diff --git a/mediapipe/python/BUILD b/mediapipe/python/BUILD
index 3a4a90b44..331ee836c 100644
--- a/mediapipe/python/BUILD
+++ b/mediapipe/python/BUILD
@@ -86,6 +86,8 @@ cc_library(
     name = "builtin_task_graphs",
     deps = [
         "//mediapipe/tasks/cc/vision/object_detector:object_detector_graph",
+        "//mediapipe/tasks/cc/vision/image_classification:image_classifier_graph",
+        "//mediapipe/tasks/cc/vision/image_segmenter:image_segmenter_graph",
     ],
 )
 
diff --git a/mediapipe/tasks/python/components/BUILD b/mediapipe/tasks/python/components/BUILD
new file mode 100644
index 000000000..eb8714a93
--- /dev/null
+++ b/mediapipe/tasks/python/components/BUILD
@@ -0,0 +1,28 @@
+# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
+#
+# 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
+#
+#     http://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.
+
+# Placeholder for internal Python strict library compatibility macro.
+
+package(default_visibility = ["//mediapipe/tasks:internal"])
+
+licenses(["notice"])
+
+py_library(
+    name = "segmenter_options",
+    srcs = ["segmenter_options.py"],
+    deps = [
+        "//mediapipe/tasks/cc/components:segmenter_options_py_pb2",
+        "//mediapipe/tasks/python/core:optional_dependencies",
+    ],
+)
diff --git a/mediapipe/tasks/python/components/segmenter_options.py b/mediapipe/tasks/python/components/segmenter_options.py
new file mode 100644
index 000000000..5b94a256d
--- /dev/null
+++ b/mediapipe/tasks/python/components/segmenter_options.py
@@ -0,0 +1,78 @@
+# Copyright 2022 The TensorFlow Authors. All Rights Reserved.
+#
+# 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
+#
+#     http://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.
+"""Segmenter options data class."""
+
+import dataclasses
+import enum
+from typing import Any, Optional
+
+from mediapipe.tasks.cc.components import segmenter_options_pb2
+from mediapipe.tasks.python.core.optional_dependencies import doc_controls
+
+_SegmenterOptionsProto = segmenter_options_pb2.SegmenterOptions
+
+
+class OutputType(enum.Enum):
+  UNSPECIFIED = 0
+  CATEGORY_MASK = 1
+  CONFIDENCE_MASK = 2
+
+
+class Activation(enum.Enum):
+  NONE = 0
+  SIGMOID = 1
+  SOFTMAX = 2
+
+
+@dataclasses.dataclass
+class SegmenterOptions:
+  """Options for segmentation processor.
+  Attributes:
+    output_type: The output mask type allows specifying the type of
+      post-processing to perform on the raw model results.
+    activation: Activation function to apply to input tensor.
+  """
+
+  output_type: Optional[OutputType] = OutputType.CATEGORY_MASK
+  activation: Optional[Activation] = Activation.NONE
+
+  @doc_controls.do_not_generate_docs
+  def to_pb2(self) -> _SegmenterOptionsProto:
+    """Generates a protobuf object to pass to the C++ layer."""
+    return _SegmenterOptionsProto(
+        output_type=self.output_type.value,
+        activation=self.activation.value
+    )
+
+  @classmethod
+  @doc_controls.do_not_generate_docs
+  def create_from_pb2(
+      cls, pb2_obj: _SegmenterOptionsProto) -> "SegmenterOptions":
+    """Creates a `SegmenterOptions` object from the given protobuf object."""
+    return SegmenterOptions(
+        output_type=OutputType(pb2_obj.output_type),
+        activation=Activation(pb2_obj.output_type)
+    )
+
+  def __eq__(self, other: Any) -> bool:
+    """Checks if this object is equal to the given object.
+    Args:
+      other: The object to be compared with.
+    Returns:
+      True if the objects are equal.
+    """
+    if not isinstance(other, SegmenterOptions):
+      return False
+
+    return self.to_pb2().__eq__(other.to_pb2())
diff --git a/mediapipe/tasks/python/test/vision/BUILD b/mediapipe/tasks/python/test/vision/BUILD
index bb495338d..403e00a37 100644
--- a/mediapipe/tasks/python/test/vision/BUILD
+++ b/mediapipe/tasks/python/test/vision/BUILD
@@ -18,4 +18,40 @@ package(default_visibility = ["//mediapipe/tasks:internal"])
 
 licenses(["notice"])
 
-# TODO: This test fails in OSS
+py_test(
+    name = "object_detector_test",
+    srcs = ["object_detector_test.py"],
+    data = [
+        "//mediapipe/tasks/testdata/vision:test_images",
+        "//mediapipe/tasks/testdata/vision:test_models",
+    ],
+    deps = [
+        # build rule placeholder: numpy dep,
+        "//mediapipe/tasks/python/components/containers:bounding_box",
+        "//mediapipe/tasks/python/components/containers:category",
+        "//mediapipe/tasks/python/components/containers:detections",
+        "//mediapipe/tasks/python/core:base_options",
+        "//mediapipe/tasks/python/test:test_util",
+        "//mediapipe/tasks/python/vision:object_detector",
+        "//mediapipe/tasks/python/vision/core:vision_task_running_mode",
+        "@absl_py//absl/testing:parameterized",
+    ],
+)
+
+py_test(
+    name = "image_segmenter_test",
+    srcs = ["image_segmenter_test.py"],
+    data = [
+        "//mediapipe/tasks/testdata/vision:test_images",
+        "//mediapipe/tasks/testdata/vision:test_models",
+    ],
+    deps = [
+        # build rule placeholder: numpy dep,
+        "//mediapipe/tasks/python/core:base_options",
+        "//mediapipe/tasks/python/test:test_util",
+        "//mediapipe/tasks/python/components:segmenter_options",
+        "//mediapipe/tasks/python/vision:image_segmenter",
+        "//mediapipe/tasks/python/vision/core:vision_task_running_mode",
+        "@absl_py//absl/testing:parameterized",
+    ],
+)
diff --git a/mediapipe/tasks/python/test/vision/image_segmenter_test.py b/mediapipe/tasks/python/test/vision/image_segmenter_test.py
new file mode 100644
index 000000000..704b0fc57
--- /dev/null
+++ b/mediapipe/tasks/python/test/vision/image_segmenter_test.py
@@ -0,0 +1,118 @@
+# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
+#
+# 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
+#
+#     http://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.
+"""Tests for image segmenter."""
+
+import enum
+
+from absl.testing import absltest
+from absl.testing import parameterized
+import numpy as np
+
+from mediapipe.python._framework_bindings import image as image_module
+from mediapipe.tasks.python.components import segmenter_options
+from mediapipe.tasks.python.core import base_options as base_options_module
+from mediapipe.tasks.python.test import test_util
+from mediapipe.tasks.python.vision import image_segmenter
+from mediapipe.tasks.python.vision.core import vision_task_running_mode as running_mode_module
+
+_BaseOptions = base_options_module.BaseOptions
+_Image = image_module.Image
+_OutputType = segmenter_options.OutputType
+_Activation = segmenter_options.Activation
+_ImageSegmenter = image_segmenter.ImageSegmenter
+_ImageSegmenterOptions = image_segmenter.ImageSegmenterOptions
+_RUNNING_MODE = running_mode_module.VisionTaskRunningMode
+
+_MODEL_FILE = 'deeplabv3.tflite'
+_IMAGE_FILE = 'segmentation_input_rotation0.jpg'
+_SEGMENTATION_FILE = 'segmentation_golden_rotation0.png'
+_MASK_MAGNIFICATION_FACTOR = 10
+_MATCH_PIXELS_THRESHOLD = 0.01
+
+
+class ModelFileType(enum.Enum):
+  FILE_CONTENT = 1
+  FILE_NAME = 2
+
+
+class ImageSegmenterTest(parameterized.TestCase):
+
+  def setUp(self):
+    super().setUp()
+    self.test_image = test_util.read_test_image(
+        test_util.get_test_data_path(_IMAGE_FILE))
+    self.model_path = test_util.get_test_data_path(_MODEL_FILE)
+
+  def test_create_from_file_succeeds_with_valid_model_path(self):
+    # Creates with default option and valid model file successfully.
+    with _ImageSegmenter.create_from_model_path(self.model_path) as segmenter:
+      self.assertIsInstance(segmenter, _ImageSegmenter)
+
+  def test_create_from_options_succeeds_with_valid_model_path(self):
+    # Creates with options containing model file successfully.
+    base_options = _BaseOptions(file_name=self.model_path)
+    options = _ImageSegmenterOptions(base_options=base_options)
+    with _ImageSegmenter.create_from_options(options) as segmenter:
+      self.assertIsInstance(segmenter, _ImageSegmenter)
+
+  def test_create_from_options_fails_with_invalid_model_path(self):
+    # Invalid empty model path.
+    with self.assertRaisesRegex(
+        ValueError,
+        r"ExternalFile must specify at least one of 'file_content', "
+        r"'file_name' or 'file_descriptor_meta'."):
+      base_options = _BaseOptions(file_name='')
+      options = _ImageSegmenterOptions(base_options=base_options)
+      _ImageSegmenter.create_from_options(options)
+
+  def test_create_from_options_succeeds_with_valid_model_content(self):
+    # Creates with options containing model content successfully.
+    with open(self.model_path, 'rb') as f:
+      base_options = _BaseOptions(file_content=f.read())
+      options = _ImageSegmenterOptions(base_options=base_options)
+      segmenter = _ImageSegmenter.create_from_options(options)
+      self.assertIsInstance(segmenter, _ImageSegmenter)
+
+  @parameterized.parameters(
+      (ModelFileType.FILE_NAME, 4),
+      (ModelFileType.FILE_CONTENT, 4))
+  def succeeds_with_category_mask(self, model_file_type, max_results):
+    # Creates segmenter.
+    if model_file_type is ModelFileType.FILE_NAME:
+      base_options = _BaseOptions(file_name=self.model_path)
+    elif model_file_type is ModelFileType.FILE_CONTENT:
+      with open(self.model_path, 'rb') as f:
+        model_content = f.read()
+      base_options = _BaseOptions(file_content=model_content)
+    else:
+      # Should never happen
+      raise ValueError('model_file_type is invalid.')
+
+    options = _ImageSegmenterOptions(base_options=base_options,
+                                     output_type=_OutputType.CATEGORY_MASK)
+    segmenter = _ImageSegmenter.create_from_options(options)
+
+    # Performs image segmentation on the input.
+    image_result = segmenter.segment(self.test_image)
+
+    # Comparing results.
+    print(image_result)
+
+    # Closes the segmenter explicitly when the segmenter is not used in
+    # a context.
+    segmenter.close()
+
+
+if __name__ == '__main__':
+  absltest.main()
diff --git a/mediapipe/tasks/python/vision/BUILD b/mediapipe/tasks/python/vision/BUILD
index 7ff818610..ce59763d6 100644
--- a/mediapipe/tasks/python/vision/BUILD
+++ b/mediapipe/tasks/python/vision/BUILD
@@ -36,3 +36,22 @@ py_library(
         "//mediapipe/tasks/python/vision/core:vision_task_running_mode",
     ],
 )
+
+py_library(
+    name = "image_segmenter",
+    srcs = [
+        "image_segmenter.py",
+    ],
+    deps = [
+        "//mediapipe/python:_framework_bindings",
+        "//mediapipe/python:packet_creator",
+        "//mediapipe/python:packet_getter",
+        "//mediapipe/tasks/cc/vision/image_segmenter/proto:image_segmenter_options_py_pb2",
+        "//mediapipe/tasks/python/components:segmenter_options",
+        "//mediapipe/tasks/python/core:base_options",
+        "//mediapipe/tasks/python/core:optional_dependencies",
+        "//mediapipe/tasks/python/core:task_info",
+        "//mediapipe/tasks/python/vision/core:base_vision_task_api",
+        "//mediapipe/tasks/python/vision/core:vision_task_running_mode",
+    ],
+)
diff --git a/mediapipe/tasks/python/vision/image_segmenter.py b/mediapipe/tasks/python/vision/image_segmenter.py
new file mode 100644
index 000000000..ea40d85c1
--- /dev/null
+++ b/mediapipe/tasks/python/vision/image_segmenter.py
@@ -0,0 +1,205 @@
+# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
+#
+# 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
+#
+#     http://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.
+"""MediaPipe image segmenter task."""
+
+import dataclasses
+from typing import Callable, List, Mapping, Optional
+
+from mediapipe.python import packet_creator
+from mediapipe.python import packet_getter
+from mediapipe.python._framework_bindings import image as image_module
+from mediapipe.python._framework_bindings import packet as packet_module
+from mediapipe.python._framework_bindings import task_runner as task_runner_module
+from mediapipe.tasks.cc.vision.image_segmenter.proto import image_segmenter_options_pb2
+from mediapipe.tasks.python.components import segmenter_options
+from mediapipe.tasks.python.core import base_options as base_options_module
+from mediapipe.tasks.python.core import task_info as task_info_module
+from mediapipe.tasks.python.core.optional_dependencies import doc_controls
+from mediapipe.tasks.python.vision.core import base_vision_task_api
+from mediapipe.tasks.python.vision.core import vision_task_running_mode as running_mode_module
+
+_BaseOptions = base_options_module.BaseOptions
+_ImageSegmenterOptionsProto = image_segmenter_options_pb2.ImageSegmenterOptions
+_SegmenterOptions = segmenter_options.SegmenterOptions
+_RunningMode = running_mode_module.VisionTaskRunningMode
+_TaskInfo = task_info_module.TaskInfo
+_TaskRunner = task_runner_module.TaskRunner
+
+_SEGMENTATION_OUT_STREAM_NAME = 'segmented_masks'
+_SEGMENTATION_TAG = 'SEGMENTATION'
+_GROUPED_SEGMENTATION_TAG = 'GROUPED_SEGMENTATION'
+_IMAGE_IN_STREAM_NAME = 'image_in'
+_IMAGE_OUT_STREAM_NAME = 'image_out'
+_IMAGE_TAG = 'IMAGE'
+_TASK_GRAPH_NAME = 'mediapipe.tasks.vision.ImageSegmenterGraph'
+
+
+@dataclasses.dataclass
+class ImageSegmenterOptions:
+  """Options for the image segmenter task.
+
+  Attributes:
+    base_options: Base options for the image segmenter task.
+    running_mode: The running mode of the task. Default to the image mode.
+      Image segmenter task has three running modes:
+      1) The image mode for detecting objects on single image inputs.
+      2) The video mode for detecting objects on the decoded frames of a video.
+      3) The live stream mode for detecting objects on a live stream of input
+         data, such as from camera.
+    output_type: Optional output mask type.
+    activation: Activation function to apply to input tensor.
+    result_callback: The user-defined result callback for processing live stream
+      data. The result callback should only be specified when the running mode
+      is set to the live stream mode.
+  """
+  base_options: _BaseOptions
+  running_mode: _RunningMode = _RunningMode.IMAGE
+  output_type: Optional[segmenter_options.OutputType] = segmenter_options.OutputType.CATEGORY_MASK
+  activation: Optional[segmenter_options.Activation] = segmenter_options.Activation.NONE
+  result_callback: Optional[
+      Callable[[List[image_module.Image], image_module.Image, int],
+               None]] = None
+
+  @doc_controls.do_not_generate_docs
+  def to_pb2(self) -> _ImageSegmenterOptionsProto:
+    """Generates an ImageSegmenterOptions protobuf object."""
+    base_options_proto = self.base_options.to_pb2()
+    base_options_proto.use_stream_mode = False if self.running_mode == _RunningMode.IMAGE else True
+
+    segmenter_options = _SegmenterOptions(
+        output_type=self.output_type,
+        activation=self.activation
+    )
+
+    return _ImageSegmenterOptionsProto(
+        base_options=base_options_proto,
+        segmenter_options=segmenter_options.to_pb2()
+    )
+
+
+class ImageSegmenter(base_vision_task_api.BaseVisionTaskApi):
+  """Class that performs image segmentation on images."""
+
+  @classmethod
+  def create_from_model_path(cls, model_path: str) -> 'ImageSegmenter':
+    """Creates an `ImageSegmenter` object from a TensorFlow Lite model and the default `ImageSegmenterOptions`.
+
+    Note that the created `ImageSegmenter` instance is in image mode, for
+    performing image segmentation on single image inputs.
+
+    Args:
+      model_path: Path to the model.
+
+    Returns:
+      `ImageSegmenter` object that's created from the model file and the default
+      `ImageSegmenterOptions`.
+
+    Raises:
+      ValueError: If failed to create `ImageSegmenter` object from the provided
+        file such as invalid file path.
+      RuntimeError: If other types of error occurred.
+    """
+    base_options = _BaseOptions(file_name=model_path)
+    options = ImageSegmenterOptions(
+        base_options=base_options, running_mode=_RunningMode.IMAGE)
+    return cls.create_from_options(options)
+
+  @classmethod
+  def create_from_options(cls,
+                          options: ImageSegmenterOptions) -> 'ImageSegmenter':
+    """Creates the `ImageSegmenter` object from image segmenter options.
+
+    Args:
+      options: Options for the image segmenter task.
+
+    Returns:
+      `ImageSegmenter` object that's created from `options`.
+
+    Raises:
+      ValueError: If failed to create `ImageSegmenter` object from
+        `ImageSegmenterOptions` such as missing the model.
+      RuntimeError: If other types of error occurred.
+    """
+
+    def packets_callback(output_packets: Mapping[str, packet_module.Packet]):
+      if output_packets[_IMAGE_OUT_STREAM_NAME].is_empty():
+        return
+      segmentation_result = packet_getter.get_proto_list(
+          output_packets[_SEGMENTATION_OUT_STREAM_NAME])
+      image = packet_getter.get_image(output_packets[_IMAGE_OUT_STREAM_NAME])
+      timestamp = output_packets[_IMAGE_OUT_STREAM_NAME].timestamp
+      options.result_callback(segmentation_result, image, timestamp)
+
+    task_info = _TaskInfo(
+        task_graph=_TASK_GRAPH_NAME,
+        input_streams=[':'.join([_IMAGE_TAG, _IMAGE_IN_STREAM_NAME])],
+        output_streams=[
+            ':'.join([_SEGMENTATION_TAG, _SEGMENTATION_OUT_STREAM_NAME]),
+            ':'.join([_IMAGE_TAG, _IMAGE_OUT_STREAM_NAME])
+        ],
+        task_options=options)
+    return cls(
+        task_info.generate_graph_config(
+            enable_flow_limiting=options.running_mode ==
+            _RunningMode.LIVE_STREAM), options.running_mode,
+        packets_callback if options.result_callback else None)
+
+  # TODO: Create an Image class for MediaPipe Tasks.
+  def segment(self,
+              image: image_module.Image) -> List[image_module.Image]:
+    """Performs the actual segmentation task on the provided MediaPipe Image.
+
+    Args:
+      image: MediaPipe Image.
+
+    Returns:
+      A segmentation result object that contains a list of segmentation masks
+      as images.
+
+    Raises:
+      ValueError: If any of the input arguments is invalid.
+      RuntimeError: If object detection failed to run.
+    """
+    output_packets = self._process_image_data(
+        {_IMAGE_IN_STREAM_NAME: packet_creator.create_image(image)})
+    segmentation_result = packet_getter.get_proto_list(
+        output_packets[_SEGMENTATION_OUT_STREAM_NAME])
+    return segmentation_result
+
+  # def segment_async(self, image: image_module.Image, timestamp_ms: int) -> None:
+  #   """Sends live image data (an Image with a unique timestamp) to perform image segmentation.
+  #
+  #   This method will return immediately after the input image is accepted. The
+  #   results will be available via the `result_callback` provided in the
+  #   `ImageSegmenterOptions`. The `segment_async` method is designed to process
+  #   live stream data such as camera input. To lower the overall latency, image
+  #   segmenter may drop the input images if needed. In other words, it's not
+  #   guaranteed to have output per input image. The `result_callback` provides:
+  #     - A segmentation result object that contains a list of segmentation masks
+  #       as images.
+  #     - The input image that the image segmenter runs on.
+  #     - The input timestamp in milliseconds.
+  #
+  #   Args:
+  #     image: MediaPipe Image.
+  #     timestamp_ms: The timestamp of the input image in milliseconds.
+  #
+  #   Raises:
+  #     ValueError: If the current input timestamp is smaller than what the object
+  #       detector has already processed.
+  #   """
+  #   self._send_live_stream_data({
+  #       _IMAGE_IN_STREAM_NAME:
+  #           packet_creator.create_image(image).at(timestamp_ms)
+  #   })