diff --git a/gluoncv/auto/estimators/torch_image_classification/torch_image_classification.py b/gluoncv/auto/estimators/torch_image_classification/torch_image_classification.py
index 6fe9559c9..387cfb111 100644
--- a/gluoncv/auto/estimators/torch_image_classification/torch_image_classification.py
+++ b/gluoncv/auto/estimators/torch_image_classification/torch_image_classification.py
@@ -549,7 +549,9 @@ def _evaluate(self, val_data):
         return self.validate(self.net, val_data, validate_loss_fn, amp_autocast=self._amp_autocast)
 
     def _predict(self, x, **kwargs):
-        if isinstance(x, pd.DataFrame):
+        if isinstance(x, str):
+            return self._predict((x,))
+        elif isinstance(x, pd.DataFrame):
             assert 'image' in x.columns, "Expect column `image` for input images"
             df = self._predict(tuple(x['image']))
             return df.reset_index(drop=True)
@@ -576,7 +578,6 @@ def _predict(self, x, **kwargs):
                     input = input.to(self.ctx[0])
                     labels = self.net(input)
                     for l in labels:
-                        print(l)
                         probs = nn.functional.softmax(l, dim=0).cpu().numpy().flatten()
                         topk_inds = l.topk(topk)[1].cpu().numpy().flatten()
                         results.extend([{'class': self.classes[topk_inds[k]],
@@ -586,7 +587,7 @@ def _predict(self, x, **kwargs):
                                         for k in range(topk)])
                         idx += 1
             return pd.DataFrame(results)
-        elif not isinstance(x, torch.tensor):
+        elif not isinstance(x, torch.Tensor):
             raise ValueError('Input is not supported: {}'.format(type(x)))
         with torch.no_grad():
             input = x.to(self.ctx[0])
@@ -602,7 +603,9 @@ def _predict(self, x, **kwargs):
 
 
     def _predict_feature(self, x, **kwargs):
-        if isinstance(x, pd.DataFrame):
+        if isinstance(x, str):
+            return self._predict_feature((x,))
+        elif isinstance(x, pd.DataFrame):
             assert 'image' in x.columns, "Expect column `image` for input images"
             df = self._predict_feature(tuple(x['image']))
             df = df.set_index(x.index)
@@ -638,7 +641,7 @@ def _predict_feature(self, x, **kwargs):
             df = pd.DataFrame(results)
             df['image'] = x
             return df
-        elif not isinstance(x, torch.tensor):
+        elif not isinstance(x, torch.Tensor):
             raise ValueError('Input is not supported: {}'.format(type(x)))
         with torch.no_grad():
             input = x.to(self.ctx[0])
diff --git a/tests/auto/test_torch_auto_estimators.py b/tests/auto/test_torch_auto_estimators.py
index 458cef7b7..a3e47a254 100644
--- a/tests/auto/test_torch_auto_estimators.py
+++ b/tests/auto/test_torch_auto_estimators.py
@@ -30,6 +30,7 @@ def test_image_classification_estimator():
     est = TorchImageClassificationEstimator({'img_cls': {'model': 'resnet18'}, 'train': {'epochs': 1}, 'gpus': list(range(get_gpu_count()))})
     res = est.fit(IMAGE_CLASS_DATASET)
     est.predict(IMAGE_CLASS_TEST)
+    est.predict(IMAGE_CLASS_TEST.iloc[0]['image'])
     est.predict_feature(IMAGE_CLASS_TEST)
     _save_load_test(est, 'test.pkl')