diff --git a/.gitignore b/.gitignore
index 82f9275..202aa2b 100644
--- a/.gitignore
+++ b/.gitignore
@@ -3,6 +3,9 @@ __pycache__/
 *.py[cod]
 *$py.class
 
+*.jpg
+*.png
+
 # C extensions
 *.so
 
diff --git a/LICENSE b/LICENSE
new file mode 100644
index 0000000..20d40b6
--- /dev/null
+++ b/LICENSE
@@ -0,0 +1,674 @@
+                    GNU GENERAL PUBLIC LICENSE
+                       Version 3, 29 June 2007
+
+ Copyright (C) 2007 Free Software Foundation, Inc. <http://fsf.org/>
+ Everyone is permitted to copy and distribute verbatim copies
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+PURPOSE.  THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM
+IS WITH YOU.  SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF
+ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
+
+  16. Limitation of Liability.
+
+  IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
+WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
+THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
+GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
+USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
+DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
+PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
+EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
+SUCH DAMAGES.
+
+  17. Interpretation of Sections 15 and 16.
+
+  If the disclaimer of warranty and limitation of liability provided
+above cannot be given local legal effect according to their terms,
+reviewing courts shall apply local law that most closely approximates
+an absolute waiver of all civil liability in connection with the
+Program, unless a warranty or assumption of liability accompanies a
+copy of the Program in return for a fee.
+
+                     END OF TERMS AND CONDITIONS
+
+            How to Apply These Terms to Your New Programs
+
+  If you develop a new program, and you want it to be of the greatest
+possible use to the public, the best way to achieve this is to make it
+free software which everyone can redistribute and change under these terms.
+
+  To do so, attach the following notices to the program.  It is safest
+to attach them to the start of each source file to most effectively
+state the exclusion of warranty; and each file should have at least
+the "copyright" line and a pointer to where the full notice is found.
+
+    <one line to give the program's name and a brief idea of what it does.>
+    Copyright (C) <year>  <name of author>
+
+    This program is free software: you can redistribute it and/or modify
+    it under the terms of the GNU General Public License as published by
+    the Free Software Foundation, either version 3 of the License, or
+    (at your option) any later version.
+
+    This program is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+    GNU General Public License for more details.
+
+    You should have received a copy of the GNU General Public License
+    along with this program.  If not, see <http://www.gnu.org/licenses/>.
+
+Also add information on how to contact you by electronic and paper mail.
+
+  If the program does terminal interaction, make it output a short
+notice like this when it starts in an interactive mode:
+
+    <program>  Copyright (C) <year>  <name of author>
+    This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
+    This is free software, and you are welcome to redistribute it
+    under certain conditions; type `show c' for details.
+
+The hypothetical commands `show w' and `show c' should show the appropriate
+parts of the General Public License.  Of course, your program's commands
+might be different; for a GUI interface, you would use an "about box".
+
+  You should also get your employer (if you work as a programmer) or school,
+if any, to sign a "copyright disclaimer" for the program, if necessary.
+For more information on this, and how to apply and follow the GNU GPL, see
+<http://www.gnu.org/licenses/>.
+
+  The GNU General Public License does not permit incorporating your program
+into proprietary programs.  If your program is a subroutine library, you
+may consider it more useful to permit linking proprietary applications with
+the library.  If this is what you want to do, use the GNU Lesser General
+Public License instead of this License.  But first, please read
+<http://www.gnu.org/philosophy/why-not-lgpl.html>.
\ No newline at end of file
diff --git a/easy_viz_cnn/__init__.py b/easy_viz_cnn/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/easy_viz_cnn/colors.py b/easy_viz_cnn/colors.py
new file mode 100644
index 0000000..750fdab
--- /dev/null
+++ b/easy_viz_cnn/colors.py
@@ -0,0 +1,50 @@
+from itertools import cycle
+from typing import NamedTuple
+
+
+class RGB(NamedTuple):
+    red: float
+    green: float
+    blue: float
+    alpha: float = 1.0
+
+
+DARK_LIGHT = cycle([RGB(0.4, 0.4, 0.4), RGB(0.8, 0.8, 0.8)])
+BLUE_GRAY = cycle([RGB(0.654, 0.780, 0.905), RGB(0.8, 0.8, 0.8)])
+GREEN_GRAY = cycle([RGB(0.5, 0.8, 0.5), RGB(0.8, 0.8, 0.8)])
+ORANGE_BROWN = cycle([RGB(0.9, 0.6, 0.4), RGB(0.6, 0.4, 0.2)])
+PURPLE_PINK = cycle([RGB(0.7, 0.5, 0.9), RGB(0.9, 0.7, 0.7)])
+TEAL_GRAY = cycle([RGB(0.5, 0.8, 0.8), RGB(0.8, 0.8, 0.8)])
+RED_BEIGE = cycle([RGB(0.9, 0.4, 0.4), RGB(0.9, 0.8, 0.7)])
+SUNSET = cycle([
+    RGB(0.9, 0.6, 0.3),  # Orange
+    RGB(0.8, 0.4, 0.4),  # Red
+    RGB(0.9, 0.7, 0.7),  # Pink
+    RGB(0.7, 0.5, 0.9)   # Purple
+])
+OCEAN = cycle([
+    RGB(0.2, 0.6, 0.8),  # Light Blue
+    RGB(0.0, 0.5, 0.5),  # Teal
+    RGB(0.4, 0.8, 0.6),  # Light Green
+    RGB(0.2, 0.4, 0.7)   # Darker Blue
+])
+FOREST = cycle([
+    RGB(0.3, 0.7, 0.3),  # Green
+    RGB(0.4, 0.3, 0.2),  # Brown
+    RGB(0.1, 0.4, 0.2),  # Dark Green
+    RGB(0.1, 0.2, 0.4)   # Dark Blue
+])
+PASTEL = cycle([
+    RGB(0.9, 0.7, 0.8),  # Pastel Pink
+    RGB(0.7, 0.8, 0.9),  # Pastel Blue
+    RGB(0.9, 0.9, 0.7),  # Pastel Yellow
+    RGB(0.7, 0.9, 0.8)   # Pastel Green
+])
+RAINBOW = cycle([
+    RGB(1.0, 0.0, 0.0),  # Red
+    RGB(1.0, 0.5, 0.0),  # Orange
+    RGB(1.0, 1.0, 0.0),  # Yellow
+    RGB(0.0, 1.0, 0.0),  # Green
+    RGB(0.0, 0.0, 1.0),  # Blue
+    RGB(0.5, 0.0, 1.0)   # Purple
+])
diff --git a/easy_viz_cnn/layers.py b/easy_viz_cnn/layers.py
new file mode 100644
index 0000000..e05b4b4
--- /dev/null
+++ b/easy_viz_cnn/layers.py
@@ -0,0 +1,180 @@
+from typing import TypeVar
+
+
+T = TypeVar("T", int, tuple[int, int])
+
+
+class Layer:
+    """Base class for all layers in the CNN model."""
+
+    def __str__(self) -> str:
+        raise NotImplementedError("Subclasses should implement this!")
+
+    def output_shape(self, input_size):
+        raise NotImplementedError("Subclasses should implement this!")
+
+
+class InputLayer(Layer):
+    """Represents an input layer in a CNN."""
+
+    def __init__(self, input_size):
+        self.input_size = input_size
+
+    def __str__(self) -> str:
+        return f"Input({self.input_size})"
+
+    def output_shape(self, *args, **kwargs):
+        return self.input_size
+
+
+class Conv1DLayer(Layer):
+    """
+    Represents a 1D convolutional layer in a CNN.
+
+    Parameters
+    ----------
+    in_channels : int
+        Number of input channels.
+    out_channels : int
+        Number of output channels.
+    kernel_size : int
+        Size of the kernel.
+    stride : int, optional
+        Stride of the convolution. Default is 1.
+    padding : int, optional
+        Padding of the convolution. Default is 0.
+    """
+
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        kernel_size: int,
+        stride: int = 1,
+        padding: int = 0,
+    ):
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.kernel_size = kernel_size
+        self.stride = stride
+        self.padding = padding
+
+    def __str__(self) -> str:
+        return f"Conv1D({self.in_channels}, {self.out_channels}, {self.kernel_size}, {self.stride}, {self.padding})"
+
+    def output_shape(self, input_size):
+        output_size = (input_size - self.kernel_size + 2 * self.padding) // self.stride + 1
+        return (output_size, self.out_channels)
+
+
+class Conv2DLayer(Layer):
+    """
+    Represents a 2D convolutional layer in a CNN.
+
+    Parameters
+    ----------
+    in_channels : int
+        Number of input channels.
+    out_channels : int
+        Number of output channels.
+    kernel_size : int or tuple[int, int]
+        Size of the kernel.
+    stride : int or tuple[int, int], optional
+        Stride of the convolution. Default is 1.
+    padding : int or tuple[int, int], optional
+        Padding of the convolution. Default is 0.
+    """
+
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        kernel_size: int | tuple[int, int],
+        stride: int | tuple[int, int] = 1,
+        padding: int | tuple[int, int] = 0,
+    ):
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+
+        if isinstance(kernel_size, int):
+            self.kernel_size = (kernel_size, kernel_size)
+        else:
+            self.kernel_size = kernel_size
+
+        if isinstance(stride, int):
+            self.stride = (stride, stride)
+        else:
+            self.stride = stride
+
+        if isinstance(padding, int):
+            self.padding = (padding, padding)
+        else:
+            self.padding = padding
+
+    def __str__(self) -> str:
+        return f"Conv2D({self.in_channels}, {self.out_channels}, {self.kernel_size}, {self.stride}, {self.padding})"
+
+    def output_shape(self, input_size):
+        output_height = (input_size[0] - self.kernel_size[0] + 2 * self.padding[0]) // self.stride[
+            0
+        ] + 1
+        output_width = (input_size[1] - self.kernel_size[1] + 2 * self.padding[0]) // self.stride[
+            0
+        ] + 1
+        return (output_height, output_width, self.out_channels)
+
+
+class PoolLayer(Layer):
+    """
+    Represents a pooling layer in a CNN.
+
+    Parameters
+    ----------
+    kernel_size : int
+        Size of the kernel.
+    stride : int, optional
+        Stride of the pooling. Default is the same as the kernel size.
+    pool_type : str, optional
+        Type of the pooling. Can be "max" or "avg". Default is "max".
+    """
+
+    def __init__(
+        self,
+        kernel_size: int,
+        stride: int = None,
+        pool_type: str = "max",
+    ):
+        self.kernel_size = kernel_size
+        self.stride = stride or kernel_size
+        self.pool_type = pool_type
+
+    def __str__(self) -> str:
+        return f"{self.pool_type.capitalize()}Pool({self.kernel_size}, {self.stride})"
+
+    def output_shape(self, input_size):
+        output_height = (input_size[0] - self.kernel_size) // self.stride + 1
+        output_width = (input_size[1] - self.kernel_size) // self.stride + 1
+        return (output_height, output_width, input_size[2])
+
+
+class FCLayer(Layer):
+    """
+    Represents a fully connected (dense) layer in a CNN.
+
+    Parameters
+    ----------
+    in_features : int
+        Number of input features.
+    out_features : int
+        Number of output features.
+    """
+
+    def __init__(self, in_features: int, out_features: int):
+        self.in_features = in_features
+        self.out_features = out_features
+
+    def __str__(self) -> str:
+        return f"FC({self.in_features}, {self.out_features})"
+
+    def output_shape(self, input_size: int) -> tuple[int, int]:
+        return (1, self.out_features)
diff --git a/easy_viz_cnn/models.py b/easy_viz_cnn/models.py
new file mode 100644
index 0000000..bad0300
--- /dev/null
+++ b/easy_viz_cnn/models.py
@@ -0,0 +1,76 @@
+from matplotlib.figure import Figure
+import matplotlib.pyplot as plt
+
+from easy_viz_cnn.layers import Layer
+
+
+class SimpleCNNModel:
+    """
+    Represents a simple CNN model composed of various layers.
+
+    Parameters
+    ----------
+    layers : list[Layer]
+        List of layers in the model.
+    """
+
+    def __init__(self, layers: list[Layer]):
+        self.layers = layers
+
+    def __str__(self) -> str:
+        return " -> ".join([str(layer) for layer in self.layers])
+
+    def features(self):
+        input_size = None
+        for layer in self.layers:
+            input_size = layer.output_shape(input_size)
+            yield input_size
+
+    def create_figure(
+        self,
+        title: str = "CNN Architecture",
+        title_fontsize: int = 12,
+        layer_color: str = "lightblue",
+        layer_fontsize: int = 10,
+    ) -> Figure:
+        """
+        Plot the architecture of the CNN model.
+
+        Parameters
+        ----------
+        title : str, optional
+            Title of the plot. Default is "CNN Architecture".
+        title_fontsize : int, optional
+            Font size of the title. Default is 12.
+        layer_color : str, optional
+            Background color of the layer boxes. Default is "lightblue".
+        layer_fontsize : int, optional
+            Font size of the layer labels. Default is 10.
+        """
+
+        fig, ax = plt.subplots(figsize=(8, 6))
+        ax.set_title(title, fontsize=title_fontsize, pad=5)
+        ax.axis("off")
+
+        x_offset = 0.5
+        y_offset = 0.80  # Start lower to fit more layers
+        layer_height = 0.12  # Larger layer height
+        layer_spacing = layer_height + 0.02  # Tight spacing between layers
+
+        for layer in self.layers:
+            ax.text(
+                x_offset,
+                y_offset,
+                str(layer),
+                ha="center",
+                va="center",
+                fontsize=layer_fontsize,
+                bbox=dict(
+                    facecolor=layer_color,
+                    edgecolor="black",
+                    boxstyle=f"round,pad={layer_height * 5}",
+                ),
+            )
+            y_offset -= layer_spacing
+
+        return fig
diff --git a/easy_viz_cnn/utils.py b/easy_viz_cnn/utils.py
new file mode 100644
index 0000000..6a2897c
--- /dev/null
+++ b/easy_viz_cnn/utils.py
@@ -0,0 +1,93 @@
+import numpy as np
+import matplotlib.pyplot as plt
+
+from easy_viz_cnn.colors import DARK_LIGHT
+
+OneUnitCNN = 0.03
+OFFSET = 0.2
+
+
+# Function to draw a rectangle
+def draw_rectangle(ax, center, width, height, color):
+    rectangle = plt.Rectangle(
+        center - np.array([width / 2, height / 2]),
+        width,
+        height,
+        color=color,
+        ec="black",
+        linewidth=1.2,
+    )
+    ax.add_patch(rectangle)
+
+
+def draw_conv_layer(
+    ax,
+    # conv_layer: ConvLayer,
+    feature_map_size,
+    center=np.array([0.5, 0.5]),
+    colors=DARK_LIGHT,
+    title="",
+    offset=OFFSET,
+):
+    if title:
+        title += "\n"
+    if title != "Input\n":
+        vline_x = center[0] - 1 * offset / 2
+        ax.plot([vline_x, vline_x], [0.5, 1], color="#808080")
+    width = OneUnitCNN * feature_map_size[0]
+    height = OneUnitCNN * feature_map_size[1]
+    center[0] += offset + width / 2
+    if len(feature_map_size) == 3:
+        num_channels = feature_map_size[-1]
+        shift = 0.05
+        center[1] += (
+            num_channels // 2 - 1
+        ) * shift  # Center adjustment based on the number of channels
+
+        for i, color in zip(range(num_channels), colors):
+
+            current_center = center + i * np.array(
+                [shift, -shift]
+            )  # Shift the center for each channel
+            draw_rectangle(
+                ax, current_center, width, height, color
+            )  # Draw rectangle for each channel
+            if (num_channels // 2 - 1) == i:
+                ax.plot(
+                    [center[0] - width / 2, current_center[0] - width / 2],
+                    [0.5, 0.5],
+                    color="#808080",
+                    zorder=1,
+                )
+        # Calculate the midpoint for text justification
+        x_min = center[0] - width / 2  # Left boundary of the x-lim area
+        x_max = current_center[0] + width / 2  # Right boundary of the x-lim area
+        x_mid = (x_min + x_max) / 2  # Midpoint between the two
+
+        # Place feature map size at the midpoint of the x-lim area
+        ax.text(
+            x_mid,
+            -1.5,  # Position the text below the layer at the calculated midpoint
+            f"Feature Map\n({feature_map_size[0]}x{feature_map_size[1]}x{feature_map_size[2]})",  # Text showing the size
+            ha="center",
+            fontsize=8,
+            color="black",
+        )
+        return current_center[0] + width  # Return x-coordinate of the last rectangle
+
+    if feature_map_size[0] == 1:
+        width += OFFSET / 2
+        center[0] += OFFSET / 2
+        height *= 0.5
+    draw_rectangle(ax, center, width, height, next(colors))
+    # Place feature map size below the single rectangle
+    ax.text(
+        center[0],
+        -1.5,  # Position the text below the layer
+        f"{title}{feature_map_size[0]}x{feature_map_size[1]}",  # Text showing the size
+        ha="center",
+        fontsize=8,
+        color="black",
+    )
+
+    return center[0] + width
diff --git a/ex2.py b/ex2.py
new file mode 100644
index 0000000..b4fd5ae
--- /dev/null
+++ b/ex2.py
@@ -0,0 +1,39 @@
+import matplotlib.pyplot as plt
+import numpy as np
+
+from easy_viz_cnn.utils import draw_rectangle
+
+
+# Create a figure and axis
+fig, ax = plt.subplots()
+ax.set_aspect('equal')
+
+# Number of rectangles
+num_rectangles = 5
+shift = 0.05  # Shift amount for each rectangle
+
+# Generate rectangles with alternating dark and light colors
+for i in range(num_rectangles):
+    width = 0.7  # the width
+    height = 0.4  # the height
+    # Alternate between dark and light colors
+    color = (0.4, 0.4, 0.4) if i % 2 == 1 else (0.8, 0.8, 0.8)
+    center = np.array([0.5, 0.5]) + i * np.array([shift, -shift])
+    draw_rectangle(ax, center, width, height, color)
+    
+for i in range(num_rectangles+4):
+    width = 0.3  # the width
+    height = 0.2 # the height
+    # Alternate between dark and light colors
+    color = (0.4, 0.4, 0.4) if i % 2 == 0 else (0.8, 0.8, 0.8)
+    center = np.array([1.5, 0.6]) + i * np.array([shift, -shift])
+    draw_rectangle(ax, center, width, height, color)
+    
+    
+plt.ylim(-0.5, 1)
+plt.xlim(0, 2.7)
+# Remove axes
+#ax.axis('off')
+
+# Display the plot
+plt.show()
diff --git a/example-1.py b/example-1.py
new file mode 100644
index 0000000..e8f8b74
--- /dev/null
+++ b/example-1.py
@@ -0,0 +1,29 @@
+import matplotlib.pyplot as plt
+
+from easy_viz_cnn.layers import Conv2DLayer as ConvLayer, InputLayer, PoolLayer, FCLayer
+from easy_viz_cnn.models import SimpleCNNModel
+
+# Define a simple CNN model using our custom layer classes
+model = SimpleCNNModel(
+    [
+        InputLayer((32, 32)),
+        ConvLayer(1, 32, 3),
+        PoolLayer(2),
+        ConvLayer(32, 64, 3),
+        PoolLayer(2),
+        FCLayer(64 * 6 * 6, 128),
+        FCLayer(128, 10),
+    ]
+)
+
+# Visualize the model
+fig = model.create_figure(title="My Sample Model")
+
+# Optionally customize the figure further
+fig.suptitle("An Additional Super Title")
+
+# Save the figure to a file instead of showing it
+fig.savefig("cnn_model_visualization.png", dpi=300)
+
+# Explicitly call plt.show() to keep the window open
+plt.show()
diff --git a/example-3.py b/example-3.py
new file mode 100644
index 0000000..6ce76b8
--- /dev/null
+++ b/example-3.py
@@ -0,0 +1,52 @@
+from matplotlib import pyplot as plt
+import numpy as np
+from easy_viz_cnn import colors
+from easy_viz_cnn.layers import Conv2DLayer as ConvLayer, FCLayer, PoolLayer, InputLayer
+from easy_viz_cnn.models import SimpleCNNModel
+from easy_viz_cnn.utils import draw_conv_layer
+
+# Create a figure and axis
+fig, ax = plt.subplots()
+ax.set_aspect("equal")
+OFFSET = 0.2
+
+
+lenet_model = SimpleCNNModel(
+    [
+        InputLayer((32, 32)),
+        ConvLayer(1, 6, kernel_size=(5, 5), stride=(1, 1), padding=(0, 0)),
+        PoolLayer(kernel_size=2, stride=2),
+        ConvLayer(6, 16, kernel_size=(5, 5), stride=(1, 1), padding=(0, 0)),
+        PoolLayer(kernel_size=2, stride=2),
+        FCLayer(16 * 4 * 4, 120),
+    ]
+)
+
+x = 0
+
+plt.plot([-1, 8], [0.5, 0.5], color="#808080", zorder=1)
+
+for idx, feature_map in enumerate(lenet_model.features()):
+    if idx == 0:
+        title = "Input"
+    else:
+        title = "Fully Connected"
+    x = draw_conv_layer(
+        ax,
+        feature_map,
+        np.array([x, 0.5]),
+        colors=colors.TEAL_GRAY,
+        title=title,
+        offset=OFFSET,
+    )
+    print(x, feature_map)
+
+# plt.xlim(-1, 8)
+# plt.ylim(-0.5, 2)
+
+# Remove axes
+ax.axis("off")
+
+print(list(lenet_model.features()))
+
+plt.show()
diff --git a/poetry.lock b/poetry.lock
new file mode 100644
index 0000000..df12998
--- /dev/null
+++ b/poetry.lock
@@ -0,0 +1,829 @@
+# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand.
+
+[[package]]
+name = "black"
+version = "24.8.0"
+description = "The uncompromising code formatter."
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "black-24.8.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:09cdeb74d494ec023ded657f7092ba518e8cf78fa8386155e4a03fdcc44679e6"},
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+[package.extras]
+docs = ["furo", "olefile", "sphinx (>=7.3)", "sphinx-copybutton", "sphinx-inline-tabs", "sphinxext-opengraph"]
+fpx = ["olefile"]
+mic = ["olefile"]
+tests = ["check-manifest", "coverage", "defusedxml", "markdown2", "olefile", "packaging", "pyroma", "pytest", "pytest-cov", "pytest-timeout"]
+typing = ["typing-extensions"]
+xmp = ["defusedxml"]
+
+[[package]]
+name = "platformdirs"
+version = "4.2.2"
+description = "A small Python package for determining appropriate platform-specific dirs, e.g. a `user data dir`."
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "platformdirs-4.2.2-py3-none-any.whl", hash = "sha256:2d7a1657e36a80ea911db832a8a6ece5ee53d8de21edd5cc5879af6530b1bfee"},
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+[package.extras]
+docs = ["furo (>=2023.9.10)", "proselint (>=0.13)", "sphinx (>=7.2.6)", "sphinx-autodoc-typehints (>=1.25.2)"]
+test = ["appdirs (==1.4.4)", "covdefaults (>=2.3)", "pytest (>=7.4.3)", "pytest-cov (>=4.1)", "pytest-mock (>=3.12)"]
+type = ["mypy (>=1.8)"]
+
+[[package]]
+name = "pluggy"
+version = "1.5.0"
+description = "plugin and hook calling mechanisms for python"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "pluggy-1.5.0-py3-none-any.whl", hash = "sha256:44e1ad92c8ca002de6377e165f3e0f1be63266ab4d554740532335b9d75ea669"},
+    {file = "pluggy-1.5.0.tar.gz", hash = "sha256:2cffa88e94fdc978c4c574f15f9e59b7f4201d439195c3715ca9e2486f1d0cf1"},
+]
+
+[package.extras]
+dev = ["pre-commit", "tox"]
+testing = ["pytest", "pytest-benchmark"]
+
+[[package]]
+name = "pycodestyle"
+version = "2.12.1"
+description = "Python style guide checker"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "pycodestyle-2.12.1-py2.py3-none-any.whl", hash = "sha256:46f0fb92069a7c28ab7bb558f05bfc0110dac69a0cd23c61ea0040283a9d78b3"},
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+]
+
+[[package]]
+name = "pyflakes"
+version = "3.2.0"
+description = "passive checker of Python programs"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "pyflakes-3.2.0-py2.py3-none-any.whl", hash = "sha256:84b5be138a2dfbb40689ca07e2152deb896a65c3a3e24c251c5c62489568074a"},
+    {file = "pyflakes-3.2.0.tar.gz", hash = "sha256:1c61603ff154621fb2a9172037d84dca3500def8c8b630657d1701f026f8af3f"},
+]
+
+[[package]]
+name = "pyparsing"
+version = "3.1.4"
+description = "pyparsing module - Classes and methods to define and execute parsing grammars"
+optional = false
+python-versions = ">=3.6.8"
+files = [
+    {file = "pyparsing-3.1.4-py3-none-any.whl", hash = "sha256:a6a7ee4235a3f944aa1fa2249307708f893fe5717dc603503c6c7969c070fb7c"},
+    {file = "pyparsing-3.1.4.tar.gz", hash = "sha256:f86ec8d1a83f11977c9a6ea7598e8c27fc5cddfa5b07ea2241edbbde1d7bc032"},
+]
+
+[package.extras]
+diagrams = ["jinja2", "railroad-diagrams"]
+
+[[package]]
+name = "pytest"
+version = "7.4.4"
+description = "pytest: simple powerful testing with Python"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "pytest-7.4.4-py3-none-any.whl", hash = "sha256:b090cdf5ed60bf4c45261be03239c2c1c22df034fbffe691abe93cd80cea01d8"},
+    {file = "pytest-7.4.4.tar.gz", hash = "sha256:2cf0005922c6ace4a3e2ec8b4080eb0d9753fdc93107415332f50ce9e7994280"},
+]
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+[package.dependencies]
+colorama = {version = "*", markers = "sys_platform == \"win32\""}
+iniconfig = "*"
+packaging = "*"
+pluggy = ">=0.12,<2.0"
+
+[package.extras]
+testing = ["argcomplete", "attrs (>=19.2.0)", "hypothesis (>=3.56)", "mock", "nose", "pygments (>=2.7.2)", "requests", "setuptools", "xmlschema"]
+
+[[package]]
+name = "python-dateutil"
+version = "2.9.0.post0"
+description = "Extensions to the standard Python datetime module"
+optional = false
+python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,>=2.7"
+files = [
+    {file = "python-dateutil-2.9.0.post0.tar.gz", hash = "sha256:37dd54208da7e1cd875388217d5e00ebd4179249f90fb72437e91a35459a0ad3"},
+    {file = "python_dateutil-2.9.0.post0-py2.py3-none-any.whl", hash = "sha256:a8b2bc7bffae282281c8140a97d3aa9c14da0b136dfe83f850eea9a5f7470427"},
+]
+
+[package.dependencies]
+six = ">=1.5"
+
+[[package]]
+name = "six"
+version = "1.16.0"
+description = "Python 2 and 3 compatibility utilities"
+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*"
+files = [
+    {file = "six-1.16.0-py2.py3-none-any.whl", hash = "sha256:8abb2f1d86890a2dfb989f9a77cfcfd3e47c2a354b01111771326f8aa26e0254"},
+    {file = "six-1.16.0.tar.gz", hash = "sha256:1e61c37477a1626458e36f7b1d82aa5c9b094fa4802892072e49de9c60c4c926"},
+]
+
+[[package]]
+name = "typing-extensions"
+version = "4.12.2"
+description = "Backported and Experimental Type Hints for Python 3.8+"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "typing_extensions-4.12.2-py3-none-any.whl", hash = "sha256:04e5ca0351e0f3f85c6853954072df659d0d13fac324d0072316b67d7794700d"},
+    {file = "typing_extensions-4.12.2.tar.gz", hash = "sha256:1a7ead55c7e559dd4dee8856e3a88b41225abfe1ce8df57b7c13915fe121ffb8"},
+]
+
+[metadata]
+lock-version = "2.0"
+python-versions = "^3.11"
+content-hash = "a896bd605371775aae3bd0ba5a7206985bac145cd2e803022f80558f42aaa923"
diff --git a/poetry.toml b/poetry.toml
new file mode 100644
index 0000000..ab1033b
--- /dev/null
+++ b/poetry.toml
@@ -0,0 +1,2 @@
+[virtualenvs]
+in-project = true
diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 0000000..e80543a
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,93 @@
+
+
+[tool.poetry]
+name = "easy_viz_cnn"
+version = "0.1.0"
+description = "A simple tool for visualizing CNN architectures."
+authors = ["Sadegh Yazdani <silverstar10@gmail.com>"]
+license = "GPLv3"
+readme = "README.md"
+homepage = "https://github.com/AvidDevelopers/EasyVizCNN"
+repository = "https://github.com/AvidDevelopers/EasyVizCNN"
+documentation = "https://AvidDevelopers.github.io/EasyVizCNN"
+
+[tool.poetry.dependencies]
+python = "^3.11"
+matplotlib = "^3.0"
+
+[tool.poetry.group.dev.dependencies]
+pytest = "^7.4"
+mypy = "^1.0"
+black = "^24.8"
+flake8-pyproject = "^1.2.3"
+
+[build-system]
+requires = ["poetry-core>=1.0.0"]
+build-backend = "poetry.core.masonry.api"
+
+[tool.mypy]
+python_version = "3.11"       # Specify the Python version you're targeting
+ignore_missing_imports = true # Ignore missing imports for third-party packages
+
+
+[tool.mypy.easy_viz_cnn]
+ignore_missing_imports = false
+
+
+[tool.pylint.messages_control]
+max-line-length = 120
+disable = [
+  "import-outside-toplevel",
+  "broad-except",
+  "bare-except",
+  "unused-import",
+  "unused-variable",
+  "missing-docstring",
+  "unused-argument",
+  "invalid-name",
+  "no-value-for-parameter",
+  "no-member",
+  "no-self-use",
+  "import-error",
+  "line-too-long",
+  "protected-access",
+  "too-few-public-methods",
+]
+
+[tool.pylint.design]
+# limiting the number of returns might discourage
+# the use of guard clauses. So we increase the
+# allowed number of returns from 6 to 8
+max-returns = 8
+# Maximum number of arguments for function / method.
+max-args=15
+
+max-parents=15
+
+# Maximum number of attributes for a class (see R0902).
+max-attributes=15
+
+# Maximum number of boolean expressions in an if statement.
+max-bool-expr=5
+
+# Maximum number of locals for function / method body.
+max-locals=25
+
+# Maximum number of statements in function / method body.
+max-statements=50
+
+# Minimum number of public methods for a class (see R0903).
+min-public-methods=1
+
+
+[tool.black]
+line-length = 99
+
+
+[tool.flake8]
+ignore = ['E231', 'E241']
+max-line-length = 99
+per-file-ignores = [
+  # '__init__.py:F401',
+]
+# count = true
diff --git a/setup.py b/setup.py
new file mode 100644
index 0000000..83fafbf
--- /dev/null
+++ b/setup.py
@@ -0,0 +1,33 @@
+from setuptools import setup, find_packages
+
+
+with open("README.md", "r", encoding="utf-8") as fh:
+    long_description = fh.read()
+
+setup(
+    name="easy_viz_cnn",
+    version="0.1.0",
+    author=[
+        "Sadegh Yazdani",
+        "Hossein ShahAbadi",
+        "Mojtaba ZarrinPour",
+    ],
+    author_email=[
+        "silverstar10@gmail.com",
+    ],
+    description="A simple tool for visualizing CNN architectures",
+    long_description=long_description,
+    long_description_content_type="text/markdown",
+    url="https://github.com/AvidDevelopers/EasyVizCNN",
+    packages=find_packages(),
+    include_package_data=True,
+    install_requires=[
+        "matplotlib>=3.0",
+    ],
+    classifiers=[
+        "Programming Language :: Python :: 3",
+        "License :: OSI Approved :: GPLv3 License",
+        "Operating System :: OS Independent",
+    ],
+    python_requires=">=3.11",
+)
diff --git a/tests/conftest.py b/tests/conftest.py
new file mode 100644
index 0000000..ab3f0eb
--- /dev/null
+++ b/tests/conftest.py
@@ -0,0 +1,28 @@
+from easy_viz_cnn.layers import Conv2DLayer as ConvLayer, FCLayer, PoolLayer, InputLayer
+from easy_viz_cnn.models import SimpleCNNModel
+from easy_viz_cnn.utils import draw_conv_layer
+
+from pytest import fixture
+
+
+# LeNet implementation
+
+
+@fixture(scope="session")
+def lenet_model():
+    # Define the LeNet model with the desired outputs
+    lenet_model = SimpleCNNModel(
+        [
+            InputLayer((32, 32)),
+            ConvLayer(
+                1, 6, kernel_size=(5, 5), stride=1, padding=0
+            ),  # Conv1: (32x32x1) -> (28x28x6)
+            PoolLayer(kernel_size=2, stride=2),  # Pool1: (28x28x6) -> (14x14x6)
+            ConvLayer(
+                6, 16, kernel_size=(5, 5), stride=1, padding=0
+            ),  # Conv2: (14x14x6) -> (10x10x16)
+            PoolLayer(kernel_size=2, stride=2),  # Pool2: (10x10x16) -> (5x5x16)
+            FCLayer(5 * 5 * 16, 120),  # FC1: (5x5x16) -> (1x120)
+        ]
+    )
+    return lenet_model
diff --git a/tests/test_feature_map.py b/tests/test_feature_map.py
new file mode 100644
index 0000000..ecb1c3a
--- /dev/null
+++ b/tests/test_feature_map.py
@@ -0,0 +1,14 @@
+from easy_viz_cnn.models import SimpleCNNModel
+
+
+def test_lenet_feature_map(lenet_model: SimpleCNNModel):
+    calc = [size for size in lenet_model.features()]
+    real = [
+        (32, 32),
+        (28, 28, 6),
+        (14, 14, 6),
+        (10, 10, 16),
+        (5, 5, 16),
+        (1, 120),
+    ]
+    assert real == calc