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# Pointly-Supervised Instance Segmentation |
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Bowen Cheng, Omkar Parkhi, Alexander Kirillov |
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[[`arXiv`](https://arxiv.org/abs/2104.06404)] [[`Project`](https://bowenc0221.github.io/point-sup)] [[`BibTeX`](#CitingPointSup)] |
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<div align="center"> |
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<img src="https://bowenc0221.github.io/images/cheng2021pointly.png" width="50%" height="50%"/> |
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</div><br/> |
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## Data preparation |
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Please follow these steps to prepare your datasets: |
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1. Follow official Detectron2 instruction to prepare COCO dataset. Set up `DETECTRON2_DATASETS` environment variable to the location of your Detectron2 dataset. |
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2. Generate 10-points annotations for COCO by running: `python tools/prepare_coco_point_annotations_without_masks.py 10` |
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## Training |
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To train a model with 8 GPUs run: |
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```bash |
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python train_net.py --config-file configs/mask_rcnn_R_50_FPN_3x_point_sup_point_aug_coco.yaml --num-gpus 8 |
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``` |
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## Evaluation |
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Model evaluation can be done similarly: |
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```bash |
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python train_net.py --config-file configs/mask_rcnn_R_50_FPN_3x_point_sup_point_aug_coco.yaml --eval-only MODEL.WEIGHTS /path/to/model_checkpoint |
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``` |
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## <a name="CitingPointSup"></a>Citing Pointly-Supervised Instance Segmentation |
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If you use PointSup, please use the following BibTeX entry. |
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```BibTeX |
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@article{cheng2021pointly, |
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title={Pointly-Supervised Instance Segmentation}, |
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author={Bowen Cheng and Omkar Parkhi and Alexander Kirillov}, |
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journal={arXiv}, |
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year={2021} |
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} |
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``` |
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