ILAPE-RBF-k-DPP

To run the code you will have to download the AnimalPose Dataset (https://sites.google.com/view/animal-pose/). Extract the keypoints and store them in csv files using the utils.py files. You can further generate classwise csv files if you wish to using generate_classwise_df.py

After generating the data you can run the incremental learning setup using the following command. [Please refer to the config file stored in ILAPE/configs]

python train_incremental.py --exp-id 10 --cfg ./configs/rbf_dpp_50_aug.yaml 

Website link for the project - https://sites.google.com/view/ilape-rbf-kdpp/home

Citation

Please cite these papers in your publications if it helps your research:

@inproceedings{fang2017rmpe,
  title={Incremental Learning for Animal Pose Estimation using {RBF} k-DPP},
  author    = {Gaurav Kumar Nayak and
            Het Shah and
            Anirban Chakraborty},
  booktitle={BMVC},
  year={2021}
}

References:

[1] AlphaPose - https://github.com/MVIG-SJTU/AlphaPose

[2] DPPy - https://github.com/guilgautier/DPPy

[3] Python Thin Plate Splines - https://github.com/cheind/py-thin-plate-spline

[4] Pose Resnet - https://github.com/microsoft/human-pose-estimation.pytorch

[5] DET - https://github.com/aimagelab/mammoth