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  Please see [Shaokai Ye et al. 2023](https://arxiv.org/abs/2203.07436) for details.
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  • The there are three models:
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- - One is an HRNet-w32 trained within mmpose on our Quadruped-80K dataset.
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- - pose_model is an HRNet-w32 compatable for DLC3.0+ Pytorch code, trained on our Quadruped-80K dataset.
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- - Detector is a Faster R-CNN that can be used as a detector for top-down estimation.
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  • Full training details can be found in Ye et al. 2023.
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  You can use the pose_model and detector simply with our light-weight loading package called [DLCLibrary](https://github.com/DeepLabCut/DLClibrary).
 
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  Please see [Shaokai Ye et al. 2023](https://arxiv.org/abs/2203.07436) for details.
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  • The there are three models:
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+ - `pose_model.pth` is an HRNet-w32 compatable for DLC3.0+ Pytorch code, trained on our Quadruped-80K dataset.
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+ - `detector.pt` is a Faster R-CNN that can be used as a detector for top-down detection.
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+ - `hrnet_w32_quadruped80k.pth` is an HRNet-w32 trained with mmpose on our Quadruped-80K dataset.
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  • Full training details can be found in Ye et al. 2023.
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  You can use the pose_model and detector simply with our light-weight loading package called [DLCLibrary](https://github.com/DeepLabCut/DLClibrary).