diff --git a/keras_nlp/api/models/__init__.py b/keras_nlp/api/models/__init__.py
index e079aa7c9e..f352a01b92 100644
--- a/keras_nlp/api/models/__init__.py
+++ b/keras_nlp/api/models/__init__.py
@@ -161,6 +161,12 @@
     MistralPreprocessor,
 )
 from keras_nlp.src.models.mistral.mistral_tokenizer import MistralTokenizer
+from keras_nlp.src.models.mix_transformer.mix_transformer_backbone import (
+    MiTBackbone,
+)
+from keras_nlp.src.models.mix_transformer.mix_transformer_classifier import (
+    MiTImageClassifier,
+)
 from keras_nlp.src.models.opt.opt_backbone import OPTBackbone
 from keras_nlp.src.models.opt.opt_causal_lm import OPTCausalLM
 from keras_nlp.src.models.opt.opt_causal_lm_preprocessor import (
diff --git a/keras_nlp/src/models/mix_transformer/__init__.py b/keras_nlp/src/models/mix_transformer/__init__.py
new file mode 100644
index 0000000000..3364a6bd16
--- /dev/null
+++ b/keras_nlp/src/models/mix_transformer/__init__.py
@@ -0,0 +1,13 @@
+# Copyright 2024 The KerasNLP Authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
diff --git a/keras_nlp/src/models/mix_transformer/mix_transformer_backbone.py b/keras_nlp/src/models/mix_transformer/mix_transformer_backbone.py
new file mode 100644
index 0000000000..2cfe7f6761
--- /dev/null
+++ b/keras_nlp/src/models/mix_transformer/mix_transformer_backbone.py
@@ -0,0 +1,181 @@
+# Copyright 2024 The KerasNLP Authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import keras
+import numpy as np
+from keras import ops
+
+from keras_nlp.src.api_export import keras_nlp_export
+from keras_nlp.src.models.feature_pyramid_backbone import FeaturePyramidBackbone
+from keras_nlp.src.models.mix_transformer.mix_transformer_layers import (
+    HierarchicalTransformerEncoder,
+)
+from keras_nlp.src.models.mix_transformer.mix_transformer_layers import (
+    OverlappingPatchingAndEmbedding,
+)
+
+
+@keras_nlp_export("keras_nlp.models.MiTBackbone")
+class MiTBackbone(FeaturePyramidBackbone):
+    def __init__(
+        self,
+        depths,
+        num_layers,
+        blockwise_num_heads,
+        blockwise_sr_ratios,
+        end_value,
+        patch_sizes,
+        strides,
+        include_rescaling=True,
+        image_shape=(224, 224, 3),
+        hidden_dims=None,
+        **kwargs,
+    ):
+        """A Backbone implementing the MixTransformer.
+
+        This architecture to be used as a backbone for the SegFormer
+        architecture [SegFormer: Simple and Efficient Design for Semantic
+        Segmentation with Transformers](https://arxiv.org/abs/2105.15203)
+        [Based on the TensorFlow implementation from DeepVision](
+            https://github.com/DavidLandup0/deepvision/tree/main/deepvision/models/classification/mix_transformer)
+
+        Args:
+            depths: The number of transformer encoders to be used per layer in the
+                network.
+            num_layers: int. The number of Transformer layers.
+            blockwise_num_heads: list of integers, the number of heads to use
+            in the attention computation for each layer.
+            blockwise_sr_ratios: list of integers, the sequence reduction
+                ratio to perform for each layer on the sequence before key and
+                value projections. If set to > 1, a `Conv2D` layer is used to
+                reduce the length of the sequence.
+            end_value: The end value of the sequence.
+            include_rescaling: bool, whether to rescale the inputs. If set
+                to `True`, inputs will be passed through a `Rescaling(1/255.0)`
+                layer. Defaults to `True`.
+            image_shape: optional shape tuple, defaults to (224, 224, 3).
+            hidden_dims: the embedding dims per hierarchical layer, used as
+                the levels of the feature pyramid.
+            patch_sizes: list of integers, the patch_size to apply for each layer.
+            strides: list of integers, stride to apply for each layer.
+
+        Examples:
+
+        Using the class with a `backbone`:
+
+        ```python
+        images = np.ones(shape=(1, 96, 96, 3))
+        labels = np.zeros(shape=(1, 96, 96, 1))
+        backbone = keras_nlp.models.MiTBackbone.from_preset("mit_b0_imagenet")
+
+        # Evaluate model
+        model(images)
+
+        # Train model
+        model.compile(
+            optimizer="adam",
+            loss=keras.losses.BinaryCrossentropy(from_logits=False),
+            metrics=["accuracy"],
+        )
+        model.fit(images, labels, epochs=3)
+        ```
+        """
+        dpr = [x for x in np.linspace(0.0, end_value, sum(depths))]
+
+        # === Layers ===
+        cur = 0
+        patch_embedding_layers = []
+        transformer_blocks = []
+        layer_norms = []
+
+        for i in range(num_layers):
+            patch_embed_layer = OverlappingPatchingAndEmbedding(
+                project_dim=hidden_dims[i],
+                patch_size=patch_sizes[i],
+                stride=strides[i],
+                name=f"patch_and_embed_{i}",
+            )
+            patch_embedding_layers.append(patch_embed_layer)
+
+            transformer_block = [
+                HierarchicalTransformerEncoder(
+                    project_dim=hidden_dims[i],
+                    num_heads=blockwise_num_heads[i],
+                    sr_ratio=blockwise_sr_ratios[i],
+                    drop_prob=dpr[cur + k],
+                    name=f"hierarchical_encoder_{i}_{k}",
+                )
+                for k in range(depths[i])
+            ]
+            transformer_blocks.append(transformer_block)
+            cur += depths[i]
+            layer_norms.append(keras.layers.LayerNormalization())
+
+        # === Functional Model ===
+        image_input = keras.layers.Input(shape=image_shape)
+        x = image_input
+
+        if include_rescaling:
+            x = keras.layers.Rescaling(scale=1 / 255)(x)
+
+        pyramid_outputs = {}
+        for i in range(num_layers):
+            # Compute new height/width after the `proj`
+            # call in `OverlappingPatchingAndEmbedding`
+            stride = strides[i]
+            new_height, new_width = (
+                int(ops.shape(x)[1] / stride),
+                int(ops.shape(x)[2] / stride),
+            )
+
+            x = patch_embedding_layers[i](x)
+            for blk in transformer_blocks[i]:
+                x = blk(x)
+            x = layer_norms[i](x)
+            x = keras.layers.Reshape(
+                (new_height, new_width, -1), name=f"output_level_{i}"
+            )(x)
+            pyramid_outputs[f"P{i + 1}"] = x
+
+        super().__init__(inputs=image_input, outputs=x, **kwargs)
+
+        # === Config ===
+        self.depths = depths
+        self.include_rescaling = include_rescaling
+        self.image_shape = image_shape
+        self.hidden_dims = hidden_dims
+        self.pyramid_outputs = pyramid_outputs
+        self.num_layers = num_layers
+        self.blockwise_num_heads = blockwise_num_heads
+        self.blockwise_sr_ratios = blockwise_sr_ratios
+        self.end_value = end_value
+        self.patch_sizes = patch_sizes
+        self.strides = strides
+
+    def get_config(self):
+        config = super().get_config()
+        config.update(
+            {
+                "depths": self.depths,
+                "include_rescaling": self.include_rescaling,
+                "hidden_dims": self.hidden_dims,
+                "image_shape": self.image_shape,
+                "num_layers": self.num_layers,
+                "blockwise_num_heads": self.blockwise_num_heads,
+                "blockwise_sr_ratios": self.blockwise_sr_ratios,
+                "end_value": self.end_value,
+                "patch_sizes": self.patch_sizes,
+                "strides": self.strides,
+            }
+        )
+        return config
diff --git a/keras_nlp/src/models/mix_transformer/mix_transformer_backbone_test.py b/keras_nlp/src/models/mix_transformer/mix_transformer_backbone_test.py
new file mode 100644
index 0000000000..4f1955297f
--- /dev/null
+++ b/keras_nlp/src/models/mix_transformer/mix_transformer_backbone_test.py
@@ -0,0 +1,75 @@
+# Copyright 2024 The KerasNLP Authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import numpy as np
+import pytest
+from keras import models
+
+from keras_nlp.src.models.mix_transformer.mix_transformer_backbone import (
+    MiTBackbone,
+)
+from keras_nlp.src.tests.test_case import TestCase
+
+
+class MiTBackboneTest(TestCase):
+    def setUp(self):
+        self.init_kwargs = {
+            "depths": [2, 2],
+            "include_rescaling": True,
+            "image_shape": (16, 16, 3),
+            "hidden_dims": [4, 8],
+            "num_layers": 2,
+            "blockwise_num_heads": [1, 2],
+            "blockwise_sr_ratios": [8, 4],
+            "end_value": 0.1,
+            "patch_sizes": [7, 3],
+            "strides": [4, 2],
+        }
+        self.input_size = 16
+        self.input_data = np.ones(
+            (2, self.input_size, self.input_size, 3), dtype="float32"
+        )
+
+    def test_backbone_basics(self):
+        self.run_backbone_test(
+            cls=MiTBackbone,
+            init_kwargs=self.init_kwargs,
+            input_data=self.input_data,
+            expected_output_shape=(2, 2, 2, 8),
+            run_quantization_check=False,
+            run_mixed_precision_check=False,
+        )
+
+    def test_pyramid_output_format(self):
+        init_kwargs = self.init_kwargs
+        backbone = MiTBackbone(**init_kwargs)
+        model = models.Model(backbone.inputs, backbone.pyramid_outputs)
+        output_data = model(self.input_data)
+
+        self.assertIsInstance(output_data, dict)
+        self.assertEqual(
+            list(output_data.keys()), list(backbone.pyramid_outputs.keys())
+        )
+        self.assertEqual(list(output_data.keys()), ["P1", "P2"])
+        for k, v in output_data.items():
+            size = self.input_size // (2 ** (int(k[1:]) + 1))
+            self.assertEqual(tuple(v.shape[:3]), (2, size, size))
+
+    @pytest.mark.large
+    def test_saved_model(self):
+        self.run_model_saving_test(
+            cls=MiTBackbone,
+            init_kwargs=self.init_kwargs,
+            input_data=self.input_data,
+        )
diff --git a/keras_nlp/src/models/mix_transformer/mix_transformer_classifier.py b/keras_nlp/src/models/mix_transformer/mix_transformer_classifier.py
new file mode 100644
index 0000000000..a9a51b63ba
--- /dev/null
+++ b/keras_nlp/src/models/mix_transformer/mix_transformer_classifier.py
@@ -0,0 +1,133 @@
+# Copyright 2024 The KerasNLP Authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import keras
+
+from keras_nlp.src.api_export import keras_nlp_export
+from keras_nlp.src.models.image_classifier import ImageClassifier
+from keras_nlp.src.models.mix_transformer.mix_transformer_backbone import (
+    MiTBackbone,
+)
+
+
+@keras_nlp_export("keras_nlp.models.MiTImageClassifier")
+class MiTImageClassifier(ImageClassifier):
+    """MiTImageClassifier image classifier model.
+
+    Args:
+        backbone: A `keras_nlp.models.MiTBackbone` instance.
+        num_classes: int. The number of classes to predict.
+        activation: `None`, str or callable. The activation function to use on
+            the `Dense` layer. Set `activation=None` to return the output
+            logits. Defaults to `"softmax"`.
+
+    To fine-tune with `fit()`, pass a dataset containing tuples of `(x, y)`
+    where `x` is a tensor and `y` is a integer from `[0, num_classes)`.
+    All `ImageClassifier` tasks include a `from_preset()` constructor which can
+    be used to load a pre-trained config and weights.
+
+    Examples:
+
+    Call `predict()` to run inference.
+    ```python
+    # Load preset and train
+    images = np.ones((2, 224, 224, 3), dtype="float32")
+    classifier = keras_nlp.models.MiTImageClassifier.from_preset(
+        "mit_b0_imagenet")
+    classifier.predict(images)
+    ```
+
+    Call `fit()` on a single batch.
+    ```python
+    # Load preset and train
+    images = np.ones((2, 224, 224, 3), dtype="float32")
+    labels = [0, 3]
+    classifier = keras_nlp.models.MixTransformerImageClassifier.from_preset(
+        "mit_b0_imagenet")
+    classifier.fit(x=images, y=labels, batch_size=2)
+    ```
+
+    Call `fit()` with custom loss, optimizer and backbone.
+    ```python
+    classifier = keras_nlp.models.MiTImageClassifier.from_preset(
+        "mit_b0_imagenet")
+    classifier.compile(
+        loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),
+        optimizer=keras.optimizers.Adam(5e-5),
+    )
+    classifier.backbone.trainable = False
+    classifier.fit(x=images, y=labels, batch_size=2)
+    ```
+
+    Custom backbone.
+    ```python
+    images = np.ones((2, 224, 224, 3), dtype="float32")
+    labels = [0, 3]
+    backbone = keras_nlp.models.MiTBackbone(
+        stackwise_num_filters=[128, 256, 512, 1024],
+        stackwise_depth=[3, 9, 9, 3],
+        include_rescaling=False,
+        block_type="basic_block",
+        image_shape = (224, 224, 3),
+    )
+    classifier = keras_nlp.models.MiTImageClassifier(
+        backbone=backbone,
+        num_classes=4,
+    )
+    classifier.fit(x=images, y=labels, batch_size=2)
+    ```
+    """
+
+    backbone_cls = MiTBackbone
+
+    def __init__(
+        self,
+        backbone,
+        num_classes,
+        activation="softmax",
+        preprocessor=None,  # adding this dummy arg for saved model test
+        # TODO: once preprocessor flow is figured out, this needs to be updated
+        **kwargs,
+    ):
+        # === Layers ===
+        self.backbone = backbone
+        self.output_dense = keras.layers.Dense(
+            num_classes,
+            activation=activation,
+            name="predictions",
+        )
+
+        # === Functional Model ===
+        inputs = self.backbone.input
+        x = self.backbone(inputs)
+        outputs = self.output_dense(x)
+        super().__init__(
+            inputs=inputs,
+            outputs=outputs,
+            **kwargs,
+        )
+
+        # === Config ===
+        self.num_classes = num_classes
+        self.activation = activation
+
+    def get_config(self):
+        # Backbone serialized in `super`
+        config = super().get_config()
+        config.update(
+            {
+                "num_classes": self.num_classes,
+                "activation": self.activation,
+            }
+        )
+        return config
diff --git a/keras_nlp/src/models/mix_transformer/mix_transformer_classifier_test.py b/keras_nlp/src/models/mix_transformer/mix_transformer_classifier_test.py
new file mode 100644
index 0000000000..57b0671be2
--- /dev/null
+++ b/keras_nlp/src/models/mix_transformer/mix_transformer_classifier_test.py
@@ -0,0 +1,70 @@
+# Copyright 2024 The KerasNLP Authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import numpy as np
+import pytest
+
+from keras_nlp.src.models.mix_transformer.mix_transformer_backbone import (
+    MiTBackbone,
+)
+from keras_nlp.src.models.mix_transformer.mix_transformer_classifier import (
+    MiTImageClassifier,
+)
+from keras_nlp.src.tests.test_case import TestCase
+
+
+class MiTImageClassifierTest(TestCase):
+    def setUp(self):
+        # Setup model.
+        self.images = np.ones((2, 16, 16, 3), dtype="float32")
+        self.labels = [0, 3]
+        self.backbone = MiTBackbone(
+            depths=[2, 2, 2, 2],
+            include_rescaling=True,
+            image_shape=(16, 16, 3),
+            hidden_dims=[4, 8],
+            num_layers=2,
+            blockwise_num_heads=[1, 2],
+            blockwise_sr_ratios=[8, 4],
+            end_value=0.1,
+            patch_sizes=[7, 3],
+            strides=[4, 2],
+        )
+        self.init_kwargs = {
+            "backbone": self.backbone,
+            "num_classes": 2,
+            "activation": "softmax",
+        }
+        self.train_data = (
+            self.images,
+            self.labels,
+        )
+
+    def test_classifier_basics(self):
+        pytest.skip(
+            reason="TODO: enable after preprocessor flow is figured out"
+        )
+        self.run_task_test(
+            cls=MiTImageClassifier,
+            init_kwargs=self.init_kwargs,
+            train_data=self.train_data,
+            expected_output_shape=(2, 2),
+        )
+
+    @pytest.mark.large
+    def test_saved_model(self):
+        self.run_model_saving_test(
+            cls=MiTImageClassifier,
+            init_kwargs=self.init_kwargs,
+            input_data=self.images,
+        )
diff --git a/keras_nlp/src/models/mix_transformer/mix_transformer_layers.py b/keras_nlp/src/models/mix_transformer/mix_transformer_layers.py
new file mode 100644
index 0000000000..53d99fe484
--- /dev/null
+++ b/keras_nlp/src/models/mix_transformer/mix_transformer_layers.py
@@ -0,0 +1,300 @@
+# Copyright 2024 The KerasNLP Authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import math
+
+import keras
+from keras import ops
+from keras import random
+
+
+class OverlappingPatchingAndEmbedding(keras.layers.Layer):
+    def __init__(self, project_dim=32, patch_size=7, stride=4, **kwargs):
+        """Overlapping Patching and Embedding layer.
+
+        Differs from `PatchingAndEmbedding` in that the patch size does not
+        affect the sequence length. It's fully derived from the `stride`
+        parameter. Additionally, no positional embedding is done
+        as part of the layer - only a projection using a `Conv2D` layer.
+
+        Args:
+            project_dim: integer, the dimensionality of the projection.
+                Defaults to `32`.
+            patch_size: integer, the size of the patches to encode.
+                Defaults to `7`.
+            stride: integer, the stride to use for the patching before
+                projection. Defaults to `5`.
+        """
+        super().__init__(**kwargs)
+
+        self.project_dim = project_dim
+        self.patch_size = patch_size
+        self.stride = stride
+
+        self.proj = keras.layers.Conv2D(
+            filters=project_dim,
+            kernel_size=patch_size,
+            strides=stride,
+            padding="same",
+        )
+        self.norm = keras.layers.LayerNormalization()
+
+    def call(self, x):
+        x = self.proj(x)
+        # B, H, W, C
+        shape = x.shape
+        x = ops.reshape(x, (-1, shape[1] * shape[2], shape[3]))
+        x = self.norm(x)
+        return x
+
+    def get_config(self):
+        config = super().get_config()
+        config.update(
+            {
+                "project_dim": self.project_dim,
+                "patch_size": self.patch_size,
+                "stride": self.stride,
+            }
+        )
+        return config
+
+
+class HierarchicalTransformerEncoder(keras.layers.Layer):
+    """Hierarchical transformer encoder block implementation as a Keras Layer.
+
+    The layer uses `SegFormerMultiheadAttention` as a `MultiHeadAttention`
+    alternative for computational efficiency, and is meant to be used
+    within the SegFormer architecture.
+
+    Args:
+        project_dim: integer, the dimensionality of the projection of the
+            encoder, and output of the `SegFormerMultiheadAttention` layer.
+            Due to the residual addition the input dimensionality has to be
+            equal to the output dimensionality.
+        num_heads: integer, the number of heads for the
+            `SegFormerMultiheadAttention` layer.
+        drop_prob: float, the probability of dropping a random
+            sample using the `DropPath` layer. Defaults to `0.0`.
+        layer_norm_epsilon: float, the epsilon for
+            `LayerNormalization` layers. Defaults to `1e-06`
+        sr_ratio: integer, the ratio to use within
+            `SegFormerMultiheadAttention`. If set to > 1, a `Conv2D`
+             layer is used to reduce the length of the sequence. Defaults to `1`.
+    """
+
+    def __init__(
+        self,
+        project_dim,
+        num_heads,
+        sr_ratio=1,
+        drop_prob=0.0,
+        layer_norm_epsilon=1e-6,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+        self.project_dim = project_dim
+        self.num_heads = num_heads
+        self.drop_prop = drop_prob
+
+        self.norm1 = keras.layers.LayerNormalization(epsilon=layer_norm_epsilon)
+        self.attn = SegFormerMultiheadAttention(
+            project_dim, num_heads, sr_ratio
+        )
+        self.drop_path = DropPath(drop_prob)
+        self.norm2 = keras.layers.LayerNormalization(epsilon=layer_norm_epsilon)
+        self.mlp = MixFFN(
+            channels=project_dim,
+            mid_channels=int(project_dim * 4),
+        )
+
+    def build(self, input_shape):
+        super().build(input_shape)
+        self.H = ops.sqrt(ops.cast(input_shape[1], "float32"))
+        self.W = ops.sqrt(ops.cast(input_shape[2], "float32"))
+
+    def call(self, x):
+        x = x + self.drop_path(self.attn(self.norm1(x)))
+        x = x + self.drop_path(self.mlp(self.norm2(x)))
+        return x
+
+    def get_config(self):
+        config = super().get_config()
+        config.update(
+            {
+                "mlp": keras.saving.serialize_keras_object(self.mlp),
+                "project_dim": self.project_dim,
+                "num_heads": self.num_heads,
+                "drop_prop": self.drop_prop,
+            }
+        )
+        return config
+
+
+class MixFFN(keras.layers.Layer):
+    def __init__(self, channels, mid_channels):
+        super().__init__()
+        self.fc1 = keras.layers.Dense(mid_channels)
+        self.dwconv = keras.layers.DepthwiseConv2D(
+            kernel_size=3,
+            strides=1,
+            padding="same",
+        )
+        self.fc2 = keras.layers.Dense(channels)
+
+    def call(self, x):
+        x = self.fc1(x)
+        shape = ops.shape(x)
+        H, W = int(math.sqrt(shape[1])), int(math.sqrt(shape[1]))
+        B, C = shape[0], shape[2]
+        x = ops.reshape(x, (B, H, W, C))
+        x = self.dwconv(x)
+        x = ops.reshape(x, (B, -1, C))
+        x = ops.nn.gelu(x)
+        x = self.fc2(x)
+        return x
+
+
+class SegFormerMultiheadAttention(keras.layers.Layer):
+    def __init__(self, project_dim, num_heads, sr_ratio):
+        """Efficient MultiHeadAttention implementation as a Keras layer.
+
+        A huge bottleneck in scaling transformers is the self-attention layer
+        with an O(n^2) complexity.
+
+        SegFormerMultiheadAttention performs a sequence reduction (SR) operation
+        with a given ratio, to reduce the sequence length before performing key
+        and value projections, reducing the O(n^2) complexity to O(n^2/R) where
+        R is the sequence reduction ratio.
+
+        Args:
+            project_dim: integer, the dimensionality of the projection
+                of the `SegFormerMultiheadAttention` layer.
+            num_heads: integer, the number of heads to use in the
+                attention computation.
+            sr_ratio: integer, the sequence reduction ratio to perform
+                on the sequence before key and value projections.
+        """
+        super().__init__()
+        self.num_heads = num_heads
+        self.sr_ratio = sr_ratio
+        self.scale = (project_dim // num_heads) ** -0.5
+        self.q = keras.layers.Dense(project_dim)
+        self.k = keras.layers.Dense(project_dim)
+        self.v = keras.layers.Dense(project_dim)
+        self.proj = keras.layers.Dense(project_dim)
+
+        if sr_ratio > 1:
+            self.sr = keras.layers.Conv2D(
+                filters=project_dim,
+                kernel_size=sr_ratio,
+                strides=sr_ratio,
+                padding="same",
+            )
+            self.norm = keras.layers.LayerNormalization()
+
+    def call(self, x):
+        input_shape = ops.shape(x)
+        H, W = int(math.sqrt(input_shape[1])), int(math.sqrt(input_shape[1]))
+        B, C = input_shape[0], input_shape[2]
+
+        q = self.q(x)
+        q = ops.reshape(
+            q,
+            (
+                input_shape[0],
+                input_shape[1],
+                self.num_heads,
+                input_shape[2] // self.num_heads,
+            ),
+        )
+        q = ops.transpose(q, [0, 2, 1, 3])
+
+        if self.sr_ratio > 1:
+            x = ops.reshape(
+                ops.transpose(x, [0, 2, 1]),
+                (B, H, W, C),
+            )
+            x = self.sr(x)
+            x = ops.reshape(x, [input_shape[0], input_shape[2], -1])
+            x = ops.transpose(x, [0, 2, 1])
+            x = self.norm(x)
+
+        k = self.k(x)
+        v = self.v(x)
+
+        k = ops.transpose(
+            ops.reshape(
+                k,
+                [B, -1, self.num_heads, C // self.num_heads],
+            ),
+            [0, 2, 1, 3],
+        )
+
+        v = ops.transpose(
+            ops.reshape(
+                v,
+                [B, -1, self.num_heads, C // self.num_heads],
+            ),
+            [0, 2, 1, 3],
+        )
+
+        attn = (q @ ops.transpose(k, [0, 1, 3, 2])) * self.scale
+        attn = ops.nn.softmax(attn, axis=-1)
+
+        attn = attn @ v
+        attn = ops.reshape(
+            ops.transpose(attn, [0, 2, 1, 3]),
+            [input_shape[0], input_shape[1], input_shape[2]],
+        )
+
+        x = self.proj(attn)
+        return x
+
+
+class DropPath(keras.layers.Layer):
+    """Implements the DropPath layer.
+
+    DropPath randomly drops samples during
+    training with a probability of `rate`. Note that this layer drops individual
+    samples within a batch and not the entire batch, whereas StochasticDepth
+    randomly drops the entire batch.
+
+    Args:
+        rate: float, the probability of the residual branch being dropped.
+        seed: (Optional) integer. Used to create a random seed.
+    """
+
+    def __init__(self, rate=0.5, seed=None, **kwargs):
+        super().__init__(**kwargs)
+        self.rate = rate
+        self._seed_val = seed
+        self.seed = random.SeedGenerator(seed=seed)
+
+    def call(self, x, training=None):
+        if self.rate == 0.0 or not training:
+            return x
+        else:
+            batch_size = x.shape[0] or ops.shape(x)[0]
+            drop_map_shape = (batch_size,) + (1,) * (len(x.shape) - 1)
+            drop_map = ops.cast(
+                random.uniform(drop_map_shape, seed=self.seed) > self.rate,
+                x.dtype,
+            )
+            x = x / (1.0 - self.rate)
+            x = x * drop_map
+            return x
+
+    def get_config(self):
+        config = super().get_config()
+        config.update({"rate": self.rate, "seed": self._seed_val})
+        return config