diff --git a/test/test_data_pipeline.py b/test/test_data_pipeline.py
index 1368fdec..bf414457 100644
--- a/test/test_data_pipeline.py
+++ b/test/test_data_pipeline.py
@@ -2,17 +2,20 @@
 from pathlib import Path
 import unittest
 
-import torch
+import torch.utils.data
+from torch import Tensor
 
+from yolort.data import DetectionDataModule
 from yolort.data.coco import CocoDetection
 from yolort.data.transforms import collate_fn, default_train_transforms
 from yolort.utils import prepare_coco128
 
+from .dataset_utils import DummyCOCODetectionDataset
+
 from typing import Dict
 
 
 class DataPipelineTester(unittest.TestCase):
-
     def test_vanilla_dataloader(self):
         # Acquire the images and labels from the coco128 dataset
         data_path = Path('data-bin')
@@ -27,19 +30,44 @@ def test_vanilla_dataloader(self):
         dataset = CocoDetection(image_root, annotation_file, default_train_transforms())
         # Test the datasets
         image, target = next(iter(dataset))
-        self.assertIsInstance(image, torch.Tensor)
+        self.assertIsInstance(image, Tensor)
         self.assertIsInstance(target, Dict)
 
         batch_size = 4
         sampler = torch.utils.data.RandomSampler(dataset)
         batch_sampler = torch.utils.data.BatchSampler(sampler, batch_size, drop_last=True)
-        loader = torch.utils.data.DataLoader(
+        data_loader = torch.utils.data.DataLoader(
             dataset, batch_sampler=batch_sampler, collate_fn=collate_fn, num_workers=0)
         # Test the dataloader
-        images, targets = next(iter(loader))
+        images, targets = next(iter(data_loader))
+
+        self.assertEqual(len(images), batch_size)
+        self.assertIsInstance(images[0], Tensor)
+        self.assertEqual(len(images[0]), 3)
+        self.assertEqual(len(targets), batch_size)
+        self.assertIsInstance(targets[0], Dict)
+        self.assertIsInstance(targets[0]["image_id"], Tensor)
+        self.assertIsInstance(targets[0]["boxes"], Tensor)
+        self.assertIsInstance(targets[0]["labels"], Tensor)
+        self.assertIsInstance(targets[0]["orig_size"], Tensor)
+
+    def test_detection_data_module(self):
+        # Setup the DataModule
+        batch_size = 4
+        train_dataset = DummyCOCODetectionDataset(num_samples=128)
+        data_module = DetectionDataModule(train_dataset, batch_size=batch_size)
+        self.assertEqual(data_module.batch_size, batch_size)
 
+        data_loader = data_module.train_dataloader(batch_size=batch_size)
+        images, targets = next(iter(data_loader))
         self.assertEqual(len(images), batch_size)
+        self.assertIsInstance(images[0], Tensor)
+        self.assertEqual(len(images[0]), 3)
         self.assertEqual(len(targets), batch_size)
+        self.assertIsInstance(targets[0], Dict)
+        self.assertIsInstance(targets[0]["image_id"], Tensor)
+        self.assertIsInstance(targets[0]["boxes"], Tensor)
+        self.assertIsInstance(targets[0]["labels"], Tensor)
 
     def test_prepare_coco128(self):
         data_path = Path('data-bin')