detectron/projects/TensorMask/README.md

• TensorMask in Detectron2
• • Installation
  • Training
  • Evaluation
• Model Zoo and Baselines
• • Citing TensorMask

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

To install, first setup Detectron 2 following INSTALL.md. Then to compile the TensorMask-specific op (swap_align2nat):

cd /path/to/detectron2/projects/TensorMask
python setup.py build develop

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 model.pth

Model Zoo and Baselines

(coming soon)

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}
}