mirror of https://github.com/RE-OWOD/RE-OWOD
64 lines
3.0 KiB
Markdown
64 lines
3.0 KiB
Markdown
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# TensorMask in Detectron2
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**A Foundation for Dense Object Segmentation**
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Xinlei Chen, Ross Girshick, Kaiming He, Piotr Dollár
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[[`arXiv`](https://arxiv.org/abs/1903.12174)] [[`BibTeX`](#CitingTensorMask)]
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<div align="center">
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<img src="http://xinleic.xyz/images/tmask.png" width="700px" />
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</div>
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In this repository, we release code for TensorMask in Detectron2.
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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.
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## Installation
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First install Detectron2 following the [documentation](https://detectron2.readthedocs.io/tutorials/install.html) and
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[setup the dataset](../../datasets). Then compile the TensorMask-specific op (`swap_align2nat`):
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```bash
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pip install -e /path/to/detectron2/projects/TensorMask
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```
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## Training
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To train a model, run:
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```bash
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python /path/to/detectron2/projects/TensorMask/train_net.py --config-file <config.yaml>
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```
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For example, to launch TensorMask BiPyramid training (1x schedule) with ResNet-50 backbone on 8 GPUs,
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one should execute:
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```bash
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python /path/to/detectron2/projects/TensorMask/train_net.py --config-file configs/tensormask_R_50_FPN_1x.yaml --num-gpus 8
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```
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## Evaluation
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Model evaluation can be done similarly (6x schedule with scale augmentation):
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```bash
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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
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```
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# Pretrained Models
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| Backbone | lr sched | AP box | AP mask | download |
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| -------- | -------- | -- | --- | -------- |
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| 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> |
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| 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> |
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## <a name="CitingTensorMask"></a>Citing TensorMask
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If you use TensorMask, please use the following BibTeX entry.
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```
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@InProceedings{chen2019tensormask,
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title={Tensormask: A Foundation for Dense Object Segmentation},
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author={Chen, Xinlei and Girshick, Ross and He, Kaiming and Doll{\'a}r, Piotr},
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journal={The International Conference on Computer Vision (ICCV)},
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year={2019}
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}
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```
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