diff --git a/mediapipe/model_maker/python/core/data/classification_dataset.py b/mediapipe/model_maker/python/core/data/classification_dataset.py
index af761d9e..073e7963 100644
--- a/mediapipe/model_maker/python/core/data/classification_dataset.py
+++ b/mediapipe/model_maker/python/core/data/classification_dataset.py
@@ -13,7 +13,7 @@
 # limitations under the License.
 """Common classification dataset library."""
 
-from typing import Any, Tuple
+from typing import List, Tuple
 
 import tensorflow as tf
 
@@ -21,19 +21,20 @@ from mediapipe.model_maker.python.core.data import dataset as ds
 
 
 class ClassificationDataset(ds.Dataset):
-  """DataLoader for classification models."""
+  """Dataset Loader for classification models."""
 
-  def __init__(self, dataset: tf.data.Dataset, size: int, index_by_label: Any):
+  def __init__(self, dataset: tf.data.Dataset, size: int,
+               label_names: List[str]):
     super().__init__(dataset, size)
-    self._index_by_label = index_by_label
+    self._label_names = label_names
 
   @property
   def num_classes(self: ds._DatasetT) -> int:
-    return len(self._index_by_label)
+    return len(self._label_names)
 
   @property
-  def index_by_label(self: ds._DatasetT) -> Any:
-    return self._index_by_label
+  def label_names(self: ds._DatasetT) -> List[str]:
+    return self._label_names
 
   def split(self: ds._DatasetT,
             fraction: float) -> Tuple[ds._DatasetT, ds._DatasetT]:
@@ -48,4 +49,4 @@ class ClassificationDataset(ds.Dataset):
     Returns:
       The splitted two sub datasets.
     """
-    return self._split(fraction, self._index_by_label)
+    return self._split(fraction, self._label_names)
diff --git a/mediapipe/model_maker/python/core/data/classification_dataset_test.py b/mediapipe/model_maker/python/core/data/classification_dataset_test.py
index 0fd8575f..82e74b04 100644
--- a/mediapipe/model_maker/python/core/data/classification_dataset_test.py
+++ b/mediapipe/model_maker/python/core/data/classification_dataset_test.py
@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from typing import Any, Tuple, TypeVar
+from typing import Any, List, Tuple, TypeVar
 
 # Dependency imports
 
@@ -37,26 +37,22 @@ class ClassificationDatasetTest(tf.test.TestCase):
       """
 
       def __init__(self, dataset: tf.data.Dataset, size: int,
-                   index_by_label: Any, value: Any):
-        super().__init__(
-            dataset=dataset, size=size, index_by_label=index_by_label)
+                   label_names: List[str], value: Any):
+        super().__init__(dataset=dataset, size=size, label_names=label_names)
         self.value = value
 
       def split(self, fraction: float) -> Tuple[_DatasetT, _DatasetT]:
-        return self._split(fraction, self.index_by_label, self.value)
+        return self._split(fraction, self.label_names, self.value)
 
     # Some dummy inputs.
     magic_value = 42
     num_classes = 2
-    index_by_label = (False, True)
+    label_names = ['foo', 'bar']
 
     # Create data loader from sample data.
     ds = tf.data.Dataset.from_tensor_slices([[0, 1], [1, 1], [0, 0], [1, 0]])
     data = MagicClassificationDataset(
-        dataset=ds,
-        size=len(ds),
-        index_by_label=index_by_label,
-        value=magic_value)
+        dataset=ds, size=len(ds), label_names=label_names, value=magic_value)
 
     # Train/Test data split.
     fraction = .25
@@ -73,7 +69,7 @@ class ClassificationDatasetTest(tf.test.TestCase):
 
     # Make sure attributes propagated correctly.
     self.assertEqual(train_data.num_classes, num_classes)
-    self.assertEqual(test_data.index_by_label, index_by_label)
+    self.assertEqual(test_data.label_names, label_names)
     self.assertEqual(train_data.value, magic_value)
     self.assertEqual(test_data.value, magic_value)
 
diff --git a/mediapipe/model_maker/python/core/tasks/classifier.py b/mediapipe/model_maker/python/core/tasks/classifier.py
index c327b7ea..f83d4059 100644
--- a/mediapipe/model_maker/python/core/tasks/classifier.py
+++ b/mediapipe/model_maker/python/core/tasks/classifier.py
@@ -29,22 +29,22 @@ from mediapipe.model_maker.python.core.tasks import custom_model
 class Classifier(custom_model.CustomModel):
   """An abstract base class that represents a TensorFlow classifier."""
 
-  def __init__(self, model_spec: Any, index_by_label: List[str], shuffle: bool,
+  def __init__(self, model_spec: Any, label_names: List[str], shuffle: bool,
                full_train: bool):
     """Initilizes a classifier with its specifications.
 
     Args:
         model_spec: Specification for the model.
-        index_by_label: A list that map from index to label class name.
+        label_names: A list of label names for the classes.
         shuffle: Whether the dataset should be shuffled.
         full_train: If true, train the model end-to-end including the backbone
           and the classification layers on top. Otherwise, only train the top
           classification layers.
     """
     super(Classifier, self).__init__(model_spec, shuffle)
-    self._index_by_label = index_by_label
+    self._label_names = label_names
     self._full_train = full_train
-    self._num_classes = len(index_by_label)
+    self._num_classes = len(label_names)
 
   def evaluate(self, data: dataset.Dataset, batch_size: int = 32) -> Any:
     """Evaluates the classifier with the provided evaluation dataset.
@@ -74,4 +74,4 @@ class Classifier(custom_model.CustomModel):
     label_filepath = os.path.join(export_dir, label_filename)
     tf.compat.v1.logging.info('Saving labels in %s', label_filepath)
     with tf.io.gfile.GFile(label_filepath, 'w') as f:
-      f.write('\n'.join(self._index_by_label))
+      f.write('\n'.join(self._label_names))
diff --git a/mediapipe/model_maker/python/core/tasks/classifier_test.py b/mediapipe/model_maker/python/core/tasks/classifier_test.py
index 1484e8e8..52a3b97d 100644
--- a/mediapipe/model_maker/python/core/tasks/classifier_test.py
+++ b/mediapipe/model_maker/python/core/tasks/classifier_test.py
@@ -36,10 +36,10 @@ class ClassifierTest(tf.test.TestCase):
 
   def setUp(self):
     super(ClassifierTest, self).setUp()
-    index_by_label = ['cat', 'dog']
+    label_names = ['cat', 'dog']
     self.model = MockClassifier(
         model_spec=None,
-        index_by_label=index_by_label,
+        label_names=label_names,
         shuffle=False,
         full_train=False)
     self.model.model = test_util.build_model(input_shape=[4], num_classes=2)
diff --git a/mediapipe/model_maker/python/vision/image_classifier/dataset.py b/mediapipe/model_maker/python/vision/image_classifier/dataset.py
index 4ae8dcfd..071fe483 100644
--- a/mediapipe/model_maker/python/vision/image_classifier/dataset.py
+++ b/mediapipe/model_maker/python/vision/image_classifier/dataset.py
@@ -106,4 +106,4 @@ class Dataset(classification_dataset.ClassificationDataset):
         'Load image with size: %d, num_label: %d, labels: %s.', all_image_size,
         all_label_size, ', '.join(label_names))
     return Dataset(
-        dataset=image_label_ds, size=all_image_size, index_by_label=label_names)
+        dataset=image_label_ds, size=all_image_size, label_names=label_names)
diff --git a/mediapipe/model_maker/python/vision/image_classifier/dataset_test.py b/mediapipe/model_maker/python/vision/image_classifier/dataset_test.py
index 6a0b696f..0eed547e 100644
--- a/mediapipe/model_maker/python/vision/image_classifier/dataset_test.py
+++ b/mediapipe/model_maker/python/vision/image_classifier/dataset_test.py
@@ -49,27 +49,27 @@ class DatasetTest(tf.test.TestCase):
 
   def test_split(self):
     ds = tf.data.Dataset.from_tensor_slices([[0, 1], [1, 1], [0, 0], [1, 0]])
-    data = dataset.Dataset(dataset=ds, size=4, index_by_label=['pos', 'neg'])
+    data = dataset.Dataset(dataset=ds, size=4, label_names=['pos', 'neg'])
     train_data, test_data = data.split(fraction=0.5)
 
     self.assertLen(train_data, 2)
     for i, elem in enumerate(train_data._dataset):
       self.assertTrue((elem.numpy() == np.array([i, 1])).all())
     self.assertEqual(train_data.num_classes, 2)
-    self.assertEqual(train_data.index_by_label, ['pos', 'neg'])
+    self.assertEqual(train_data.label_names, ['pos', 'neg'])
 
     self.assertLen(test_data, 2)
     for i, elem in enumerate(test_data._dataset):
       self.assertTrue((elem.numpy() == np.array([i, 0])).all())
     self.assertEqual(test_data.num_classes, 2)
-    self.assertEqual(test_data.index_by_label, ['pos', 'neg'])
+    self.assertEqual(test_data.label_names, ['pos', 'neg'])
 
   def test_from_folder(self):
     data = dataset.Dataset.from_folder(dirname=self.image_path)
 
     self.assertLen(data, 2)
     self.assertEqual(data.num_classes, 2)
-    self.assertEqual(data.index_by_label, ['daisy', 'tulips'])
+    self.assertEqual(data.label_names, ['daisy', 'tulips'])
     for image, label in data.gen_tf_dataset():
       self.assertTrue(label.numpy() == 1 or label.numpy() == 0)
       if label.numpy() == 0:
@@ -88,19 +88,19 @@ class DatasetTest(tf.test.TestCase):
     self.assertIsInstance(train_data.gen_tf_dataset(), tf.data.Dataset)
     self.assertLen(train_data, 1034)
     self.assertEqual(train_data.num_classes, 3)
-    self.assertEqual(train_data.index_by_label,
+    self.assertEqual(train_data.label_names,
                      ['angular_leaf_spot', 'bean_rust', 'healthy'])
 
     self.assertIsInstance(validation_data.gen_tf_dataset(), tf.data.Dataset)
     self.assertLen(validation_data, 133)
     self.assertEqual(validation_data.num_classes, 3)
-    self.assertEqual(validation_data.index_by_label,
+    self.assertEqual(validation_data.label_names,
                      ['angular_leaf_spot', 'bean_rust', 'healthy'])
 
     self.assertIsInstance(test_data.gen_tf_dataset(), tf.data.Dataset)
     self.assertLen(test_data, 128)
     self.assertEqual(test_data.num_classes, 3)
-    self.assertEqual(test_data.index_by_label,
+    self.assertEqual(test_data.label_names,
                      ['angular_leaf_spot', 'bean_rust', 'healthy'])
 
 
diff --git a/mediapipe/model_maker/python/vision/image_classifier/image_classifier.py b/mediapipe/model_maker/python/vision/image_classifier/image_classifier.py
index dd8929a7..a3282ebf 100644
--- a/mediapipe/model_maker/python/vision/image_classifier/image_classifier.py
+++ b/mediapipe/model_maker/python/vision/image_classifier/image_classifier.py
@@ -13,7 +13,7 @@
 # limitations under the License.
 """APIs to train image classifier model."""
 
-from typing import Any, List, Optional
+from typing import List, Optional
 
 import tensorflow as tf
 import tensorflow_hub as hub
@@ -31,18 +31,18 @@ from mediapipe.model_maker.python.vision.image_classifier import train_image_cla
 class ImageClassifier(classifier.Classifier):
   """ImageClassifier for building image classification model."""
 
-  def __init__(self, model_spec: ms.ModelSpec, index_by_label: List[Any],
+  def __init__(self, model_spec: ms.ModelSpec, label_names: List[str],
                hparams: hp.HParams):
     """Initializes ImageClassifier class.
 
     Args:
       model_spec: Specification for the model.
-      index_by_label: A list that maps from index to label class name.
+      label_names: A list of label names for the classes.
       hparams: The hyperparameters for training image classifier.
     """
     super().__init__(
         model_spec=model_spec,
-        index_by_label=index_by_label,
+        label_names=label_names,
         shuffle=hparams.shuffle,
         full_train=hparams.do_fine_tuning)
     self._hparams = hparams
@@ -80,9 +80,7 @@ class ImageClassifier(classifier.Classifier):
 
     spec = ms.SupportedModels.get(model_spec)
     image_classifier = cls(
-        model_spec=spec,
-        index_by_label=train_data.index_by_label,
-        hparams=hparams)
+        model_spec=spec, label_names=train_data.label_names, hparams=hparams)
 
     image_classifier._create_model()
 
diff --git a/mediapipe/python/BUILD b/mediapipe/python/BUILD
index 5da630cd..6c2bc1c3 100644
--- a/mediapipe/python/BUILD
+++ b/mediapipe/python/BUILD
@@ -88,6 +88,7 @@ cc_library(
     name = "builtin_task_graphs",
     deps = [
         "//mediapipe/tasks/cc/vision/image_classifier:image_classifier_graph",
+        "//mediapipe/tasks/cc/vision/image_segmenter:image_segmenter_graph",
         "//mediapipe/tasks/cc/vision/object_detector:object_detector_graph",
         "//mediapipe/tasks/cc/vision/gesture_recognizer:gesture_recognizer_graph",
     ],
diff --git a/mediapipe/tasks/cc/vision/gesture_recognizer/BUILD b/mediapipe/tasks/cc/vision/gesture_recognizer/BUILD
index 6296017d..a88d0d72 100644
--- a/mediapipe/tasks/cc/vision/gesture_recognizer/BUILD
+++ b/mediapipe/tasks/cc/vision/gesture_recognizer/BUILD
@@ -62,14 +62,18 @@ cc_library(
         "//mediapipe/tasks/cc/components:image_preprocessing",
         "//mediapipe/tasks/cc/components/processors:classification_postprocessing_graph",
         "//mediapipe/tasks/cc/components/processors/proto:classification_postprocessing_graph_options_cc_proto",
+        "//mediapipe/tasks/cc/components/processors/proto:classifier_options_cc_proto",
         "//mediapipe/tasks/cc/core:model_asset_bundle_resources",
         "//mediapipe/tasks/cc/core:model_resources",
         "//mediapipe/tasks/cc/core:model_resources_cache",
         "//mediapipe/tasks/cc/core:model_task_graph",
         "//mediapipe/tasks/cc/core:utils",
+        "//mediapipe/tasks/cc/core/proto:base_options_cc_proto",
         "//mediapipe/tasks/cc/core/proto:external_file_cc_proto",
         "//mediapipe/tasks/cc/core/proto:inference_subgraph_cc_proto",
         "//mediapipe/tasks/cc/metadata/utils:zip_utils",
+        "//mediapipe/tasks/cc/vision/gesture_recognizer/calculators:combined_prediction_calculator",
+        "//mediapipe/tasks/cc/vision/gesture_recognizer/calculators:combined_prediction_calculator_cc_proto",
         "//mediapipe/tasks/cc/vision/gesture_recognizer/calculators:handedness_to_matrix_calculator",
         "//mediapipe/tasks/cc/vision/gesture_recognizer/calculators:landmarks_to_matrix_calculator",
         "//mediapipe/tasks/cc/vision/gesture_recognizer/calculators:landmarks_to_matrix_calculator_cc_proto",
@@ -77,8 +81,6 @@ cc_library(
         "//mediapipe/tasks/cc/vision/gesture_recognizer/proto:gesture_embedder_graph_options_cc_proto",
         "//mediapipe/tasks/cc/vision/gesture_recognizer/proto:hand_gesture_recognizer_graph_options_cc_proto",
         "//mediapipe/tasks/cc/vision/hand_landmarker:hand_landmarks_detector_graph",
-        "//mediapipe/tasks/cc/vision/hand_landmarker/proto:hand_landmarker_graph_options_cc_proto",
-        "//mediapipe/tasks/cc/vision/hand_landmarker/proto:hand_landmarks_detector_graph_options_cc_proto",
         "//mediapipe/tasks/metadata:metadata_schema_cc",
         "@com_google_absl//absl/status",
         "@com_google_absl//absl/status:statusor",
diff --git a/mediapipe/tasks/cc/vision/gesture_recognizer/calculators/combined_prediction_calculator.cc b/mediapipe/tasks/cc/vision/gesture_recognizer/calculators/combined_prediction_calculator.cc
index c7147ea6..cb95091d 100644
--- a/mediapipe/tasks/cc/vision/gesture_recognizer/calculators/combined_prediction_calculator.cc
+++ b/mediapipe/tasks/cc/vision/gesture_recognizer/calculators/combined_prediction_calculator.cc
@@ -153,13 +153,12 @@ class CombinedPredictionCalculator : public Node {
     // After loop, if have winning prediction return. Otherwise empty packet.
     std::unique_ptr<ClassificationList> first_winning_prediction = nullptr;
     auto collection = kClassificationListIn(cc);
-    for (int idx = 0; idx < collection.Count(); ++idx) {
-      const auto& packet = collection[idx];
-      if (packet.IsEmpty()) {
+    for (const auto& input : collection) {
+      if (input.IsEmpty() || input.Get().classification_size() == 0) {
         continue;
       }
       auto prediction = GetWinningPrediction(
-          packet.Get(), classwise_thresholds_, options_.background_label(),
+          input.Get(), classwise_thresholds_, options_.background_label(),
           options_.default_global_threshold());
       if (prediction->classification(0).label() !=
           options_.background_label()) {
diff --git a/mediapipe/tasks/cc/vision/gesture_recognizer/gesture_recognizer.cc b/mediapipe/tasks/cc/vision/gesture_recognizer/gesture_recognizer.cc
index d4ab16ac..fa1fc69c 100644
--- a/mediapipe/tasks/cc/vision/gesture_recognizer/gesture_recognizer.cc
+++ b/mediapipe/tasks/cc/vision/gesture_recognizer/gesture_recognizer.cc
@@ -146,6 +146,10 @@ ConvertGestureRecognizerGraphOptionsProto(GestureRecognizerOptions* options) {
         ->mutable_canned_gesture_classifier_graph_options()
         ->mutable_classifier_options()
         ->set_score_threshold(options->min_gesture_confidence);
+    hand_gesture_recognizer_graph_options
+        ->mutable_custom_gesture_classifier_graph_options()
+        ->mutable_classifier_options()
+        ->set_score_threshold(options->min_gesture_confidence);
   }
   return options_proto;
 }
diff --git a/mediapipe/tasks/cc/vision/gesture_recognizer/hand_gesture_recognizer_graph.cc b/mediapipe/tasks/cc/vision/gesture_recognizer/hand_gesture_recognizer_graph.cc
index 7b774695..6e83f9ee 100644
--- a/mediapipe/tasks/cc/vision/gesture_recognizer/hand_gesture_recognizer_graph.cc
+++ b/mediapipe/tasks/cc/vision/gesture_recognizer/hand_gesture_recognizer_graph.cc
@@ -30,14 +30,17 @@ limitations under the License.
 #include "mediapipe/tasks/cc/common.h"
 #include "mediapipe/tasks/cc/components/processors/classification_postprocessing_graph.h"
 #include "mediapipe/tasks/cc/components/processors/proto/classification_postprocessing_graph_options.pb.h"
+#include "mediapipe/tasks/cc/components/processors/proto/classifier_options.pb.h"
 #include "mediapipe/tasks/cc/core/model_asset_bundle_resources.h"
 #include "mediapipe/tasks/cc/core/model_resources.h"
 #include "mediapipe/tasks/cc/core/model_resources_cache.h"
 #include "mediapipe/tasks/cc/core/model_task_graph.h"
+#include "mediapipe/tasks/cc/core/proto/base_options.pb.h"
 #include "mediapipe/tasks/cc/core/proto/external_file.pb.h"
 #include "mediapipe/tasks/cc/core/proto/inference_subgraph.pb.h"
 #include "mediapipe/tasks/cc/core/utils.h"
 #include "mediapipe/tasks/cc/metadata/utils/zip_utils.h"
+#include "mediapipe/tasks/cc/vision/gesture_recognizer/calculators/combined_prediction_calculator.pb.h"
 #include "mediapipe/tasks/cc/vision/gesture_recognizer/calculators/landmarks_to_matrix_calculator.pb.h"
 #include "mediapipe/tasks/cc/vision/gesture_recognizer/proto/gesture_classifier_graph_options.pb.h"
 #include "mediapipe/tasks/cc/vision/gesture_recognizer/proto/gesture_embedder_graph_options.pb.h"
@@ -58,6 +61,7 @@ using ::mediapipe::api2::builder::Source;
 using ::mediapipe::tasks::components::processors::
     ConfigureTensorsToClassificationCalculator;
 using ::mediapipe::tasks::core::ModelAssetBundleResources;
+using ::mediapipe::tasks::core::proto::BaseOptions;
 using ::mediapipe::tasks::metadata::SetExternalFile;
 using ::mediapipe::tasks::vision::gesture_recognizer::proto::
     HandGestureRecognizerGraphOptions;
@@ -78,13 +82,20 @@ constexpr char kVectorTag[] = "VECTOR";
 constexpr char kIndexTag[] = "INDEX";
 constexpr char kIterableTag[] = "ITERABLE";
 constexpr char kBatchEndTag[] = "BATCH_END";
+constexpr char kPredictionTag[] = "PREDICTION";
+constexpr char kBackgroundLabel[] = "None";
 constexpr char kGestureEmbedderTFLiteName[] = "gesture_embedder.tflite";
 constexpr char kCannedGestureClassifierTFLiteName[] =
     "canned_gesture_classifier.tflite";
+constexpr char kCustomGestureClassifierTFLiteName[] =
+    "custom_gesture_classifier.tflite";
 
 struct SubTaskModelResources {
-  const core::ModelResources* gesture_embedder_model_resource;
-  const core::ModelResources* canned_gesture_classifier_model_resource;
+  const core::ModelResources* gesture_embedder_model_resource = nullptr;
+  const core::ModelResources* canned_gesture_classifier_model_resource =
+      nullptr;
+  const core::ModelResources* custom_gesture_classifier_model_resource =
+      nullptr;
 };
 
 Source<std::vector<Tensor>> ConvertMatrixToTensor(Source<Matrix> matrix,
@@ -94,41 +105,21 @@ Source<std::vector<Tensor>> ConvertMatrixToTensor(Source<Matrix> matrix,
   return node[Output<std::vector<Tensor>>{"TENSORS"}];
 }
 
-// Sets the base options in the sub tasks.
-absl::Status SetSubTaskBaseOptions(const ModelAssetBundleResources& resources,
-                                   HandGestureRecognizerGraphOptions* options,
-                                   bool is_copy) {
-  ASSIGN_OR_RETURN(const auto gesture_embedder_file,
-                   resources.GetModelFile(kGestureEmbedderTFLiteName));
-  auto* gesture_embedder_graph_options =
-      options->mutable_gesture_embedder_graph_options();
-  SetExternalFile(gesture_embedder_file,
-                  gesture_embedder_graph_options->mutable_base_options()
-                      ->mutable_model_asset(),
-                  is_copy);
-  gesture_embedder_graph_options->mutable_base_options()
-      ->mutable_acceleration()
-      ->CopyFrom(options->base_options().acceleration());
-  gesture_embedder_graph_options->mutable_base_options()->set_use_stream_mode(
-      options->base_options().use_stream_mode());
-
-  ASSIGN_OR_RETURN(const auto canned_gesture_classifier_file,
-                   resources.GetModelFile(kCannedGestureClassifierTFLiteName));
-  auto* canned_gesture_classifier_graph_options =
-      options->mutable_canned_gesture_classifier_graph_options();
-  SetExternalFile(
-      canned_gesture_classifier_file,
-      canned_gesture_classifier_graph_options->mutable_base_options()
-          ->mutable_model_asset(),
-      is_copy);
-  canned_gesture_classifier_graph_options->mutable_base_options()
-      ->mutable_acceleration()
-      ->CopyFrom(options->base_options().acceleration());
-  canned_gesture_classifier_graph_options->mutable_base_options()
-      ->set_use_stream_mode(options->base_options().use_stream_mode());
+absl::Status ConfigureCombinedPredictionCalculator(
+    CombinedPredictionCalculatorOptions* options) {
+  options->set_background_label(kBackgroundLabel);
   return absl::OkStatus();
 }
 
+void PopulateAccelerationAndUseStreamMode(
+    const BaseOptions& parent_base_options,
+    BaseOptions* sub_task_base_options) {
+  sub_task_base_options->mutable_acceleration()->CopyFrom(
+      parent_base_options.acceleration());
+  sub_task_base_options->set_use_stream_mode(
+      parent_base_options.use_stream_mode());
+}
+
 }  // namespace
 
 // A
@@ -212,6 +203,56 @@ class SingleHandGestureRecognizerGraph : public core::ModelTaskGraph {
   }
 
  private:
+  // Sets the base options in the sub tasks.
+  absl::Status SetSubTaskBaseOptions(const ModelAssetBundleResources& resources,
+                                     HandGestureRecognizerGraphOptions* options,
+                                     bool is_copy) {
+    ASSIGN_OR_RETURN(const auto gesture_embedder_file,
+                     resources.GetModelFile(kGestureEmbedderTFLiteName));
+    auto* gesture_embedder_graph_options =
+        options->mutable_gesture_embedder_graph_options();
+    SetExternalFile(gesture_embedder_file,
+                    gesture_embedder_graph_options->mutable_base_options()
+                        ->mutable_model_asset(),
+                    is_copy);
+    PopulateAccelerationAndUseStreamMode(
+        options->base_options(),
+        gesture_embedder_graph_options->mutable_base_options());
+
+    ASSIGN_OR_RETURN(
+        const auto canned_gesture_classifier_file,
+        resources.GetModelFile(kCannedGestureClassifierTFLiteName));
+    auto* canned_gesture_classifier_graph_options =
+        options->mutable_canned_gesture_classifier_graph_options();
+    SetExternalFile(
+        canned_gesture_classifier_file,
+        canned_gesture_classifier_graph_options->mutable_base_options()
+            ->mutable_model_asset(),
+        is_copy);
+    PopulateAccelerationAndUseStreamMode(
+        options->base_options(),
+        canned_gesture_classifier_graph_options->mutable_base_options());
+
+    const auto custom_gesture_classifier_file =
+        resources.GetModelFile(kCustomGestureClassifierTFLiteName);
+    if (custom_gesture_classifier_file.ok()) {
+      has_custom_gesture_classifier = true;
+      auto* custom_gesture_classifier_graph_options =
+          options->mutable_custom_gesture_classifier_graph_options();
+      SetExternalFile(
+          custom_gesture_classifier_file.value(),
+          custom_gesture_classifier_graph_options->mutable_base_options()
+              ->mutable_model_asset(),
+          is_copy);
+      PopulateAccelerationAndUseStreamMode(
+          options->base_options(),
+          custom_gesture_classifier_graph_options->mutable_base_options());
+    } else {
+      LOG(INFO) << "Custom gesture classifier is not defined.";
+    }
+    return absl::OkStatus();
+  }
+
   absl::StatusOr<SubTaskModelResources> CreateSubTaskModelResources(
       SubgraphContext* sc) {
     auto* options = sc->MutableOptions<HandGestureRecognizerGraphOptions>();
@@ -237,6 +278,19 @@ class SingleHandGestureRecognizerGraph : public core::ModelTaskGraph {
             std::make_unique<core::proto::ExternalFile>(
                 std::move(canned_gesture_classifier_model_asset)),
             "_canned_gesture_classifier"));
+    if (has_custom_gesture_classifier) {
+      auto& custom_gesture_classifier_model_asset =
+          *options->mutable_custom_gesture_classifier_graph_options()
+               ->mutable_base_options()
+               ->mutable_model_asset();
+      ASSIGN_OR_RETURN(
+          sub_task_model_resources.custom_gesture_classifier_model_resource,
+          CreateModelResources(
+              sc,
+              std::make_unique<core::proto::ExternalFile>(
+                  std::move(custom_gesture_classifier_model_asset)),
+              "_custom_gesture_classifier"));
+    }
     return sub_task_model_resources;
   }
 
@@ -302,7 +356,7 @@ class SingleHandGestureRecognizerGraph : public core::ModelTaskGraph {
     hand_world_landmarks_tensor >> concatenate_tensor_vector.In(2);
     auto concatenated_tensors = concatenate_tensor_vector.Out("");
 
-    // Inference for static hand gesture recognition.
+    // Inference for gesture embedder.
     auto& gesture_embedder_inference =
         AddInference(*sub_task_model_resources.gesture_embedder_model_resource,
                      graph_options.gesture_embedder_graph_options()
@@ -310,34 +364,64 @@ class SingleHandGestureRecognizerGraph : public core::ModelTaskGraph {
                          .acceleration(),
                      graph);
     concatenated_tensors >> gesture_embedder_inference.In(kTensorsTag);
-    auto embedding_tensors = gesture_embedder_inference.Out(kTensorsTag);
+    auto embedding_tensors =
+        gesture_embedder_inference.Out(kTensorsTag).Cast<Tensor>();
 
-    auto& canned_gesture_classifier_inference = AddInference(
-        *sub_task_model_resources.canned_gesture_classifier_model_resource,
-        graph_options.canned_gesture_classifier_graph_options()
-            .base_options()
-            .acceleration(),
-        graph);
-    embedding_tensors >> canned_gesture_classifier_inference.In(kTensorsTag);
-    auto inference_output_tensors =
-        canned_gesture_classifier_inference.Out(kTensorsTag);
+    auto& combine_predictions = graph.AddNode("CombinedPredictionCalculator");
+    MP_RETURN_IF_ERROR(ConfigureCombinedPredictionCalculator(
+        &combine_predictions
+             .GetOptions<CombinedPredictionCalculatorOptions>()));
 
+    int classifier_nums = 0;
+    // Inference for custom gesture classifier if it exists.
+    if (has_custom_gesture_classifier) {
+      ASSIGN_OR_RETURN(
+          auto gesture_clasification_list,
+          GetGestureClassificationList(
+              sub_task_model_resources.custom_gesture_classifier_model_resource,
+              graph_options.custom_gesture_classifier_graph_options(),
+              embedding_tensors, graph));
+      gesture_clasification_list >> combine_predictions.In(classifier_nums++);
+    }
+
+    // Inference for canned gesture classifier.
+    ASSIGN_OR_RETURN(
+        auto gesture_clasification_list,
+        GetGestureClassificationList(
+            sub_task_model_resources.canned_gesture_classifier_model_resource,
+            graph_options.canned_gesture_classifier_graph_options(),
+            embedding_tensors, graph));
+    gesture_clasification_list >> combine_predictions.In(classifier_nums++);
+
+    auto combined_classification_list =
+        combine_predictions.Out(kPredictionTag).Cast<ClassificationList>();
+
+    return combined_classification_list;
+  }
+
+  absl::StatusOr<Source<ClassificationList>> GetGestureClassificationList(
+      const core::ModelResources* model_resources,
+      const proto::GestureClassifierGraphOptions& options,
+      Source<Tensor>& embedding_tensors, Graph& graph) {
+    auto& custom_gesture_classifier_inference = AddInference(
+        *model_resources, options.base_options().acceleration(), graph);
+    embedding_tensors >> custom_gesture_classifier_inference.In(kTensorsTag);
+    auto custom_gesture_inference_out_tensors =
+        custom_gesture_classifier_inference.Out(kTensorsTag);
     auto& tensors_to_classification =
         graph.AddNode("TensorsToClassificationCalculator");
     MP_RETURN_IF_ERROR(ConfigureTensorsToClassificationCalculator(
-        graph_options.canned_gesture_classifier_graph_options()
-            .classifier_options(),
-        *sub_task_model_resources.canned_gesture_classifier_model_resource
-             ->GetMetadataExtractor(),
+        options.classifier_options(), *model_resources->GetMetadataExtractor(),
         0,
         &tensors_to_classification.GetOptions<
             mediapipe::TensorsToClassificationCalculatorOptions>()));
-    inference_output_tensors >> tensors_to_classification.In(kTensorsTag);
-    auto classification_list =
-        tensors_to_classification[Output<ClassificationList>(
-            "CLASSIFICATIONS")];
-    return classification_list;
+    custom_gesture_inference_out_tensors >>
+        tensors_to_classification.In(kTensorsTag);
+    return tensors_to_classification.Out("CLASSIFICATIONS")
+        .Cast<ClassificationList>();
   }
+
+  bool has_custom_gesture_classifier = false;
 };
 
 // clang-format off
diff --git a/mediapipe/tasks/java/com/google/mediapipe/tasks/vision/gesturerecognizer/GestureRecognitionResult.java b/mediapipe/tasks/java/com/google/mediapipe/tasks/vision/gesturerecognizer/GestureRecognitionResult.java
index fd764cb1..e9e1ebe8 100644
--- a/mediapipe/tasks/java/com/google/mediapipe/tasks/vision/gesturerecognizer/GestureRecognitionResult.java
+++ b/mediapipe/tasks/java/com/google/mediapipe/tasks/vision/gesturerecognizer/GestureRecognitionResult.java
@@ -31,6 +31,8 @@ import java.util.List;
 @AutoValue
 public abstract class GestureRecognitionResult implements TaskResult {
 
+  private static final int kGestureDefaultIndex = -1;
+
   /**
    * Creates a {@link GestureRecognitionResult} instance from the lists of landmarks, handedness,
    * and gestures protobuf messages.
@@ -97,7 +99,9 @@ public abstract class GestureRecognitionResult implements TaskResult {
         gestures.add(
             Category.create(
                 classification.getScore(),
-                classification.getIndex(),
+                // Gesture index is not used, because the final gesture result comes from multiple
+                // classifiers.
+                kGestureDefaultIndex,
                 classification.getLabel(),
                 classification.getDisplayName()));
       }
@@ -123,6 +127,10 @@ public abstract class GestureRecognitionResult implements TaskResult {
   /** Handedness of detected hands. */
   public abstract List<List<Category>> handednesses();
 
-  /** Recognized hand gestures of detected hands */
+  /**
+   * Recognized hand gestures of detected hands. Note that the index of the gesture is always -1,
+   * because the raw indices from multiple gesture classifiers cannot consolidate to a meaningful
+   * index.
+   */
   public abstract List<List<Category>> gestures();
 }
diff --git a/mediapipe/tasks/javatests/com/google/mediapipe/tasks/vision/gesturerecognizer/GestureRecognizerTest.java b/mediapipe/tasks/javatests/com/google/mediapipe/tasks/vision/gesturerecognizer/GestureRecognizerTest.java
index eca5d35c..2d4b3a50 100644
--- a/mediapipe/tasks/javatests/com/google/mediapipe/tasks/vision/gesturerecognizer/GestureRecognizerTest.java
+++ b/mediapipe/tasks/javatests/com/google/mediapipe/tasks/vision/gesturerecognizer/GestureRecognizerTest.java
@@ -46,19 +46,24 @@ import org.junit.runners.Suite.SuiteClasses;
 @SuiteClasses({GestureRecognizerTest.General.class, GestureRecognizerTest.RunningModeTest.class})
 public class GestureRecognizerTest {
   private static final String GESTURE_RECOGNIZER_BUNDLE_ASSET_FILE = "gesture_recognizer.task";
+  private static final String GESTURE_RECOGNIZER_WITH_CUSTOM_CLASSIFIER_BUNDLE_ASSET_FILE =
+      "gesture_recognizer_with_custom_classifier.task";
   private static final String TWO_HANDS_IMAGE = "right_hands.jpg";
   private static final String THUMB_UP_IMAGE = "thumb_up.jpg";
   private static final String POINTING_UP_ROTATED_IMAGE = "pointing_up_rotated.jpg";
   private static final String NO_HANDS_IMAGE = "cats_and_dogs.jpg";
+  private static final String FIST_IMAGE = "fist.jpg";
   private static final String THUMB_UP_LANDMARKS = "thumb_up_landmarks.pb";
+  private static final String FIST_LANDMARKS = "fist_landmarks.pb";
   private static final String TAG = "Gesture Recognizer Test";
   private static final String THUMB_UP_LABEL = "Thumb_Up";
-  private static final int THUMB_UP_INDEX = 5;
   private static final String POINTING_UP_LABEL = "Pointing_Up";
-  private static final int POINTING_UP_INDEX = 3;
+  private static final String FIST_LABEL = "Closed_Fist";
+  private static final String ROCK_LABEL = "Rock";
   private static final float LANDMARKS_ERROR_TOLERANCE = 0.03f;
   private static final int IMAGE_WIDTH = 382;
   private static final int IMAGE_HEIGHT = 406;
+  private static final int GESTURE_EXPECTED_INDEX = -1;
 
   @RunWith(AndroidJUnit4.class)
   public static final class General extends GestureRecognizerTest {
@@ -77,7 +82,7 @@ public class GestureRecognizerTest {
       GestureRecognitionResult actualResult =
           gestureRecognizer.recognize(getImageFromAsset(THUMB_UP_IMAGE));
       GestureRecognitionResult expectedResult =
-          getExpectedGestureRecognitionResult(THUMB_UP_LANDMARKS, THUMB_UP_LABEL, THUMB_UP_INDEX);
+          getExpectedGestureRecognitionResult(THUMB_UP_LANDMARKS, THUMB_UP_LABEL);
       assertActualResultApproximatelyEqualsToExpectedResult(actualResult, expectedResult);
     }
 
@@ -108,16 +113,14 @@ public class GestureRecognizerTest {
                   BaseOptions.builder()
                       .setModelAssetPath(GESTURE_RECOGNIZER_BUNDLE_ASSET_FILE)
                       .build())
-              // TODO update the confidence to be in range [0,1] after embedding model
-              // and scoring calculator is integrated.
-              .setMinGestureConfidence(2.0f)
+              .setMinGestureConfidence(0.5f)
               .build();
       GestureRecognizer gestureRecognizer =
           GestureRecognizer.createFromOptions(ApplicationProvider.getApplicationContext(), options);
       GestureRecognitionResult actualResult =
           gestureRecognizer.recognize(getImageFromAsset(THUMB_UP_IMAGE));
       GestureRecognitionResult expectedResult =
-          getExpectedGestureRecognitionResult(THUMB_UP_LANDMARKS, THUMB_UP_LABEL, THUMB_UP_INDEX);
+          getExpectedGestureRecognitionResult(THUMB_UP_LANDMARKS, THUMB_UP_LABEL);
       // Only contains one top scoring gesture.
       assertThat(actualResult.gestures().get(0)).hasSize(1);
       assertActualGestureEqualExpectedGesture(
@@ -159,10 +162,48 @@ public class GestureRecognizerTest {
           gestureRecognizer.recognize(
               getImageFromAsset(POINTING_UP_ROTATED_IMAGE), imageProcessingOptions);
       assertThat(actualResult.gestures()).hasSize(1);
-      assertThat(actualResult.gestures().get(0).get(0).index()).isEqualTo(POINTING_UP_INDEX);
       assertThat(actualResult.gestures().get(0).get(0).categoryName()).isEqualTo(POINTING_UP_LABEL);
     }
 
+    @Test
+    public void recognize_successWithCannedGestureFist() throws Exception {
+      GestureRecognizerOptions options =
+          GestureRecognizerOptions.builder()
+              .setBaseOptions(
+                  BaseOptions.builder()
+                      .setModelAssetPath(GESTURE_RECOGNIZER_BUNDLE_ASSET_FILE)
+                      .build())
+              .setNumHands(1)
+              .build();
+      GestureRecognizer gestureRecognizer =
+          GestureRecognizer.createFromOptions(ApplicationProvider.getApplicationContext(), options);
+      GestureRecognitionResult actualResult =
+          gestureRecognizer.recognize(getImageFromAsset(FIST_IMAGE));
+      GestureRecognitionResult expectedResult =
+          getExpectedGestureRecognitionResult(FIST_LANDMARKS, FIST_LABEL);
+      assertActualResultApproximatelyEqualsToExpectedResult(actualResult, expectedResult);
+    }
+
+    @Test
+    public void recognize_successWithCustomGestureRock() throws Exception {
+      GestureRecognizerOptions options =
+          GestureRecognizerOptions.builder()
+              .setBaseOptions(
+                  BaseOptions.builder()
+                      .setModelAssetPath(
+                          GESTURE_RECOGNIZER_WITH_CUSTOM_CLASSIFIER_BUNDLE_ASSET_FILE)
+                      .build())
+              .setNumHands(1)
+              .build();
+      GestureRecognizer gestureRecognizer =
+          GestureRecognizer.createFromOptions(ApplicationProvider.getApplicationContext(), options);
+      GestureRecognitionResult actualResult =
+          gestureRecognizer.recognize(getImageFromAsset(FIST_IMAGE));
+      GestureRecognitionResult expectedResult =
+          getExpectedGestureRecognitionResult(FIST_LANDMARKS, ROCK_LABEL);
+      assertActualResultApproximatelyEqualsToExpectedResult(actualResult, expectedResult);
+    }
+
     @Test
     public void recognize_failsWithRegionOfInterest() throws Exception {
       GestureRecognizerOptions options =
@@ -331,7 +372,7 @@ public class GestureRecognizerTest {
     GestureRecognitionResult actualResult =
         gestureRecognizer.recognize(getImageFromAsset(THUMB_UP_IMAGE));
     GestureRecognitionResult expectedResult =
-        getExpectedGestureRecognitionResult(THUMB_UP_LANDMARKS, THUMB_UP_LABEL, THUMB_UP_INDEX);
+        getExpectedGestureRecognitionResult(THUMB_UP_LANDMARKS, THUMB_UP_LABEL);
     assertActualResultApproximatelyEqualsToExpectedResult(actualResult, expectedResult);
   }
 
@@ -348,7 +389,7 @@ public class GestureRecognizerTest {
     GestureRecognizer gestureRecognizer =
         GestureRecognizer.createFromOptions(ApplicationProvider.getApplicationContext(), options);
     GestureRecognitionResult expectedResult =
-        getExpectedGestureRecognitionResult(THUMB_UP_LANDMARKS, THUMB_UP_LABEL, THUMB_UP_INDEX);
+        getExpectedGestureRecognitionResult(THUMB_UP_LANDMARKS, THUMB_UP_LABEL);
     for (int i = 0; i < 3; i++) {
       GestureRecognitionResult actualResult =
           gestureRecognizer.recognizeForVideo(
@@ -361,7 +402,7 @@ public class GestureRecognizerTest {
   public void recognize_failsWithOutOfOrderInputTimestamps() throws Exception {
     MPImage image = getImageFromAsset(THUMB_UP_IMAGE);
     GestureRecognitionResult expectedResult =
-        getExpectedGestureRecognitionResult(THUMB_UP_LANDMARKS, THUMB_UP_LABEL, THUMB_UP_INDEX);
+        getExpectedGestureRecognitionResult(THUMB_UP_LANDMARKS, THUMB_UP_LABEL);
     GestureRecognizerOptions options =
         GestureRecognizerOptions.builder()
             .setBaseOptions(
@@ -393,7 +434,7 @@ public class GestureRecognizerTest {
   public void recognize_successWithLiveSteamMode() throws Exception {
     MPImage image = getImageFromAsset(THUMB_UP_IMAGE);
     GestureRecognitionResult expectedResult =
-        getExpectedGestureRecognitionResult(THUMB_UP_LANDMARKS, THUMB_UP_LABEL, THUMB_UP_INDEX);
+        getExpectedGestureRecognitionResult(THUMB_UP_LANDMARKS, THUMB_UP_LABEL);
     GestureRecognizerOptions options =
         GestureRecognizerOptions.builder()
             .setBaseOptions(
@@ -423,7 +464,7 @@ public class GestureRecognizerTest {
   }
 
   private static GestureRecognitionResult getExpectedGestureRecognitionResult(
-      String filePath, String gestureLabel, int gestureIndex) throws Exception {
+      String filePath, String gestureLabel) throws Exception {
     AssetManager assetManager = ApplicationProvider.getApplicationContext().getAssets();
     InputStream istr = assetManager.open(filePath);
     LandmarksDetectionResult landmarksDetectionResultProto =
@@ -431,9 +472,7 @@ public class GestureRecognizerTest {
     ClassificationProto.ClassificationList gesturesProto =
         ClassificationProto.ClassificationList.newBuilder()
             .addClassification(
-                ClassificationProto.Classification.newBuilder()
-                    .setLabel(gestureLabel)
-                    .setIndex(gestureIndex))
+                ClassificationProto.Classification.newBuilder().setLabel(gestureLabel))
             .build();
     return GestureRecognitionResult.create(
         Arrays.asList(landmarksDetectionResultProto.getLandmarks()),
@@ -479,8 +518,8 @@ public class GestureRecognizerTest {
 
   private static void assertActualGestureEqualExpectedGesture(
       Category actualGesture, Category expectedGesture) {
-    assertThat(actualGesture.index()).isEqualTo(actualGesture.index());
-    assertThat(expectedGesture.categoryName()).isEqualTo(expectedGesture.categoryName());
+    assertThat(actualGesture.categoryName()).isEqualTo(expectedGesture.categoryName());
+    assertThat(actualGesture.index()).isEqualTo(GESTURE_EXPECTED_INDEX);
   }
 
   private static void assertImageSizeIsExpected(MPImage inputImage) {
diff --git a/mediapipe/tasks/python/test/vision/BUILD b/mediapipe/tasks/python/test/vision/BUILD
index 9b0fab6c..40afe22b 100644
--- a/mediapipe/tasks/python/test/vision/BUILD
+++ b/mediapipe/tasks/python/test/vision/BUILD
@@ -57,6 +57,22 @@ py_test(
     ],
 )
 
+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 = [
+        "//mediapipe/python:_framework_bindings",
+        "//mediapipe/tasks/python/core:base_options",
+        "//mediapipe/tasks/python/test:test_utils",
+        "//mediapipe/tasks/python/vision:image_segmenter",
+        "//mediapipe/tasks/python/vision/core:vision_task_running_mode",
+    ],
+)
+
 py_test(
     name = "gesture_recognizer_test",
     srcs = ["gesture_recognizer_test.py"],
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 00000000..b1fe4f75
--- /dev/null
+++ b/mediapipe/tasks/python/test/vision/image_segmenter_test.py
@@ -0,0 +1,353 @@
+# 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 typing import List
+from unittest import mock
+
+from absl.testing import absltest
+from absl.testing import parameterized
+import cv2
+import numpy as np
+
+from mediapipe.python._framework_bindings import image as image_module
+from mediapipe.python._framework_bindings import image_frame
+from mediapipe.tasks.python.core import base_options as base_options_module
+from mediapipe.tasks.python.test import test_utils
+from mediapipe.tasks.python.vision import image_segmenter
+from mediapipe.tasks.python.vision.core import vision_task_running_mode
+
+_BaseOptions = base_options_module.BaseOptions
+_Image = image_module.Image
+_ImageFormat = image_frame.ImageFormat
+_OutputType = image_segmenter.OutputType
+_Activation = image_segmenter.Activation
+_ImageSegmenter = image_segmenter.ImageSegmenter
+_ImageSegmenterOptions = image_segmenter.ImageSegmenterOptions
+_RUNNING_MODE = vision_task_running_mode.VisionTaskRunningMode
+
+_MODEL_FILE = 'deeplabv3.tflite'
+_IMAGE_FILE = 'segmentation_input_rotation0.jpg'
+_SEGMENTATION_FILE = 'segmentation_golden_rotation0.png'
+_MASK_MAGNIFICATION_FACTOR = 10
+_MASK_SIMILARITY_THRESHOLD = 0.98
+
+
+def _similar_to_uint8_mask(actual_mask, expected_mask):
+  actual_mask_pixels = actual_mask.numpy_view().flatten()
+  expected_mask_pixels = expected_mask.numpy_view().flatten()
+
+  consistent_pixels = 0
+  num_pixels = len(expected_mask_pixels)
+
+  for index in range(num_pixels):
+    consistent_pixels += (
+        actual_mask_pixels[index] *
+        _MASK_MAGNIFICATION_FACTOR == expected_mask_pixels[index])
+
+  return consistent_pixels / num_pixels >= _MASK_SIMILARITY_THRESHOLD
+
+
+class ModelFileType(enum.Enum):
+  FILE_CONTENT = 1
+  FILE_NAME = 2
+
+
+class ImageSegmenterTest(parameterized.TestCase):
+
+  def setUp(self):
+    super().setUp()
+    # Load the test input image.
+    self.test_image = _Image.create_from_file(
+        test_utils.get_test_data_path(_IMAGE_FILE))
+    # Loads ground truth segmentation file.
+    gt_segmentation_data = cv2.imread(
+        test_utils.get_test_data_path(_SEGMENTATION_FILE), cv2.IMREAD_GRAYSCALE)
+    self.test_seg_image = _Image(_ImageFormat.GRAY8, gt_segmentation_data)
+    self.model_path = test_utils.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(model_asset_path=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', 'file_pointer_meta' or 'file_descriptor_meta'."):
+      base_options = _BaseOptions(model_asset_path='')
+      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(model_asset_buffer=f.read())
+      options = _ImageSegmenterOptions(base_options=base_options)
+      segmenter = _ImageSegmenter.create_from_options(options)
+      self.assertIsInstance(segmenter, _ImageSegmenter)
+
+  @parameterized.parameters((ModelFileType.FILE_NAME,),
+                            (ModelFileType.FILE_CONTENT,))
+  def test_segment_succeeds_with_category_mask(self, model_file_type):
+    # Creates segmenter.
+    if model_file_type is ModelFileType.FILE_NAME:
+      base_options = _BaseOptions(model_asset_path=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(model_asset_buffer=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.
+    category_masks = segmenter.segment(self.test_image)
+    self.assertLen(category_masks, 1)
+    category_mask = category_masks[0]
+    result_pixels = category_mask.numpy_view().flatten()
+
+    # Check if data type of `category_mask` is correct.
+    self.assertEqual(result_pixels.dtype, np.uint8)
+
+    self.assertTrue(
+        _similar_to_uint8_mask(category_masks[0], self.test_seg_image),
+        f'Number of pixels in the candidate mask differing from that of the '
+        f'ground truth mask exceeds {_MASK_SIMILARITY_THRESHOLD}.')
+
+    # Closes the segmenter explicitly when the segmenter is not used in
+    # a context.
+    segmenter.close()
+
+  def test_segment_succeeds_with_confidence_mask(self):
+    # Creates segmenter.
+    base_options = _BaseOptions(model_asset_path=self.model_path)
+
+    # Run segmentation on the model in CATEGORY_MASK mode.
+    options = _ImageSegmenterOptions(
+        base_options=base_options, output_type=_OutputType.CATEGORY_MASK)
+    segmenter = _ImageSegmenter.create_from_options(options)
+    category_masks = segmenter.segment(self.test_image)
+    category_mask = category_masks[0].numpy_view()
+
+    # Run segmentation on the model in CONFIDENCE_MASK mode.
+    options = _ImageSegmenterOptions(
+        base_options=base_options,
+        output_type=_OutputType.CONFIDENCE_MASK,
+        activation=_Activation.SOFTMAX)
+    segmenter = _ImageSegmenter.create_from_options(options)
+    confidence_masks = segmenter.segment(self.test_image)
+
+    # Check if confidence mask shape is correct.
+    self.assertLen(
+        confidence_masks, 21,
+        'Number of confidence masks must match with number of categories.')
+
+    # Gather the confidence masks in a single array `confidence_mask_array`.
+    confidence_mask_array = np.array(
+        [confidence_mask.numpy_view() for confidence_mask in confidence_masks])
+
+    # Check if data type of `confidence_masks` are correct.
+    self.assertEqual(confidence_mask_array.dtype, np.float32)
+
+    # Compute the category mask from the created confidence mask.
+    calculated_category_mask = np.argmax(confidence_mask_array, axis=0)
+    self.assertListEqual(
+        calculated_category_mask.tolist(), category_mask.tolist(),
+        'Confidence mask does not match with the category mask.')
+
+    # Closes the segmenter explicitly when the segmenter is not used in
+    # a context.
+    segmenter.close()
+
+  @parameterized.parameters((ModelFileType.FILE_NAME),
+                            (ModelFileType.FILE_CONTENT))
+  def test_segment_in_context(self, model_file_type):
+    if model_file_type is ModelFileType.FILE_NAME:
+      base_options = _BaseOptions(model_asset_path=self.model_path)
+    elif model_file_type is ModelFileType.FILE_CONTENT:
+      with open(self.model_path, 'rb') as f:
+        model_contents = f.read()
+      base_options = _BaseOptions(model_asset_buffer=model_contents)
+    else:
+      # Should never happen
+      raise ValueError('model_file_type is invalid.')
+
+    options = _ImageSegmenterOptions(
+        base_options=base_options, output_type=_OutputType.CATEGORY_MASK)
+    with _ImageSegmenter.create_from_options(options) as segmenter:
+      # Performs image segmentation on the input.
+      category_masks = segmenter.segment(self.test_image)
+      self.assertLen(category_masks, 1)
+
+      self.assertTrue(
+          _similar_to_uint8_mask(category_masks[0], self.test_seg_image),
+          f'Number of pixels in the candidate mask differing from that of the '
+          f'ground truth mask exceeds {_MASK_SIMILARITY_THRESHOLD}.')
+
+  def test_missing_result_callback(self):
+    options = _ImageSegmenterOptions(
+        base_options=_BaseOptions(model_asset_path=self.model_path),
+        running_mode=_RUNNING_MODE.LIVE_STREAM)
+    with self.assertRaisesRegex(ValueError,
+                                r'result callback must be provided'):
+      with _ImageSegmenter.create_from_options(options) as unused_segmenter:
+        pass
+
+  @parameterized.parameters((_RUNNING_MODE.IMAGE), (_RUNNING_MODE.VIDEO))
+  def test_illegal_result_callback(self, running_mode):
+    options = _ImageSegmenterOptions(
+        base_options=_BaseOptions(model_asset_path=self.model_path),
+        running_mode=running_mode,
+        result_callback=mock.MagicMock())
+    with self.assertRaisesRegex(ValueError,
+                                r'result callback should not be provided'):
+      with _ImageSegmenter.create_from_options(options) as unused_segmenter:
+        pass
+
+  def test_calling_segment_for_video_in_image_mode(self):
+    options = _ImageSegmenterOptions(
+        base_options=_BaseOptions(model_asset_path=self.model_path),
+        running_mode=_RUNNING_MODE.IMAGE)
+    with _ImageSegmenter.create_from_options(options) as segmenter:
+      with self.assertRaisesRegex(ValueError,
+                                  r'not initialized with the video mode'):
+        segmenter.segment_for_video(self.test_image, 0)
+
+  def test_calling_segment_async_in_image_mode(self):
+    options = _ImageSegmenterOptions(
+        base_options=_BaseOptions(model_asset_path=self.model_path),
+        running_mode=_RUNNING_MODE.IMAGE)
+    with _ImageSegmenter.create_from_options(options) as segmenter:
+      with self.assertRaisesRegex(ValueError,
+                                  r'not initialized with the live stream mode'):
+        segmenter.segment_async(self.test_image, 0)
+
+  def test_calling_segment_in_video_mode(self):
+    options = _ImageSegmenterOptions(
+        base_options=_BaseOptions(model_asset_path=self.model_path),
+        running_mode=_RUNNING_MODE.VIDEO)
+    with _ImageSegmenter.create_from_options(options) as segmenter:
+      with self.assertRaisesRegex(ValueError,
+                                  r'not initialized with the image mode'):
+        segmenter.segment(self.test_image)
+
+  def test_calling_segment_async_in_video_mode(self):
+    options = _ImageSegmenterOptions(
+        base_options=_BaseOptions(model_asset_path=self.model_path),
+        running_mode=_RUNNING_MODE.VIDEO)
+    with _ImageSegmenter.create_from_options(options) as segmenter:
+      with self.assertRaisesRegex(ValueError,
+                                  r'not initialized with the live stream mode'):
+        segmenter.segment_async(self.test_image, 0)
+
+  def test_segment_for_video_with_out_of_order_timestamp(self):
+    options = _ImageSegmenterOptions(
+        base_options=_BaseOptions(model_asset_path=self.model_path),
+        running_mode=_RUNNING_MODE.VIDEO)
+    with _ImageSegmenter.create_from_options(options) as segmenter:
+      unused_result = segmenter.segment_for_video(self.test_image, 1)
+      with self.assertRaisesRegex(
+          ValueError, r'Input timestamp must be monotonically increasing'):
+        segmenter.segment_for_video(self.test_image, 0)
+
+  def test_segment_for_video(self):
+    options = _ImageSegmenterOptions(
+        base_options=_BaseOptions(model_asset_path=self.model_path),
+        output_type=_OutputType.CATEGORY_MASK,
+        running_mode=_RUNNING_MODE.VIDEO)
+    with _ImageSegmenter.create_from_options(options) as segmenter:
+      for timestamp in range(0, 300, 30):
+        category_masks = segmenter.segment_for_video(self.test_image, timestamp)
+        self.assertLen(category_masks, 1)
+        self.assertTrue(
+            _similar_to_uint8_mask(category_masks[0], self.test_seg_image),
+            f'Number of pixels in the candidate mask differing from that of the '
+            f'ground truth mask exceeds {_MASK_SIMILARITY_THRESHOLD}.')
+
+  def test_calling_segment_in_live_stream_mode(self):
+    options = _ImageSegmenterOptions(
+        base_options=_BaseOptions(model_asset_path=self.model_path),
+        running_mode=_RUNNING_MODE.LIVE_STREAM,
+        result_callback=mock.MagicMock())
+    with _ImageSegmenter.create_from_options(options) as segmenter:
+      with self.assertRaisesRegex(ValueError,
+                                  r'not initialized with the image mode'):
+        segmenter.segment(self.test_image)
+
+  def test_calling_segment_for_video_in_live_stream_mode(self):
+    options = _ImageSegmenterOptions(
+        base_options=_BaseOptions(model_asset_path=self.model_path),
+        running_mode=_RUNNING_MODE.LIVE_STREAM,
+        result_callback=mock.MagicMock())
+    with _ImageSegmenter.create_from_options(options) as segmenter:
+      with self.assertRaisesRegex(ValueError,
+                                  r'not initialized with the video mode'):
+        segmenter.segment_for_video(self.test_image, 0)
+
+  def test_segment_async_calls_with_illegal_timestamp(self):
+    options = _ImageSegmenterOptions(
+        base_options=_BaseOptions(model_asset_path=self.model_path),
+        running_mode=_RUNNING_MODE.LIVE_STREAM,
+        result_callback=mock.MagicMock())
+    with _ImageSegmenter.create_from_options(options) as segmenter:
+      segmenter.segment_async(self.test_image, 100)
+      with self.assertRaisesRegex(
+          ValueError, r'Input timestamp must be monotonically increasing'):
+        segmenter.segment_async(self.test_image, 0)
+
+  def test_segment_async_calls(self):
+    observed_timestamp_ms = -1
+
+    def check_result(result: List[image_module.Image], output_image: _Image,
+                     timestamp_ms: int):
+      # Get the output category mask.
+      category_mask = result[0]
+      self.assertEqual(output_image.width, self.test_image.width)
+      self.assertEqual(output_image.height, self.test_image.height)
+      self.assertEqual(output_image.width, self.test_seg_image.width)
+      self.assertEqual(output_image.height, self.test_seg_image.height)
+      self.assertTrue(
+          _similar_to_uint8_mask(category_mask, self.test_seg_image),
+          f'Number of pixels in the candidate mask differing from that of the '
+          f'ground truth mask exceeds {_MASK_SIMILARITY_THRESHOLD}.')
+      self.assertLess(observed_timestamp_ms, timestamp_ms)
+      self.observed_timestamp_ms = timestamp_ms
+
+    options = _ImageSegmenterOptions(
+        base_options=_BaseOptions(model_asset_path=self.model_path),
+        output_type=_OutputType.CATEGORY_MASK,
+        running_mode=_RUNNING_MODE.LIVE_STREAM,
+        result_callback=check_result)
+    with _ImageSegmenter.create_from_options(options) as segmenter:
+      for timestamp in range(0, 300, 30):
+        segmenter.segment_async(self.test_image, timestamp)
+
+
+if __name__ == '__main__':
+  absltest.main()
diff --git a/mediapipe/tasks/python/vision/BUILD b/mediapipe/tasks/python/vision/BUILD
index f0b3c9f5..87de5b98 100644
--- a/mediapipe/tasks/python/vision/BUILD
+++ b/mediapipe/tasks/python/vision/BUILD
@@ -59,6 +59,25 @@ py_library(
     ],
 )
 
+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/components/proto:segmenter_options_py_pb2",
+        "//mediapipe/tasks/cc/vision/image_segmenter/proto:image_segmenter_options_py_pb2",
+        "//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",
+    ],
+)
+
 py_library(
     name = "gesture_recognizer",
     srcs = [
diff --git a/mediapipe/tasks/python/vision/image_segmenter.py b/mediapipe/tasks/python/vision/image_segmenter.py
new file mode 100644
index 00000000..b7022b85
--- /dev/null
+++ b/mediapipe/tasks/python/vision/image_segmenter.py
@@ -0,0 +1,253 @@
+# 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
+import enum
+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
+from mediapipe.python._framework_bindings import task_runner
+from mediapipe.tasks.cc.components.proto import segmenter_options_pb2
+from mediapipe.tasks.cc.vision.image_segmenter.proto import image_segmenter_options_pb2
+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
+
+_BaseOptions = base_options_module.BaseOptions
+_SegmenterOptionsProto = segmenter_options_pb2.SegmenterOptions
+_ImageSegmenterOptionsProto = image_segmenter_options_pb2.ImageSegmenterOptions
+_RunningMode = vision_task_running_mode.VisionTaskRunningMode
+_TaskInfo = task_info_module.TaskInfo
+_TaskRunner = task_runner.TaskRunner
+
+_SEGMENTATION_OUT_STREAM_NAME = 'segmented_mask_out'
+_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'
+_MICRO_SECONDS_PER_MILLISECOND = 1000
+
+
+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 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 segmenting
+      objects on single image inputs. 2) The video mode for segmenting objects
+      on the decoded frames of a video. 3) The live stream mode for segmenting
+      objects on a live stream of input data, such as from camera.
+    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.
+    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[OutputType] = OutputType.CATEGORY_MASK
+  activation: Optional[Activation] = 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_proto = _SegmenterOptionsProto(
+        output_type=self.output_type.value, activation=self.activation.value)
+    return _ImageSegmenterOptionsProto(
+        base_options=base_options_proto,
+        segmenter_options=segmenter_options_proto)
+
+
+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(model_asset_path=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.Packet]):
+      if output_packets[_IMAGE_OUT_STREAM_NAME].is_empty():
+        return
+      segmentation_result = packet_getter.get_image_list(
+          output_packets[_SEGMENTATION_OUT_STREAM_NAME])
+      image = packet_getter.get_image(output_packets[_IMAGE_OUT_STREAM_NAME])
+      timestamp = output_packets[_SEGMENTATION_OUT_STREAM_NAME].timestamp
+      options.result_callback(segmentation_result, image,
+                              timestamp.value // _MICRO_SECONDS_PER_MILLISECOND)
+
+    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)
+
+  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:
+      If the output_type is CATEGORY_MASK, the returned vector of images is
+      per-category segmented image mask.
+      If the output_type is CONFIDENCE_MASK, the returned vector of images
+      contains only one confidence image mask. 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 image segmentation failed to run.
+    """
+    output_packets = self._process_image_data(
+        {_IMAGE_IN_STREAM_NAME: packet_creator.create_image(image)})
+    segmentation_result = packet_getter.get_image_list(
+        output_packets[_SEGMENTATION_OUT_STREAM_NAME])
+    return segmentation_result
+
+  def segment_for_video(self, image: image_module.Image,
+                        timestamp_ms: int) -> List[image_module.Image]:
+    """Performs segmentation on the provided video frames.
+
+    Only use this method when the ImageSegmenter is created with the video
+    running mode. It's required to provide the video frame's timestamp (in
+    milliseconds) along with the video frame. The input timestamps should be
+    monotonically increasing for adjacent calls of this method.
+
+    Args:
+      image: MediaPipe Image.
+      timestamp_ms: The timestamp of the input video frame in milliseconds.
+
+    Returns:
+      If the output_type is CATEGORY_MASK, the returned vector of images is
+      per-category segmented image mask.
+      If the output_type is CONFIDENCE_MASK, the returned vector of images
+      contains only one confidence image mask. 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 image segmentation failed to run.
+    """
+    output_packets = self._process_video_data({
+        _IMAGE_IN_STREAM_NAME:
+            packet_creator.create_image(image).at(
+                timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND)
+    })
+    segmentation_result = packet_getter.get_image_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.
+
+    Only use this method when the ImageSegmenter is created with the live stream
+    running mode. The input timestamps should be monotonically increasing for
+    adjacent calls of this method. 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` prvoides:
+      - 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 image
+        segmenter has already processed.
+    """
+    self._send_live_stream_data({
+        _IMAGE_IN_STREAM_NAME:
+            packet_creator.create_image(image).at(
+                timestamp_ms * _MICRO_SECONDS_PER_MILLISECOND)
+    })
diff --git a/mediapipe/tasks/testdata/vision/BUILD b/mediapipe/tasks/testdata/vision/BUILD
index 71be2881..0545c5cc 100644
--- a/mediapipe/tasks/testdata/vision/BUILD
+++ b/mediapipe/tasks/testdata/vision/BUILD
@@ -37,6 +37,7 @@ mediapipe_files(srcs = [
     "coco_ssd_mobilenet_v1_1.0_quant_2018_06_29.tflite",
     "coco_ssd_mobilenet_v1_1.0_quant_2018_06_29_with_dummy_score_calibration.tflite",
     "deeplabv3.tflite",
+    "fist.jpg",
     "hand_landmark_full.tflite",
     "hand_landmark_lite.tflite",
     "left_hands.jpg",
@@ -64,6 +65,7 @@ mediapipe_files(srcs = [
     "selfie_segm_144_256_3.tflite",
     "selfie_segm_144_256_3_expected_mask.jpg",
     "thumb_up.jpg",
+    "victory.jpg",
 ])
 
 exports_files(
@@ -91,6 +93,7 @@ filegroup(
         "cats_and_dogs.jpg",
         "cats_and_dogs_no_resizing.jpg",
         "cats_and_dogs_rotated.jpg",
+        "fist.jpg",
         "hand_landmark_full.tflite",
         "hand_landmark_lite.tflite",
         "left_hands.jpg",
@@ -107,6 +110,7 @@ filegroup(
         "selfie_segm_128_128_3_expected_mask.jpg",
         "selfie_segm_144_256_3_expected_mask.jpg",
         "thumb_up.jpg",
+        "victory.jpg",
     ],
     visibility = [
         "//mediapipe/python:__subpackages__",
@@ -149,6 +153,7 @@ filegroup(
         "expected_left_up_hand_rotated_landmarks.prototxt",
         "expected_right_down_hand_landmarks.prototxt",
         "expected_right_up_hand_landmarks.prototxt",
+        "fist_landmarks.pbtxt",
         "hand_detector_result_one_hand.pbtxt",
         "hand_detector_result_one_hand_rotated.pbtxt",
         "hand_detector_result_two_hands.pbtxt",
@@ -156,5 +161,6 @@ filegroup(
         "pointing_up_rotated_landmarks.pbtxt",
         "thumb_up_landmarks.pbtxt",
         "thumb_up_rotated_landmarks.pbtxt",
+        "victory_landmarks.pbtxt",
     ],
 )
diff --git a/mediapipe/tasks/testdata/vision/fist_landmarks.pbtxt b/mediapipe/tasks/testdata/vision/fist_landmarks.pbtxt
new file mode 100644
index 00000000..a24358c3
--- /dev/null
+++ b/mediapipe/tasks/testdata/vision/fist_landmarks.pbtxt
@@ -0,0 +1,223 @@
+classifications {
+  classification {
+    score: 1.0
+    label: "Left"
+    display_name: "Left"
+  }
+}
+
+landmarks {
+  landmark {
+    x: 0.47709703
+    y: 0.66129065
+    z: -3.3540672e-07
+  }
+  landmark {
+    x: 0.6125982
+    y: 0.5578249
+    z: -0.041392017
+  }
+  landmark {
+    x: 0.71123487
+    y: 0.4316616
+    z: -0.064544134
+  }
+  landmark {
+    x: 0.6836403
+    y: 0.3199585
+    z: -0.08752567
+  }
+  landmark {
+    x: 0.5593274
+    y: 0.3206453
+    z: -0.09880819
+  }
+  landmark {
+    x: 0.60828537
+    y: 0.3068749
+    z: -0.014799656
+  }
+  landmark {
+    x: 0.62940764
+    y: 0.21414441
+    z: -0.06007311
+  }
+  landmark {
+    x: 0.6244353
+    y: 0.32872596
+    z: -0.08326768
+  }
+  landmark {
+    x: 0.60784453
+    y: 0.3684796
+    z: -0.09658983
+  }
+  landmark {
+    x: 0.5156504
+    y: 0.32194698
+    z: -0.021699267
+  }
+  landmark {
+    x: 0.52931
+    y: 0.24767634
+    z: -0.062571
+  }
+  landmark {
+    x: 0.5484773
+    y: 0.3805329
+    z: -0.07028895
+  }
+  landmark {
+    x: 0.54428184
+    y: 0.3881125
+    z: -0.07458326
+  }
+  landmark {
+    x: 0.43159598
+    y: 0.34918433
+    z: -0.037482508
+  }
+  landmark {
+    x: 0.4486106
+    y: 0.27649382
+    z: -0.08174769
+  }
+  landmark {
+    x: 0.47723144
+    y: 0.3964985
+    z: -0.06496752
+  }
+  landmark {
+    x: 0.46794242
+    y: 0.4082967
+    z: -0.04897496
+  }
+  landmark {
+    x: 0.34826216
+    y: 0.37813392
+    z: -0.057438444
+  }
+  landmark {
+    x: 0.3861837
+    y: 0.32820183
+    z: -0.07282783
+  }
+  landmark {
+    x: 0.41143674
+    y: 0.39734486
+    z: -0.047633167
+  }
+  landmark {
+    x: 0.39401984
+    y: 0.41149133
+    z: -0.029640475
+  }
+}
+
+world_landmarks {
+  landmark {
+    x: -0.008604452
+    y: 0.08165767
+    z: 0.0061365655
+  }
+  landmark {
+    x: 0.027301773
+    y: 0.061905317
+    z: -0.00872007
+  }
+  landmark {
+    x: 0.049898714
+    y: 0.035359327
+    z: -0.016682662
+  }
+  landmark {
+    x: 0.050297678
+    y: 0.005200807
+    z: -0.028928496
+  }
+  landmark {
+    x: 0.015639625
+    y: -0.0063155442
+    z: -0.03174634
+  }
+  landmark {
+    x: 0.029161729
+    y: -0.0024596984
+    z: 0.0011553494
+  }
+  landmark {
+    x: 0.034491
+    y: -0.017581237
+    z: -0.020781275
+  }
+  landmark {
+    x: 0.034020264
+    y: -0.0059247985
+    z: -0.02573838
+  }
+  landmark {
+    x: 0.02867364
+    y: 0.011137734
+    z: -0.009430941
+  }
+  landmark {
+    x: 0.0015385814
+    y: -0.004778851
+    z: 0.0056454404
+  }
+  landmark {
+    x: 0.010490709
+    y: -0.019680617
+    z: -0.027034117
+  }
+  landmark {
+    x: 0.0132071925
+    y: 0.0071370844
+    z: -0.034802448
+  }
+  landmark {
+    x: 0.0139978565
+    y: 0.011672501
+    z: -0.0040006908
+  }
+  landmark {
+    x: -0.019919239
+    y: -0.0006897822
+    z: -0.0003317799
+  }
+  landmark {
+    x: -0.01088193
+    y: -0.008502296
+    z: -0.02873486
+  }
+  landmark {
+    x: -0.005327127
+    y: 0.012745364
+    z: -0.034153957
+  }
+  landmark {
+    x: -0.0027040644
+    y: 0.02167169
+    z: -0.011669062
+  }
+  landmark {
+    x: -0.038813893
+    y: 0.011925209
+    z: -0.0076287366
+  }
+  landmark {
+    x: -0.030842202
+    y: 0.0010964936
+    z: -0.022697516
+  }
+  landmark {
+    x: -0.01829514
+    y: 0.013929318
+    z: -0.032819964
+  }
+  landmark {
+    x: -0.024175374
+    y: 0.022456694
+    z: -0.02357186
+  }
+}
diff --git a/mediapipe/tasks/testdata/vision/gesture_recognizer_with_custom_classifier.task b/mediapipe/tasks/testdata/vision/gesture_recognizer_with_custom_classifier.task
new file mode 100644
index 00000000..3c1da7b3
Binary files /dev/null and b/mediapipe/tasks/testdata/vision/gesture_recognizer_with_custom_classifier.task differ
diff --git a/mediapipe/tasks/testdata/vision/hand_gesture_recognizer_with_custom_classifier.task b/mediapipe/tasks/testdata/vision/hand_gesture_recognizer_with_custom_classifier.task
new file mode 100644
index 00000000..1390ca88
Binary files /dev/null and b/mediapipe/tasks/testdata/vision/hand_gesture_recognizer_with_custom_classifier.task differ
diff --git a/mediapipe/tasks/testdata/vision/victory_landmarks.pbtxt b/mediapipe/tasks/testdata/vision/victory_landmarks.pbtxt
new file mode 100644
index 00000000..7a704ee3
--- /dev/null
+++ b/mediapipe/tasks/testdata/vision/victory_landmarks.pbtxt
@@ -0,0 +1,223 @@
+classifications {
+  classification {
+    score: 1.0
+    label: "Left"
+    display_name: "Left"
+  }
+}
+
+landmarks {
+  landmark {
+    x: 0.5164316
+    y: 0.804093
+    z: 8.7653416e-07
+  }
+  landmark {
+    x: 0.6063608
+    y: 0.7111354
+    z: -0.044089418
+  }
+  landmark {
+    x: 0.6280186
+    y: 0.588498
+    z: -0.062358405
+  }
+  landmark {
+    x: 0.5265348
+    y: 0.52083343
+    z: -0.08526791
+  }
+  landmark {
+    x: 0.4243384
+    y: 0.4993468
+    z: -0.1077741
+  }
+  landmark {
+    x: 0.5605667
+    y: 0.4489705
+    z: -0.016151091
+  }
+  landmark {
+    x: 0.5766643
+    y: 0.32260323
+    z: -0.049342215
+  }
+  landmark {
+    x: 0.5795845
+    y: 0.24180722
+    z: -0.07323826
+  }
+  landmark {
+    x: 0.5827511
+    y: 0.16940045
+    z: -0.09069163
+  }
+  landmark {
+    x: 0.4696163
+    y: 0.4599558
+    z: -0.032168437
+  }
+  landmark {
+    x: 0.44361597
+    y: 0.31689578
+    z: -0.075698614
+  }
+  landmark {
+    x: 0.42695498
+    y: 0.22273324
+    z: -0.10819675
+  }
+  landmark {
+    x: 0.40697217
+    y: 0.14279765
+    z: -0.12666894
+  }
+  landmark {
+    x: 0.39543492
+    y: 0.50612336
+    z: -0.055138163
+  }
+  landmark {
+    x: 0.3618012
+    y: 0.4388296
+    z: -0.1298119
+  }
+  landmark {
+    x: 0.4154368
+    y: 0.52674913
+    z: -0.1463017
+  }
+  landmark {
+    x: 0.44916254
+    y: 0.59442246
+    z: -0.13470782
+  }
+  landmark {
+    x: 0.33178204
+    y: 0.5731769
+    z: -0.08103096
+  }
+  landmark {
+    x: 0.3092102
+    y: 0.5040002
+    z: -0.13258384
+  }
+  landmark {
+    x: 0.35576707
+    y: 0.5576498
+    z: -0.12714732
+  }
+  landmark {
+    x: 0.393444
+    y: 0.6118667
+    z: -0.11102459
+  }
+}
+
+world_landmarks {
+  landmark {
+    x: 0.01299962
+    y: 0.09162361
+    z: 0.011185312
+  }
+  landmark {
+    x: 0.03726317
+    y: 0.0638103
+    z: -0.010005756
+  }
+  landmark {
+    x: 0.03975261
+    y: 0.03712649
+    z: -0.02906275
+  }
+  landmark {
+    x: 0.018798776
+    y: 0.012429599
+    z: -0.048737116
+  }
+  landmark {
+    x: -0.0128555335
+    y: 0.001022811
+    z: -0.044505004
+  }
+  landmark {
+    x: 0.025658218
+    y: -0.008031519
+    z: -0.0058278795
+  }
+  landmark {
+    x: 0.028017294
+    y: -0.038120236
+    z: -0.010376478
+  }
+  landmark {
+    x: 0.030067094
+    y: -0.059907563
+    z: -0.014568218
+  }
+  landmark {
+    x: 0.027284538
+    y: -0.07803874
+    z: -0.032692235
+  }
+  landmark {
+    x: 0.0013260426
+    y: -0.005039873
+    z: 0.005567288
+  }
+  landmark {
+    x: -0.002380834
+    y: -0.044605374
+    z: -0.0038231965
+  }
+  landmark {
+    x: -0.009240147
+    y: -0.066279344
+    z: -0.02161214
+  }
+  landmark {
+    x: -0.0092535615
+    y: -0.08933755
+    z: -0.037401434
+  }
+  landmark {
+    x: -0.01751284
+    y: 0.0037118336
+    z: 0.0047480655
+  }
+  landmark {
+    x: -0.02195602
+    y: -0.010006189
+    z: -0.02371484
+  }
+  landmark {
+    x: -0.012851426
+    y: 0.008346066
+    z: -0.037721373
+  }
+  landmark {
+    x: -0.00018795021
+    y: 0.026816685
+    z: -0.03732748
+  }
+  landmark {
+    x: -0.034864448
+    y: 0.022316
+    z: -0.0002774651
+  }
+  landmark {
+    x: -0.035896845
+    y: 0.01066218
+    z: -0.017325373
+  }
+  landmark {
+    x: -0.02358637
+    y: 0.018667895
+    z: -0.028403495
+  }
+  landmark {
+    x: -0.013704676
+    y: 0.033456434
+    z: -0.02595728
+  }
+}
diff --git a/third_party/external_files.bzl b/third_party/external_files.bzl
index 4b7309ee..4dcbc3bd 100644
--- a/third_party/external_files.bzl
+++ b/third_party/external_files.bzl
@@ -244,6 +244,18 @@ def external_files():
         urls = ["https://storage.googleapis.com/mediapipe-assets/feature_tensor_meta.json?generation=1665422818797346"],
     )
 
+    http_file(
+        name = "com_google_mediapipe_fist_jpg",
+        sha256 = "43fa1cabf3f90d574accc9a56986e2ee48638ce59fc65af1846487f73bb2ef24",
+        urls = ["https://storage.googleapis.com/mediapipe-assets/fist.jpg?generation=1666999359066679"],
+    )
+
+    http_file(
+        name = "com_google_mediapipe_fist_landmarks_pbtxt",
+        sha256 = "76d6489e6163211ce5e9080e51983165bb9b24ff50146cc7487bd629f011c598",
+        urls = ["https://storage.googleapis.com/mediapipe-assets/fist_landmarks.pbtxt?generation=1666999360561864"],
+    )
+
     http_file(
         name = "com_google_mediapipe_general_meta_json",
         sha256 = "b95363e4bae89b9c2af484498312aaad4efc7ff57c7eadcc4e5e7adca641445f",
@@ -838,6 +850,18 @@ def external_files():
         urls = ["https://storage.googleapis.com/mediapipe-assets/universal_sentence_encoder_qa_with_metadata.tflite?generation=1665445919252005"],
     )
 
+    http_file(
+        name = "com_google_mediapipe_victory_jpg",
+        sha256 = "84cb8853e3df614e0cb5c93a25e3e2f38ea5e4f92fd428ee7d867ed3479d5764",
+        urls = ["https://storage.googleapis.com/mediapipe-assets/victory.jpg?generation=1666999364225126"],
+    )
+
+    http_file(
+        name = "com_google_mediapipe_victory_landmarks_pbtxt",
+        sha256 = "b25ab4f222674489f543afb6454396ecbc1437a7ae6213dbf0553029ae939ab0",
+        urls = ["https://storage.googleapis.com/mediapipe-assets/victory_landmarks.pbtxt?generation=1666999366036622"],
+    )
+
     http_file(
         name = "com_google_mediapipe_vocab_for_regex_tokenizer_txt",
         sha256 = "b1134b10927a53ce4224bbc30ccf075c9969c94ebf40c368966d1dcf445ca923",