diff --git a/finetuner/__init__.py b/finetuner/__init__.py
index e0d0773bd..922726175 100644
--- a/finetuner/__init__.py
+++ b/finetuner/__init__.py
@@ -10,7 +10,7 @@
 
 if TYPE_CHECKING:
     from .helper import AnyDNN, DocumentArrayLike
-    from .tuner.summary import SummaryCollection
+    from .tuner.summary import Summary
 
 
 # fit interface generated from Tuner
@@ -26,7 +26,7 @@ def fit(
     optimizer: str = 'adam',
     optimizer_kwargs: Optional[Dict] = None,
     device: str = 'cpu',
-) -> 'SummaryCollection':
+) -> 'Summary':
     ...
 
 
@@ -49,7 +49,7 @@ def fit(
     output_dim: Optional[int] = None,
     freeze: bool = False,
     device: str = 'cpu',
-) -> 'SummaryCollection':
+) -> 'Summary':
     ...
 
 
@@ -97,7 +97,7 @@ def fit(
 
 def fit(
     model: 'AnyDNN', train_data: 'DocumentArrayLike', *args, **kwargs
-) -> Optional['SummaryCollection']:
+) -> Optional['Summary']:
     if kwargs.get('to_embedding_model', False):
         from .tailor import to_embedding_model
 
diff --git a/finetuner/tuner/__init__.py b/finetuner/tuner/__init__.py
index 06752ff82..4be3f647d 100644
--- a/finetuner/tuner/__init__.py
+++ b/finetuner/tuner/__init__.py
@@ -4,7 +4,7 @@
 
 if TYPE_CHECKING:
     from .base import BaseTuner
-    from .summary import SummaryCollection
+    from .summary import Summary
 
 
 def _get_tuner_class(dnn_model: AnyDNN) -> Type['BaseTuner']:
@@ -36,7 +36,7 @@ def fit(
     optimizer_kwargs: Optional[Dict] = None,
     device: str = 'cpu',
     **kwargs,
-) -> 'SummaryCollection':
+) -> 'Summary':
     """Finetune the model on the training data.
 
     :param embed_model: an embedding model
diff --git a/finetuner/tuner/base.py b/finetuner/tuner/base.py
index e34a98fdb..3bd5d24fb 100644
--- a/finetuner/tuner/base.py
+++ b/finetuner/tuner/base.py
@@ -9,7 +9,7 @@
 )
 
 from ..helper import AnyDNN, AnyDataLoader, AnyOptimizer, DocumentArrayLike
-from .summary import SummaryCollection
+from .summary import Summary
 
 
 class BaseLoss:
@@ -101,7 +101,7 @@ def fit(
         batch_size: int = 256,
         *args,
         **kwargs,
-    ) -> SummaryCollection:
+    ) -> Summary:
         """Fit the :py:attr:`.embed_model` on labeled data.
 
         Note that fitting changes the weights in :py:attr:`.embed_model` in-place. This allows one to consecutively
diff --git a/finetuner/tuner/keras/__init__.py b/finetuner/tuner/keras/__init__.py
index 3f388f3a2..6a9e4ad68 100644
--- a/finetuner/tuner/keras/__init__.py
+++ b/finetuner/tuner/keras/__init__.py
@@ -8,7 +8,7 @@
 from . import losses, datasets
 from ..base import BaseTuner, BaseLoss
 from ..dataset.helper import get_dataset
-from ..summary import ScalarSummary, SummaryCollection
+from ..summary import ScalarSequence, Summary
 from ...helper import DocumentArrayLike, AnyDataLoader
 
 
@@ -64,10 +64,10 @@ def _get_optimizer(
 
     def _train(
         self, data: AnyDataLoader, optimizer: Optimizer, description: str
-    ) -> ScalarSummary:
+    ) -> ScalarSequence:
         """Train the model on given labeled data"""
 
-        _summary = ScalarSummary('Train Loss')
+        _summary = ScalarSequence('Train Loss')
         with ProgressBar(
             description,
             message_on_done=_summary.__str__,
@@ -96,11 +96,11 @@ def _eval(
         self,
         data: AnyDataLoader,
         description: str = 'Evaluating',
-        train_loss: Optional[ScalarSummary] = None,
-    ) -> ScalarSummary:
+        train_loss: Optional[ScalarSequence] = None,
+    ) -> ScalarSequence:
         """Evaluate the model on given labeled data"""
 
-        _summary = ScalarSummary('Eval Loss')
+        _summary = ScalarSequence('Eval Loss')
 
         with ProgressBar(
             description,
@@ -130,7 +130,7 @@ def fit(
         optimizer_kwargs: Optional[Dict] = None,
         device: str = 'cpu',
         **kwargs,
-    ) -> SummaryCollection:
+    ) -> Summary:
         """Finetune the model on the training data.
 
         :param train_data: Data on which to train the model
@@ -171,8 +171,8 @@ def fit(
 
         _optimizer = self._get_optimizer(optimizer, optimizer_kwargs, learning_rate)
 
-        m_train_loss = ScalarSummary('train')
-        m_eval_loss = ScalarSummary('eval')
+        m_train_loss = ScalarSequence('train')
+        m_eval_loss = ScalarSequence('eval')
 
         with get_device(device):
             for epoch in range(epochs):
@@ -187,7 +187,7 @@ def fit(
                     le = self._eval(_eval_data, train_loss=m_train_loss)
                     m_eval_loss += le
 
-        return SummaryCollection(m_train_loss, m_eval_loss)
+        return Summary(m_train_loss, m_eval_loss)
 
     def save(self, *args, **kwargs):
         """Save the embedding model.
diff --git a/finetuner/tuner/paddle/__init__.py b/finetuner/tuner/paddle/__init__.py
index f999c7531..47d99eb2f 100644
--- a/finetuner/tuner/paddle/__init__.py
+++ b/finetuner/tuner/paddle/__init__.py
@@ -8,7 +8,7 @@
 from . import losses, datasets
 from ..base import BaseTuner, BaseLoss
 from ..dataset.helper import get_dataset
-from ..summary import ScalarSummary, SummaryCollection
+from ..summary import ScalarSequence, Summary
 from ...helper import DocumentArrayLike, AnyDataLoader
 
 
@@ -63,13 +63,13 @@ def _eval(
         self,
         data: AnyDataLoader,
         description: str = 'Evaluating',
-        train_loss: Optional[ScalarSummary] = None,
-    ) -> ScalarSummary:
+        train_loss: Optional[ScalarSequence] = None,
+    ) -> ScalarSequence:
         """Evaluate the model on given labeled data"""
 
         self._embed_model.eval()
 
-        _summary = ScalarSummary('Eval Loss')
+        _summary = ScalarSequence('Eval Loss')
 
         with ProgressBar(
             description,
@@ -90,12 +90,12 @@ def _eval(
 
     def _train(
         self, data: AnyDataLoader, optimizer: Optimizer, description: str
-    ) -> ScalarSummary:
+    ) -> ScalarSequence:
         """Train the model on given labeled data"""
 
         self._embed_model.train()
 
-        _summary = ScalarSummary('Train Loss')
+        _summary = ScalarSequence('Train Loss')
         with ProgressBar(
             description,
             message_on_done=_summary.__str__,
@@ -130,7 +130,7 @@ def fit(
         optimizer_kwargs: Optional[Dict] = None,
         device: str = 'cpu',
         **kwargs,
-    ) -> SummaryCollection:
+    ) -> Summary:
         """Finetune the model on the training data.
 
         :param train_data: Data on which to train the model
@@ -164,8 +164,8 @@ def fit(
 
         _optimizer = self._get_optimizer(optimizer, optimizer_kwargs, learning_rate)
 
-        m_train_loss = ScalarSummary('train')
-        m_eval_loss = ScalarSummary('eval')
+        m_train_loss = ScalarSequence('train')
+        m_eval_loss = ScalarSequence('eval')
 
         for epoch in range(epochs):
             _data = self._get_data_loader(
@@ -186,7 +186,7 @@ def fit(
                 le = self._eval(_data, train_loss=m_train_loss)
                 m_eval_loss += le
 
-        return SummaryCollection(m_train_loss, m_eval_loss)
+        return Summary(m_train_loss, m_eval_loss)
 
     def save(self, *args, **kwargs):
         """Save the embedding model.
diff --git a/finetuner/tuner/pytorch/__init__.py b/finetuner/tuner/pytorch/__init__.py
index f99f7905d..6daa900d8 100644
--- a/finetuner/tuner/pytorch/__init__.py
+++ b/finetuner/tuner/pytorch/__init__.py
@@ -8,7 +8,7 @@
 from . import losses, datasets
 from ..base import BaseTuner, BaseLoss
 from ..dataset.helper import get_dataset
-from ..summary import ScalarSummary, SummaryCollection
+from ..summary import ScalarSequence, Summary
 from ...helper import DocumentArrayLike, AnyDataLoader
 
 
@@ -67,13 +67,13 @@ def _eval(
         self,
         data: AnyDataLoader,
         description: str = 'Evaluating',
-        train_loss: Optional[ScalarSummary] = None,
-    ) -> ScalarSummary:
+        train_loss: Optional[ScalarSequence] = None,
+    ) -> ScalarSequence:
         """Evaluate the model on given labeled data"""
 
         self._embed_model.eval()
 
-        _summary = ScalarSummary('Eval Loss')
+        _summary = ScalarSequence('Eval Loss')
 
         with ProgressBar(
             description,
@@ -99,12 +99,12 @@ def _eval(
 
     def _train(
         self, data: AnyDataLoader, optimizer: Optimizer, description: str
-    ) -> ScalarSummary:
+    ) -> ScalarSequence:
         """Train the model on given labeled data"""
 
         self._embed_model.train()
 
-        _summary = ScalarSummary('Train Loss')
+        _summary = ScalarSequence('Train Loss')
         with ProgressBar(
             description,
             message_on_done=_summary.__str__,
@@ -142,7 +142,7 @@ def fit(
         optimizer_kwargs: Optional[Dict] = None,
         device: str = 'cpu',
         **kwargs,
-    ) -> SummaryCollection:
+    ) -> Summary:
         """Finetune the model on the training data.
 
         :param train_data: Data on which to train the model
@@ -179,8 +179,8 @@ def fit(
         # Get optimizer
         _optimizer = self._get_optimizer(optimizer, optimizer_kwargs, learning_rate)
 
-        m_train_loss = ScalarSummary('train')
-        m_eval_loss = ScalarSummary('eval')
+        m_train_loss = ScalarSequence('train')
+        m_eval_loss = ScalarSequence('eval')
 
         for epoch in range(epochs):
             _data = self._get_data_loader(
@@ -201,7 +201,7 @@ def fit(
                 le = self._eval(_data, train_loss=m_train_loss)
                 m_eval_loss += le
 
-        return SummaryCollection(m_train_loss, m_eval_loss)
+        return Summary(m_train_loss, m_eval_loss)
 
     def save(self, *args, **kwargs):
         """Save the embedding model.
diff --git a/finetuner/tuner/summary.py b/finetuner/tuner/summary.py
index 326b0fa9c..b84aa40dc 100644
--- a/finetuner/tuner/summary.py
+++ b/finetuner/tuner/summary.py
@@ -8,43 +8,45 @@
 ]  #: The type of numerics including numpy data type
 
 
-class ScalarSummary:
-    def __init__(self, name: str = '', data: Optional[List[NumericType]] = None):
+class ScalarSequence:
+    def __init__(self, name: str):
         """Create a record for storing a list of scalar values e.g. losses/metrics
 
         :param name: the name of that record
-        :param data: the data record to initialize from
         """
 
-        self._name = name or ''
-        self._record = data or []
+        self.name = name
+        self._record = []
 
-    def __iadd__(self, other: Union[List[NumericType], float, 'ScalarSummary']):
+    def __iadd__(self, other: Union[List[NumericType], float, 'ScalarSequence']):
         if isinstance(other, list):
             self._record += other
-        elif isinstance(other, ScalarSummary):
+        elif isinstance(other, ScalarSequence):
             self._record += other._record
+        elif isinstance(other, np.ndarray) and np.squeeze(other).ndim == 1:
+            self._record += [v for v in np.squeeze(other)]
         else:
             self._record.append(other)
         return self
 
     def __str__(self):
         if self._record:
-            return (
-                f'{self._name}: {np.mean([float(loss) for loss in self._record]):.2f}'
-            )
+            return f'{self.name}: {np.mean([float(loss) for loss in self._record]):.2f}'
         else:
-            return f'{self._name} has no record'
+            return f'{self.name} has no record'
 
     def floats(self) -> List[NumericType]:
         """Return all numbers as a list of Python native float """
         return [float(v) for v in self._record]
 
+    def __bool__(self):
+        return bool(self._record)
+
 
-class SummaryCollection:
-    def __init__(self, *records: ScalarSummary):
+class Summary:
+    def __init__(self, *records: ScalarSequence):
         """Create a collection of summaries. """
-        self._records = records
+        self._records = [r for r in records if r]
 
     def save(self, filepath: str):
         """Store all summary into a JSON file"""
@@ -56,4 +58,57 @@ def save(self, filepath: str):
 
     def dict(self) -> Dict[str, List[NumericType]]:
         """Return all summaries as a Dictionary, where key is the name and value is the record"""
-        return {r._name: r.floats() for r in self._records}
+        return {r.name: r.floats() for r in self._records}
+
+    def plot(
+        self,
+        output: Optional[str] = None,
+        max_plot_points: Optional[int] = None,
+        **kwargs,
+    ):
+        """Plot all records in the summary into one plot.
+
+        .. note::
+            This function requires ``matplotlib`` to be installed.
+
+        :param output: Optional path to store the visualization. If not given, show in UI
+        :param max_plot_points: the maximum number of points to plot. When the actual number of plots is larger than
+            given number, then a linspace sampling is conducted first to get the actual number of points for plotting.
+        :param kwargs: extra kwargs pass to matplotlib.plot
+        """
+        import matplotlib.pyplot as plt
+
+        fig, axes = plt.subplots(
+            1,
+            len(self._records),
+            figsize=(6 * len(self._records), 6),
+            constrained_layout=True,
+        )
+        if not isinstance(axes, np.ndarray):
+            # when only one record, axes is not a list, so wrap it
+            axes = [axes]
+
+        plt_kwargs = dict(alpha=0.8, linewidth=1)
+        plt_kwargs.update(kwargs)
+
+        for idx, record in enumerate(self._records):
+            axes[idx].plot(
+                *self._sample_points(record.floats(), max_len=max_plot_points),
+                **plt_kwargs,
+            )
+            axes[idx].set_ylabel(record.name)
+            axes[idx].set_box_aspect(1)
+            axes[idx].set_xlabel('Steps')
+
+        if output:
+            plt.savefig(output, bbox_inches='tight', pad_inches=0.1)
+        else:
+            plt.show()
+
+    @staticmethod
+    def _sample_points(arr, max_len: int):
+        if not max_len or max_len > len(arr):
+            return list(range(0, len(arr))), arr
+        else:
+            idx = np.round(np.linspace(0, len(arr) - 1, max_len)).astype(int)
+            return idx, [arr[j] for j in idx]
diff --git a/setup.py b/setup.py
index c2292ecc1..14cb358d9 100644
--- a/setup.py
+++ b/setup.py
@@ -39,7 +39,7 @@
     long_description_content_type='text/markdown',
     zip_safe=False,
     setup_requires=['setuptools>=18.0', 'wheel'],
-    install_requires=['jina>=2.1.11'],
+    install_requires=['jina>=2.1.11', 'matplotlib'],
     classifiers=[
         'Development Status :: 5 - Production/Stable',
         'Intended Audience :: Developers',
diff --git a/tests/unit/tuner/test_summary.py b/tests/unit/tuner/test_summary.py
new file mode 100644
index 000000000..7e81efefd
--- /dev/null
+++ b/tests/unit/tuner/test_summary.py
@@ -0,0 +1,23 @@
+import os.path
+
+from finetuner.tuner.summary import ScalarSequence, Summary
+import numpy as np
+
+
+def test_summary(tmpdir):
+    s1 = ScalarSequence('s1')
+    s2 = ScalarSequence('s2')
+    s1 += np.random.random((10,))
+    assert s1.floats()
+    s2 += np.random.random((100,))
+    assert s1.floats()
+
+    s3 = ScalarSequence('empty')
+
+    sm = Summary(s1, s2, s3)
+    assert len(sm.dict()) == 2  #: empty record is not counted
+    sm.plot()
+    sm.plot(max_plot_points=5)
+    sm.plot(max_plot_points=1000)
+    sm.plot(tmpdir / 'sm.png')
+    assert os.path.exists(tmpdir / 'sm.png')