diff --git a/references/detection/train_tensorflow.py b/references/detection/train_tensorflow.py
index d6eff53464..6330425de0 100644
--- a/references/detection/train_tensorflow.py
+++ b/references/detection/train_tensorflow.py
@@ -178,9 +178,11 @@ def main(args):
     with open(os.path.join(args.val_path, "labels.json"), "rb") as f:
         val_hash = hashlib.sha256(f.read()).hexdigest()
 
-    batch_transforms = T.Compose([
-        T.Normalize(mean=(0.798, 0.785, 0.772), std=(0.264, 0.2749, 0.287)),
-    ])
+    batch_transforms = T.Compose(
+        [
+            T.Normalize(mean=(0.798, 0.785, 0.772), std=(0.264, 0.2749, 0.287)),
+        ]
+    )
 
     # Load doctr model
     model = detection.__dict__[args.arch](
@@ -219,18 +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(), 0.4),
-            T.RandomApply(T.GaussianBlur(kernel_shape=3, std=(0.1, 0.1)), 0.3),
-            T.RandomSaturation(0.3),
-            T.RandomContrast(0.3),
-            T.RandomBrightness(0.3),
-            T.RandomApply(T.ToGray(num_output_channels=3), 0.1),
-        ]),
+        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(), 0.4),
+                # T.RandomApply(T.GaussianBlur(kernel_shape=3, std=(0.1, 0.1)), 0.3),
+                T.RandomSaturation(0.3),
+                T.RandomContrast(0.3),
+                T.RandomBrightness(0.3),
+                T.RandomApply(T.ToGray(num_output_channels=3), 0.1),
+            ]
+        ),
         sample_transforms=T.SampleCompose(
             (
                 [T.Resize((args.input_size, args.input_size), preserve_aspect_ratio=True, symmetric_pad=True)]
@@ -342,12 +346,14 @@ 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,
+                }
+            )
 
         # ClearML
         if args.clearml: