diff --git a/docs/source/conf.py b/docs/source/conf.py
index 7a239919..378a2248 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -19,8 +19,8 @@
 # -- Project information -----------------------------------------------------
 
 project = "DaCapo"
-copyright = "2024, Caroline Malin-Mayor, Jeff Rhoades, Marwan Zouinkhi, William Patton, David Ackerman, Jan Funke"
-author = "Caroline Malin-Mayor, Jeff Rhoades, Marwan Zouinkhi, William Patton, David Ackerman, Jan Funke"
+copyright = "2024, William Patton, Jeff Rhoades, Marwan Zouinkhi,  David Ackerman, Caroline Malin-Mayor, Jan Funke"
+author = " William Patton, Jeff Rhoades, Marwan Zouinkhi, David Ackerman, Caroline Malin-Mayor, Jan Funke"
 
 
 # -- General configuration ---------------------------------------------------
diff --git a/docs/source/index.rst b/docs/source/index.rst
index 3a94253d..9a354e7a 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -10,6 +10,7 @@
    overview
    install
    notebooks/minimal_tutorial
+   unet_architectures
    tutorial
    docker
    aws
diff --git a/docs/source/unet_architectures.rst b/docs/source/unet_architectures.rst
new file mode 100644
index 00000000..7d9c180e
--- /dev/null
+++ b/docs/source/unet_architectures.rst
@@ -0,0 +1,125 @@
+UNet Models
+===========
+
+This section explains how to configure and use UNet models in DaCapo. Several configurations for different types of UNet architectures are demonstrated below.
+
+Overview
+--------
+
+UNet is a popular architecture for image segmentation tasks, particularly in biomedical imaging. DaCapo provides support for configuring various types of UNet models with customizable parameters.
+
+Examples
+--------
+
+Here are some examples of UNet configurations:
+
+1. **Upsample UNet**
+
+.. code-block:: python
+
+    from dacapo.experiments.architectures import CNNectomeUNetConfig
+    from funlib.geometry import Coordinate
+
+    architecture_config = CNNectomeUNetConfig(
+        name="upsample_unet",
+        input_shape=Coordinate(216, 216, 216),
+        eval_shape_increase=Coordinate(72, 72, 72),
+        fmaps_in=1,
+        num_fmaps=12,
+        fmaps_out=72,
+        fmap_inc_factor=6,
+        downsample_factors=[(2, 2, 2), (3, 3, 3), (3, 3, 3)],
+        constant_upsample=True,
+        upsample_factors=[(2, 2, 2)],
+    )
+
+2. **Yoshi UNet**
+
+.. code-block:: python
+
+    yoshi_unet_config = CNNectomeUNetConfig(
+        name="yoshi-unet",
+        input_shape=Coordinate(188, 188, 188),
+        eval_shape_increase=Coordinate(72, 72, 72),
+        fmaps_in=1,
+        num_fmaps=12,
+        fmaps_out=72,
+        fmap_inc_factor=6,
+        downsample_factors=[(2, 2, 2), (2, 2, 2), (2, 2, 2)],
+        constant_upsample=True,
+        upsample_factors=[],
+    )
+
+3. **Attention Upsample UNet**
+
+.. code-block:: python
+
+    attention_upsample_config = CNNectomeUNetConfig(
+        name="attention-upsample-unet",
+        input_shape=Coordinate(216, 216, 216),
+        eval_shape_increase=Coordinate(72, 72, 72),
+        fmaps_in=1,
+        num_fmaps=12,
+        fmaps_out=72,
+        fmap_inc_factor=6,
+        downsample_factors=[(2, 2, 2), (3, 3, 3), (3, 3, 3)],
+        constant_upsample=True,
+        upsample_factors=[(2, 2, 2)],
+        use_attention=True,
+    )
+
+4. **2D UNet**
+
+.. code-block:: python
+
+    architecture_config = CNNectomeUNetConfig(
+        name="2d_unet",
+        input_shape=(2, 132, 132),
+        eval_shape_increase=(8, 32, 32),
+        fmaps_in=2,
+        num_fmaps=8,
+        fmaps_out=8,
+        fmap_inc_factor=2,
+        downsample_factors=[(1, 4, 4), (1, 4, 4)],
+        kernel_size_down=[[(1, 3, 3)] * 2] * 3,
+        kernel_size_up=[[(1, 3, 3)] * 2] * 2,
+        constant_upsample=True,
+        padding="valid",
+    )
+
+5. **UNet with Batch Normalization**
+
+.. code-block:: python
+
+    architecture_config = CNNectomeUNetConfig(
+        name="unet_norm",
+        input_shape=Coordinate(216, 216, 216),
+        eval_shape_increase=Coordinate(72, 72, 72),
+        fmaps_in=1,
+        num_fmaps=2,
+        fmaps_out=2,
+        fmap_inc_factor=2,
+        downsample_factors=[(2, 2, 2), (3, 3, 3), (3, 3, 3)],
+        constant_upsample=True,
+        upsample_factors=[],
+        batch_norm=False,
+    )
+
+Configuration Parameters
+------------------------
+
+- **name**: A unique identifier for the configuration.
+- **input_shape**: The shape of the input data.
+- **eval_shape_increase**: Increase in shape during evaluation.
+- **fmaps_in**: Number of input feature maps.
+- **num_fmaps**: Number of feature maps in the first layer.
+- **fmaps_out**: Number of output feature maps.
+- **fmap_inc_factor**: Factor by which feature maps increase in each layer.
+- **downsample_factors**: Factors by which the input is downsampled at each layer.
+- **upsample_factors**: Factors by which the input is upsampled at each layer.
+- **constant_upsample**: Whether to use constant upsampling.
+- **use_attention**: Whether to use attention mechanisms.
+- **batch_norm**: Whether to use batch normalization.
+- **padding**: Padding mode for convolutional layers.
+
+This page should serve as a reference for configuring UNet models in DaCapo. Adjust the parameters as per your dataset and task requirements.