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diff --git a/.gitignore b/.gitignore
index f071240..0f03f96 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,2 +1,3 @@
 __pycache__
-.pytest_cache
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+.pytest_cache
+dist/
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diff --git a/README.md b/README.md
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--- a/README.md
+++ b/README.md
@@ -0,0 +1,18 @@
+# k3-addons: Additional multi-backend functionality for Keras 3.
+![Logo](.assets/k3-addons.png)
+
+# Includes:
+- Layers
+    - Pooling:
+        - `AdaptiveAveragePooling1D`
+        - `AdaptiveMaxPooling1D`
+        - `AdaptiveAveragePooling2D`
+        - `AdaptiveMaxPooling2D`
+    - Attention:
+        - `DoubleAttention`
+        - `AFTFull`
+        - `ChannelAttention2D`
+        - `SpatialAttention2D`
+        - `ECAAttention`
+        - `ExternalAttention`
+        - `ResidualAttention`
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diff --git a/k3_addons/layers/attention/cbam.py b/k3_addons/layers/attention/cbam.py
index e69de29..d8d1c62 100644
--- a/k3_addons/layers/attention/cbam.py
+++ b/k3_addons/layers/attention/cbam.py
@@ -0,0 +1,68 @@
+from keras import layers, ops, Sequential, backend
+
+from k3_addons.layers.pooling.adaptive_pooling import (
+    AdaptiveMaxPool2D,
+    AdaptiveAveragePool2D,
+)
+from k3_addons.api_export import k3_export
+
+@k3_export('k3_addons.layers.ChannelAttention2D')
+class ChannelAttention(layers.Layer):
+    def __init__(self, reduction=16):
+        super().__init__()
+        self.reduction = reduction
+        self.maxpool = AdaptiveMaxPool2D(1)
+        self.avgpool = AdaptiveAveragePool2D(1)
+        self.data_format = backend.image_data_format()
+
+    def build(self, input_shape):
+        if self.data_format == "channels_last":
+            input_dim = input_shape[3]
+        else:
+            input_dim = input_shape[1]
+        self.se = Sequential(
+            [
+                layers.Conv2D(input_dim // self.reduction, 1, use_bias=False),
+                layers.Activation("relu"),
+                layers.Conv2D(input_dim, 1, use_bias=False),
+            ]
+        )
+
+    def call(self, x):
+        max_result = self.maxpool(x)
+        avg_result = self.avgpool(x)
+        max_out = self.se(max_result)
+        avg_out = self.se(avg_result)
+        output = ops.sigmoid(max_out + avg_out)
+        return output
+
+@k3_export('k3_addons.layers.SpatialAttention2D')
+class SpatialAttention(layers.Layer):
+    def __init__(self, kernel_size=7):
+        super().__init__()
+        self.conv = layers.Conv2D(1, kernel_size=kernel_size, padding='same')
+
+    def call(self, x):
+        if backend.image_data_format() == "channels_first":
+            axis = 1
+        else:
+            axis = 3
+        max_result = ops.max(x, axis=axis, keepdims=True)
+        avg_result = ops.mean(x, axis=axis, keepdims=True)
+        result = ops.concatenate([max_result, avg_result], axis)
+        output = self.conv(result)
+        output = ops.sigmoid(output)
+        return output
+
+@k3_export('k3_addons.layers.CBAM')
+class CBAMBlock(layers.Layer):
+    def __init__(self, reduction=16, kernel_size=49):
+        super().__init__()
+        self.channel_attention = ChannelAttention(reduction=reduction)
+        self.spatial_attention = SpatialAttention(kernel_size=kernel_size)
+
+    def call(self, x):
+        residual = x
+        out = x * self.channel_attention(x)
+        out = out * self.spatial_attention(out)
+        return out + residual
diff --git a/k3_addons/layers/attention/eca.py b/k3_addons/layers/attention/eca.py
index e69de29..d0207f4 100644
--- a/k3_addons/layers/attention/eca.py
+++ b/k3_addons/layers/attention/eca.py
@@ -0,0 +1,23 @@
+from keras import layers, ops, Sequential
+from k3_addons.layers.pooling.adaptive_pooling import AdaptiveAveragePool2D
+from k3_addons.api_export import k3_export
+
+
+@k3_export('k3_addons.layers.ECAAttention')
+class ECAAttention(layers.Layer):
+
+    def __init__(self, kernel_size=3):
+        super().__init__()
+        self.pooling = AdaptiveAveragePool2D(1)
+        self.conv=layers.Conv1D(1, kernel_size=kernel_size, padding='same')
+
+
+    def call(self, x):
+        y = self.pooling(x) #b,1, 1, c
+        y = ops.squeeze(y, axis=2) # b, 1, c
+        y = ops.transpose(y, axes=[0, 2, 1]) # b, c, 1
+        y = self.conv(y) #b, c, 1
+        y = ops.sigmoid(y) # b, c, 1
+        y = ops.transpose(y, axes=[0, 2, 1]) # b, 1, c
+        y= ops.expand_dims(y, axis=2) #bs, 1, 1, c
+        return x * ops.broadcast_to(y, ops.shape(x))
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