diff --git a/art/estimators/classification/pytorch.py b/art/estimators/classification/pytorch.py
index c1fc0117f4..9c43f36cfe 100644
--- a/art/estimators/classification/pytorch.py
+++ b/art/estimators/classification/pytorch.py
@@ -375,6 +375,7 @@ def fit(  # pylint: disable=W0221
         training_mode: bool = True,
         drop_last: bool = False,
         scheduler: Optional["torch.optim.lr_scheduler._LRScheduler"] = None,
+        verbose: bool = False,
         **kwargs,
     ) -> None:
         """
@@ -390,14 +391,13 @@ def fit(  # pylint: disable=W0221
                           the batch size. If ``False`` and the size of dataset is not divisible by the batch size, then
                           the last batch will be smaller. (default: ``False``)
         :param scheduler: Learning rate scheduler to run at the start of every epoch.
-        :param kwargs: Dictionary of framework-specific arguments. Currently supports "display_progress_bar" to
-                       display training progress.
+        :param verbose: If to display the progress bar information.
+        :param kwargs: Dictionary of framework-specific arguments. This parameter is not currently supported for PyTorch
+                       and providing it takes no effect.
         """
         import torch
         from torch.utils.data import TensorDataset, DataLoader
 
-        display_progress_bar = kwargs.get("display_progress_bar", False)
-
         # Set model mode
         self._model.train(mode=training_mode)
 
@@ -419,8 +419,8 @@ def fit(  # pylint: disable=W0221
         dataloader = DataLoader(dataset=dataset, batch_size=batch_size, shuffle=True, drop_last=drop_last)
 
         # Start training
-        for _ in tqdm(range(nb_epochs), disable=not display_progress_bar, desc="Epochs"):
-            for x_batch, y_batch in tqdm(dataloader, disable=not display_progress_bar, desc="Batches"):
+        for _ in tqdm(range(nb_epochs), disable=not verbose, desc="Epochs"):
+            for x_batch, y_batch in tqdm(dataloader, disable=not verbose, desc="Batches"):
                 # Move inputs to device
                 x_batch = x_batch.to(self._device)
                 y_batch = y_batch.to(self._device)
@@ -456,20 +456,19 @@ def fit(  # pylint: disable=W0221
             if scheduler is not None:
                 scheduler.step()
 
-    def fit_generator(self, generator: "DataGenerator", nb_epochs: int = 20, **kwargs) -> None:
+    def fit_generator(self, generator: "DataGenerator", nb_epochs: int = 20, verbose: bool = False, **kwargs) -> None:
         """
         Fit the classifier using the generator that yields batches as specified.
 
         :param generator: Batch generator providing `(x, y)` for each epoch.
         :param nb_epochs: Number of epochs to use for training.
-        :param kwargs: Dictionary of framework-specific arguments. Currently supports "display_progress_bar" to
-                       display training progress.
+        :param verbose: If to display the progress bar information.
+        :param kwargs: Dictionary of framework-specific arguments. This parameter is not currently supported for PyTorch
+                       and providing it takes no effect.
         """
         import torch
         from art.data_generators import PyTorchDataGenerator
 
-        display_progress_bar = kwargs.get("display_progress_bar", False)
-
         # Put the model in the training mode
         self._model.train()
 
@@ -490,8 +489,8 @@ def fit_generator(self, generator: "DataGenerator", nb_epochs: int = 20, **kwarg
                 == (0, 1)
             )
         ):
-            for _ in tqdm(range(nb_epochs), disable=not display_progress_bar, desc="Epochs"):
-                for i_batch, o_batch in tqdm(generator.iterator, disable=not display_progress_bar, desc="Batches"):
+            for _ in tqdm(range(nb_epochs), disable=not verbose, desc="Epochs"):
+                for i_batch, o_batch in tqdm(generator.iterator, disable=not verbose, desc="Batches"):
                     if isinstance(i_batch, np.ndarray):
                         i_batch = torch.from_numpy(i_batch).to(self._device)
                     else:
@@ -500,7 +499,10 @@ def fit_generator(self, generator: "DataGenerator", nb_epochs: int = 20, **kwarg
                     if isinstance(o_batch, np.ndarray):
                         o_batch = torch.argmax(torch.from_numpy(o_batch).to(self._device), dim=1)
                     else:
-                        o_batch = torch.argmax(o_batch.to(self._device), dim=1)
+                        if o_batch.dim() > 1:
+                            o_batch = torch.argmax(o_batch.to(self._device), dim=1)
+                        else:
+                            o_batch = o_batch.to(self._device)
 
                     # Zero the parameter gradients
                     self._optimizer.zero_grad()
diff --git a/art/estimators/classification/tensorflow.py b/art/estimators/classification/tensorflow.py
index f4e2aece0f..16fc4d69d5 100644
--- a/art/estimators/classification/tensorflow.py
+++ b/art/estimators/classification/tensorflow.py
@@ -266,7 +266,9 @@ def predict(  # pylint: disable=W0221
 
         return predictions
 
-    def fit(self, x: np.ndarray, y: np.ndarray, batch_size: int = 128, nb_epochs: int = 10, **kwargs) -> None:
+    def fit(
+        self, x: np.ndarray, y: np.ndarray, batch_size: int = 128, nb_epochs: int = 10, verbose: bool = False, **kwargs
+    ) -> None:
         """
         Fit the classifier on the training set `(x, y)`.
 
@@ -275,6 +277,7 @@ def fit(self, x: np.ndarray, y: np.ndarray, batch_size: int = 128, nb_epochs: in
                   shape (nb_samples,).
         :param batch_size: Size of batches.
         :param nb_epochs: Number of epochs to use for training.
+        :param verbose: If to display the progress bar information.
         :param kwargs: Dictionary of framework-specific arguments. This parameter is not currently supported for
                TensorFlow and providing it takes no effect.
         """
@@ -298,12 +301,12 @@ def fit(self, x: np.ndarray, y: np.ndarray, batch_size: int = 128, nb_epochs: in
         ind = np.arange(len(x_preprocessed)).tolist()
 
         # Start training
-        for _ in range(nb_epochs):
+        for _ in tqdm(range(nb_epochs), disable=not verbose, desc="Epochs"):
             # Shuffle the examples
             random.shuffle(ind)
 
             # Train for one epoch
-            for m in range(num_batch):
+            for m in tqdm(range(num_batch), disable=not verbose, desc="Batches"):
                 i_batch = x_preprocessed[ind[m * batch_size : (m + 1) * batch_size]]
                 o_batch = y_preprocessed[ind[m * batch_size : (m + 1) * batch_size]]
 
@@ -314,13 +317,14 @@ def fit(self, x: np.ndarray, y: np.ndarray, batch_size: int = 128, nb_epochs: in
                 # Run train step
                 self._sess.run(self.train, feed_dict=feed_dict)
 
-    def fit_generator(self, generator: "DataGenerator", nb_epochs: int = 20, **kwargs) -> None:
+    def fit_generator(self, generator: "DataGenerator", nb_epochs: int = 20, verbose: bool = False, **kwargs) -> None:
         """
         Fit the classifier using the generator that yields batches as specified.
 
         :param generator: Batch generator providing `(x, y)` for each epoch. If the generator can be used for native
                           training in TensorFlow, it will.
         :param nb_epochs: Number of epochs to use for training.
+        :param verbose: If to display the progress bar information.
         :param kwargs: Dictionary of framework-specific arguments. This parameter is not currently supported for
                TensorFlow and providing it takes no effect.
         """
@@ -343,8 +347,8 @@ def fit_generator(self, generator: "DataGenerator", nb_epochs: int = 20, **kwarg
                 == (0, 1)
             )
         ):
-            for _ in range(nb_epochs):
-                for _ in range(int(generator.size / generator.batch_size)):  # type: ignore
+            for _ in tqdm(range(nb_epochs), disable=not verbose, desc="Epochs"):
+                for _ in tqdm(range(int(generator.size / generator.batch_size)), disable=not verbose, desc="Batches"):  # type: ignore
                     i_batch, o_batch = generator.get_batch()
 
                     if self._reduce_labels:
@@ -953,7 +957,9 @@ def _predict_framework(self, x: "tf.Tensor", training_mode: bool = False) -> "tf
 
         return self._model(x_preprocessed, training=training_mode)
 
-    def fit(self, x: np.ndarray, y: np.ndarray, batch_size: int = 128, nb_epochs: int = 10, **kwargs) -> None:
+    def fit(
+        self, x: np.ndarray, y: np.ndarray, batch_size: int = 128, nb_epochs: int = 10, verbose: bool = False, **kwargs
+    ) -> None:
         """
         Fit the classifier on the training set `(x, y)`.
 
@@ -962,14 +968,13 @@ def fit(self, x: np.ndarray, y: np.ndarray, batch_size: int = 128, nb_epochs: in
                   shape (nb_samples,).
         :param batch_size: Size of batches.
         :param nb_epochs: Number of epochs to use for training.
+        :param verbose: If to display progress bar information.
         :param kwargs: Dictionary of framework-specific arguments. This parameter currently supports
                        "scheduler" which is an optional function that will be called at the end of every
-                       epoch to adjust the learning rate, and "display_progress_bar" to display training progress.
+                       epoch to adjust the learning rate.
         """
         import tensorflow as tf
 
-        display_progress_bar = kwargs.get("display_progress_bar", False)
-
         if self._train_step is None:  # pragma: no cover
             if self._loss_object is None:  # pragma: no cover
                 raise TypeError(
@@ -1006,29 +1011,28 @@ def train_step(model, images, labels):
 
         train_ds = tf.data.Dataset.from_tensor_slices((x_preprocessed, y_preprocessed)).shuffle(10000).batch(batch_size)
 
-        for epoch in tqdm(range(nb_epochs), disable=not display_progress_bar, desc="Epochs"):
-            for images, labels in tqdm(train_ds, disable=not display_progress_bar, desc="Batches"):
+        for epoch in tqdm(range(nb_epochs), disable=not verbose, desc="Epochs"):
+            for images, labels in tqdm(train_ds, disable=not verbose, desc="Batches"):
                 train_step(self.model, images, labels)
 
             if scheduler is not None:
                 scheduler(epoch)
 
-    def fit_generator(self, generator: "DataGenerator", nb_epochs: int = 20, **kwargs) -> None:
+    def fit_generator(self, generator: "DataGenerator", nb_epochs: int = 20, verbose: bool = False, **kwargs) -> None:
         """
         Fit the classifier using the generator that yields batches as specified.
 
         :param generator: Batch generator providing `(x, y)` for each epoch. If the generator can be used for native
                           training in TensorFlow, it will.
         :param nb_epochs: Number of epochs to use for training.
+        :param verbose: If to display progress bar information
         :param kwargs: Dictionary of framework-specific arguments. This parameter currently supports
                        "scheduler" which is an optional function that will be called at the end of every
-                       epoch to adjust the learning rate, and "display_progress_bar" to display training progress.
+                       epoch to adjust the learning rate.
         """
         import tensorflow as tf
         from art.data_generators import TensorFlowV2DataGenerator
 
-        display_progress_bar = kwargs.get("display_progress_bar", False)
-
         if self._train_step is None:  # pragma: no cover
             if self._loss_object is None:  # pragma: no cover
                 raise TypeError(
@@ -1068,8 +1072,8 @@ def train_step(model, images, labels):
                 == (0, 1)
             )
         ):
-            for epoch in tqdm(range(nb_epochs), disable=not display_progress_bar, desc="Epochs"):
-                for i_batch, o_batch in tqdm(generator.iterator, disable=not display_progress_bar, desc="Batches"):
+            for epoch in tqdm(range(nb_epochs), disable=not verbose, desc="Epochs"):
+                for i_batch, o_batch in tqdm(generator.iterator, disable=not verbose, desc="Batches"):
                     if self._reduce_labels:
                         o_batch = tf.math.argmax(o_batch, axis=1)
                     train_step(self._model, i_batch, o_batch)
diff --git a/tests/estimators/classification/test_deeplearning_common.py b/tests/estimators/classification/test_deeplearning_common.py
index 2f051ecf77..3b40966d3f 100644
--- a/tests/estimators/classification/test_deeplearning_common.py
+++ b/tests/estimators/classification/test_deeplearning_common.py
@@ -202,11 +202,7 @@ def get_lr(_):
         # Test a valid callback
         classifier, _ = image_dl_estimator(from_logits=True)
 
-        # Keras fit has its own kwarg arguments
-        if framework in ["kerastf", "keras"]:
-            kwargs = {"callbacks": [LearningRateScheduler(get_lr)]}
-        else:
-            kwargs = {"callbacks": [LearningRateScheduler(get_lr)], "display_progress_bar": True}
+        kwargs = {"callbacks": [LearningRateScheduler(get_lr)], "verbose": True}
         classifier.fit(x_train_mnist, y_train_mnist, batch_size=default_batch_size, nb_epochs=1, **kwargs)
 
         # Test failure for invalid parameters: does not apply to many frameworks which allow arbitrary kwargs