detectron2-maskrcnn

# PointRend: Image Segmentation as Rendering Alexander Kirillov, Yuxin Wu, Kaiming He, Ross Girshick [[`arXiv`](https://arxiv.org/abs/1912.08193)] [[`BibTeX`](#CitingPointRend)] <div align="center"> <img src="https://alexander-kirillov.github.io/images/kirillov2019pointrend.jpg"/> </div><br/> In this repository, we release code for PointRend in Detectron2. PointRend can be flexibly applied to both instance and semantic segmentation tasks by building on top of existing state-of-the-art models. ## Installation Install Detectron2 following [the instructions](https://detectron2.readthedocs.io/tutorials/install.html). You are ready to go! ## Quick start and visualization This [Colab Notebook](https://colab.research.google.com/drive/1isGPL5h5_cKoPPhVL9XhMokRtHDvmMVL) tutorial contains examples of PointRend usage and visualizations of its point sampling stages. ## Training To train a model with 8 GPUs run: ```bash cd /path/to/detectron2/projects/PointRend python train_net.py --config-file configs/InstanceSegmentation/pointrend_rcnn_R_50_FPN_1x_coco.yaml --num-gpus 8 ``` ## Evaluation Model evaluation can be done similarly: ```bash cd /path/to/detectron2/projects/PointRend python train_net.py --config-file configs/InstanceSegmentation/pointrend_rcnn_R_50_FPN_1x_coco.yaml --eval-only MODEL.WEIGHTS /path/to/model_checkpoint ``` # Pretrained Models ## Instance Segmentation #### COCO <table><tbody> <!-- START TABLE --> <!-- TABLE HEADER --> <th valign="bottom">Mask<br/>head</th> <th valign="bottom">Backbone</th> <th valign="bottom">lr<br/>sched</th> <th valign="bottom">Output<br/>resolution</th> <th valign="bottom">mask<br/>AP</th> <th valign="bottom">mask<br/>AP&ast;</th> <th valign="bottom">model id</th> <th valign="bottom">download</th> <!-- TABLE BODY --> <tr><td align="left"><a href="configs/InstanceSegmentation/pointrend_rcnn_R_50_FPN_1x_coco.yaml">PointRend</a></td> <td align="center">R50-FPN</td> <td align="center">1&times;</td> <td align="center">224&times;224</td> <td align="center">36.2</td> <td align="center">39.7</td> <td align="center">164254221</td> <td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/PointRend/InstanceSegmentation/pointrend_rcnn_R_50_FPN_1x_coco/164254221/model_final_88c6f8.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/PointRend/InstanceSegmentation/pointrend_rcnn_R_50_FPN_1x_coco/164254221/metrics.json">metrics</a></td> </tr> <tr><td align="left"><a href="configs/InstanceSegmentation/pointrend_rcnn_R_50_FPN_3x_coco.yaml">PointRend</a></td> <td align="center">R50-FPN</td> <td align="center">3&times;</td> <td align="center">224&times;224</td> <td align="center">38.3</td> <td align="center">41.6</td> <td align="center">164955410</td> <td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/PointRend/InstanceSegmentation/pointrend_rcnn_R_50_FPN_3x_coco/164955410/model_final_3c3198.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/PointRend/InstanceSegmentation/pointrend_rcnn_R_50_FPN_3x_coco/164955410/metrics.json">metrics</a></td> </tr> </tbody></table> AP&ast; is COCO mask AP evaluated against the higher-quality LVIS annotations; see the paper for details. Run `python detectron2/datasets/prepare_cocofied_lvis.py` to prepare GT files for AP&ast; evaluation. Since LVIS annotations are not exhaustive `lvis-api` and not `cocoapi` should be used to evaluate AP&ast;. #### Cityscapes Cityscapes model is trained with ImageNet pretraining. <table><tbody> <!-- START TABLE --> <!-- TABLE HEADER --> <th valign="bottom">Mask<br/>head</th> <th valign="bottom">Backbone</th> <th valign="bottom">lr<br/>sched</th> <th valign="bottom">Output<br/>resolution</th> <th valign="bottom">mask<br/>AP</th> <th valign="bottom">model id</th> <th valign="bottom">download</th> <!-- TABLE BODY --> <tr><td align="left"><a href="configs/InstanceSegmentation/pointrend_rcnn_R_50_FPN_1x_cityscapes.yaml">PointRend</a></td> <td align="center">R50-FPN</td> <td align="center">1&times;</td> <td align="center">224&times;224</td> <td align="center">35.9</td> <td align="center">164255101</td> <td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/PointRend/InstanceSegmentation/pointrend_rcnn_R_50_FPN_1x_cityscapes/164255101/model_final_318a02.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/PointRend/InstanceSegmentation/pointrend_rcnn_R_50_FPN_1x_cityscapes/164255101/metrics.json">metrics</a></td> </tr> </tbody></table> ## Semantic Segmentation #### Cityscapes Cityscapes model is trained with ImageNet pretraining. <table><tbody> <!-- START TABLE --> <!-- TABLE HEADER --> <th valign="bottom">Method</th> <th valign="bottom">Backbone</th> <th valign="bottom">Output<br/>resolution</th> <th valign="bottom">mIoU</th> <th valign="bottom">model id</th> <th valign="bottom">download</th> <!-- TABLE BODY --> <tr><td align="left"><a href="configs/SemanticSegmentation/pointrend_semantic_R_101_FPN_1x_cityscapes.yaml">SemanticFPN + PointRend</a></td> <td align="center">R101-FPN</td> <td align="center">1024&times;2048</td> <td align="center">78.6</td> <td align="center">186480235</td> <td align="center"><a href="https://dl.fbaipublicfiles.com/detectron2/PointRend/SemanticSegmentation/pointrend_semantic_R_101_FPN_1x_cityscapes/186480235/model_final_5f3665.pkl">model</a>&nbsp;|&nbsp;<a href="https://dl.fbaipublicfiles.com/detectron2/PointRend/SemanticSegmentation/pointrend_semantic_R_101_FPN_1x_cityscapes/186480235/metrics.json">metrics</a></td> </tr> </tbody></table> ## <a name="CitingPointRend"></a>Citing PointRend If you use PointRend, please use the following BibTeX entry. ```BibTeX @InProceedings{kirillov2019pointrend, title={{PointRend}: Image Segmentation as Rendering}, author={Alexander Kirillov and Yuxin Wu and Kaiming He and Ross Girshick}, journal={ArXiv:1912.08193}, year={2019} } ```