TensorMask in Detectron2

A Foundation for Dense Object Segmentation

Xinlei Chen, Ross Girshick, Kaiming He, Piotr Dollár

[arXiv] [BibTeX]

In this repository, we release code for TensorMask in Detectron2. TensorMask is a dense sliding-window instance segmentation framework that, for the first time, achieves results close to the well-developed Mask R-CNN framework -- both qualitatively and quantitatively. It establishes a conceptually complementary direction for object instance segmentation research.

Installation

First install Detectron2 following the documentation and setup the dataset. Then compile the TensorMask-specific op (swap_align2nat):

pip install -e /path/to/detectron2/projects/TensorMask

Training

To train a model, run:

python /path/to/detectron2/projects/TensorMask/train_net.py --config-file <config.yaml>

For example, to launch TensorMask BiPyramid training (1x schedule) with ResNet-50 backbone on 8 GPUs, one should execute:

python /path/to/detectron2/projects/TensorMask/train_net.py --config-file configs/tensormask_R_50_FPN_1x.yaml --num-gpus 8

Evaluation

Model evaluation can be done similarly (6x schedule with scale augmentation):

python /path/to/detectron2/projects/TensorMask/train_net.py --config-file configs/tensormask_R_50_FPN_6x.yaml --eval-only MODEL.WEIGHTS /path/to/model_checkpoint

Pretrained Models

Backbone	lr sched	AP box	AP mask	download
R50	1x	37.6	32.4	model \| metrics
R50	6x	41.4	35.8	model \| metrics

Citing TensorMask

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

@InProceedings{chen2019tensormask,
  title={Tensormask: A Foundation for Dense Object Segmentation},
  author={Chen, Xinlei and Girshick, Ross and He, Kaiming and Doll{\'a}r, Piotr},
  journal={The International Conference on Computer Vision (ICCV)},
  year={2019}
}