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license: cc-by-nc-sa-4.0 |
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--- |
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# Model Card for Oriented R-CNN pretrained on DOTA 1.0 |
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<!-- Provide a quick summary of what the model is/does. [Optional] --> |
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The original paper is [Oriented R-CNN for Object Detection](https://openaccess.thecvf.com/content/ICCV2021/papers/Xie_Oriented_R-CNN_for_Object_Detection_ICCV_2021_paper.pdf). |
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This implementation of this model has been developed by [OpenMMLab](https://openmmlab.com/) in the [MMRotate](https://github.com/open-mmlab/mmrotate) framework. |
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The model has been trained on [DOTA 1.0](https://captain-whu.github.io/DOTA/) |
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The performance measured as mAP is 75.69. |
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- **Developed by:** OpenMMLab |
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- **Model type:** Object Detection model |
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- **License:** cc-by-nc-sa-4.0 |
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- **Resources for more information:** More information needed |
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- [GitHub Repo](https://github.com/open-mmlab/mmrotate/) |
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- [Associated Paper](https://openaccess.thecvf.com/content/ICCV2021/papers/Xie_Oriented_R-CNN_for_Object_Detection_ICCV_2021_paper.pdf) |
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# How to Get Started with the Model |
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Use the code below to get started with the model. |
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``` |
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from mmdet.apis import init_detector, inference_detector |
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import mmrotate |
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config_file = 'oriented_rcnn_r50_fpn_1x_dota_le90.py' |
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checkpoint_file = 'oriented_rcnn_r50_fpn_1x_dota_le90-6d2b2ce0.pth' |
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model = init_detector(config_file, checkpoint_file, device='cuda:0') |
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inference_detector(model, 'demo/demo.jpg') |
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``` |
