| # 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> \| <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> \| <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} | |
| } | |
| ``` | |