diff --git a/official/vision/beta/configs/video_classification.py b/official/vision/beta/configs/video_classification.py
index e097788106228bb2e4442c25451037c5e7ebb012..496c51ef962b4313394d2c7da188ff33bba6281f 100644
--- a/official/vision/beta/configs/video_classification.py
+++ b/official/vision/beta/configs/video_classification.py
@@ -50,6 +50,17 @@ class DataConfig(cfg.DataConfig):
   min_image_size: int = 256
 
 
+def kinetics400(is_training):
+  """Generated Kinectics 400 dataset configs."""
+  return DataConfig(
+      name='kinetics400',
+      num_classes=400,
+      is_training=is_training,
+      split='train' if is_training else 'valid',
+      num_examples=215570 if is_training else 17706,
+      feature_shape=(64, 224, 224, 3) if is_training else (250, 224, 224, 3))
+
+
 def kinetics600(is_training):
   """Generated Kinectics 600 dataset configs."""
   return DataConfig(
@@ -153,9 +164,35 @@ def video_classification() -> cfg.ExperimentConfig:
       ])
 
 
+@exp_factory.register_config_factory('video_classification_kinetics400')
+def video_classification_kinetics400() -> cfg.ExperimentConfig:
+  """Video classification on Kinectics 400 with resnet."""
+  train_dataset = kinetics400(is_training=True)
+  validation_dataset = kinetics400(is_training=False)
+  task = VideoClassificationTask(
+      model=VideoClassificationModel(
+          backbone=backbones_3d.Backbone3D(
+              type='resnet_3d', resnet_3d=backbones_3d.ResNet3D50()),
+          norm_activation=common.NormActivation(
+              norm_momentum=0.9, norm_epsilon=1e-5)),
+      losses=Losses(l2_weight_decay=1e-4),
+      train_data=train_dataset,
+      validation_data=validation_dataset)
+  config = cfg.ExperimentConfig(
+      runtime=cfg.RuntimeConfig(mixed_precision_dtype='bfloat16'),
+      task=task,
+      restrictions=[
+          'task.train_data.is_training != None',
+          'task.validation_data.is_training != None',
+          'task.train_data.num_classes == task.validation_data.num_classes',
+      ])
+  add_trainer(config, train_batch_size=1024, eval_batch_size=64)
+  return config
+
+
 @exp_factory.register_config_factory('video_classification_kinetics600')
 def video_classification_kinetics600() -> cfg.ExperimentConfig:
-  """Video classification on Videonet with resnet."""
+  """Video classification on Kinectics 600 with resnet."""
   train_dataset = kinetics600(is_training=True)
   validation_dataset = kinetics600(is_training=False)
   task = VideoClassificationTask(
@@ -176,5 +213,4 @@ def video_classification_kinetics600() -> cfg.ExperimentConfig:
           'task.train_data.num_classes == task.validation_data.num_classes',
       ])
   add_trainer(config, train_batch_size=1024, eval_batch_size=64)
-
   return config