diff --git a/keras_hub/api/layers/__init__.py b/keras_hub/api/layers/__init__.py
index f389052f8e..0e0d31d3a7 100644
--- a/keras_hub/api/layers/__init__.py
+++ b/keras_hub/api/layers/__init__.py
@@ -35,6 +35,9 @@
 from keras_hub.src.layers.preprocessing.random_deletion import RandomDeletion
 from keras_hub.src.layers.preprocessing.random_swap import RandomSwap
 from keras_hub.src.layers.preprocessing.start_end_packer import StartEndPacker
+from keras_hub.src.models.basnet.basnet_image_converter import (
+    BASNetImageConverter,
+)
 from keras_hub.src.models.clip.clip_image_converter import CLIPImageConverter
 from keras_hub.src.models.deeplab_v3.deeplab_v3_image_converter import (
     DeepLabV3ImageConverter,
diff --git a/keras_hub/api/models/__init__.py b/keras_hub/api/models/__init__.py
index a37bad2e38..7c7adbf97c 100644
--- a/keras_hub/api/models/__init__.py
+++ b/keras_hub/api/models/__init__.py
@@ -29,6 +29,9 @@
     BartSeq2SeqLMPreprocessor,
 )
 from keras_hub.src.models.bart.bart_tokenizer import BartTokenizer
+from keras_hub.src.models.basnet.basnet import BASNetImageSegmenter
+from keras_hub.src.models.basnet.basnet_backbone import BASNetBackbone
+from keras_hub.src.models.basnet.basnet_preprocessor import BASNetPreprocessor
 from keras_hub.src.models.bert.bert_backbone import BertBackbone
 from keras_hub.src.models.bert.bert_masked_lm import BertMaskedLM
 from keras_hub.src.models.bert.bert_masked_lm_preprocessor import (
diff --git a/keras_hub/src/models/basnet/__init__.py b/keras_hub/src/models/basnet/__init__.py
new file mode 100644
index 0000000000..953164a973
--- /dev/null
+++ b/keras_hub/src/models/basnet/__init__.py
@@ -0,0 +1,5 @@
+from keras_hub.src.models.basnet.basnet_backbone import BASNetBackbone
+from keras_hub.src.models.basnet.basnet_presets import basnet_presets
+from keras_hub.src.utils.preset_utils import register_presets
+
+register_presets(basnet_presets, BASNetBackbone)
diff --git a/keras_hub/src/models/basnet/basnet.py b/keras_hub/src/models/basnet/basnet.py
new file mode 100644
index 0000000000..6ca7bd6851
--- /dev/null
+++ b/keras_hub/src/models/basnet/basnet.py
@@ -0,0 +1,122 @@
+import keras
+
+from keras_hub.src.api_export import keras_hub_export
+from keras_hub.src.models.basnet.basnet_backbone import BASNetBackbone
+from keras_hub.src.models.basnet.basnet_preprocessor import BASNetPreprocessor
+from keras_hub.src.models.image_segmenter import ImageSegmenter
+
+
+@keras_hub_export("keras_hub.models.BASNetImageSegmenter")
+class BASNetImageSegmenter(ImageSegmenter):
+    """BASNet image segmentation task.
+
+    Args:
+        backbone: A `keras_hub.models.BASNetBackbone` instance.
+        preprocessor: `None`, a `keras_hub.models.Preprocessor` instance,
+            a `keras.Layer` instance, or a callable. If `None` no preprocessing
+            will be applied to the inputs.
+
+    Example:
+    ```python
+    import keras_hub
+
+    images = np.ones(shape=(1, 288, 288, 3))
+    labels = np.zeros(shape=(1, 288, 288, 1))
+
+    image_encoder = keras_hub.models.ResNetBackbone.from_preset(
+        "resnet_18_imagenet",
+        load_weights=False
+    )
+    backbone = keras_hub.models.BASNetBackbone(
+        image_encoder,
+        num_classes=1,
+        image_shape=[288, 288, 3]
+    )
+    model = keras_hub.models.BASNetImageSegmenter(backbone)
+
+    # Evaluate the model
+    pred_labels = model(images)
+
+    # Train the model
+    model.compile(
+        optimizer="adam",
+        loss=keras.losses.BinaryCrossentropy(from_logits=False),
+        metrics=["accuracy"],
+    )
+    model.fit(images, labels, epochs=3)
+    ```
+    """
+
+    backbone_cls = BASNetBackbone
+    preprocessor_cls = BASNetPreprocessor
+
+    def __init__(
+        self,
+        backbone,
+        preprocessor=None,
+        **kwargs,
+    ):
+        # === Functional Model ===
+        x = backbone.input
+        outputs = backbone(x)
+        # only return the refinement module's output as final prediction
+        outputs = outputs["refine_out"]
+        super().__init__(inputs=x, outputs=outputs, **kwargs)
+
+        # === Config ===
+        self.backbone = backbone
+        self.preprocessor = preprocessor
+
+    def compute_loss(self, x, y, y_pred, *args, **kwargs):
+        # train BASNet's prediction and refinement module outputs against the
+        # same ground truth data
+        outputs = self.backbone(x)
+        losses = []
+        for output in outputs.values():
+            losses.append(super().compute_loss(x, y, output, *args, **kwargs))
+        return keras.ops.sum(losses, axis=0)
+
+    def compile(
+        self,
+        optimizer="auto",
+        loss="auto",
+        metrics="auto",
+        **kwargs,
+    ):
+        """Configures the `BASNet` task for training.
+
+        `BASNet` extends the default compilation signature
+        of `keras.Model.compile` with defaults for `optimizer` and `loss`. To
+        override these defaults, pass any value to these arguments during
+        compilation.
+
+        Args:
+            optimizer: `"auto"`, an optimizer name, or a `keras.Optimizer`
+                instance. Defaults to `"auto"`, which uses the default
+                optimizer for `BASNet`. See `keras.Model.compile` and
+                `keras.optimizers` for more info on possible `optimizer`
+                values.
+            loss: `"auto"`, a loss name, or a `keras.losses.Loss` instance.
+                Defaults to `"auto"`, in which case the default loss
+                computation of `BASNet` will be applied.
+                See `keras.Model.compile` and `keras.losses` for more info on
+                possible `loss` values.
+            metrics: `"auto"`, or a list of metrics to be evaluated by
+                the model during training and testing. Defaults to `"auto"`,
+                where a `keras.metrics.Accuracy` will be applied to track the
+                accuracy of the model during training.
+                See `keras.Model.compile` and `keras.metrics` for
+                more info on possible `metrics` values.
+            **kwargs: See `keras.Model.compile` for a full list of arguments
+                supported by the compile method.
+        """
+        if loss == "auto":
+            loss = keras.losses.BinaryCrossentropy()
+        if metrics == "auto":
+            metrics = [keras.metrics.Accuracy()]
+        super().compile(
+            optimizer=optimizer,
+            loss=loss,
+            metrics=metrics,
+            **kwargs,
+        )
diff --git a/keras_hub/src/models/basnet/basnet_backbone.py b/keras_hub/src/models/basnet/basnet_backbone.py
new file mode 100644
index 0000000000..3ae15fc9bb
--- /dev/null
+++ b/keras_hub/src/models/basnet/basnet_backbone.py
@@ -0,0 +1,366 @@
+import keras
+
+from keras_hub.src.api_export import keras_hub_export
+from keras_hub.src.models.backbone import Backbone
+from keras_hub.src.models.resnet.resnet_backbone import (
+    apply_basic_block as resnet_basic_block,
+)
+
+
+@keras_hub_export("keras_hub.models.BASNetBackbone")
+class BASNetBackbone(Backbone):
+    """BASNet architecture for semantic segmentation.
+
+    A Keras model implementing the BASNet architecture described in [BASNet:
+    Boundary-Aware Segmentation Network for Mobile and Web Applications](
+    https://arxiv.org/abs/2101.04704). BASNet uses a predict-refine
+    architecture for highly accurate image segmentation.
+
+    Args:
+        image_encoder: A `keras_hub.models.ResNetBackbone` instance. The
+            backbone network for the model that is used as a feature extractor
+            for BASNet prediction encoder. Currently supported backbones are
+            ResNet18 and ResNet34.
+            (Note: Do not specify `image_shape` within the backbone.
+            Please provide these while initializing the 'BASNetBackbone' model)
+        num_classes: int, the number of classes for the segmentation model.
+        image_shape: optional shape tuple, defaults to (None, None, 3).
+        projection_filters: int, number of filters in the convolution layer
+            projecting low-level features from the `backbone`.
+        prediction_heads: (Optional) List of `keras.layers.Layer` defining
+            the prediction module head for the model. If not provided, a
+            default head is created with a Conv2D layer followed by resizing.
+        refinement_head: (Optional) a `keras.layers.Layer` defining the
+            refinement module head for the model. If not provided, a default
+            head is created with a Conv2D layer.
+        dtype: `None` or str or `keras.mixed_precision.DTypePolicy`. The dtype
+            to use for the model's computations and weights.
+    """
+
+    def __init__(
+        self,
+        image_encoder,
+        num_classes,
+        image_shape=(None, None, 3),
+        projection_filters=64,
+        prediction_heads=None,
+        refinement_head=None,
+        dtype=None,
+        **kwargs,
+    ):
+        if not isinstance(image_encoder, keras.layers.Layer) or not isinstance(
+            image_encoder, keras.Model
+        ):
+            raise ValueError(
+                "Argument `image_encoder` must be a `keras.layers.Layer`"
+                f" instance or `keras.Model`. Received instead"
+                f" image_encoder={image_encoder} (of type"
+                f" {type(image_encoder)})."
+            )
+
+        if tuple(image_encoder.image_shape) != (None, None, 3):
+            raise ValueError(
+                "Do not specify `image_shape` within the"
+                " `BASNetBackbone`'s image_encoder. \nPlease provide"
+                " `image_shape` while initializing the 'BASNetBackbone' model."
+            )
+
+        # === Functional Model ===
+        inputs = keras.layers.Input(shape=image_shape)
+        x = inputs
+
+        if prediction_heads is None:
+            prediction_heads = []
+            for size in (1, 2, 4, 8, 16, 32, 32):
+                head_layers = [
+                    keras.layers.Conv2D(
+                        num_classes,
+                        kernel_size=(3, 3),
+                        padding="same",
+                        dtype=dtype,
+                    )
+                ]
+                if size != 1:
+                    head_layers.append(
+                        keras.layers.UpSampling2D(
+                            size=size, interpolation="bilinear", dtype=dtype
+                        )
+                    )
+                prediction_heads.append(keras.Sequential(head_layers))
+
+        if refinement_head is None:
+            refinement_head = keras.Sequential(
+                [
+                    keras.layers.Conv2D(
+                        num_classes,
+                        kernel_size=(3, 3),
+                        padding="same",
+                        dtype=dtype,
+                    ),
+                ]
+            )
+
+        # Prediction model.
+        predict_model = basnet_predict(
+            x, image_encoder, projection_filters, prediction_heads, dtype=dtype
+        )
+
+        # Refinement model.
+        refine_model = basnet_rrm(
+            predict_model, projection_filters, refinement_head, dtype=dtype
+        )
+
+        outputs = refine_model.outputs  # Combine outputs.
+        outputs.extend(predict_model.outputs)
+
+        output_names = ["refine_out"] + [
+            f"predict_out_{i}" for i in range(1, len(outputs))
+        ]
+
+        outputs = {
+            output_name: keras.layers.Activation(
+                "sigmoid", name=output_name, dtype=dtype
+            )(output)
+            for output, output_name in zip(outputs, output_names)
+        }
+
+        super().__init__(inputs=inputs, outputs=outputs, dtype=dtype, **kwargs)
+
+        # === Config ===
+        self.image_encoder = image_encoder
+        self.num_classes = num_classes
+        self.image_shape = image_shape
+        self.projection_filters = projection_filters
+        self.prediction_heads = prediction_heads
+        self.refinement_head = refinement_head
+
+    def get_config(self):
+        config = super().get_config()
+        config.update(
+            {
+                "image_encoder": keras.saving.serialize_keras_object(
+                    self.image_encoder
+                ),
+                "num_classes": self.num_classes,
+                "image_shape": self.image_shape,
+                "projection_filters": self.projection_filters,
+                "prediction_heads": [
+                    keras.saving.serialize_keras_object(prediction_head)
+                    for prediction_head in self.prediction_heads
+                ],
+                "refinement_head": keras.saving.serialize_keras_object(
+                    self.refinement_head
+                ),
+            }
+        )
+        return config
+
+    @classmethod
+    def from_config(cls, config):
+        if "image_encoder" in config:
+            config["image_encoder"] = keras.layers.deserialize(
+                config["image_encoder"]
+            )
+        if "prediction_heads" in config and isinstance(
+            config["prediction_heads"], list
+        ):
+            for i in range(len(config["prediction_heads"])):
+                if isinstance(config["prediction_heads"][i], dict):
+                    config["prediction_heads"][i] = keras.layers.deserialize(
+                        config["prediction_heads"][i]
+                    )
+
+        if "refinement_head" in config and isinstance(
+            config["refinement_head"], dict
+        ):
+            config["refinement_head"] = keras.layers.deserialize(
+                config["refinement_head"]
+            )
+        return super().from_config(config)
+
+
+def convolution_block(x_input, filters, dilation=1, dtype=None):
+    """Apply convolution + batch normalization + ReLU activation.
+
+    Args:
+        x_input: Input keras tensor.
+        filters: int, number of output filters in the convolution.
+        dilation: int, dilation rate for the convolution operation.
+            Defaults to 1.
+        dtype: `None` or str or `keras.mixed_precision.DTypePolicy`. The dtype
+            to use for the model's computations and weights.
+
+    Returns:
+        A tensor with convolution, batch normalization, and ReLU
+        activation applied.
+    """
+    x = keras.layers.Conv2D(
+        filters, (3, 3), padding="same", dilation_rate=dilation, dtype=dtype
+    )(x_input)
+    x = keras.layers.BatchNormalization(dtype=dtype)(x)
+    return keras.layers.Activation("relu", dtype=dtype)(x)
+
+
+def get_resnet_block(_resnet, block_num):
+    """Extract and return a specific ResNet block.
+
+    Args:
+        _resnet: `keras.Model`. ResNet model instance.
+        block_num: int, block number to extract.
+
+    Returns:
+        A Keras Model representing the specified ResNet block.
+    """
+
+    extractor_levels = ["P2", "P3", "P4", "P5"]
+    num_blocks = _resnet.stackwise_num_blocks
+    if block_num == 0:
+        x = _resnet.get_layer("pool1_pool").output
+    else:
+        x = _resnet.pyramid_outputs[extractor_levels[block_num - 1]]
+    y = _resnet.get_layer(
+        f"stack{block_num}_block{num_blocks[block_num]-1}_add"
+    ).output
+    return keras.models.Model(
+        inputs=x,
+        outputs=y,
+        name=f"resnet_block{block_num + 1}",
+    )
+
+
+def basnet_predict(x_input, backbone, filters, segmentation_heads, dtype=None):
+    """BASNet Prediction Module.
+
+    This module outputs a coarse label map by integrating heavy
+    encoder, bridge, and decoder blocks.
+
+    Args:
+        x_input: Input keras tensor.
+        backbone: `keras.Model`. The backbone network used as a feature
+            extractor for BASNet prediction encoder.
+        filters: int, the number of filters.
+        segmentation_heads: List of `keras.layers.Layer`, A list of Keras
+            layers serving as the segmentation head for prediction module.
+        dtype: `None` or str or `keras.mixed_precision.DTypePolicy`. The dtype
+            to use for the model's computations and weights.
+
+
+    Returns:
+        A Keras Model that integrates the encoder, bridge, and decoder
+        blocks for coarse label map prediction.
+    """
+    num_stages = 6
+
+    x = x_input
+
+    # -------------Encoder--------------
+    x = keras.layers.Conv2D(
+        filters, kernel_size=(3, 3), padding="same", dtype=dtype
+    )(x)
+
+    encoder_blocks = []
+    for i in range(num_stages):
+        if i < 4:  # First four stages are adopted from ResNet backbone.
+            x = get_resnet_block(backbone, i)(x)
+            encoder_blocks.append(x)
+        else:  # Last 2 stages consist of three basic resnet blocks.
+            x = keras.layers.MaxPool2D(
+                pool_size=(2, 2), strides=(2, 2), dtype=dtype
+            )(x)
+            for j in range(3):
+                x = resnet_basic_block(
+                    x,
+                    filters=x.shape[3],
+                    conv_shortcut=False,
+                    name=f"v1_basic_block_{i + 1}_{j + 1}",
+                    dtype=dtype,
+                )
+            encoder_blocks.append(x)
+
+    # -------------Bridge-------------
+    x = convolution_block(x, filters=filters * 8, dilation=2, dtype=dtype)
+    x = convolution_block(x, filters=filters * 8, dilation=2, dtype=dtype)
+    x = convolution_block(x, filters=filters * 8, dilation=2, dtype=dtype)
+    encoder_blocks.append(x)
+
+    # -------------Decoder-------------
+    decoder_blocks = []
+    for i in reversed(range(num_stages)):
+        if i != (num_stages - 1):  # Except first, scale other decoder stages.
+            x = keras.layers.UpSampling2D(
+                size=2, interpolation="bilinear", dtype=dtype
+            )(x)
+
+        x = keras.layers.concatenate([encoder_blocks[i], x], axis=-1)
+        x = convolution_block(x, filters=filters * 8, dtype=dtype)
+        x = convolution_block(x, filters=filters * 8, dtype=dtype)
+        x = convolution_block(x, filters=filters * 8, dtype=dtype)
+        decoder_blocks.append(x)
+
+    decoder_blocks.reverse()  # Change order from last to first decoder stage.
+    decoder_blocks.append(encoder_blocks[-1])  # Copy bridge to decoder.
+
+    # -------------Side Outputs--------------
+    decoder_blocks = [
+        segmentation_head(decoder_block)  # Prediction segmentation head.
+        for segmentation_head, decoder_block in zip(
+            segmentation_heads, decoder_blocks
+        )
+    ]
+
+    return keras.models.Model(inputs=[x_input], outputs=decoder_blocks)
+
+
+def basnet_rrm(base_model, filters, segmentation_head, dtype=None):
+    """BASNet Residual Refinement Module (RRM).
+
+    This module outputs a fine label map by integrating light encoder,
+    bridge, and decoder blocks.
+
+    Args:
+        base_model: Keras model used as the base or coarse label map.
+        filters: int, the number of filters.
+        segmentation_head: a `keras.layers.Layer`, A Keras layer serving
+            as the segmentation head for refinement module.
+        dtype: `None` or str or `keras.mixed_precision.DTypePolicy`. The dtype
+            to use for the model's computations and weights.
+
+    Returns:
+        A Keras Model that constructs the Residual Refinement Module (RRM).
+    """
+    num_stages = 4
+
+    x_input = base_model.output[0]
+
+    # -------------Encoder--------------
+    x = keras.layers.Conv2D(
+        filters, kernel_size=(3, 3), padding="same", dtype=dtype
+    )(x_input)
+
+    encoder_blocks = []
+    for _ in range(num_stages):
+        x = convolution_block(x, filters=filters)
+        encoder_blocks.append(x)
+        x = keras.layers.MaxPool2D(
+            pool_size=(2, 2), strides=(2, 2), dtype=dtype
+        )(x)
+
+    # -------------Bridge--------------
+    x = convolution_block(x, filters=filters, dtype=dtype)
+
+    # -------------Decoder--------------
+    for i in reversed(range(num_stages)):
+        x = keras.layers.UpSampling2D(
+            size=2, interpolation="bilinear", dtype=dtype
+        )(x)
+        x = keras.layers.concatenate([encoder_blocks[i], x], axis=-1)
+        x = convolution_block(x, filters=filters)
+
+    x = segmentation_head(x)  # Refinement segmentation head.
+
+    # ------------- refined = coarse + residual
+    x = keras.layers.Add(dtype=dtype)(
+        [x_input, x]
+    )  # Add prediction + refinement output
+
+    return keras.models.Model(inputs=base_model.input, outputs=[x])
diff --git a/keras_hub/src/models/basnet/basnet_backbone_test.py b/keras_hub/src/models/basnet/basnet_backbone_test.py
new file mode 100644
index 0000000000..dfb665d2c3
--- /dev/null
+++ b/keras_hub/src/models/basnet/basnet_backbone_test.py
@@ -0,0 +1,36 @@
+import numpy as np
+
+from keras_hub.src.models.basnet.basnet_backbone import BASNetBackbone
+from keras_hub.src.models.resnet.resnet_backbone import ResNetBackbone
+from keras_hub.src.tests.test_case import TestCase
+
+
+class BASNetBackboneTest(TestCase):
+    def setUp(self):
+        self.images = np.ones((2, 64, 64, 3))
+        self.image_encoder = ResNetBackbone(
+            input_conv_filters=[64],
+            input_conv_kernel_sizes=[7],
+            stackwise_num_filters=[64, 128, 256, 512],
+            stackwise_num_blocks=[2, 2, 2, 2],
+            stackwise_num_strides=[1, 2, 2, 2],
+            block_type="basic_block",
+        )
+        self.init_kwargs = {
+            "image_encoder": self.image_encoder,
+            "num_classes": 1,
+        }
+
+    def test_backbone_basics(self):
+        output_names = ["refine_out"] + [
+            f"predict_out_{i}" for i in range(1, 8)
+        ]
+        expected_output_shape = {name: (2, 64, 64, 1) for name in output_names}
+        self.run_backbone_test(
+            cls=BASNetBackbone,
+            init_kwargs=self.init_kwargs,
+            input_data=self.images,
+            expected_output_shape=expected_output_shape,
+            run_mixed_precision_check=False,
+            run_quantization_check=False,
+        )
diff --git a/keras_hub/src/models/basnet/basnet_image_converter.py b/keras_hub/src/models/basnet/basnet_image_converter.py
new file mode 100644
index 0000000000..399858c174
--- /dev/null
+++ b/keras_hub/src/models/basnet/basnet_image_converter.py
@@ -0,0 +1,8 @@
+from keras_hub.src.api_export import keras_hub_export
+from keras_hub.src.layers.preprocessing.image_converter import ImageConverter
+from keras_hub.src.models.basnet.basnet_backbone import BASNetBackbone
+
+
+@keras_hub_export("keras_hub.layers.BASNetImageConverter")
+class BASNetImageConverter(ImageConverter):
+    backbone_cls = BASNetBackbone
diff --git a/keras_hub/src/models/basnet/basnet_preprocessor.py b/keras_hub/src/models/basnet/basnet_preprocessor.py
new file mode 100644
index 0000000000..cfe580aa4b
--- /dev/null
+++ b/keras_hub/src/models/basnet/basnet_preprocessor.py
@@ -0,0 +1,14 @@
+from keras_hub.src.api_export import keras_hub_export
+from keras_hub.src.models.basnet.basnet_backbone import BASNetBackbone
+from keras_hub.src.models.basnet.basnet_image_converter import (
+    BASNetImageConverter,
+)
+from keras_hub.src.models.image_segmenter_preprocessor import (
+    ImageSegmenterPreprocessor,
+)
+
+
+@keras_hub_export("keras_hub.models.BASNetPreprocessor")
+class BASNetPreprocessor(ImageSegmenterPreprocessor):
+    backbone_cls = BASNetBackbone
+    image_converter_cls = BASNetImageConverter
diff --git a/keras_hub/src/models/basnet/basnet_presets.py b/keras_hub/src/models/basnet/basnet_presets.py
new file mode 100644
index 0000000000..3d96ab7885
--- /dev/null
+++ b/keras_hub/src/models/basnet/basnet_presets.py
@@ -0,0 +1,3 @@
+"""BASNet model preset configurations."""
+
+basnet_presets = {}
diff --git a/keras_hub/src/models/basnet/basnet_test.py b/keras_hub/src/models/basnet/basnet_test.py
new file mode 100644
index 0000000000..4147d43c7c
--- /dev/null
+++ b/keras_hub/src/models/basnet/basnet_test.py
@@ -0,0 +1,66 @@
+import numpy as np
+import pytest
+
+from keras_hub.src.models.basnet.basnet import BASNetImageSegmenter
+from keras_hub.src.models.basnet.basnet_backbone import BASNetBackbone
+from keras_hub.src.models.basnet.basnet_preprocessor import BASNetPreprocessor
+from keras_hub.src.models.resnet.resnet_backbone import ResNetBackbone
+from keras_hub.src.tests.test_case import TestCase
+
+
+class BASNetTest(TestCase):
+    def setUp(self):
+        self.images = np.ones((2, 64, 64, 3))
+        self.labels = np.concatenate(
+            (np.zeros((2, 32, 64, 1)), np.ones((2, 32, 64, 1))), axis=1
+        )
+        self.image_encoder = ResNetBackbone(
+            input_conv_filters=[64],
+            input_conv_kernel_sizes=[7],
+            stackwise_num_filters=[64, 128, 256, 512],
+            stackwise_num_blocks=[2, 2, 2, 2],
+            stackwise_num_strides=[1, 2, 2, 2],
+            block_type="basic_block",
+        )
+        self.backbone = BASNetBackbone(
+            image_encoder=self.image_encoder,
+            num_classes=1,
+        )
+        self.preprocessor = BASNetPreprocessor()
+        self.init_kwargs = {
+            "backbone": self.backbone,
+            "preprocessor": self.preprocessor,
+        }
+        self.train_data = (self.images, self.labels)
+
+    def test_basics(self):
+        self.run_task_test(
+            cls=BASNetImageSegmenter,
+            init_kwargs=self.init_kwargs,
+            train_data=self.train_data,
+            expected_output_shape=(2, 64, 64, 1),
+        )
+
+    @pytest.mark.large
+    def test_saved_model(self):
+        self.run_model_saving_test(
+            cls=BASNetImageSegmenter,
+            init_kwargs=self.init_kwargs,
+            input_data=self.images,
+        )
+
+    def test_end_to_end_model_predict(self):
+        model = BASNetImageSegmenter(**self.init_kwargs)
+        output = model.predict(self.images)
+        self.assertAllEqual(output.shape, (2, 64, 64, 1))
+
+    @pytest.mark.skip(reason="disabled until preset's been uploaded to Kaggle")
+    @pytest.mark.extra_large
+    def test_all_presets(self):
+        for preset in BASNetImageSegmenter.presets:
+            self.run_preset_test(
+                cls=BASNetImageSegmenter,
+                preset=preset,
+                input_data=self.images,
+                expected_output_shape=(2, 64, 64, 1),
+            )