diff --git a/doctr/models/detection/differentiable_binarization/pytorch.py b/doctr/models/detection/differentiable_binarization/pytorch.py
index 9e4b81ef92..17686bb28e 100644
--- a/doctr/models/detection/differentiable_binarization/pytorch.py
+++ b/doctr/models/detection/differentiable_binarization/pytorch.py
@@ -57,24 +57,28 @@ def __init__(
 
         conv_layer = DeformConv2d if deform_conv else nn.Conv2d
 
-        self.in_branches = nn.ModuleList([
-            nn.Sequential(
-                conv_layer(chans, out_channels, 1, bias=False),
-                nn.BatchNorm2d(out_channels),
-                nn.ReLU(inplace=True),
-            )
-            for idx, chans in enumerate(in_channels)
-        ])
+        self.in_branches = nn.ModuleList(
+            [
+                nn.Sequential(
+                    conv_layer(chans, out_channels, 1, bias=False),
+                    nn.BatchNorm2d(out_channels),
+                    nn.ReLU(inplace=True),
+                )
+                for idx, chans in enumerate(in_channels)
+            ]
+        )
         self.upsample = nn.Upsample(scale_factor=2, mode="bilinear", align_corners=True)
-        self.out_branches = nn.ModuleList([
-            nn.Sequential(
-                conv_layer(out_channels, out_chans, 3, padding=1, bias=False),
-                nn.BatchNorm2d(out_chans),
-                nn.ReLU(inplace=True),
-                nn.Upsample(scale_factor=2**idx, mode="bilinear", align_corners=True),
-            )
-            for idx, chans in enumerate(in_channels)
-        ])
+        self.out_branches = nn.ModuleList(
+            [
+                nn.Sequential(
+                    conv_layer(out_channels, out_chans, 3, padding=1, bias=False),
+                    nn.BatchNorm2d(out_chans),
+                    nn.ReLU(inplace=True),
+                    nn.Upsample(scale_factor=2**idx, mode="bilinear", align_corners=True),
+                )
+                for idx, chans in enumerate(in_channels)
+            ]
+        )
 
     def forward(self, x: List[torch.Tensor]) -> torch.Tensor:
         if len(x) != len(self.out_branches):
diff --git a/references/detection/train_pytorch.py b/references/detection/train_pytorch.py
index 4f64011518..e3fe2c9f8d 100644
--- a/references/detection/train_pytorch.py
+++ b/references/detection/train_pytorch.py
@@ -266,15 +266,17 @@ def main(args):
     train_set = DetectionDataset(
         img_folder=os.path.join(args.train_path, "images"),
         label_path=os.path.join(args.train_path, "labels.json"),
-        img_transforms=Compose([
-            # Augmentations
-            T.RandomApply(T.ColorInversion(), 0.1),
-            T.RandomApply(T.GaussianNoise(mean=0.1, std=0.1), 0.1),
-            T.RandomApply(T.RandomShadow(), 0.1),
-            T.RandomApply(GaussianBlur(kernel_size=3), 0.1),
-            RandomPhotometricDistort(p=0.05),
-            RandomGrayscale(p=0.05),
-        ]),
+        img_transforms=Compose(
+            [
+                # Augmentations
+                T.RandomApply(T.ColorInversion(), 0.1),
+                T.RandomApply(T.GaussianNoise(mean=0.1, std=0.1), 0.1),
+                T.RandomApply(T.RandomShadow(), 0.1),
+                T.RandomApply(GaussianBlur(kernel_size=3), 0.1),
+                RandomPhotometricDistort(p=0.05),
+                RandomGrayscale(p=0.05),
+            ]
+        ),
         sample_transforms=T.SampleCompose(
             (
                 [T.Resize((args.input_size, args.input_size), preserve_aspect_ratio=True, symmetric_pad=True)]
@@ -390,12 +392,14 @@ def main(args):
         print(log_msg)
         # W&B
         if args.wb:
-            wandb.log({
-                "val_loss": val_loss,
-                "recall": recall,
-                "precision": precision,
-                "mean_iou": mean_iou,
-            })
+            wandb.log(
+                {
+                    "val_loss": val_loss,
+                    "recall": recall,
+                    "precision": precision,
+                    "mean_iou": mean_iou,
+                }
+            )
         if args.early_stop and early_stopper.early_stop(val_loss):
             print("Training halted early due to reaching patience limit.")
             break
diff --git a/references/detection/train_tensorflow.py b/references/detection/train_tensorflow.py
index 21b27d1bed..fd0271c188 100644
--- a/references/detection/train_tensorflow.py
+++ b/references/detection/train_tensorflow.py
@@ -221,18 +221,20 @@ def main(args):
     train_set = DetectionDataset(
         img_folder=os.path.join(args.train_path, "images"),
         label_path=os.path.join(args.train_path, "labels.json"),
-        img_transforms=T.Compose([
-            # Augmentations
-            T.RandomApply(T.ColorInversion(), 0.1),
-            T.RandomJpegQuality(60),
-            #T.RandomApply(T.GaussianNoise(mean=0.1, std=0.1), 0.1),
-            #T.RandomApply(T.RandomShadow(), 0.1),
-            #T.RandomApply(T.GaussianBlur(kernel_shape=3, std=(0.1, 0.1)), 0.1),
-            T.RandomSaturation(0.3),
-            T.RandomContrast(0.3),
-            T.RandomBrightness(0.3),
-            T.RandomApply(T.ToGray(num_output_channels=3), 0.05),
-        ]),
+        img_transforms=T.Compose(
+            [
+                # Augmentations
+                T.RandomApply(T.ColorInversion(), 0.1),
+                T.RandomJpegQuality(60),
+                # T.RandomApply(T.GaussianNoise(mean=0.1, std=0.1), 0.1),
+                # T.RandomApply(T.RandomShadow(), 0.1),
+                # T.RandomApply(T.GaussianBlur(kernel_shape=3, std=(0.1, 0.1)), 0.1),
+                T.RandomSaturation(0.3),
+                T.RandomContrast(0.3),
+                T.RandomBrightness(0.3),
+                T.RandomApply(T.ToGray(num_output_channels=3), 0.05),
+            ]
+        ),
         sample_transforms=T.SampleCompose(
             (
                 [T.Resize((args.input_size, args.input_size), preserve_aspect_ratio=True, symmetric_pad=True)]