diff --git a/flash/tabular/classification/input.py b/flash/tabular/classification/input.py
index 4b7b57b575..b66303d428 100644
--- a/flash/tabular/classification/input.py
+++ b/flash/tabular/classification/input.py
@@ -67,7 +67,7 @@ def load_data(
             )
 
 
-class TabularClassificationDictInput(TabularDataFrameInput):
+class TabularClassificationDictInput(TabularClassificationDataFrameInput):
     def load_data(
         self,
         data: Dict[str, Union[Any, List[Any]]],
diff --git a/tests/tabular/classification/test_data.py b/tests/tabular/classification/test_data.py
index d4a9c9a164..c235f1a9dc 100644
--- a/tests/tabular/classification/test_data.py
+++ b/tests/tabular/classification/test_data.py
@@ -26,23 +26,22 @@
     from flash.tabular import TabularClassificationData
     from flash.tabular.classification.utils import _categorize, _compute_normalization, _generate_codes, _normalize
 
-    TEST_DF_1 = pd.DataFrame(
-        data={
-            "category": ["a", "b", "c", "a", None, "c"],
-            "scalar_a": [0.0, 1.0, 2.0, 3.0, None, 5.0],
-            "scalar_b": [5.0, 4.0, 3.0, 2.0, None, 1.0],
-            "label": [0, 1, 0, 1, 0, 1],
-        }
-    )
+    TEST_DICT_1 = {
+        "category": ["a", "b", "c", "a", None, "c"],
+        "scalar_a": [0.0, 1.0, 2.0, 3.0, None, 5.0],
+        "scalar_b": [5.0, 4.0, 3.0, 2.0, None, 1.0],
+        "label": [0, 1, 0, 1, 0, 1],
+    }
 
-    TEST_DF_2 = pd.DataFrame(
-        data={
-            "category": ["d", "e", "f"],
-            "scalar_a": [0.0, 1.0, 2.0],
-            "scalar_b": [0.0, 4.0, 2.0],
-            "label": [0, 1, 0],
-        }
-    )
+    TEST_DICT_2 = {
+        "category": ["d", "e", "f"],
+        "scalar_a": [0.0, 1.0, 2.0],
+        "scalar_b": [0.0, 4.0, 2.0],
+        "label": [0, 1, 0],
+    }
+
+    TEST_DF_1 = pd.DataFrame(data=TEST_DICT_1)
+    TEST_DF_2 = pd.DataFrame(data=TEST_DICT_2)
 
 
 @pytest.mark.skipif(not _TABULAR_TESTING, reason="tabular dependencies are required")
@@ -164,6 +163,27 @@ def test_from_csv(tmpdir):
         assert target.shape == (1,)
 
 
+@pytest.mark.skipif(not _TABULAR_TESTING, reason="tabular dependencies are required")
+def test_from_dict():
+    dm = TabularClassificationData.from_dict(
+        categorical_fields=["category"],
+        numerical_fields=["scalar_a", "scalar_b"],
+        target_fields="label",
+        train_data=TEST_DICT_1,
+        val_data=TEST_DICT_2,
+        test_data=TEST_DICT_2,
+        num_workers=0,
+        batch_size=1,
+    )
+    for dl in [dm.train_dataloader(), dm.val_dataloader(), dm.test_dataloader()]:
+        data = next(iter(dl))
+        (cat, num) = data[DataKeys.INPUT]
+        target = data[DataKeys.TARGET]
+        assert cat.shape == (1, 1)
+        assert num.shape == (1, 2)
+        assert target.shape == (1,)
+
+
 @pytest.mark.skipif(not _TABULAR_TESTING, reason="tabular dependencies are required")
 def test_empty_inputs():
     train_data_frame = TEST_DF_1.copy()