diff --git a/test/test_datasets.py b/test/test_datasets.py
index 60bc7613e53..407a6bfb338 100644
--- a/test/test_datasets.py
+++ b/test/test_datasets.py
@@ -1032,5 +1032,65 @@ def test_not_found_or_corrupted(self):
         self.skipTest("Dataset currently does not handle the case of no found videos.")
 
 
+class HMDB51TestCase(datasets_utils.VideoDatasetTestCase):
+    DATASET_CLASS = datasets.HMDB51
+
+    CONFIGS = datasets_utils.combinations_grid(fold=(1, 2, 3), train=(True, False))
+
+    _VIDEO_FOLDER = "videos"
+    _SPLITS_FOLDER = "splits"
+    _CLASSES = ("brush_hair", "wave")
+
+    def dataset_args(self, tmpdir, config):
+        tmpdir = pathlib.Path(tmpdir)
+        root = tmpdir / self._VIDEO_FOLDER
+        annotation_path = tmpdir / self._SPLITS_FOLDER
+        return root, annotation_path
+
+    def inject_fake_data(self, tmpdir, config):
+        tmpdir = pathlib.Path(tmpdir)
+
+        video_folder = tmpdir / self._VIDEO_FOLDER
+        os.makedirs(video_folder)
+        video_files = self._create_videos(video_folder)
+
+        splits_folder = tmpdir / self._SPLITS_FOLDER
+        os.makedirs(splits_folder)
+        num_examples = self._create_split_files(splits_folder, video_files, config["fold"], config["train"])
+
+        return num_examples
+
+    def _create_videos(self, root, num_examples_per_class=3):
+        def file_name_fn(cls, idx, clips_per_group=2):
+            return f"{cls}_{(idx // clips_per_group) + 1:d}_{(idx % clips_per_group) + 1:d}.avi"
+
+        return [
+            (
+                cls,
+                datasets_utils.create_video_folder(
+                    root,
+                    cls,
+                    lambda idx: file_name_fn(cls, idx),
+                    num_examples_per_class,
+                ),
+            )
+            for cls in self._CLASSES
+        ]
+
+    def _create_split_files(self, root, video_files, fold, train):
+        num_videos = num_train_videos = 0
+
+        for cls, videos in video_files:
+            num_videos += len(videos)
+
+            train_videos = set(random.sample(videos, random.randrange(1, len(videos) - 1)))
+            num_train_videos += len(train_videos)
+
+            with open(pathlib.Path(root) / f"{cls}_test_split{fold}.txt", "w") as fh:
+                fh.writelines(f"{file.name} {1 if file in train_videos else 2}\n" for file in videos)
+
+        return num_train_videos if train else (num_videos - num_train_videos)
+
+
 if __name__ == "__main__":
     unittest.main()