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deeplabcut.pose_estimation_pytorch.config.enums

Classes:

Name Description
DatasetType

Enumeration of dataset types.
DetectorType

Enumeration of detector types.
MethodType

Enumeration of pose estimation method types.
NetType

Enumeration of network architecture types as stored in configs.

DatasetType

Bases: str, Enum

Enumeration of dataset types.

Source code in deeplabcut/pose_estimation_pytorch/config/enums.py 
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class DatasetType(str, Enum):
    """Enumeration of dataset types."""

    # TODO @deruyter92 2026-02-05: Add other dataset types as needed.
    MULTIANIMAL_IMGAUG = "multi-animal-imgaug"

DetectorType

Bases: str, Enum

Enumeration of detector types.

Source code in deeplabcut/pose_estimation_pytorch/config/enums.py 
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class DetectorType(str, Enum):
    """Enumeration of detector types."""

    SSDLITE = "ssdlite"
    FASTERRCNN_RESNET50_FPN_V2 = "fasterrcnn_resnet50_fpn_v2"
    FASTERRCNN_MOBILENET_V3_LARGE_FPN = "fasterrcnn_mobilenet_v3_large_fpn"

MethodType

Bases: str, Enum

Enumeration of pose estimation method types.

Source code in deeplabcut/pose_estimation_pytorch/config/enums.py 
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class MethodType(str, Enum):
    """Enumeration of pose estimation method types."""

    BOTTOM_UP = "bu"
    TOP_DOWN = "td"
    CONDITIONAL_TOP_DOWN = "ctd"

NetType

Bases: str, Enum

Enumeration of network architecture types as stored in configs.

Note

Aliases (e.g. top_down_resnet_50) are user-facing names that map to a canonical member plus an optional top-down flag. See alias, from_alias, and available_aliases

Methods:

Name Description
alias

User-facing name (e.g. top_down_resnet_50 for backbone + TD).
available_aliases

All selectable model names for GUI / docs / create_training_dataset.
from_alias

Parse user-facing / legacy name → (canonical enum, top_down).
Source code in deeplabcut/pose_estimation_pytorch/config/enums.py 
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class NetType(str, Enum):
    """Enumeration of network architecture types as stored in configs.

    Note:
        Aliases (e.g. ``top_down_resnet_50``) are user-facing names that map to
        a canonical member plus an optional top-down flag. See ``alias``,
        ``from_alias``, and ``available_aliases``
    """

    RESNET_50 = "resnet_50"
    RESNET_101 = "resnet_101"

    HRNET_W18 = "hrnet_w18"
    HRNET_W32 = "hrnet_w32"
    HRNET_W48 = "hrnet_w48"

    CSPNEXT_S = "cspnext_s"
    CSPNEXT_M = "cspnext_m"
    CSPNEXT_X = "cspnext_x"

    DEKR_W18 = "dekr_w18"
    DEKR_W32 = "dekr_w32"
    DEKR_W48 = "dekr_w48"

    CTD_COAM_W32 = "ctd_coam_w32"
    CTD_COAM_W48 = "ctd_coam_w48"
    CTD_COAM_W48_HUMAN = "ctd_coam_w48_human"
    CTD_PRENET_HRNET_W32 = "ctd_prenet_hrnet_w32"
    CTD_PRENET_HRNET_W48 = "ctd_prenet_hrnet_w48"
    CTD_PRENET_RTMPOSE_S = "ctd_prenet_rtmpose_s"
    CTD_PRENET_RTMPOSE_M = "ctd_prenet_rtmpose_m"
    CTD_PRENET_RTMPOSE_X = "ctd_prenet_rtmpose_x"
    CTD_PRENET_RTMPOSE_X_HUMAN = "ctd_prenet_rtmpose_x_human"

    DLCRNET_STRIDE16_MS5 = "dlcrnet_stride16_ms5"
    DLCRNET_STRIDE32_MS5 = "dlcrnet_stride32_ms5"

    RTMPOSE_S = "rtmpose_s"
    RTMPOSE_M = "rtmpose_m"
    RTMPOSE_X = "rtmpose_x"

    ANIMALTOKENPOSE_BASE = "animaltokenpose_base"

    @functools.cached_property
    def is_backbone(self) -> bool:
        from deeplabcut.pose_estimation_pytorch.config.utils import (
            get_config_folder_path,
            load_backbones,
        )

        return self.value in frozenset(load_backbones(get_config_folder_path()))

    def alias(self, *, top_down: bool = False) -> str:
        """User-facing name (e.g. ``top_down_resnet_50`` for backbone + TD)."""
        if top_down and self.is_backbone:
            return f"{_TOP_DOWN_PREFIX}{self.value}"
        return self.value

    @classmethod
    def from_alias(cls, label: str) -> tuple[NetType, bool]:
        """Parse user-facing / legacy name → (canonical enum, top_down)."""
        label = _LEGACY_NET_TYPE_ALIASES.get(label, label)
        label_has_td_prefix = False
        if label.startswith(_TOP_DOWN_PREFIX):
            label = label.removeprefix(_TOP_DOWN_PREFIX)
            label_has_td_prefix = True
        return cls(label), label_has_td_prefix

    @classmethod
    def available_aliases(cls) -> list[str]:
        """All selectable model names for GUI / docs / ``create_training_dataset``."""
        labels: list[str] = []
        for net_type in cls:
            labels.append(net_type.alias(top_down=False))
            if net_type.is_backbone:
                labels.append(net_type.alias(top_down=True))
        return sorted(labels)

alias 

alias(*, top_down: bool = False) -> str

User-facing name (e.g. top_down_resnet_50 for backbone + TD).

Source code in deeplabcut/pose_estimation_pytorch/config/enums.py 
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def alias(self, *, top_down: bool = False) -> str:
    """User-facing name (e.g. ``top_down_resnet_50`` for backbone + TD)."""
    if top_down and self.is_backbone:
        return f"{_TOP_DOWN_PREFIX}{self.value}"
    return self.value

available_aliases classmethod 

available_aliases() -> list[str]

All selectable model names for GUI / docs / create_training_dataset.

Source code in deeplabcut/pose_estimation_pytorch/config/enums.py 
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@classmethod
def available_aliases(cls) -> list[str]:
    """All selectable model names for GUI / docs / ``create_training_dataset``."""
    labels: list[str] = []
    for net_type in cls:
        labels.append(net_type.alias(top_down=False))
        if net_type.is_backbone:
            labels.append(net_type.alias(top_down=True))
    return sorted(labels)

from_alias classmethod 

from_alias(label: str) -> tuple[NetType, bool]

Parse user-facing / legacy name → (canonical enum, top_down).

Source code in deeplabcut/pose_estimation_pytorch/config/enums.py 
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@classmethod
def from_alias(cls, label: str) -> tuple[NetType, bool]:
    """Parse user-facing / legacy name → (canonical enum, top_down)."""
    label = _LEGACY_NET_TYPE_ALIASES.get(label, label)
    label_has_td_prefix = False
    if label.startswith(_TOP_DOWN_PREFIX):
        label = label.removeprefix(_TOP_DOWN_PREFIX)
        label_has_td_prefix = True
    return cls(label), label_has_td_prefix