dataset_info = dict(
    dataset_name="animalpose",
    paper_info=dict(
        author="Cao, Jinkun and Tang, Hongyang and Fang, Hao-Shu and "
        "Shen, Xiaoyong and Lu, Cewu and Tai, Yu-Wing",
        title="Cross-Domain Adaptation for Animal Pose Estimation",
        container="The IEEE International Conference on " "Computer Vision (ICCV)",
        year="2019",
        homepage="https://sites.google.com/view/animal-pose/",
    ),
    keypoint_info={
        0: dict(name="L_Eye", id=0, color=[0, 255, 0], type="upper", swap="R_Eye"),
        1: dict(name="R_Eye", id=1, color=[255, 128, 0], type="upper", swap="L_Eye"),
        2: dict(
            name="L_EarBase", id=2, color=[0, 255, 0], type="upper", swap="R_EarBase"
        ),
        3: dict(
            name="R_EarBase", id=3, color=[255, 128, 0], type="upper", swap="L_EarBase"
        ),
        4: dict(name="Nose", id=4, color=[51, 153, 255], type="upper", swap=""),
        5: dict(name="Throat", id=5, color=[51, 153, 255], type="upper", swap=""),
        6: dict(name="TailBase", id=6, color=[51, 153, 255], type="lower", swap=""),
        7: dict(name="Withers", id=7, color=[51, 153, 255], type="upper", swap=""),
        8: dict(
            name="L_F_Elbow", id=8, color=[0, 255, 0], type="upper", swap="R_F_Elbow"
        ),
        9: dict(
            name="R_F_Elbow", id=9, color=[255, 128, 0], type="upper", swap="L_F_Elbow"
        ),
        10: dict(
            name="L_B_Elbow", id=10, color=[0, 255, 0], type="lower", swap="R_B_Elbow"
        ),
        11: dict(
            name="R_B_Elbow", id=11, color=[255, 128, 0], type="lower", swap="L_B_Elbow"
        ),
        12: dict(
            name="L_F_Knee", id=12, color=[0, 255, 0], type="upper", swap="R_F_Knee"
        ),
        13: dict(
            name="R_F_Knee", id=13, color=[255, 128, 0], type="upper", swap="L_F_Knee"
        ),
        14: dict(
            name="L_B_Knee", id=14, color=[0, 255, 0], type="lower", swap="R_B_Knee"
        ),
        15: dict(
            name="R_B_Knee", id=15, color=[255, 128, 0], type="lower", swap="L_B_Knee"
        ),
        16: dict(
            name="L_F_Paw", id=16, color=[0, 255, 0], type="upper", swap="R_F_Paw"
        ),
        17: dict(
            name="R_F_Paw", id=17, color=[255, 128, 0], type="upper", swap="L_F_Paw"
        ),
        18: dict(
            name="L_B_Paw", id=18, color=[0, 255, 0], type="lower", swap="R_B_Paw"
        ),
        19: dict(
            name="R_B_Paw", id=19, color=[255, 128, 0], type="lower", swap="L_B_Paw"
        ),
    },
    skeleton_info={
        0: dict(link=("L_Eye", "R_Eye"), id=0, color=[51, 153, 255]),
        1: dict(link=("L_Eye", "L_EarBase"), id=1, color=[0, 255, 0]),
        2: dict(link=("R_Eye", "R_EarBase"), id=2, color=[255, 128, 0]),
        3: dict(link=("L_Eye", "Nose"), id=3, color=[0, 255, 0]),
        4: dict(link=("R_Eye", "Nose"), id=4, color=[255, 128, 0]),
        5: dict(link=("Nose", "Throat"), id=5, color=[51, 153, 255]),
        6: dict(link=("Throat", "Withers"), id=6, color=[51, 153, 255]),
        7: dict(link=("TailBase", "Withers"), id=7, color=[51, 153, 255]),
        8: dict(link=("Throat", "L_F_Elbow"), id=8, color=[0, 255, 0]),
        9: dict(link=("L_F_Elbow", "L_F_Knee"), id=9, color=[0, 255, 0]),
        10: dict(link=("L_F_Knee", "L_F_Paw"), id=10, color=[0, 255, 0]),
        11: dict(link=("Throat", "R_F_Elbow"), id=11, color=[255, 128, 0]),
        12: dict(link=("R_F_Elbow", "R_F_Knee"), id=12, color=[255, 128, 0]),
        13: dict(link=("R_F_Knee", "R_F_Paw"), id=13, color=[255, 128, 0]),
        14: dict(link=("TailBase", "L_B_Elbow"), id=14, color=[0, 255, 0]),
        15: dict(link=("L_B_Elbow", "L_B_Knee"), id=15, color=[0, 255, 0]),
        16: dict(link=("L_B_Knee", "L_B_Paw"), id=16, color=[0, 255, 0]),
        17: dict(link=("TailBase", "R_B_Elbow"), id=17, color=[255, 128, 0]),
        18: dict(link=("R_B_Elbow", "R_B_Knee"), id=18, color=[255, 128, 0]),
        19: dict(link=("R_B_Knee", "R_B_Paw"), id=19, color=[255, 128, 0]),
    },
    joint_weights=[
        1.0,
        1.0,
        1.0,
        1.0,
        1.0,
        1.0,
        1.0,
        1.0,
        1.0,
        1.0,
        1.0,
        1.0,
        1.2,
        1.2,
        1.2,
        1.2,
        1.5,
        1.5,
        1.5,
        1.5,
    ],
    # Note: The original paper did not provide enough information about
    # the sigmas. We modified from 'https://github.com/cocodataset/'
    # 'cocoapi/blob/master/PythonAPI/pycocotools/cocoeval.py#L523'
    sigmas=[
        0.025,
        0.025,
        0.026,
        0.035,
        0.035,
        0.10,
        0.10,
        0.10,
        0.107,
        0.107,
        0.107,
        0.107,
        0.087,
        0.087,
        0.087,
        0.087,
        0.089,
        0.089,
        0.089,
        0.089,
    ],
)
