \
    --confidence-threshold 0.35 \
    --opts MODEL.WEIGHTS blendmask_r101_dcni3_5x.pth

Training and evaluation

To train a model with "train_net.py", first setup the corresponding datasets following datasets/README.md,

Then follow these steps to generate blendmask format annotations for instance segmentation.

then run:

OMP_NUM_THREADS=1 python tools/train_net.py \
    --config-file configs/BlendMask/R_50_1x.yaml \
    --num-gpus 4 \
    OUTPUT_DIR training_dir/blendmask_R_50_1x

To evaluate the model after training, run:

OMP_NUM_THREADS=1 python tools/train_net.py \
    --config-file configs/BlendMask/R_50_1x.yaml \
    --eval-only \
    --num-gpus 4 \
    OUTPUT_DIR training_dir/blendmask_R_50_1x \
    MODEL.WEIGHTS training_dir/blendmask_R_50_1x/model_final.pth

Models

COCO Instance Segmentation Baselines

Model Name inf. time box AP mask AP download
Mask R-CNN R_50_1x 13 FPS 38.6 35.2
BlendMask R_50_1x 14 FPS 39.9 35.8 model
Mask R-CNN R_50_3x 13 FPS 41.0 37.2
BlendMask R_50_3x 14 FPS 42.7 37.8 model
Mask R-CNN R_101_3x 10 FPS 42.9 38.6
BlendMask R_101_3x 11 FPS 44.8 39.5 model
BlendMask R_101_dcni3_5x 10 FPS 46.8 41.1 model

BlendMask Real-time Models

Model Name inf. time box AP mask AP download
Mask R-CNN 550_R_50_3x 16 FPS 39.1 35.3
BlendMask 550_R_50_3x 28 FPS 38.7 34.5 model
BlendMask RT_R_50_4x_syncbn_shtw 31 FPS 39.3 35.1 model
BlendMask RT_R_50_4x_bn-head_syncbn_shtw 31 FPS 39.3 35.1 model
BlendMask DLA_34_4x 32 FPS 40.8 36.3 model

COCO Panoptic Segmentation Baselines with BlendMask

Model Name PQ PQTh PQSt download
Panoptic FPN R_50_3x 41.5 48.3 31.2
BlendMask R_50_3x 42.5 49.5 32.0 model
Panoptic FPN R_101_3x 43.0 49.7 32.9
BlendMask R_101_3x 44.3 51.6 33.2 model
BlendMask R_101_dcni3_5x 46.0 52.9 35.5 model

Citing BlendMask

If you use BlendMask in your research or wish to refer to the baseline results, please use the following BibTeX entries.

@inproceedings{chen2020blendmask,
  title     =  {{BlendMask}: Top-Down Meets Bottom-Up for Instance Segmentation},
  author    =  {Chen, Hao and Sun, Kunyang and Tian, Zhi and Shen, Chunhua and Huang, Yongming and Yan, Youliang},
  booktitle =  {Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR)},
  year      =  {2020}
}