# TensorMask in Detectron2
**A Foundation for Dense Object Segmentation**
Xinlei Chen, Ross Girshick, Kaiming He, Piotr Dollár
[[`arXiv`](https://arxiv.org/abs/1903.12174)] [[`BibTeX`](#CitingTensorMask)]
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](https://detectron2.readthedocs.io/tutorials/install.html) and
[setup the dataset](../../datasets). Then compile the TensorMask-specific op (`swap_align2nat`):
```bash
cd /path/to/detectron2/projects/TensorMask
python setup.py build develop
```
## Training
To train a model, run:
```bash
python /path/to/detectron2/projects/TensorMask/train_net.py --config-file
```
For example, to launch TensorMask BiPyramid training (1x schedule) with ResNet-50 backbone on 8 GPUs,
one should execute:
```bash
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):
```bash
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}
}
```