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| # BlendMask: Top-Down Meets Bottom-Up for Instance Segmentation | ||
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| BlendMask: Top-Down Meets Bottom-Up for Instance Segmentation; | ||
| Hao Chen, Kunyang Sun, Zhi Tian, Chunhua Shen, Yongming Huang, and Youliang Yan; | ||
| In: Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2020. | ||
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| [[`Paper`](https://arxiv.org/abs/2001.00309)] [[`BibTeX`](#citing-blendmask)] | ||
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| This project contains training BlendMask for instance segmentation and panoptic segmentation on COCO and configs for segmenting persons on PIC. | ||
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| ## Quick Start | ||
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| ### Demo | ||
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| ``` | ||
| wget -O blendmask_r101_dcni3_5x.pth https://cloudstor.aarnet.edu.au/plus/s/vbnKnQtaGlw8TKv/download | ||
| python demo/demo.py \ | ||
| --config-file configs/BlendMask/R_101_dcni3_5x.yaml \ | ||
| --input datasets/coco/val2017/000000005992.jpg \ | ||
| --confidence-threshold 0.35 \ | ||
| --opts MODEL.WEIGHTS blendmask_r101_dcni3_5x.pth | ||
| ``` | ||
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| ### Training and evaluation | ||
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| To train a model with "train_net.py", first | ||
| setup the corresponding datasets following | ||
| [datasets/README.md](https://github.com/facebookresearch/detectron2/blob/master/datasets/README.md), | ||
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| Then follow [these steps](https://github.com/aim-uofa/AdelaiDet/tree/master/datasets#expected-dataset-structure-for-adelaidet-instance-detection) to generate blendmask format annotations for instance segmentation. | ||
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| then run: | ||
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| ``` | ||
| OMP_NUM_THREADS=1 python tools/train_net.py \ | ||
| --config-file configs/BlendMask/R_50_1x.yaml \ | ||
| --num-gpus 4 \ | ||
| OUTPUT_DIR training_dir/blendmask_R_50_1x | ||
| ``` | ||
| To evaluate the model after training, run: | ||
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| ``` | ||
| OMP_NUM_THREADS=1 python tools/train_net.py \ | ||
| --config-file configs/BlendMask/R_50_1x.yaml \ | ||
| --eval-only \ | ||
| --num-gpus 4 \ | ||
| OUTPUT_DIR training_dir/blendmask_R_50_1x \ | ||
| MODEL.WEIGHTS training_dir/blendmask_R_50_1x/model_final.pth | ||
| ``` | ||
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| ## Models | ||
| ### COCO Instance Segmentation Baselines | ||
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| Model | Name | inf. time | box AP | download | ||
| --- |:---:|:---:|:---:|:---: | ||
| Mask R-CNN | [R_50_1x](https://github.com/facebookresearch/detectron2/blob/master/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml) | 13 FPS | 38.6 | 35.2 | | ||
| BlendMask | [R_50_1x](configs/BlendMask/R_50_1x.yaml) | 14 FPS | 39.9 | 35.8 | [model](https://cloudstor.aarnet.edu.au/plus/s/zoxXPnr6Hw3OJgK/download) | ||
| Mask R-CNN | [R_50_3x](https://github.com/facebookresearch/detectron2/blob/master/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml) | 13 FPS | 41.0 | 37.2 | | ||
| BlendMask | [R_50_3x](configs/BlendMask/R_50_3x.yaml) | 14 FPS | 42.7 | 37.8 | [model](https://cloudstor.aarnet.edu.au/plus/s/ZnaInHFEKst6mvg/download) | ||
| Mask R-CNN | [R_101_3x](https://github.com/facebookresearch/detectron2/blob/master/configs/COCO-InstanceSegmentation/mask_rcnn_R_101_FPN_3x.yaml) | 10 FPS | 42.9 | 38.6 | | ||
| BlendMask | [R_101_3x](configs/BlendMask/R_101_3x.yaml) | 11 FPS | 44.8 | 39.5 | [model](https://cloudstor.aarnet.edu.au/plus/s/e4fXrliAcMtyEBy/download) | ||
| BlendMask | [R_101_dcni3_5x](configs/BlendMask/R_101_dcni3_5x.yaml) | 10 FPS | 46.8 | 41.1 | [model](https://cloudstor.aarnet.edu.au/plus/s/vbnKnQtaGlw8TKv/download) | ||
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| ### BlendMask Real-time Models | ||
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| Model | Name | inf. time | box AP | download | ||
| --- |:---:|:---:|:---:|:---: | ||
| Mask R-CNN | [550_R_50_3x](configs/RCNN/550_R_50_FPN_3x.yaml) | 16 FPS | 39.1 | 35.3 | | ||
| BlendMask | [550_R_50_3x](configs/BlendMask/550_R_50_3x.yaml) | 28 FPS | 38.7 | 34.5 | [model](https://cloudstor.aarnet.edu.au/plus/s/R3Qintf7N8UCiIt/download) | ||
| BlendMask | [RT_R_50_4x](configs/BlendMask/RT_R_50_4x.yaml) | 30 FPS | 40.1 | 34.6 | [model](https://cloudstor.aarnet.edu.au/plus/s/fmmciLkyaOoY1Tc/download) | ||
| BlendMask | [DLA_34_4x](configs/BlendMask/DLA_34_syncbn_4x.yaml) | 32 FPS | 40.9 | 35.2 | [model](https://cloudstor.aarnet.edu.au/plus/s/Lx94rWNnZ8TRd2Y/download) | ||
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| # Citing BlendMask | ||
| If you use BlendMask in your research or wish to refer to the baseline results, please use the following BibTeX entries. | ||
| ```BibTeX | ||
| @inproceedings{chen2020blendmask, | ||
| title = {{BlendMask}: Top-Down Meets Bottom-Up for Instance Segmentation}, | ||
| author = {Chen, Hao and Sun, Kunyang and Tian, Zhi and Shen, Chunhua and Huang, Yongming and Yan, Youliang}, | ||
| booktitle = {Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR)}, | ||
| year = {2020} | ||
| } | ||
| ``` |