
# 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)]

<div align="center">
  <img src="http://xinleic.xyz/images/tmask.png" width="700px" />
</div>

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

## Training

To train a model, run:
```bash
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:
```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 | <a href="https://dl.fbaipublicfiles.com/detectron2/TensorMask/tensormask_R_50_FPN_1x/152549419/model_final_8f325c.pkl">model</a>&nbsp;\| &nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/TensorMask/tensormask_R_50_FPN_1x/152549419/metrics.json">metrics</a> |
| R50      | 6x       | 41.4   | 35.8 | <a href="https://dl.fbaipublicfiles.com/detectron2/TensorMask/tensormask_R_50_FPN_6x/153538791/model_final_e8df31.pkl">model</a>&nbsp;\| &nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/TensorMask/tensormask_R_50_FPN_6x/153538791/metrics.json">metrics</a> |


## <a name="CitingTensorMask"></a>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}
}
```
