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README.md

PointRend: Image Segmentation as Rendering

Alexander Kirillov, Yuxin Wu, Kaiming He, Ross Girshick

[arXiv] [BibTeX]


In this repository, we release code for PointRend in Detectron2. PointRend can be flexibly applied to both instance and semantic segmentation tasks by building on top of existing state-of-the-art models.

Quick start and visualization

This Colab Notebook tutorial contains examples of PointRend usage and visualizations of its point sampling stages.

Training

To train a model with 8 GPUs run:

cd /path/to/detectron2/projects/PointRend
python train_net.py --config-file configs/InstanceSegmentation/pointrend_rcnn_R_50_FPN_1x_coco.yaml --num-gpus 8

Evaluation

Model evaluation can be done similarly:

cd /path/to/detectron2/projects/PointRend
python train_net.py --config-file configs/InstanceSegmentation/pointrend_rcnn_R_50_FPN_1x_coco.yaml --eval-only MODEL.WEIGHTS /path/to/model_checkpoint

Pretrained Models

Instance Segmentation

COCO

Mask
head		 Backbone lr
sched		 Output
resolution		 mask
AP		 mask
AP*		 model id download
PointRend R50-FPN 1× 224×224 36.2 39.7 164254221 model | metrics
PointRend R50-FPN 3× 224×224 38.3 41.6 164955410 model | metrics
PointRend R101-FPN 3× 224×224 40.1 43.8 model | metrics
PointRend X101-FPN 3× 224×224 41.1 44.7 model | metrics

AP* is COCO mask AP evaluated against the higher-quality LVIS annotations; see the paper for details. Run python detectron2/datasets/prepare_cocofied_lvis.py to prepare GT files for AP* evaluation. Since LVIS annotations are not exhaustive, lvis-api and not cocoapi should be used to evaluate AP*.

Cityscapes

Cityscapes model is trained with ImageNet pretraining.

Mask
head		 Backbone lr
sched		 Output
resolution		 mask
AP		 model id download
PointRend R50-FPN 1× 224×224 35.9 164255101 model | metrics

Semantic Segmentation

Cityscapes

Cityscapes model is trained with ImageNet pretraining.

Method Backbone Output
resolution		 mIoU model id download
SemanticFPN + PointRend R101-FPN 1024×2048 78.9 202576688 model | metrics

Citing PointRend

If you use PointRend, please use the following BibTeX entry.

@InProceedings{kirillov2019pointrend,
  title={{PointRend}: Image Segmentation as Rendering},
  author={Alexander Kirillov and Yuxin Wu and Kaiming He and Ross Girshick},
  journal={ArXiv:1912.08193},
  year={2019}
}

Citing Implicit PointRend

If you use Implicit PointRend, please use the following BibTeX entry.

@InProceedings{cheng2021pointly,
  title={Pointly-Supervised Instance Segmentation,
  author={Bowen Cheng and Omkar Parkhi and Alexander Kirillov},
  journal={ArXiv},
  year={2021}
}