DeepLabCut · C-Achard · Jun 12, 2026 · Jun 12, 2026 · Jun 12, 2026 · Jun 12, 2026
diff --git a/_toc.yml b/_toc.yml
@@ -1,10 +1,12 @@
 format: jb-book
 root: README
+
 parts:
 - caption: Getting Started
   chapters:
   - file: docs/UseOverviewGuide
   - file: docs/course
+
 - caption: Installation
   chapters:
   - file: docs/installation
@@ -31,40 +33,41 @@ parts:
   - file: docs/pytorch/user_guide.md
   - file: docs/pytorch/pytorch_config.md
   - file: docs/pytorch/architectures.md
+
 - caption: Quick Start Tutorials
   chapters:
   - file: docs/quick-start/single_animal_quick_guide
   - file: docs/quick-start/tutorial_maDLC
-- caption: 🚀 Beginner's Guide to DeepLabCut
+
+- caption: "🚀 Beginner's Guide to DeepLabCut"
   chapters:
   - file: docs/beginner-guides/beginners-guide
   - file: docs/beginner-guides/manage-project
   - file: docs/beginner-guides/labeling
   - file: docs/beginner-guides/Training-Evaluation
   - file: docs/beginner-guides/video-analysis
-- caption: 🚀 Main Demo Notebooks
+
+- caption: "🚀 Main Demo Notebooks"
   chapters:
   - file: examples/COLAB/COLAB_DEMO_SuperAnimal
   - file: examples/COLAB/COLAB_DEMO_mouse_openfield
   - file: examples/COLAB/COLAB_3miceDemo
   - file: examples/COLAB/COLAB_HumanPose_with_RTMPose
 
-
-
-- caption: 🚀 Notebooks For Your Data
+- caption: "🚀 Notebooks For Your Data"
   chapters:
   - file: examples/COLAB/COLAB_YOURDATA_SuperAnimal
   - file: examples/COLAB/COLAB_YOURDATA_TrainNetwork_VideoAnalysis
   - file: examples/COLAB/COLAB_YOURDATA_maDLC_TrainNetwork_VideoAnalysis
 
-- caption: 🚀 Special Feature Demos
+- caption: "🚀 Special Feature Demos"
   chapters:
   - file: examples/COLAB/COLAB_transformer_reID
   - file: examples/COLAB/COLAB_BUCTD_and_CTD_tracking
   - file: examples/JUPYTER/Demo_3D_DeepLabCut
   - file: examples/COLAB/COLAB_DLC_ModelZoo
 
-- caption: 🧑‍🍳 Cookbook (detailed helper guides)
+- caption: "🧑‍🍳 Cookbook (detailed helper guides)"
   chapters:
   - file: docs/convert_maDLC
   - file: docs/recipes/OtherData
@@ -82,6 +85,7 @@ parts:
 - caption: Hardware Tips
   chapters:
   - file: docs/recipes/TechHardware
+
 - caption: DeepLabCut-Live!
   chapters:
   - file: docs/dlc-live/deeplabcutlive
@@ -110,7 +114,7 @@ parts:
   - file: docs/benchmark
   - file: docs/pytorch/Benchmarking_shuffle_guide
 
-- caption: Mission & Contribute
+- caption: "Mission & Contribute"
   chapters:
   - file: docs/MISSION_AND_VALUES
   - file: docs/roadmap

diff --git a/deeplabcut/core/config/base_config.py b/deeplabcut/core/config/base_config.py
@@ -49,7 +49,7 @@ class DLCBaseConfig(BaseModel):
     - Field aliases from `json_schema_extra`.
     """
 
-    model_config = ConfigDict(extra="forbid", validate_assignment=True)
+    model_config = ConfigDict(extra="forbid", validate_assignment=True, arbitrary_types_allowed=True)
 
     # ------------------------------------------------------------------
     # Validation (before pydantic field validation)
@@ -392,10 +392,16 @@ def record_change_note(
         include_caller: bool = False,
         _stack_depth: int = 1,
     ) -> None:
+        field_name = self._resolve_alias(field_name)
+
+        if field_name not in type(self).model_fields:
+            raise KeyError(f"'{type(self).__name__}' has no field '{field_name}'")
+
         if include_caller:
             frame = sys._getframe(_stack_depth)
             filename = frame.f_code.co_filename.rsplit("/", 1)[-1]
             message = f"{message} [{filename}:{frame.f_lineno}]"
+
         self._change_notes[field_name] = message
 
     def log_changes(self) -> None:

diff --git a/deeplabcut/core/weight_init.py b/deeplabcut/core/weight_init.py
@@ -104,8 +104,8 @@ def from_dict(data: dict) -> WeightInitialization:
         if "snapshot_path" not in data:
             return WeightInitialization.from_dict_legacy(data)
 
-        snapshot_path = data['snapshot_path']
-        if data['snapshot_path'] is not None:
+        snapshot_path = data["snapshot_path"]
+        if data["snapshot_path"] is not None:
             snapshot_path = Path(snapshot_path)
 
         detector_snapshot_path = data.get("detector_snapshot_path")

diff --git a/deeplabcut/modelzoo/generalized_data_converter/datasets/materialize.py b/deeplabcut/modelzoo/generalized_data_converter/datasets/materialize.py
@@ -612,7 +612,6 @@ def _generic2coco(
                     os.symlink(src, dest)
                 except Exception as err:
                     print(f"Could not create a symlink from {src} to {dest}: {err}")
-                    pass
 
         image["file_name"] = file_name
         lookuptable[dest] = src

diff --git a/deeplabcut/pose_estimation_3d/camera_calibration.py b/deeplabcut/pose_estimation_3d/camera_calibration.py
@@ -435,7 +435,7 @@ def check_undistortion(config, cbrow=8, cbcol=6, plot=True):
             f2.suptitle("Undistorted corner points on camera-1 and camera-2", fontsize=25)
             ax1.imshow(cv2.cvtColor(im_remapped1, cv2.COLOR_BGR2RGB))
             ax2.imshow(cv2.cvtColor(im_remapped2, cv2.COLOR_BGR2RGB))
-            for i in range(0, cam1_undistort.shape[1]):
+            for i in range(cam1_undistort.shape[1]):
                 ax1.scatter(
                     [cam1_undistort[-1][i, 0, 0]],
                     [cam1_undistort[-1][i, 0, 1]],

diff --git a/deeplabcut/pose_estimation_pytorch/config/make_pose_config.py b/deeplabcut/pose_estimation_pytorch/config/make_pose_config.py
@@ -17,6 +17,7 @@
 
 from deeplabcut.core.config import read_config_as_dict, write_config
 from deeplabcut.core.weight_init import WeightInitialization
+from deeplabcut.pose_estimation_pytorch.config.pose import PoseConfig
 from deeplabcut.pose_estimation_pytorch.config.utils import (
     get_config_folder_path,
     load_backbones,

diff --git a/deeplabcut/pose_estimation_pytorch/data/base.py b/deeplabcut/pose_estimation_pytorch/data/base.py
@@ -10,16 +10,16 @@
 #
 from __future__ import annotations
 
+import warnings
 from abc import ABC, abstractmethod
 from pathlib import Path
-import warnings
 
 import albumentations as A
 import numpy as np
 
-from deeplabcut.pose_estimation_pytorch.config.pose import PoseConfig
-import deeplabcut.core.config as config_utils
 import deeplabcut.pose_estimation_pytorch.config as config
+from deeplabcut.core.types import DEPRECATED_ARGUMENT
+from deeplabcut.pose_estimation_pytorch.config.pose import PoseConfig
 from deeplabcut.pose_estimation_pytorch.data.dataset import (
     PoseDataset,
     PoseDatasetParameters,
@@ -34,7 +34,6 @@
     map_id_to_annotations,
 )
 from deeplabcut.pose_estimation_pytorch.task import Task
-from deeplabcut.core.types import DEPRECATED_ARGUMENT
 
 
 class Loader(ABC):
@@ -66,18 +65,20 @@ def __init__(
         Args:
             project_root: The root directory of the project.
             image_root: The root directory of the images.
-            model_config (Path | str | PoseConfig | dict): 
+            model_config (Path | str | PoseConfig | dict):
                 The pose model configuration. Can be a path to a YAML file, a PoseConfig object, or a dictionary.
             (model_config_path: The path to the pose model configuration. Deprecated, use `model_config` instead.)
         """
+
         def _resolve_legacy_args(model_config, model_config_path):
             """Support for legacy argument `model_config_path`. returns new model_config arg"""
             if model_config_path:
                 warnings.warn(
                     "argument `model_config_path` in Loader.__init__ is deprecated, use `model_config` instead",
                     DeprecationWarning,
+                    stacklevel=2,
                 )
-                if model_config is not None: 
+                if model_config is not None:
                     raise ValueError(
                         "`model_config_path` and `model_config` arguments cannot be provided together! "
                         "Please provide only `model_config`."
@@ -86,21 +87,24 @@ def _resolve_legacy_args(model_config, model_config_path):
                     model_config_path = Path(model_config_path) / "pytorch_config.yaml"
                 model_config = model_config_path
             elif model_config is None:
-                raise ValueError(
-                    "`model_config` argument must be provided."
-                )
+                raise ValueError("`model_config` argument must be provided.")
             return model_config
-        model_config = _resolve_legacy_args(model_config, model_config_path)    
+
+        model_config = _resolve_legacy_args(model_config, model_config_path)
         self.model_cfg: PoseConfig = PoseConfig.from_any(model_config)
 
         def _infer_model_config_path(model_config: PoseConfig | dict | Path | str) -> Path:
-            """Resolve the pose config path. Either the input is a path, or it is specified in `metadata.pose_config_path` field."""
+            """
+            Resolve the pose config path.
+            Either the input is a path, or it is specified in `metadata.pose_config_path` field.
+            """
             provided_path = Path(model_config) if isinstance(model_config, (Path, str)) else None
             specified_path = self.model_cfg.select("metadata.pose_config_path")
             model_config_path = provided_path or specified_path
             if model_config_path is None:
                 raise ValueError("`model_config` must contain a `metadata.pose_config_path` field.")
             return Path(model_config_path)
+
         self.model_config_path = _infer_model_config_path(model_config)
 
         self.project_root = Path(project_root)

diff --git a/deeplabcut/pose_estimation_pytorch/data/dlcloader.py b/deeplabcut/pose_estimation_pytorch/data/dlcloader.py
@@ -20,8 +20,9 @@
 import numpy as np
 import pandas as pd
 import scipy.io as sio
-from deeplabcut.core.config.project_config import ProjectConfig
+
 import deeplabcut.utils.auxiliaryfunctions as af
+from deeplabcut.core.config.project_config import ProjectConfig
 from deeplabcut.core.engine import Engine
 from deeplabcut.generate_training_dataset.trainingsetmanipulation import drop_likelihood_columns
 from deeplabcut.pose_estimation_pytorch.data.base import Loader
@@ -51,9 +52,7 @@ def __init__(
         self._project_config: ProjectConfig = ProjectConfig.from_any(config)
         self._project_root = provided_root_dir or self._project_config.project_path
         if self._project_root is None:
-            raise ValueError(
-                "`config` must contain a `project_path` field."
-            )
+            raise ValueError("`config` must contain a `project_path` field.")
 
         self._shuffle = shuffle
         self._trainset_index = trainset_index

diff --git a/deeplabcut/pose_estimation_pytorch/data/postprocessor.py b/deeplabcut/pose_estimation_pytorch/data/postprocessor.py
@@ -39,7 +39,6 @@ def __call__(self, predictions: Any, context: Context) -> Any:
         Returns:
             a single post-processed prediction
         """
-        pass
 
 
 def build_bottom_up_postprocessor(

diff --git a/deeplabcut/pose_estimation_pytorch/data/preprocessor.py b/deeplabcut/pose_estimation_pytorch/data/preprocessor.py
@@ -51,7 +51,6 @@ def __call__(self, image: Image, context: Context) -> tuple[Image, Context]:
         Returns:
             the pre-processed image (or batch of images) and their context
         """
-        pass
 
 
 def build_bottom_up_preprocessor(color_mode: str, transform: A.BaseCompose) -> Preprocessor:

diff --git a/deeplabcut/pose_estimation_pytorch/models/backbones/base.py b/deeplabcut/pose_estimation_pytorch/models/backbones/base.py
@@ -54,7 +54,6 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
         Returns:
             a feature map for the input, of shape (batch_size, c', h', w')
         """
-        pass
 
     def freeze_batch_norm_layers(self) -> None:
         """Freezes batch norm layers.

diff --git a/deeplabcut/pose_estimation_pytorch/models/detectors/base.py b/deeplabcut/pose_estimation_pytorch/models/detectors/base.py
@@ -85,7 +85,6 @@ def forward(
             losses: {'loss_name': loss_value}
             detections: for each of the b images, {"boxes": bounding_boxes}
         """
-        pass
 
     @abstractmethod
     def get_target(self, labels: dict) -> list[dict]:
@@ -97,7 +96,6 @@ def get_target(self, labels: dict) -> list[dict]:
         Returns:
             list of dictionaries, each representing target information for a single annotation.
         """
-        pass
 
     def freeze_batch_norm_layers(self) -> None:
         """Freezes batch norm layers.

diff --git a/deeplabcut/pose_estimation_pytorch/models/heads/base.py b/deeplabcut/pose_estimation_pytorch/models/heads/base.py
@@ -97,7 +97,6 @@ def forward(self, x: torch.Tensor) -> dict[str, torch.Tensor]:
         Returns:
             the head outputs (e.g. "heatmap", "locref")
         """
-        pass
 
     def get_loss(
         self,
@@ -172,4 +171,3 @@ def convert_weights(
                 )
                 ``
         """
-        pass
diff --git a/deeplabcut/pose_estimation_pytorch/models/modules/conv_block.py b/deeplabcut/pose_estimation_pytorch/models/modules/conv_block.py
@@ -50,7 +50,6 @@ def forward(self, x: torch.Tensor):
         Returns:
             Output tensor.
         """
-        pass
 
     def _init_weights(self, pretrained: str | None):
         """Method for initializing block weights from pretrained models.

diff --git a/deeplabcut/pose_estimation_pytorch/models/necks/base.py b/deeplabcut/pose_estimation_pytorch/models/necks/base.py
@@ -33,7 +33,6 @@ def forward(self, x: torch.Tensor):
         Returns:
             Output tensor.
         """
-        pass
 
     def _init_weights(self, pretrained: str):
         """Initialize the Neck with pretrained weights.

diff --git a/deeplabcut/pose_estimation_pytorch/models/predictors/base.py b/deeplabcut/pose_estimation_pytorch/models/predictors/base.py
@@ -61,4 +61,3 @@ def forward(self, stride: float, outputs: dict[str, torch.Tensor]) -> dict[str,
         Raises:
             NotImplementedError: This method must be implemented in subclasses.
         """
-        pass
diff --git a/deeplabcut/pose_estimation_pytorch/runners/inference.py b/deeplabcut/pose_estimation_pytorch/runners/inference.py
@@ -256,7 +256,7 @@ def __init__(
             self.inference_cfg = InferenceConfig.from_dict(inference_cfg)
         elif inference_cfg is None:
             self.inference_cfg = InferenceConfig()
-        else: 
+        else:
             raise ValueError(f"Invalid inference config: {inference_cfg}")
 
         if self.snapshot_path is not None and self.snapshot_path != "":
@@ -539,7 +539,11 @@ def _safe_get(self) -> Any:
                 return item
             except Empty:
                 # check if producer is still running
-                if self._stop_event.is_set() or self._preprocessing_thread is None or not self._preprocessing_thread.is_alive():
+                if (
+                    self._stop_event.is_set()
+                    or self._preprocessing_thread is None
+                    or not self._preprocessing_thread.is_alive()
+                ):
                     return None
                 continue
 

diff --git a/deeplabcut/pose_estimation_pytorch/runners/logger.py b/deeplabcut/pose_estimation_pytorch/runners/logger.py
@@ -152,7 +152,6 @@ def log_images(
             targets: the targets for each model head
             step: the current step
         """
-        pass
 
     def select_images_to_log(self, train: DataLoader, valid: DataLoader) -> None:
         """Selects the train and test images to log.
@@ -448,7 +447,6 @@ def log_config(self, config: dict = None) -> None:
         Args:
             config: Experiment config file.
         """
-        pass
 
     def _load_existing_data(self) -> None:
         """Loads existing CSV data if the log file exists."""

diff --git a/deeplabcut/pose_estimation_pytorch/runners/schedulers.py b/deeplabcut/pose_estimation_pytorch/runners/schedulers.py
@@ -13,7 +13,6 @@
 from typing import Any
 
 import torch
-from omegaconf import DictConfig, ListConfig, OmegaConf
 from torch.optim.lr_scheduler import _LRScheduler
 
 
@@ -87,9 +86,6 @@ def build_scheduler(
         # unpicklable omegaconf objects with _parent references.
         # TODO @deruyter92: decide on typed / plain list / dict in upstream code
         # Then this check can be removed.
-        if isinstance(param, (ListConfig, DictConfig)):
-            param = OmegaConf.to_container(param, resolve=True)
-
         if isinstance(param, list):
             param = [_parse_scheduler_param(p, optimizer) for p in param]
         else:

diff --git a/deeplabcut/pose_estimation_tensorflow/nnets/utils.py b/deeplabcut/pose_estimation_tensorflow/nnets/utils.py
@@ -213,5 +213,3 @@ def drop_connect(inputs, is_training, drop_connect_rate):
 
 class DepthwiseConv2D(tf.keras.layers.DepthwiseConv2D, tf.compat.v1.layers.Layer):
     """Wrap keras DepthwiseConv2D to tf.layers."""
-
-    pass
diff --git a/deeplabcut/refine_training_dataset/outlier_frames.py b/deeplabcut/refine_training_dataset/outlier_frames.py
@@ -1122,7 +1122,7 @@ def merge_datasets(config, forceiterate=None):
         else:
             print("The following folder was not manually refined,...", folder)
             flagged = True
-            pass  # this folder does not contain a MachineLabelsRefine file (not updated...)
+            # this folder does not contain a MachineLabelsRefine file (not updated...)
 
     if not flagged:
         # updates iteration by 1

diff --git a/deeplabcut/refine_training_dataset/tracklets.py b/deeplabcut/refine_training_dataset/tracklets.py
@@ -193,7 +193,7 @@ def get_frame_ind(s):
 
             # Map a tracklet # to the animal ID it belongs to or the bodypart # it corresponds to.
             self.individuals = self.cfg["individuals"] + (["single"] if len(self.cfg["uniquebodyparts"]) else [])
-            self.tracklet2id = [i for i in range(0, self.nindividuals) for _ in bodyparts_multi] + [
+            self.tracklet2id = [i for i in range(self.nindividuals) for _ in bodyparts_multi] + [
                 self.nindividuals
             ] * len(bodyparts_single)
             bps = bodyparts_multi + bodyparts_single
-Original file line number
+Diff line change
@@ Expand Up @@
             Returns:
                 a single post-processed prediction
             """
-            pass
     def build_bottom_up_postprocessor(
@@ Expand Down @@
Original file line number	Diff line number	Diff line change
Expand Up		@@ -213,5 +213,3 @@ def drop_connect(inputs, is_training, drop_connect_rate):

		class DepthwiseConv2D(tf.keras.layers.DepthwiseConv2D, tf.compat.v1.layers.Layer):
		"""Wrap keras DepthwiseConv2D to tf.layers."""

		pass