diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml
new file mode 100644
index 0000000..35721e1
--- /dev/null
+++ b/.github/workflows/docs.yml
@@ -0,0 +1,66 @@
+name: Deploy MkDocs site to GitHub Pages
+
+on:
+  push:
+    branches:
+      - main
+    paths:
+      - 'docs/**'  # Trigger hanya ketika ada perubahan di folder docs
+      - mkdocs.yml
+  workflow_dispatch:  # Menyediakan trigger manual jika dibutuhkan
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+    permissions:
+      contents: read
+      pages: write
+      id-token: write
+      actions: read
+    steps:
+      - name: Clone repository
+        uses: actions/checkout@v4
+
+      - name: Install uv and set the python version
+        uses: astral-sh/setup-uv@v5
+        with:
+          version: "0.5.24"
+          enable-cache: true
+          cache-dependency-glob: "uv.lock"
+          python-version: 3.12
+
+
+      - name: Install dependencies
+        run: |
+          uv sync
+
+      - name: Build MkDocs site
+        run: |
+          mkdocs build
+
+      - name: Setup Pages
+        uses: actions/configure-pages@v3
+
+      - name: Upload artifact
+        uses: actions/upload-pages-artifact@v3
+        with:
+          path: ./site
+
+  deploy:
+    if: github.ref == 'refs/heads/main'
+    runs-on: ubuntu-latest
+    needs: build
+
+    permissions:
+      pages: write
+      id-token: write
+      actions: read
+
+    environment:
+      name: github-pages
+      url: ${{ steps.deployment.outputs.page_url }}
+
+    steps:
+      - name: Deploy to GitHub Pages
+        id: deployment
+        uses: actions/deploy-pages@v4
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 180e211..891b14c 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -1,10 +1,21 @@
 repos:
+  - repo: https://github.com/astral-sh/uv-pre-commit
+    # uv version.
+    rev: 0.5.29
+    hooks:
+      - id: uv-lock
+        name: update uv lock file
+      - id: uv-export
+        name: export uv lock file
+
   - repo: https://github.com/pre-commit/pre-commit-hooks
     rev: v5.0.0
     hooks:
       - id: check-added-large-files
+        args: ['--maxkb=1024']  # 1MB = 1024KB
       - id: check-toml
       - id: check-yaml
+        exclude: ^mkdocs\.yml$
       - id: detect-private-key
       - id: end-of-file-fixer
         exclude: ^LICENSE|VERSION$
diff --git a/README.md b/README.md
index e900991..79e7771 100644
--- a/README.md
+++ b/README.md
@@ -1,12 +1,14 @@
 
 <div align="center">
+<!-- image logo -->
+<img src="assets/logo-v1.png" alt="Color Correction Logo" width="150"/>
 
 # Color Correction
 
 <br>
 
 [![version](https://badge.fury.io/py/color-correction.svg)](https://badge.fury.io/py/color-correction)
-[![downloads](https://img.shields.io/pypi/dm/color-correction-asdfghjkl)](https://pypistats.org/packages/color-correction-asdfghjkl)
+[![downloads](https://img.shields.io/pypi/dm/color-correction)](https://pypistats.org/packages/color-correction)
 [![python-version](https://img.shields.io/pypi/pyversions/color-correction)](https://badge.fury.io/py/color-correction)
 
 
diff --git a/assets/logo-v1.png b/assets/logo-v1.png
new file mode 100644
index 0000000..570d716
Binary files /dev/null and b/assets/logo-v1.png differ
diff --git a/color_correction/core/__init__.py b/color_correction/core/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/color_correction/core/card_detection/det_yv8_onnx.py b/color_correction/core/card_detection/det_yv8_onnx.py
index 9183510..19de9bb 100644
--- a/color_correction/core/card_detection/det_yv8_onnx.py
+++ b/color_correction/core/card_detection/det_yv8_onnx.py
@@ -14,12 +14,52 @@
 
 
 class YOLOv8CardDetector(BaseCardDetector):
-    """YOLOv8CardDetector is a class that implements card detection
-    using the YOLOv8 model.
-
-    Reference
-    ---------
-    https://github.com/ibaiGorordo/ONNX-YOLOv8-Object-Detection/blob/main/yolov8/YOLOv8.py
+    """YOLOv8CardDetector is a class that implements card detection using the YOLOv8
+    model.
+
+    This class provides functionality to detect cards in images using a YOLOv8 model
+    implemented with ONNX runtime. It handles model initialization, image preprocessing,
+    inference, and post-processing of detection results.
+
+    Parameters
+    ----------
+    conf_th : float, optional
+        Confidence threshold for filtering detections, by default 0.15
+    iou_th : float, optional
+        Intersection over Union threshold for non-maximum suppression, by default 0.7
+    path : str or None, optional
+        Path to the ONNX model file.
+        If None, downloads the default model, by default None
+    use_gpu : bool, optional
+        Whether to use GPU for inference, by default False
+
+    Attributes
+    ----------
+    conf_threshold : float
+        Threshold for filtering detections based on confidence scores
+    iou_threshold : float
+        Threshold for non-maximum suppression
+    use_gpu : bool
+        Flag indicating whether to use GPU for inference
+    session : onnxruntime.InferenceSession
+        ONNX Runtime session for model inference
+    input_names : list
+        Names of model input nodes
+    output_names : list
+        Names of model output nodes
+    input_shape : tuple
+        Shape of the input tensor
+    input_height : int
+        Height of the input image required by the model
+    input_width : int
+        Width of the input image required by the model
+    scale_to_ori : float
+        Scale factor to map predictions back to original image size
+
+    Methods
+    -------
+    detect(image)
+        Detect cards in the input image and return detection results
 
     """
 
diff --git a/color_correction/core/correction/__init__.py b/color_correction/core/correction/__init__.py
index d19cc64..5e5bdae 100644
--- a/color_correction/core/correction/__init__.py
+++ b/color_correction/core/correction/__init__.py
@@ -1,17 +1,17 @@
 from color_correction.core.correction._factory import (
     CorrectionModelFactory,
 )
-from color_correction.core.correction.affine_reg import AffineReg
+from color_correction.core.correction.affine_reg import AffineRegression
 from color_correction.core.correction.least_squares import (
     LeastSquaresRegression,
 )
-from color_correction.core.correction.linear_reg import LinearReg
+from color_correction.core.correction.linear_reg import LinearRegression
 from color_correction.core.correction.polynomial import Polynomial
 
 __all__ = [
     "CorrectionModelFactory",
     "LeastSquaresRegression",
     "Polynomial",
-    "LinearReg",
-    "AffineReg",
+    "LinearRegression",
+    "AffineRegression",
 ]
diff --git a/color_correction/core/correction/_factory.py b/color_correction/core/correction/_factory.py
index 16d796f..979b9b5 100644
--- a/color_correction/core/correction/_factory.py
+++ b/color_correction/core/correction/_factory.py
@@ -1,8 +1,8 @@
-from color_correction.core.correction.affine_reg import AffineReg
+from color_correction.core.correction.affine_reg import AffineRegression
 from color_correction.core.correction.least_squares import (
     LeastSquaresRegression,
 )
-from color_correction.core.correction.linear_reg import LinearReg
+from color_correction.core.correction.linear_reg import LinearRegression
 from color_correction.core.correction.polynomial import Polynomial
 
 
@@ -12,7 +12,7 @@ def create(model_name: str, **kwargs: dict) -> ...:
         model_registry = {
             "least_squares": LeastSquaresRegression(),
             "polynomial": Polynomial(**kwargs),
-            "linear_reg": LinearReg(),
-            "affine_reg": AffineReg(),
+            "linear_reg": LinearRegression(),
+            "affine_reg": AffineRegression(),
         }
         return model_registry.get(model_name)
diff --git a/color_correction/core/correction/affine_reg.py b/color_correction/core/correction/affine_reg.py
index 6e5b123..edace0c 100644
--- a/color_correction/core/correction/affine_reg.py
+++ b/color_correction/core/correction/affine_reg.py
@@ -10,7 +10,15 @@
 )
 
 
-class AffineReg(BaseComputeCorrection):
+class AffineRegression(BaseComputeCorrection):
+    """
+    Apply an affine (linear) regression for color correction.
+
+    This class uses a linear regression model without an intercept
+    (by adding a bias column) to compute a correction transformation
+    between input and reference patches.
+    """
+
     def __init__(self) -> None:
         self.model = None
 
@@ -19,6 +27,21 @@ def fit(
         x_patches: np.ndarray,  # input patches
         y_patches: np.ndarray,  # reference patches
     ) -> np.ndarray:
+        """
+        Fit a linear regression model using input and reference patches.
+
+        Parameters
+        ----------
+        x_patches : np.ndarray
+            Array of input patches with shape (n_samples, n_features).
+        y_patches : np.ndarray
+            Array of reference patches with shape (n_samples, n_targets).
+
+        Returns
+        -------
+        np.ndarray
+            The fitted linear regression model.
+        """
         start_time = time.perf_counter()
         x_patches = np.array(x_patches)
         print("x_patches.shape", x_patches.shape)
@@ -30,6 +53,24 @@ def fit(
         return self.model
 
     def compute_correction(self, input_image: np.ndarray) -> np.ndarray:
+        """
+        Compute the color-corrected image using the fitted regression model.
+
+        Parameters
+        ----------
+        input_image : np.ndarray
+            The input image to be corrected.
+
+        Returns
+        -------
+        np.ndarray
+            The color-corrected output image.
+
+        Raises
+        ------
+        ValueError
+            If the model has not been fitted before calling this method.
+        """
         if self.model is None:
             raise ValueError("Model is not fitted yet. Please call fit() method first.")
 
diff --git a/color_correction/core/correction/least_squares.py b/color_correction/core/correction/least_squares.py
index 76beb39..4c4eb3b 100644
--- a/color_correction/core/correction/least_squares.py
+++ b/color_correction/core/correction/least_squares.py
@@ -10,6 +10,13 @@
 
 
 class LeastSquaresRegression(BaseComputeCorrection):
+    """
+    Apply a least squares regression for color correction.
+
+    This class computes a color correction transformation by solving the least squares
+    problem to find the best fit coefficients.
+    """
+
     def __init__(self) -> None:
         self.model = None
 
@@ -18,6 +25,21 @@ def fit(
         x_patches: np.ndarray,  # input patches
         y_patches: np.ndarray,  # reference patches
     ) -> np.ndarray:
+        """
+        Fit the least squares regression model using input and reference patches.
+
+        Parameters
+        ----------
+        x_patches : np.ndarray
+            Array of input patches.
+        y_patches : np.ndarray
+            Array of reference patches.
+
+        Returns
+        -------
+        np.ndarray
+            The matrix of coefficients obtained from the least squares solution.
+        """
         start_time = time.perf_counter()
 
         self.model = np.linalg.lstsq(
@@ -31,6 +53,24 @@ def fit(
         return self.model
 
     def compute_correction(self, input_image: np.ndarray) -> np.ndarray:
+        """
+        Compute the color-corrected image using the fitted least squares model.
+
+        Parameters
+        ----------
+        input_image : np.ndarray
+            The input image represented as an array of color values.
+
+        Returns
+        -------
+        np.ndarray
+            The color-corrected output image.
+
+        Raises
+        ------
+        ValueError
+            If the model has not been fitted before calling this method.
+        """
         if self.model is None:
             raise ValueError("Model is not fitted yet. Please call fit() method first.")
 
diff --git a/color_correction/core/correction/linear_reg.py b/color_correction/core/correction/linear_reg.py
index 7ce2795..d10dd5e 100644
--- a/color_correction/core/correction/linear_reg.py
+++ b/color_correction/core/correction/linear_reg.py
@@ -1,7 +1,7 @@
 import time
 
 import numpy as np
-from sklearn.linear_model import LinearRegression
+from sklearn.linear_model import LinearRegression as LinearRegressionSklearn
 
 from color_correction.core.correction.base import BaseComputeCorrection
 from color_correction.utils.correction import (
@@ -10,24 +10,88 @@
 )
 
 
-class LinearReg(BaseComputeCorrection):
+class LinearRegression(BaseComputeCorrection):
+    """
+    LinearRegression correction using scikit-learn's LinearRegression.
+
+    This class fits a linear model to input and reference patches and computes
+    the corresponding color correction on images.
+
+    Attributes
+    ----------
+    model : sklearn.linear_model.LinearRegression, optional
+        The fitted linear regression model. Defaults to None until fitted.
+    """
+
     def __init__(self) -> None:
+        """
+        Initialize the LinearRegression correction.
+
+        Notes
+        -----
+        The model is set to None until the fit method is called.
+        """
         self.model = None
 
     def fit(
         self,
-        x_patches: np.ndarray,  # input patches
-        y_patches: np.ndarray,  # reference patches
+        x_patches: np.ndarray,
+        y_patches: np.ndarray,
     ) -> np.ndarray:
+        """
+        Fit the linear regression model on input and reference patches.
+
+        Parameters
+        ----------
+        x_patches : np.ndarray
+            Array of input patches for fitting.
+        y_patches : np.ndarray
+            Array of reference patches corresponding to the input.
+
+        Returns
+        -------
+        np.ndarray
+            The fitted model coefficients.
+
+        Notes
+        -----
+        The method uses scikit-learn's LinearRegression without intercept.
+        """
         start_time = time.perf_counter()
 
-        self.model = LinearRegression(fit_intercept=False).fit(x_patches, y_patches)
+        self.model = LinearRegressionSklearn(fit_intercept=False).fit(
+            x_patches,
+            y_patches,
+        )
 
         exc_time = time.perf_counter() - start_time
         print(f"{self.__class__.__name__} Fit: {exc_time} seconds")
         return self.model
 
     def compute_correction(self, input_image: np.ndarray) -> np.ndarray:
+        """
+        Compute the color correction for the given input image.
+
+        Parameters
+        ----------
+        input_image : np.ndarray
+            The input image array to be corrected.
+
+        Returns
+        -------
+        np.ndarray
+            The corrected image array.
+
+        Raises
+        ------
+        ValueError
+            If the model has not been fitted prior to calling this method.
+
+        Notes
+        -----
+        The method preprocesses the input image, applies the linear regression model,
+        and postprocesses the output to maintain the original image shape.
+        """
         if self.model is None:
             raise ValueError("Model is not fitted yet. Please call fit() method first.")
 
diff --git a/color_correction/core/correction/polynomial.py b/color_correction/core/correction/polynomial.py
index 5df8bf2..522bfca 100644
--- a/color_correction/core/correction/polynomial.py
+++ b/color_correction/core/correction/polynomial.py
@@ -13,7 +13,33 @@
 
 
 class Polynomial(BaseComputeCorrection):
+    """
+    Polynomial correction class using polynomial regression.
+
+    Parameters
+    ----------
+    **kwargs : dict, optional
+        Keyword arguments. Recognized keyword:
+        degree : int, optional, default 2
+            Degree of the polynomial.
+    """
+
     def __init__(self, **kwargs: dict) -> None:
+        """
+        Initialize the Polynomial correction model.
+
+        Parameters
+        ----------
+        **kwargs : dict
+            Keyword arguments for initialization.
+
+        Other Parameters
+        ----------------
+        degree : int, optional
+            Degree of the polynomial. Default is 2.
+            The more complex the polynomial, the more flexible the model.
+            But it may also lead to overfitting.
+        """
         self.model = None
         self.degree = kwargs.get("degree", 2)
 
@@ -23,19 +49,58 @@ def fit(
         y_patches: np.ndarray,  # reference patches
         **kwargs: dict,
     ) -> np.ndarray:
-        start_time = time.perf_counter()
+        """
+        Fit the polynomial regression model.
+
+        Parameters
+        ----------
+        x_patches : np.ndarray
+            Input image patches.
+        y_patches : np.ndarray
+            Reference image patches.
+        **kwargs : dict
+            Additional keyword arguments. Recognized keyword:
+            degree : int, optional
+                Degree of the polynomial.
 
+        Returns
+        -------
+        np.ndarray
+            Fitted model pipeline.
+
+        Notes
+        -----
+        The execution time for model fitting is printed.
+        """
+        start_time = time.perf_counter()
         degree = kwargs.get("degree", self.degree)
         self.model = make_pipeline(
             PolynomialFeatures(degree),
             LinearRegression(),
         ).fit(x_patches, y_patches)
-
         exc_time = time.perf_counter() - start_time
         print(f"{self.__class__.__name__} Fit: {exc_time} seconds")
         return self.model
 
     def compute_correction(self, input_image: np.ndarray) -> np.ndarray:
+        """
+        Compute and return the corrected image using the fitted model.
+
+        Parameters
+        ----------
+        input_image : np.ndarray
+            The input image to be corrected.
+
+        Returns
+        -------
+        np.ndarray
+            The corrected image.
+
+        Raises
+        ------
+        ValueError
+            If the model has not been fitted yet.
+        """
         if self.model is None:
             raise ValueError("Model is not fitted yet. Please call fit() method first.")
 
diff --git a/color_correction/services/__init__.py b/color_correction/services/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/color_correction/services/color_correction.py b/color_correction/services/color_correction.py
index 665cac2..0f12cb8 100644
--- a/color_correction/services/color_correction.py
+++ b/color_correction/services/color_correction.py
@@ -50,6 +50,12 @@ class ColorCorrection:
     **kwargs : dict
         Additional parameters for the correction model.
 
+    Other parameters
+    ----------------
+    degree : int, optional
+        Degree of `polynomial` correction model. Default is 2.
+        the more degree, the more complex the model. (e.g. 2, 3, 4, ...)
+
     Attributes
     ----------
     reference_patches : List[ColorPatchType] | None
@@ -233,12 +239,31 @@ def _create_debug_directory(self, base_dir: str) -> str:
 
     @property
     def model_name(self) -> str:
-        "Return the name of the correction model."
+        """
+        Return the name of the correction model.
+
+        Returns
+        -------
+        str
+            The name of the correction model class.
+        """
         return self.correction_model.__class__.__name__
 
     @property
     def ref_patches(self) -> np.ndarray:
-        """Return grid image of reference color patches."""
+        """
+        Return grid image of reference color patches.
+
+        Returns
+        -------
+        tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray]
+            A tuple containing:
+            - reference_patches: The array representing the reference color patches.
+            - reference_grid_image: The array depicting
+                the grid layout of the reference patches.
+            - reference_debug_image: The array used for debugging
+                the color correction process.
+        """
         return (
             self.reference_patches,
             self.reference_grid_image,
@@ -250,6 +275,27 @@ def set_reference_patches(
         image: np.ndarray | None,
         debug: bool = False,
     ) -> None:
+        """Set reference patches for color correction.
+
+        This function sets up reference color patches either from a default set of D50 BGR values
+        or by extracting patches from a provided reference image.
+
+        Parameters
+        ----------
+        image : np.ndarray | None
+            Input reference image from which to extract color patches.
+            If None, uses default D50 BGR reference values.
+        debug : bool, default=False
+            If True, generates additional debug visualization.
+
+        Returns
+        -------
+        None
+            Sets the following instance attributes:
+            - reference_patches: Color values of reference patches
+            - reference_grid_image: Grid image of reference patches
+            - reference_debug_image: Debug visualization (only if debug=True and image provided)
+        """  # noqa: E501
         if image is None:
             self.reference_patches = reference_color_d50_bgr
             self.reference_grid_image = create_patch_tiled_image(self.reference_patches)
@@ -261,6 +307,38 @@ def set_reference_patches(
             ) = self._extract_color_patches(image=image, debug=debug)
 
     def set_input_patches(self, image: np.ndarray, debug: bool = False) -> None:
+        """
+        This function processes an input image to extract color patches and generates
+        corresponding grid and debug visualizations 🔍
+
+
+        Parameters
+        ----------
+        image : np.ndarray
+            Input image to extract color patches from 📸
+        debug : bool, optional
+            If True, generates additional debug visualization, by default False 🐛
+
+        Returns
+        -------
+        tuple
+            Contains three elements:
+            - input_patches : np.ndarray
+                Extracted color patches from the image
+            - input_grid_image : np.ndarray
+                Visualization of the detected grid
+            - input_debug_image : np.ndarray
+                Debug visualization (if debug=True)
+
+        Notes
+        -----
+        The function will set class attributes:
+            - `self.input_patches`
+            - `self.input_grid_image`
+            - `self.input_debug_image`
+
+        The function first resets these attributes to None before processing 🔄
+        """
         self.input_patches = None
         self.input_grid_image = None
         self.input_debug_image = None
@@ -277,14 +355,14 @@ def fit(self) -> tuple[NDArray, list[ColorPatchType], list[ColorPatchType]]:
 
         Parameters
         ----------
-        input_image : NDArray
-            Image BGR to be corrected that contains color checker classic 24 patches.
-        reference_image : NDArray, optional
-            Image BGR to be reference that contains color checker classic 24 patches.
+        input_image : np.ndarray
+            Image `BGR` to be corrected that contains color checker classic 24 patches.
+        reference_image : np.ndarray, optional
+            Image `BGR` to be reference that contains color checker classic 24 patches.
 
         Returns
         -------
-        Tuple[NDArray, List[NDArray], List[NDArray]]
+        Tuple[np.ndarray, List[np.ndarray], List[np.ndarray]]
             Correction weights, input patches, and reference patches.
         """
         if self.reference_patches is None:
diff --git a/color_correction/services/correction_analyzer.py b/color_correction/services/correction_analyzer.py
index 96f0a61..3d8487a 100644
--- a/color_correction/services/correction_analyzer.py
+++ b/color_correction/services/correction_analyzer.py
@@ -16,12 +16,43 @@
 
 
 class ColorCorrectionAnalyzer:
+    """
+    Analyzer for benchmarking color correction methods.
+
+    This class combines multiple correction and detection techniques to analyze
+    the performance of color correction models by comparing patches and overall image
+    differences.
+
+    Parameters
+    ----------
+    list_correction_methods : list of tuple[LiteralModelCorrection, dict]
+        A list of tuples, where each tuple contains a correction method identifier
+        and its parameters.
+    list_detection_methods : list of tuple[LiteralModelDetection, dict]
+        A list of tuples, where each tuple contains a detection method identifier
+        and its parameters.
+    use_gpu : bool, optional
+        Flag to indicate whether GPU is used, by default True.
+    """
+
     def __init__(
         self,
         list_correction_methods: list[tuple[LiteralModelCorrection, dict]],
         list_detection_methods: list[tuple[LiteralModelDetection, dict]],
         use_gpu: bool = True,
     ) -> None:
+        """
+        Initialize the ColorCorrectionAnalyzer.
+
+        Parameters
+        ----------
+        list_correction_methods : list of tuple[LiteralModelCorrection, dict]]
+            List of correction methods and their parameters.
+        list_detection_methods : list of tuple[LiteralModelDetection, dict]]
+            List of detection methods and their parameters.
+        use_gpu : bool, optional
+            Whether to use GPU acceleration, by default True.
+        """
         self.list_correction_methods = list_correction_methods
         self.list_detection_methods = list_detection_methods
         self.use_gpu = use_gpu
@@ -37,6 +68,31 @@ def _run_single_exp(
         cc_params: dict,
         reference_image: np.ndarray | None = None,
     ) -> dict:
+        """
+        Run a single experiment for a given detection and correction method.
+
+        Parameters
+        ----------
+        idx : int
+            Index of the experiment.
+        input_image : np.ndarray
+            The input image array.
+        det_method : LiteralModelDetection
+            The detection method identifier.
+        det_params : dict
+            Parameters for the detection method.
+        cc_method : LiteralModelCorrection
+            The correction method identifier.
+        cc_params : dict
+            Parameters for the correction method.
+        reference_image : np.ndarray, optional
+            The reference image, by default None.
+
+        Returns
+        -------
+        dict
+            A dictionary containing evaluation data and results of the experiment.
+        """
         cc = ColorCorrection(
             correction_model=cc_method,
             detection_model=det_method,
@@ -100,8 +156,43 @@ def run(
         reference_image: np.ndarray | None = None,
     ) -> pd.DataFrame:
         """
-        Fungsi ini menjalankan benchmark untuk model color correction.
-        """
+        Run the full benchmark for color correction and generate reports.
+
+        Parameters
+        ----------
+        input_image : np.ndarray
+            The image to be processed.
+        output_dir : str, optional
+            The directory to save reports, by default "benchmark_debug".
+        reference_image : np.ndarray, optional
+            Optional reference image used for evaluation, by default None.
+
+        Returns
+        -------
+        pd.DataFrame
+            A DataFrame containing results of all experiments.
+
+        Examples
+        --------
+        >>> import numpy as np
+        >>> from color_correction.services.correction_analyzer import ColorCorrectionAnalyzer
+        >>>
+        >>> # Assume input_image is a numpy array of shape (H, W, C)
+        >>> input_image = np.random.rand(100, 100, 3)
+        >>> analyzer = ColorCorrectionAnalyzer(
+        ...     list_correction_methods=[
+        ...         ("least_squares", {}),
+        ...         ("linear_reg", {}),
+        ...         ("affine_reg", {}),
+        ...         ("polynomial", {"degree": 2})
+        ...         ("polynomial", {"degree": 3})
+        ...         ("polynomial", {"degree": 4})
+        ...     ],
+        ...     list_detection_methods=[("yolov8", {"detection_conf_th": 0.25})],
+        ... )
+        >>> results = analyzer.run(input_image=input_image, output_dir="output")
+        >>> print(results.head())
+        """  # noqa: E501
         ls_data = []
         idx = 1
         for det_method, det_params in self.list_detection_methods:
@@ -145,6 +236,7 @@ def run(
 
         print("DataFrame shape:", df_results.shape)
         print("\nDataFrame columns:", df_results.columns.tolist())
+        return df_results
 
 
 if __name__ == "__main__":
diff --git a/docs/assets/color-correction-how-it-works.png b/docs/assets/color-correction-how-it-works.png
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diff --git a/docs/assets/logo-v1.png b/docs/assets/logo-v1.png
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diff --git a/docs/assets/sample-benchmark.png b/docs/assets/sample-benchmark.png
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diff --git a/docs/assets/sample-output-debug.jpg b/docs/assets/sample-output-debug.jpg
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diff --git a/docs/assets/sample-output-usage.png b/docs/assets/sample-output-usage.png
new file mode 100644
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diff --git a/docs/index.md b/docs/index.md
new file mode 100644
index 0000000..942da0b
--- /dev/null
+++ b/docs/index.md
@@ -0,0 +1,165 @@
+# Color Correction
+
+<figure markdown="span">
+  ![Image title](assets/logo-v1.png){ width="200" }
+  <figcaption>color correction</figcaption>
+</figure>
+
+<p align="center">
+    <a href="https://badge.fury.io/py/color-correction">
+        <img src="https://badge.fury.io/py/color-correction.svg" alt="version" />
+    </a>
+    <a href="https://pypistats.org/packages/color-correction">
+        <img src="https://img.shields.io/pypi/dm/color-correction" alt="downloads" />
+    </a>
+    <a href="https://badge.fury.io/py/color-correction">
+        <img src="https://img.shields.io/pypi/pyversions/color-correction" alt="python version" />
+    </a>
+</p>
+
+!!! warning "Disclaimer"
+
+    _Package formerly published as [`color-correction-asdfghjkl`](https://pypi.org/project/color-correction-asdfghjkl/) on PyPI. The name has been simplified for better accessibility and professional recognition._
+
+This package is designed to perform color correction on images using the Color Checker Classic 24 Patch card. It provides a robust solution for ensuring accurate color representation in your images.
+
+## 📦 Installation
+
+```bash
+pip install color-correction
+```
+
+## 🏋️‍♀️ How it works
+![How it works](assets/color-correction-how-it-works.png)
+
+
+## ⚡ How to use
+
+```python
+from color_correction import ColorCorrection
+
+# Step 1: Define the path to the input image
+image_path = "asset/images/cc-19.png"
+
+# Step 2: Load the input image
+input_image = cv2.imread(image_path)
+
+# Step 3: Initialize the color correction model with specified parameters
+color_corrector = ColorCorrection(
+    detection_model="yolov8",
+    detection_conf_th=0.25,
+    correction_model="polynomial", # "least_squares", "affine_reg", "linear_reg"
+    degree=3,  # for polynomial correction model
+    use_gpu=True,
+)
+
+# Step 4: Extract color patches from the input image
+# you can set reference patches from another image (image has color checker card)
+# or use the default D50
+# color_corrector.set_reference_patches(image=None, debug=True)
+color_corrector.set_input_patches(image=input_image, debug=True)
+color_corrector.fit()
+corrected_image = color_corrector.predict(
+    input_image=input_image,
+    debug=True,
+    debug_output_dir="zzz",
+)
+
+# Step 5: Evaluate the color correction results
+eval_result = color_corrector.calc_color_diff_patches()
+print(eval_result)
+```
+
+
+??? info "Sample Evaluation Output"
+
+    ```json
+    {
+        "initial": {
+            "min": 2.254003059526461,
+            "max": 13.461066402633447,
+            "mean": 8.3072755187654,
+            "std": 3.123962754767539,
+        },
+        "corrected": {
+            "min": 0.30910031798755183,
+            "max": 5.422311999126372,
+            "mean": 1.4965478752947827,
+            "std": 1.2915738724958112,
+        },
+        "delta": {
+            "min": 1.9449027415389093,
+            "max": 8.038754403507074,
+            "mean": 6.810727643470616,
+            "std": 1.8323888822717276,
+        },
+    }
+    ```
+
+
+??? info "Sample Output Debugging Image"
+
+    ![Sample Output](assets/sample-output-debug.jpg)
+
+
+
+## 🔎 Reporting
+```python
+import cv2
+
+from color_correction import ColorCorrectionAnalyzer
+
+# input_image_path = "assets/cc-19.png"
+input_image_path = "assets/cc-1.jpg"
+
+report = ColorCorrectionAnalyzer(
+    list_correction_methods=[
+        ("least_squares", {}),
+        ("linear_reg", {}),
+        ("affine_reg", {}),
+        ("polynomial", {"degree": 2}),
+        ("polynomial", {"degree": 3}),
+        # ("polynomial", {"degree": 4}),
+        # ("polynomial", {"degree": 5}),
+    ],
+    list_detection_methods=[
+        ("yolov8", {"detection_conf_th": 0.25}),
+    ],
+)
+report.run(
+    input_image=cv2.imread(input_image_path),
+    reference_image=None,
+    output_dir="report-output",
+)
+```
+
+??? info "Sample Report Output"
+
+    ![Sample Benchmark Output](assets/sample-benchmark.png)
+
+
+## 📈 Benefits
+- **Consistency**: Ensure uniform color correction across multiple images.
+- **Accuracy**: Leverage the color correction matrix for precise color adjustments.
+- **Flexibility**: Adaptable for various image sets with different color profiles.
+
+
+## 🤸 TODO
+
+- [ ] Add Loggers
+- [ ] Add detection MCC:CCheckerDetector from opencv
+- [ ] Add Segmentation Color Checker using YOLOv11 ONNX
+- [ ] Improve validation preprocessing (e.g., auto-match-orientation CC)
+- [ ] Add more analysis and evaluation metrics _(still thinking...)_
+
+<!-- write reference -->
+
+## 📚 References
+- [Color Checker Classic 24 Patch Card](https://www.xrite.com/categories/calibration-profiling/colorchecker-classic)
+- [Color Correction Tool ML](https://github.com/collinswakholi/ML_ColorCorrection_tool/tree/Pip_package)
+- [Colour Science Python](https://www.colour-science.org/colour-checker-detection/)
+- [Fast and Robust Multiple ColorChecker Detection ()](https://github.com/pedrodiamel/colorchecker-detection)
+- [Automatic color correction with OpenCV and Python (PyImageSearch)](https://pyimagesearch.com/2021/02/15/automatic-color-correction-with-opencv-and-python/)
+- [ONNX-YOLOv8-Object-Detection](https://github.com/ibaiGorordo/ONNX-YOLOv8-Object-Detection)
+- [yolov8-triton](https://github.com/omarabid59/yolov8-triton/tree/main)
+- [Streamlined Data Science Development: Organizing, Developing and Documenting Your Code](https://medium.com/henkel-data-and-analytics/streamlined-data-science-development-organizing-developing-and-documenting-your-code-bfd69e3ef4fb)
diff --git a/docs/reference/core/card_detection/yv8_onnx.md b/docs/reference/core/card_detection/yv8_onnx.md
new file mode 100644
index 0000000..fe826f7
--- /dev/null
+++ b/docs/reference/core/card_detection/yv8_onnx.md
@@ -0,0 +1,4 @@
+# Documentation for `YOLOv8CardDetector`
+
+
+::: color_correction.core.card_detection.det_yv8_onnx.YOLOv8CardDetector
diff --git a/docs/reference/core/correction/affine_reg.md b/docs/reference/core/correction/affine_reg.md
new file mode 100644
index 0000000..bb9c7db
--- /dev/null
+++ b/docs/reference/core/correction/affine_reg.md
@@ -0,0 +1,4 @@
+# Documentation for `AffineRegression`
+
+
+::: color_correction.core.correction.AffineRegression
diff --git a/docs/reference/core/correction/least_squares.md b/docs/reference/core/correction/least_squares.md
new file mode 100644
index 0000000..b674c7f
--- /dev/null
+++ b/docs/reference/core/correction/least_squares.md
@@ -0,0 +1,3 @@
+# Documentation for `LeastSquares`
+
+::: color_correction.core.correction.LeastSquaresRegression
diff --git a/docs/reference/core/correction/linear_reg.md b/docs/reference/core/correction/linear_reg.md
new file mode 100644
index 0000000..8a5ec6f
--- /dev/null
+++ b/docs/reference/core/correction/linear_reg.md
@@ -0,0 +1,3 @@
+# Documentation for `LinearRegression`
+
+::: color_correction.core.correction.LinearRegression
diff --git a/docs/reference/core/correction/polynomial.md b/docs/reference/core/correction/polynomial.md
new file mode 100644
index 0000000..7dde95d
--- /dev/null
+++ b/docs/reference/core/correction/polynomial.md
@@ -0,0 +1,3 @@
+# Documentation for `Polynomial`
+
+::: color_correction.core.correction.Polynomial
diff --git a/docs/reference/services/color_correction.md b/docs/reference/services/color_correction.md
new file mode 100644
index 0000000..e733a10
--- /dev/null
+++ b/docs/reference/services/color_correction.md
@@ -0,0 +1,3 @@
+# Documentation for `ColorCorrection`
+
+::: color_correction.ColorCorrection
diff --git a/docs/reference/services/correction_analyzer.md b/docs/reference/services/correction_analyzer.md
new file mode 100644
index 0000000..f108d3a
--- /dev/null
+++ b/docs/reference/services/correction_analyzer.md
@@ -0,0 +1,3 @@
+# Documentation for `ColorCorrectionAnalyzer`
+
+::: color_correction.ColorCorrectionAnalyzer
diff --git a/docs/tutorials.md b/docs/tutorials.md
new file mode 100644
index 0000000..6d81d67
--- /dev/null
+++ b/docs/tutorials.md
@@ -0,0 +1,6 @@
+# Tutorial
+
+This is a tutorial.
+!!! warning "Under Construction"
+
+    This tutorial is under construction. Please check back later.
diff --git a/mkdocs.yml b/mkdocs.yml
new file mode 100644
index 0000000..badf2f4
--- /dev/null
+++ b/mkdocs.yml
@@ -0,0 +1,158 @@
+site_name: color-correction
+extra:
+  generator: false
+theme:
+  name: 'material'
+  language: en
+  logo: 'assets/logo-v1.png'
+  favicon: 'assets/logo-v1.png'
+  font:
+    text: 'Plus Jakarta Sans'
+    code: 'Fira Code'
+  palette:
+    # Palette toggle for light mode
+    - media: "(prefers-color-scheme: light)"
+      scheme: default
+      toggle:
+        icon: material/lightbulb
+        name: Switch to dark mode
+      primary: 1F69E9
+      accent: grey
+
+    # Palette toggle for dark mode
+    - media: "(prefers-color-scheme: dark)"
+      scheme: slate
+      toggle:
+        icon: material/lightbulb-outline
+        name: Switch to system preference
+      primary: 1F69E9
+      accent: white
+  icon:
+    admonition:
+      note: fontawesome/solid/note-sticky
+      abstract: fontawesome/solid/book
+      info: fontawesome/solid/circle-info
+      tip: fontawesome/solid/bullhorn
+      success: fontawesome/solid/check
+      question: fontawesome/solid/circle-question
+      warning: fontawesome/solid/triangle-exclamation
+      failure: fontawesome/solid/bomb
+      danger: fontawesome/solid/skull
+      bug: fontawesome/solid/robot
+      example: fontawesome/solid/flask
+      quote: fontawesome/regular/file-code
+
+  features:
+    - navigation.instant
+    - navigation.instant.progress
+    - navigation.tabs
+    - navigation.expand
+    - navigation.indexes
+    - navigation.top
+    - search.suggest
+    - content.code.copy
+    - content.code.annotate
+
+markdown_extensions:
+  - pymdownx.inlinehilite
+  - pymdownx.snippets
+  - pymdownx.superfences
+  - attr_list
+  - md_in_html
+  - pymdownx.blocks.caption
+  - admonition
+  - pymdownx.details
+  - pymdownx.superfences
+  - pymdownx.critic
+  - pymdownx.caret
+  - pymdownx.keys
+  - pymdownx.mark
+  - pymdownx.tilde
+  - def_list
+  - pymdownx.tasklist:
+      custom_checkbox: true
+  - pymdownx.highlight:
+      anchor_linenums: true
+      line_spans: __span
+      pygments_lang_class: true
+  - pymdownx.emoji:
+      emoji_index: !!python/name:material.extensions.emoji.twemoji
+      emoji_generator: !!python/name:material.extensions.emoji.to_svg
+  - pymdownx.superfences:
+      custom_fences:
+      - name: mermaid
+        class: mermaid
+        format: !!python/name:pymdownx.superfences.fence_code_format
+  - toc:
+      permalink: "¤"
+
+plugins:
+  - search
+  - mkdocstrings:
+      handlers:
+        python:
+          paths: [.]  # actually not needed, default
+          options:
+            filters:
+              - "!^_" # ignore objects starting with an underscore
+              - "^__" # ignore objects starting with a double underscore
+
+            # docstrings
+            docstring_style: numpy
+            docstring_section_style: table
+            show_if_no_docstring: false
+            show_docstring_classes: true
+            show_docstring_attributes: true
+            show_docstring_functions: true
+            show_docstring_modules: true
+            show_docstring_examples: true
+            show_docstring_other_parameters: true
+            show_docstring_parameters: true
+            show_docstring_raises: true
+            show_docstring_receives: true
+            show_docstring_returns: true
+            show_docstring_warns: true
+            show_docstring_yields: true
+
+            # signature
+            annotations_path: source # source, brief, full
+            line_length: 79
+            separate_signature: true
+            show_signature_annotations: true
+
+            # general
+            show_source: true
+
+            # headings
+            parameter_headings: false
+            show_bases: false
+            show_root_heading: true
+            show_root_full_path: false
+            show_root_toc_entry: false
+            show_root_members_full_path: false
+            show_object_full_path: false
+            group_by_category: true
+            show_category_heading: true
+            show_symbol_type_heading: true
+            show_symbol_type_toc: true
+
+            # members
+            members_order: source
+            show_submodules: true
+            show_labels: true
+
+nav:
+  - Home: index.md
+  - Tutorial: tutorials.md
+  - Reference:
+      - Core:
+        - Card Detection:
+          - YoloV8 ONNX: reference/core/card_detection/yv8_onnx.md
+        - Correction:
+          - Least Squares: reference/core/correction/least_squares.md
+          - Linear Regression: reference/core/correction/linear_reg.md
+          - Affine Regression: reference/core/correction/affine_reg.md
+          - Polynomial: reference/core/correction/polynomial.md
+      - Services:
+        - Color Correction: reference/services/color_correction.md
+        - Correction Analyzer: reference/services/correction_analyzer.md
diff --git a/pyproject.toml b/pyproject.toml
index 3f40028..2988dfd 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -39,6 +39,12 @@ Homepage = "https://github.com/agfianf/color-correction"
 Repository = "https://github.com/agfianf/color-correction"
 Issues = "https://github.com/agfianf/color-correction/issues"
 
+[project.optional-dependencies]
+docs = [
+    "mkdocs-material>=9.6.3",
+    "mkdocstrings[python]>=0.28.0",
+]
+
 
 [dependency-groups]
 mccdet = [
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000..2c4e2b2
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,834 @@
+# This file was autogenerated by uv via the following command:
+#    uv export --frozen --output-file=requirements.txt
+-e .
+annotated-types==0.7.0 \
+    --hash=sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53 \
+    --hash=sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89
+anyio==4.8.0 \
+    --hash=sha256:1d9fe889df5212298c0c0723fa20479d1b94883a2df44bd3897aa91083316f7a \
+    --hash=sha256:b5011f270ab5eb0abf13385f851315585cc37ef330dd88e27ec3d34d651fd47a
+certifi==2024.12.14 \
+    --hash=sha256:1275f7a45be9464efc1173084eaa30f866fe2e47d389406136d332ed4967ec56 \
+    --hash=sha256:b650d30f370c2b724812bee08008be0c4163b163ddaec3f2546c1caf65f191db
+cfgv==3.4.0 \
+    --hash=sha256:b7265b1f29fd3316bfcd2b330d63d024f2bfd8bcb8b0272f8e19a504856c48f9 \
+    --hash=sha256:e52591d4c5f5dead8e0f673fb16db7949d2cfb3f7da4582893288f0ded8fe560
+colorama==0.4.6 ; sys_platform == 'win32' \
+    --hash=sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44 \
+    --hash=sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6
+coloredlogs==15.0.1 \
+    --hash=sha256:612ee75c546f53e92e70049c9dbfcc18c935a2b9a53b66085ce9ef6a6e5c0934 \
+    --hash=sha256:7c991aa71a4577af2f82600d8f8f3a89f936baeaf9b50a9c197da014e5bf16b0
+colour-science==0.4.6 \
+    --hash=sha256:4cd90e6d500c16f3c24225da57031e1944de52fec6b484f5bb3d4ea7d0cfee08 \
+    --hash=sha256:be98c2c9b2a5caf0c443431f402599ca9e1cc7d944bb804156803bcc97af4cf0
+contourpy==1.3.1 \
+    --hash=sha256:041b640d4ec01922083645a94bb3b2e777e6b626788f4095cf21abbe266413c1 \
+    --hash=sha256:05e806338bfeaa006acbdeba0ad681a10be63b26e1b17317bfac3c5d98f36cda \
+    --hash=sha256:08d9d449a61cf53033612cb368f3a1b26cd7835d9b8cd326647efe43bca7568d \
+    --hash=sha256:0ffa84be8e0bd33410b17189f7164c3589c229ce5db85798076a3fa136d0e509 \
+    --hash=sha256:113231fe3825ebf6f15eaa8bc1f5b0ddc19d42b733345eae0934cb291beb88b6 \
+    --hash=sha256:14c102b0eab282427b662cb590f2e9340a9d91a1c297f48729431f2dcd16e14f \
+    --hash=sha256:174e758c66bbc1c8576992cec9599ce8b6672b741b5d336b5c74e35ac382b18e \
+    --hash=sha256:19c1555a6801c2f084c7ddc1c6e11f02eb6a6016ca1318dd5452ba3f613a1751 \
+    --hash=sha256:19d40d37c1c3a4961b4619dd9d77b12124a453cc3d02bb31a07d58ef684d3d86 \
+    --hash=sha256:1bf98051f1045b15c87868dbaea84f92408337d4f81d0e449ee41920ea121d3b \
+    --hash=sha256:20914c8c973f41456337652a6eeca26d2148aa96dd7ac323b74516988bea89fc \
+    --hash=sha256:287ccc248c9e0d0566934e7d606201abd74761b5703d804ff3df8935f523d546 \
+    --hash=sha256:2ba94a401342fc0f8b948e57d977557fbf4d515f03c67682dd5c6191cb2d16ec \
+    --hash=sha256:31c1b55c1f34f80557d3830d3dd93ba722ce7e33a0b472cba0ec3b6535684d8f \
+    --hash=sha256:36987a15e8ace5f58d4d5da9dca82d498c2bbb28dff6e5d04fbfcc35a9cb3a82 \
+    --hash=sha256:3a04ecd68acbd77fa2d39723ceca4c3197cb2969633836ced1bea14e219d077c \
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+    --hash=sha256:fc2ace710ba7c1dfd1a3b42530b62b9ceed115f19a1656adefce7b1782a37794
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+    --hash=sha256:12eaf48463b472c3c0f8dbacdbf906e573013df81a0ab82f0616ea4b11281908 \
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+    --hash=sha256:c55b20591ced744aa04e8c3e4b7543ea4d650b6c3c4b208c08a05b4010e8b442 \
+    --hash=sha256:c58a9622d5dbeb668f407f35f4e6bfac34bb9ecdcc81680c04d0258169747997 \
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+    --hash=sha256:fd44fc75522f58612ec4a33958a7e5552562b7705b42ef1b4f8c0818e304a363
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+    --hash=sha256:a0b2b9fe80bbcd81a6647ff13108738cfb482d481d826cc0e02f5b35e5c88d2c
+nodeenv==1.9.1 \
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+    --hash=sha256:ba11c9782d29c27c70ffbdda2d7415098754709be8a7056d79a737cd901155c9
+numpy==1.26.4 ; sys_platform == 'darwin' \
+    --hash=sha256:03a8c78d01d9781b28a6989f6fa1bb2c4f2d51201cf99d3dd875df6fbd96b23b \
+    --hash=sha256:2a02aba9ed12e4ac4eb3ea9421c420301a0c6460d9830d74a9df87efa4912010 \
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+    --hash=sha256:b3ce300f3644fb06443ee2222c2201dd3a89ea6040541412b8fa189341847218 \
+    --hash=sha256:edd8b5fe47dab091176d21bb6de568acdd906d1887a4584a15a9a96a1dca06ef
+numpy==2.2.2 ; sys_platform != 'darwin' \
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+    --hash=sha256:b6fb9c32a91ec32a689ec6410def76443e3c750e7cfc3fb2206b985ffb2b85f0 \
+    --hash=sha256:b78ea78450fd96a498f50ee096f69c75379af5138f7881a51355ab0e11286c97 \
+    --hash=sha256:bd249bc894af67cbd8bad2c22e7cbcd46cf87ddfca1f1289d1e7e54868cc785c \
+    --hash=sha256:d6d6a0910c3b4368d89dde073e630882cdb266755565155bc33520283b2d9df8 \
+    --hash=sha256:da1eeb460ecce8d5b8608826595c777728cdf28ce7b5a5a8c8ac8d949beadcf2 \
+    --hash=sha256:e0c8854b09bc4de7b041148d8550d3bd712b5c21ff6a8ed308085f190235d7ff \
+    --hash=sha256:e0d4142eb40ca6f94539e4db929410f2a46052a0fe7a2c1c59f6179c39938d2a \
+    --hash=sha256:ed6906f61834d687738d25988ae117683705636936cc605be0bb208b23df4d8f
+onnx==1.17.0 \
+    --hash=sha256:0141c2ce806c474b667b7e4499164227ef594584da432fd5613ec17c1855e311 \
+    --hash=sha256:081ec43a8b950171767d99075b6b92553901fa429d4bc5eb3ad66b36ef5dbe3a \
+    --hash=sha256:0e906e6a83437de05f8139ea7eaf366bf287f44ae5cc44b2850a30e296421f2f \
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+    --hash=sha256:3193a3672fc60f1a18c0f4c93ac81b761bc72fd8a6c2035fa79ff5969f07713e \
+    --hash=sha256:38b5df0eb22012198cdcee527cc5f917f09cce1f88a69248aaca22bd78a7f023 \
+    --hash=sha256:3d955ba2939878a520a97614bcf2e79c1df71b29203e8ced478fa78c9a9c63c2 \
+    --hash=sha256:48ca1a91ff73c1d5e3ea2eef20ae5d0e709bb8a2355ed798ffc2169753013fd3 \
+    --hash=sha256:4a183c6178be001bf398260e5ac2c927dc43e7746e8638d6c05c20e321f8c949 \
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+    --hash=sha256:fa8e459d4954f608fa26116118bb67f56b93b209c39b008277ace29937453dc9
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+    --hash=sha256:590be9e6e24d06db33a4262829edef682500ef008565a969c73d39d5f8bfb3fd
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+    --hash=sha256:61980854fd66de3a90028d679a954d5f2623e83144b5afe5ee86f43d762e5f0a
+pyreadline3==3.5.4 ; sys_platform == 'win32' \
+    --hash=sha256:8d57d53039a1c75adba8e50dd3d992b28143480816187ea5efbd5c78e6c885b7 \
+    --hash=sha256:eaf8e6cc3c49bcccf145fc6067ba8643d1df34d604a1ec0eccbf7a18e6d3fae6
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+    --hash=sha256:965370d062bce11e73868e0335abac31b4d3de0e82f4007408d242b4f8610761
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+    --hash=sha256:a8b2bc7bffae282281c8140a97d3aa9c14da0b136dfe83f850eea9a5f7470427
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+    --hash=sha256:f7b63ef50f1b690dddf550d03497b66d609393b40b564ed0d674909a68ebf16a
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+    --hash=sha256:31c7c1817eb7fae7ca4b8c7ee50c72f93aa2dd863de768e1ef4245d426aa0725
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+    --hash=sha256:fa909b1a36e000a03c382aade0bd2063fd5680ff8b8e501660c0f59f021a6415
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diff --git a/uv.lock b/uv.lock
index e31361e..f920395 100644
--- a/uv.lock
+++ b/uv.lock
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+]
+
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 version = "2024.12.14"
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+[[package]]
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+version = "8.1.8"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "colorama", marker = "sys_platform == 'win32'" },
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+wheels = [
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+]
+
 [[package]]
 name = "color-correction"
 version = "0.0.1b3"
@@ -137,6 +158,12 @@ dependencies = [
     { name = "shapely" },
 ]
 
+[package.optional-dependencies]
+docs = [
+    { name = "mkdocs-material" },
+    { name = "mkdocstrings", extra = ["python"] },
+]
+
 [package.dev-dependencies]
 dev = [
     { name = "pre-commit" },
@@ -156,6 +183,8 @@ requires-dist = [
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     { name = "httpx", specifier = ">=0.28.1" },
     { name = "matplotlib", specifier = ">=3.10.0" },
+    { name = "mkdocs-material", marker = "extra == 'docs'", specifier = ">=9.6.3" },
+    { name = "mkdocstrings", extras = ["python"], marker = "extra == 'docs'", specifier = ">=0.28.0" },
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     { name = "onnxruntime", specifier = ">=1.20.1" },
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+[[package]]
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+[[package]]
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+[[package]]
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