diff --git a/docs/vame-docs-app/docs/reference/sidebar.json b/docs/vame-docs-app/docs/reference/sidebar.json
index 1ffa7cb4..77e66f21 100644
--- a/docs/vame-docs-app/docs/reference/sidebar.json
+++ b/docs/vame-docs-app/docs/reference/sidebar.json
@@ -7,7 +7,7 @@
         "reference/vame/analysis/gif_creator",
         "reference/vame/analysis/pose_segmentation",
         "reference/vame/analysis/tree_hierarchy",
-        "reference/vame/analysis/umap_visualization",
+        "reference/vame/analysis/umap",
         "reference/vame/analysis/videowriter"
       ],
       "label": "vame.analysis",
@@ -50,7 +50,9 @@
         "reference/vame/util/align_egocentrical",
         "reference/vame/util/auxiliary",
         "reference/vame/util/csv_to_npy",
-        "reference/vame/util/gif_pose_helper"
+        "reference/vame/util/data_manipulation",
+        "reference/vame/util/gif_pose_helper",
+        "reference/vame/util/model_util"
       ],
       "label": "vame.util",
       "type": "category"
diff --git a/docs/vame-docs-app/docs/reference/vame/analysis/community_analysis.md b/docs/vame-docs-app/docs/reference/vame/analysis/community_analysis.md
index 3fcaadd4..2da540c8 100644
--- a/docs/vame-docs-app/docs/reference/vame/analysis/community_analysis.md
+++ b/docs/vame-docs-app/docs/reference/vame/analysis/community_analysis.md
@@ -51,24 +51,6 @@ Compute the transition matrix from the adjacency matrix.
 
 - `np.ndarray` - Transition matrix.
 
-#### consecutive
-
-```python
-def consecutive(data: np.ndarray, stepsize: int = 1) -> List[np.ndarray]
-```
-
-Identifies location of missing motif finding consecutive elements in an array and returns array(s) at the split.
-
-**Arguments**:
-
-- `data` _np.ndarray_ - Input array.
-- `stepsize` _int, optional_ - Step size. Defaults to 1.
-  
-
-**Returns**:
-
-- `List[np.ndarray]` - List of arrays containing consecutive elements.
-
 #### find\_zero\_labels
 
 ```python
@@ -197,7 +179,7 @@ Create community bag for given files and labels (Markov chain to tree -> comm
 #### create\_cohort\_community\_bag
 
 ```python
-def create_cohort_community_bag(files: List[str], labels: List[np.ndarray],
+def create_cohort_community_bag(labels: List[np.ndarray],
                                 trans_mat_full: np.ndarray, cut_tree: int,
                                 n_cluster: int) -> Tuple
 ```
@@ -207,7 +189,6 @@ Create cohort community bag for given labels, transition matrix, cut tree, and n
 
 **Arguments**:
 
-- `files` _List[str]_ - List of files paths (deprecated).
 - `labels` _List[np.ndarray]_ - List of label arrays.
 - `trans_mat_full` _np.ndarray_ - Full transition matrix.
 - `cut_tree` _int_ - Cut line for tree.
@@ -260,59 +241,13 @@ Transform kmeans parameterized latent vector into communities. Get cohort commun
 
 - `List[np.ndarray]` - List of cohort community labels for each file.
 
-#### umap\_embedding
-
-```python
-def umap_embedding(cfg: dict, file: str, model_name: str, n_cluster: int,
-                   parametrization: str) -> np.ndarray
-```
-
-Perform UMAP embedding for given file and parameters.
-
-**Arguments**:
-
-- `cfg` _dict_ - Configuration parameters.
-- `file` _str_ - File path.
-- `model_name` _str_ - Model name.
-- `n_cluster` _int_ - Number of clusters.
-- `parametrization` _str_ - parametrization.
-  
-
-**Returns**:
-
-- `np.ndarray` - UMAP embedding.
-
-#### umap\_vis
-
-```python
-def umap_vis(cfg: dict, file: str, embed: np.ndarray,
-             community_labels_all: np.ndarray, save_path: str | None) -> None
-```
-
-Create plotly visualizaton of UMAP embedding.
-
-**Arguments**:
-
-- `cfg` _dict_ - Configuration parameters.
-- `file` _str_ - File path.
-- `embed` _np.ndarray_ - UMAP embedding.
-- `community_labels_all` _np.ndarray_ - Community labels.
-- `save_path` - Path to save the plot. If None it will not save the plot.
-  
-
-**Returns**:
-
-  None
-
 #### community
 
 ```python
 @save_state(model=CommunityFunctionSchema)
 def community(config: str,
               cohort: bool = True,
-              show_umap: bool = False,
               cut_tree: int | None = None,
-              save_umap_figure: bool = True,
               save_logs: bool = False) -> None
 ```
 
@@ -322,7 +257,6 @@ Perform community analysis.
 
 - `config` _str_ - Path to the configuration file.
 - `cohort` _bool, optional_ - Flag indicating cohort analysis. Defaults to True.
-- `show_umap` _bool, optional_ - Flag indicating weather to show UMAP visualization. Defaults to False.
 - `cut_tree` _int, optional_ - Cut line for tree. Defaults to None.
   
 
diff --git a/docs/vame-docs-app/docs/reference/vame/analysis/generative_functions.md b/docs/vame-docs-app/docs/reference/vame/analysis/generative_functions.md
index 6e6c74a0..1b2d4e08 100644
--- a/docs/vame-docs-app/docs/reference/vame/analysis/generative_functions.md
+++ b/docs/vame-docs-app/docs/reference/vame/analysis/generative_functions.md
@@ -94,24 +94,6 @@ Visualize cluster centers.
 
   None
 
-#### load\_model
-
-```python
-def load_model(cfg: dict, model_name: str) -> torch.nn.Module
-```
-
-Load PyTorch model.
-
-**Arguments**:
-
-- `cfg` _dict_ - Configuration dictionary.
-- `model_name` _str_ - Name of the model.
-  
-
-**Returns**:
-
-- `torch.nn.Module` - Loaded PyTorch model.
-
 #### generative\_model
 
 ```python
diff --git a/docs/vame-docs-app/docs/reference/vame/analysis/pose_segmentation.md b/docs/vame-docs-app/docs/reference/vame/analysis/pose_segmentation.md
index 6e367bfc..8a520605 100644
--- a/docs/vame-docs-app/docs/reference/vame/analysis/pose_segmentation.md
+++ b/docs/vame-docs-app/docs/reference/vame/analysis/pose_segmentation.md
@@ -10,25 +10,6 @@ Leibniz Institute for Neurobiology, Magdeburg, Germany
 https://github.com/LINCellularNeuroscience/VAME
 Licensed under GNU General Public License v3.0
 
-#### load\_model
-
-```python
-def load_model(cfg: dict, model_name: str, fixed: bool) -> RNN_VAE
-```
-
-Load the VAME model.
-
-**Arguments**:
-
-- `cfg` _dict_ - Configuration dictionary.
-- `model_name` _str_ - Name of the model.
-- `fixed` _bool_ - Fixed or variable length sequences.
-  
-
-**Returns**:
-
-- `RNN_VAE` - Loaded VAME model.
-
 #### embedd\_latent\_vectors
 
 ```python
@@ -52,24 +33,6 @@ Embed latent vectors for the given files using the VAME model.
 
 - `List[np.ndarray]` - List of latent vectors for each file.
 
-#### consecutive
-
-```python
-def consecutive(data: np.ndarray, stepsize: int = 1) -> List[np.ndarray]
-```
-
-Find consecutive sequences in the data array.
-
-**Arguments**:
-
-- `data` _np.ndarray_ - Input array.
-- `stepsize` _int, optional_ - Step size. Defaults to 1.
-  
-
-**Returns**:
-
-- `List[np.ndarray]` - List of consecutive sequences.
-
 #### get\_motif\_usage
 
 ```python
diff --git a/docs/vame-docs-app/docs/reference/vame/analysis/tree_hierarchy.md b/docs/vame-docs-app/docs/reference/vame/analysis/tree_hierarchy.md
index ae916062..30d39816 100644
--- a/docs/vame-docs-app/docs/reference/vame/analysis/tree_hierarchy.md
+++ b/docs/vame-docs-app/docs/reference/vame/analysis/tree_hierarchy.md
@@ -109,42 +109,6 @@ Draw a tree.
 
   None
 
-#### traverse\_tree
-
-```python
-def traverse_tree(T: nx.Graph, root_node: str | None = None) -> str
-```
-
-Traverse a tree and return the traversal sequence.
-
-**Arguments**:
-
-- `T` _nx.Graph_ - The tree to be traversed.
-- `root_node` _str, optional_ - The root node of the tree. If None, traversal starts from the root.
-  
-
-**Returns**:
-
-- `str` - The traversal sequence.
-
-#### traverse\_tree
-
-```python
-def traverse_tree(T: nx.Graph, root_node: str | None = None) -> str
-```
-
-Traverse a tree and return the traversal sequence.
-
-**Arguments**:
-
-- `T` _nx.Graph_ - The tree to be traversed.
-- `root_node` _str, optional_ - The root node of the tree. If None, traversal starts from the root.
-  
-
-**Returns**:
-
-- `str` - The traversal sequence.
-
 #### traverse\_tree\_cutline
 
 ```python
diff --git a/docs/vame-docs-app/docs/reference/vame/analysis/umap.md b/docs/vame-docs-app/docs/reference/vame/analysis/umap.md
new file mode 100644
index 00000000..2571e693
--- /dev/null
+++ b/docs/vame-docs-app/docs/reference/vame/analysis/umap.md
@@ -0,0 +1,136 @@
+---
+sidebar_label: umap
+title: vame.analysis.umap
+---
+
+Variational Animal Motion Embedding 1.0-alpha Toolbox
+© K. Luxem & P. Bauer, Department of Cellular Neuroscience
+Leibniz Institute for Neurobiology, Magdeburg, Germany
+
+https://github.com/LINCellularNeuroscience/VAME
+Licensed under GNU General Public License v3.0
+
+#### umap\_embedding
+
+```python
+def umap_embedding(cfg: dict, file: str, model_name: str, n_cluster: int,
+                   parametrization: str) -> np.ndarray
+```
+
+Perform UMAP embedding for given file and parameters.
+
+**Arguments**:
+
+- `cfg` _dict_ - Configuration parameters.
+- `file` _str_ - File path.
+- `model_name` _str_ - Model name.
+- `n_cluster` _int_ - Number of clusters.
+- `parametrization` _str_ - parametrization.
+  
+
+**Returns**:
+
+- `np.ndarray` - UMAP embedding.
+
+#### umap\_vis\_community\_labels
+
+```python
+def umap_vis_community_labels(cfg: dict, embed: np.ndarray,
+                              community_labels_all: np.ndarray,
+                              save_path: str | None) -> None
+```
+
+Create plotly visualizaton of UMAP embedding with community labels.
+
+**Arguments**:
+
+- `cfg` _dict_ - Configuration parameters.
+- `embed` _np.ndarray_ - UMAP embedding.
+- `community_labels_all` _np.ndarray_ - Community labels.
+- `save_path` - Path to save the plot. If None it will not save the plot.
+  
+
+**Returns**:
+
+  None
+
+#### umap\_vis
+
+```python
+def umap_vis(embed: np.ndarray, num_points: int) -> None
+```
+
+Visualize UMAP embedding without labels.
+
+**Arguments**:
+
+- `embed` _np.ndarray_ - UMAP embedding.
+- `num_points` _int_ - Number of data points to visualize.
+  
+
+**Returns**:
+
+  None - Plot Visualization of UMAP embedding.
+
+#### umap\_label\_vis
+
+```python
+def umap_label_vis(embed: np.ndarray, label: np.ndarray, n_cluster: int,
+                   num_points: int) -> None
+```
+
+Visualize UMAP embedding with motif labels.
+
+**Arguments**:
+
+- `embed` _np.ndarray_ - UMAP embedding.
+- `label` _np.ndarray_ - Motif labels.
+- `n_cluster` _int_ - Number of clusters.
+- `num_points` _int_ - Number of data points to visualize.
+  
+
+**Returns**:
+
+  fig - Plot figure of UMAP visualization embedding with motif labels.
+
+#### umap\_vis\_comm
+
+```python
+def umap_vis_comm(embed: np.ndarray, community_label: np.ndarray,
+                  num_points: int) -> None
+```
+
+Visualize UMAP embedding with community labels.
+
+**Arguments**:
+
+- `embed` _np.ndarray_ - UMAP embedding.
+- `community_label` _np.ndarray_ - Community labels.
+- `num_points` _int_ - Number of data points to visualize.
+  
+
+**Returns**:
+
+  fig - Plot figure of UMAP visualization embedding with community labels.
+
+#### visualization
+
+```python
+@save_state(model=VisualizationFunctionSchema)
+def visualization(config: Union[str, Path],
+                  label: Optional[str] = None,
+                  save_logs: bool = False) -> None
+```
+
+Visualize UMAP embeddings based on configuration settings.
+
+**Arguments**:
+
+- `config` _Union[str, Path]_ - Path to the configuration file.
+- `label` _str, optional_ - Type of labels to visualize. Default is None.
+  
+
+**Returns**:
+
+  None - Plot Visualization of UMAP embeddings.
+
diff --git a/docs/vame-docs-app/docs/reference/vame/model/create_training.md b/docs/vame-docs-app/docs/reference/vame/model/create_training.md
index 2ad64049..707a7c1e 100644
--- a/docs/vame-docs-app/docs/reference/vame/model/create_training.md
+++ b/docs/vame-docs-app/docs/reference/vame/model/create_training.md
@@ -10,52 +10,11 @@ Leibniz Institute for Neurobiology, Magdeburg, Germany
 https://github.com/LINCellularNeuroscience/VAME
 Licensed under GNU General Public License v3.0
 
-#### nan\_helper
-
-```python
-def nan_helper(y: np.ndarray) -> Tuple
-```
-
-Identifies indices of NaN values in an array and provides a function to convert them to non-NaN indices.
-
-**Arguments**:
-
-- `y` _np.ndarray_ - Input array containing NaN values.
-  
-
-**Returns**:
-
-  Tuple[np.ndarray, Union[np.ndarray, None]]: A tuple containing two elements:
-  - An array of boolean values indicating the positions of NaN values.
-  - A lambda function to convert NaN indices to non-NaN indices.
-
-#### interpol
-
-```python
-def interpol(arr: np.ndarray) -> np.ndarray
-```
-
-Interpolates all NaN values in the given array.
-
-**Arguments**:
-
-- `arr` _np.ndarray_ - Input array containing NaN values.
-  
-
-**Returns**:
-
-- `np.ndarray` - Array with NaN values replaced by interpolated values.
-
 #### plot\_check\_parameter
 
 ```python
-def plot_check_parameter(cfg: dict,
-                         iqr_val: float,
-                         num_frames: int,
-                         X_true: List[np.ndarray],
-                         X_med: np.ndarray,
-                         anchor_1: int | None = None,
-                         anchor_2: int | None = None) -> None
+def plot_check_parameter(cfg: dict, iqr_val: float, num_frames: int,
+                         X_true: List[np.ndarray], X_med: np.ndarray) -> None
 ```
 
 Plot the check parameter - z-scored data and the filtered data.
@@ -79,8 +38,7 @@ Plot the check parameter - z-scored data and the filtered data.
 
 ```python
 def traindata_aligned(cfg: dict, files: List[str], testfraction: float,
-                      num_features: int, savgol_filter: bool,
-                      check_parameter: bool) -> None
+                      savgol_filter: bool, check_parameter: bool) -> None
 ```
 
 Create training dataset for aligned data.
diff --git a/docs/vame-docs-app/docs/reference/vame/model/rnn_model.md b/docs/vame-docs-app/docs/reference/vame/model/rnn_model.md
index b8672377..b74d1e80 100644
--- a/docs/vame-docs-app/docs/reference/vame/model/rnn_model.md
+++ b/docs/vame-docs-app/docs/reference/vame/model/rnn_model.md
@@ -65,8 +65,7 @@ Lambda module for computing the latent space parameters.
 #### \_\_init\_\_
 
 ```python
-def __init__(ZDIMS: int, hidden_size_layer_1: int, hidden_size_layer_2: int,
-             softplus: bool)
+def __init__(ZDIMS: int, hidden_size_layer_1: int, softplus: bool)
 ```
 
 Initialize the Lambda module.
diff --git a/docs/vame-docs-app/docs/reference/vame/model/rnn_vae.md b/docs/vame-docs-app/docs/reference/vame/model/rnn_vae.md
index fb357f87..8a5624c8 100644
--- a/docs/vame-docs-app/docs/reference/vame/model/rnn_vae.md
+++ b/docs/vame-docs-app/docs/reference/vame/model/rnn_vae.md
@@ -181,10 +181,10 @@ Train the model.
 #### test
 
 ```python
-def test(test_loader: Data.DataLoader, epoch: int, model: nn.Module,
-         optimizer: torch.optim.Optimizer, BETA: float, kl_weight: float,
-         seq_len: int, mse_red: str, kloss: str, klmbda: float,
-         future_decoder: bool, bsize: int) -> Tuple[float, float, float]
+def test(test_loader: Data.DataLoader, model: nn.Module, BETA: float,
+         kl_weight: float, seq_len: int, mse_red: str, kloss: str,
+         klmbda: float, future_decoder: bool,
+         bsize: int) -> Tuple[float, float, float]
 ```
 
 Evaluate the model on the test dataset.
diff --git a/docs/vame-docs-app/docs/reference/vame/util/align_egocentrical.md b/docs/vame-docs-app/docs/reference/vame/util/align_egocentrical.md
index c0257019..d5a9409a 100644
--- a/docs/vame-docs-app/docs/reference/vame/util/align_egocentrical.md
+++ b/docs/vame-docs-app/docs/reference/vame/util/align_egocentrical.md
@@ -10,85 +10,6 @@ Leibniz Institute for Neurobiology, Magdeburg, Germany
 https://github.com/LINCellularNeuroscience/VAME
 Licensed under GNU General Public License v3.0
 
-#### crop\_and\_flip
-
-```python
-def crop_and_flip(
-        rect: Tuple, src: np.ndarray, points: List[np.ndarray],
-        ref_index: Tuple[int, int]) -> Tuple[np.ndarray, List[np.ndarray]]
-```
-
-Crop and flip the image based on the given rectangle and points.
-
-**Arguments**:
-
-- `rect` _Tuple_ - Rectangle coordinates (center, size, theta).
-- `src` _np.ndarray_ - Source image.
-- `points` _List[np.ndarray]_ - List of points.
-- `ref_index` _Tuple[int, int]_ - Reference indices for alignment.
-  
-
-**Returns**:
-
-  Tuple[np.ndarray, List[np.ndarray]]: Cropped and flipped image, and shifted points.
-
-#### nan\_helper
-
-```python
-def nan_helper(y: np.ndarray) -> Tuple[np.ndarray, np.ndarray]
-```
-
-Helper function to identify NaN values in an array.
-
-**Arguments**:
-
-- `y` _np.ndarray_ - Input array.
-  
-
-**Returns**:
-
-  Tuple[np.ndarray, np.ndarray]: Boolean mask for NaN values and function to interpolate them.
-
-#### interpol
-
-```python
-def interpol(arr: np.ndarray) -> np.ndarray
-```
-
-Interpolates NaN values in the given array.
-
-**Arguments**:
-
-- `arr` _np.ndarray_ - Input array.
-  
-
-**Returns**:
-
-- `np.ndarray` - Array with interpolated NaN values.
-
-#### background
-
-```python
-def background(path_to_file: str,
-               filename: str,
-               video_format: str = '.mp4',
-               num_frames: int = 1000) -> np.ndarray
-```
-
-Compute the background image from a fixed camera.
-
-**Arguments**:
-
-- `path_to_file` _str_ - Path to the file directory.
-- `filename` _str_ - Name of the video file without the format.
-- `video_format` _str, optional_ - Format of the video file. Defaults to '.mp4'.
-- `num_frames` _int, optional_ - Number of frames to use for background computation. Defaults to 1000.
-  
-
-**Returns**:
-
-- `np.ndarray` - Background image.
-
 #### align\_mouse
 
 ```python
diff --git a/docs/vame-docs-app/docs/reference/vame/util/csv_to_npy.md b/docs/vame-docs-app/docs/reference/vame/util/csv_to_npy.md
index 6e914d9f..1e0c0cd7 100644
--- a/docs/vame-docs-app/docs/reference/vame/util/csv_to_npy.md
+++ b/docs/vame-docs-app/docs/reference/vame/util/csv_to_npy.md
@@ -10,42 +10,6 @@ Leibniz Institute for Neurobiology, Magdeburg, Germany
 https://github.com/LINCellularNeuroscience/VAME
 Licensed under GNU General Public License v3.0
 
-#### nan\_helper
-
-```python
-def nan_helper(y: np.ndarray) -> Tuple
-```
-
-Identifies indices of NaN values in an array and provides a function to convert them to non-NaN indices.
-
-**Arguments**:
-
-- `y` _np.ndarray_ - Input array containing NaN values.
-  
-
-**Returns**:
-
-  Tuple[np.ndarray, Union[np.ndarray, None]]: A tuple containing two elements:
-  - An array of boolean values indicating the positions of NaN values.
-  - A lambda function to convert NaN indices to non-NaN indices.
-
-#### interpol
-
-```python
-def interpol(arr: np.ndarray) -> np.ndarray
-```
-
-Interpolates all NaN values of a given array.
-
-**Arguments**:
-
-- `arr` _np.ndarray_ - A numpy array with NaN values.
-  
-
-**Returns**:
-
-- `np.ndarray` - A numpy array with interpolated NaN values.
-
 #### csv\_to\_numpy
 
 ```python
diff --git a/docs/vame-docs-app/docs/reference/vame/util/data_manipulation.md b/docs/vame-docs-app/docs/reference/vame/util/data_manipulation.md
new file mode 100644
index 00000000..2285c51e
--- /dev/null
+++ b/docs/vame-docs-app/docs/reference/vame/util/data_manipulation.md
@@ -0,0 +1,122 @@
+---
+sidebar_label: data_manipulation
+title: vame.util.data_manipulation
+---
+
+#### consecutive
+
+```python
+def consecutive(data: np.ndarray, stepsize: int = 1) -> List[np.ndarray]
+```
+
+Find consecutive sequences in the data array.
+
+**Arguments**:
+
+- `data` _np.ndarray_ - Input array.
+- `stepsize` _int, optional_ - Step size. Defaults to 1.
+  
+
+**Returns**:
+
+- `List[np.ndarray]` - List of consecutive sequences.
+
+#### nan\_helper
+
+```python
+def nan_helper(y: np.ndarray) -> Tuple
+```
+
+Identifies indices of NaN values in an array and provides a function to convert them to non-NaN indices.
+
+**Arguments**:
+
+- `y` _np.ndarray_ - Input array containing NaN values.
+  
+
+**Returns**:
+
+  Tuple[np.ndarray, Union[np.ndarray, None]]: A tuple containing two elements:
+  - An array of boolean values indicating the positions of NaN values.
+  - A lambda function to convert NaN indices to non-NaN indices.
+
+#### interpol\_all\_nans
+
+```python
+def interpol_all_nans(arr: np.ndarray) -> np.ndarray
+```
+
+Interpolates all NaN values in the given array.
+
+**Arguments**:
+
+- `arr` _np.ndarray_ - Input array containing NaN values.
+  
+
+**Returns**:
+
+- `np.ndarray` - Array with NaN values replaced by interpolated values.
+
+#### interpol\_first\_rows\_nans
+
+```python
+def interpol_first_rows_nans(arr: np.ndarray) -> np.ndarray
+```
+
+Interpolates NaN values in the given array.
+
+**Arguments**:
+
+- `arr` _np.ndarray_ - Input array with NaN values.
+  
+
+**Returns**:
+
+- `np.ndarray` - Array with interpolated NaN values.
+
+#### crop\_and\_flip
+
+```python
+def crop_and_flip(
+        rect: Tuple, src: np.ndarray, points: List[np.ndarray],
+        ref_index: Tuple[int, int]) -> Tuple[np.ndarray, List[np.ndarray]]
+```
+
+Crop and flip the image based on the given rectangle and points.
+
+**Arguments**:
+
+- `rect` _Tuple_ - Rectangle coordinates (center, size, theta).
+- `src` _np.ndarray_ - Source image.
+- `points` _List[np.ndarray]_ - List of points.
+- `ref_index` _Tuple[int, int]_ - Reference indices for alignment.
+  
+
+**Returns**:
+
+  Tuple[np.ndarray, List[np.ndarray]]: Cropped and flipped image, and shifted points.
+
+#### background
+
+```python
+def background(path_to_file: str,
+               filename: str,
+               file_format: str = '.mp4',
+               num_frames: int = 1000,
+               save_background: bool = True) -> np.ndarray
+```
+
+Compute background image from fixed camera.
+
+**Arguments**:
+
+- `path_to_file` _str_ - Path to the directory containing the video files.
+- `filename` _str_ - Name of the video file.
+- `file_format` _str, optional_ - Format of the video file. Defaults to '.mp4'.
+- `num_frames` _int, optional_ - Number of frames to use for background computation. Defaults to 1000.
+  
+
+**Returns**:
+
+- `np.ndarray` - Background image.
+
diff --git a/docs/vame-docs-app/docs/reference/vame/util/gif_pose_helper.md b/docs/vame-docs-app/docs/reference/vame/util/gif_pose_helper.md
index 36481296..9811fd4c 100644
--- a/docs/vame-docs-app/docs/reference/vame/util/gif_pose_helper.md
+++ b/docs/vame-docs-app/docs/reference/vame/util/gif_pose_helper.md
@@ -10,84 +10,6 @@ Leibniz Institute for Neurobiology, Magdeburg, Germany
 https://github.com/LINCellularNeuroscience/VAME
 Licensed under GNU General Public License v3.0
 
-#### crop\_and\_flip
-
-```python
-def crop_and_flip(rect: tuple, src: np.ndarray, points: list,
-                  ref_index: list) -> tuple
-```
-
-Crop and flip an image based on a rectangle and reference points.
-
-**Arguments**:
-
-- `rect` _tuple_ - Tuple containing rectangle information (center, size, angle).
-- `src` _np.ndarray_ - Source image to crop and flip.
-- `points` _list_ - List of points to be aligned.
-- `ref_index` _list_ - Reference indices for alignment.
-  
-
-**Returns**:
-
-- `tuple` - Cropped and flipped image, shifted points.
-
-#### background
-
-```python
-def background(path_to_file: str,
-               filename: str,
-               file_format: str = '.mp4',
-               num_frames: int = 100) -> np.ndarray
-```
-
-Compute background image from fixed camera.
-
-**Arguments**:
-
-- `path_to_file` _str_ - Path to the directory containing the video files.
-- `filename` _str_ - Name of the video file.
-- `file_format` _str, optional_ - Format of the video file. Defaults to '.mp4'.
-- `num_frames` _int, optional_ - Number of frames to use for background computation. Defaults to 1000.
-  
-
-**Returns**:
-
-- `np.ndarray` - Background image.
-
-#### nan\_helper
-
-```python
-def nan_helper(y: np.ndarray) -> tuple
-```
-
-Helper function to find indices of NaN values.
-
-**Arguments**:
-
-- `y` _np.ndarray_ - Input array.
-  
-
-**Returns**:
-
-- `tuple` - Indices of NaN values.
-
-#### interpol
-
-```python
-def interpol(arr: np.ndarray) -> np.ndarray
-```
-
-Interpolates NaN values in the given array.
-
-**Arguments**:
-
-- `arr` _np.ndarray_ - Input array with NaN values.
-  
-
-**Returns**:
-
-- `np.ndarray` - Array with interpolated NaN values.
-
 #### get\_animal\_frames
 
 ```python
diff --git a/docs/vame-docs-app/docs/reference/vame/util/model_util.md b/docs/vame-docs-app/docs/reference/vame/util/model_util.md
new file mode 100644
index 00000000..7e5dd81c
--- /dev/null
+++ b/docs/vame-docs-app/docs/reference/vame/util/model_util.md
@@ -0,0 +1,24 @@
+---
+sidebar_label: model_util
+title: vame.util.model_util
+---
+
+#### load\_model
+
+```python
+def load_model(cfg: dict, model_name: str, fixed: bool = True) -> RNN_VAE
+```
+
+Load the VAME model.
+
+**Arguments**:
+
+- `cfg` _dict_ - Configuration dictionary.
+- `model_name` _str_ - Name of the model.
+- `fixed` _bool_ - Fixed or variable length sequences.
+  
+
+**Returns**:
+
+- `RNN_VAE` - Loaded VAME model.
+