diff --git a/doctr/models/detection/differentiable_binarization/tensorflow.py b/doctr/models/detection/differentiable_binarization/tensorflow.py
index 64bda8bcd..3e78f4756 100644
--- a/doctr/models/detection/differentiable_binarization/tensorflow.py
+++ b/doctr/models/detection/differentiable_binarization/tensorflow.py
@@ -29,7 +29,7 @@
         "mean": (0.798, 0.785, 0.772),
         "std": (0.264, 0.2749, 0.287),
         "input_shape": (1024, 1024, 3),
-        "url": "https://doctr-static.mindee.com/models?id=v0.2.0/db_resnet50-adcafc63.zip&src=0",
+        "url": "https://doctr-static.mindee.com/models?id=v0.7.0/db_resnet50-84171458.zip&src=0",
     },
     "db_mobilenet_v3_large": {
         "mean": (0.798, 0.785, 0.772),
@@ -147,20 +147,24 @@ def __init__(
         _inputs = [layers.Input(shape=in_shape[1:]) for in_shape in self.feat_extractor.output_shape]
         output_shape = tuple(self.fpn(_inputs).shape)
 
-        self.probability_head = keras.Sequential([
-            *conv_sequence(64, "relu", True, kernel_size=3, input_shape=output_shape[1:]),
-            layers.Conv2DTranspose(64, 2, strides=2, use_bias=False, kernel_initializer="he_normal"),
-            layers.BatchNormalization(),
-            layers.Activation("relu"),
-            layers.Conv2DTranspose(num_classes, 2, strides=2, kernel_initializer="he_normal"),
-        ])
-        self.threshold_head = keras.Sequential([
-            *conv_sequence(64, "relu", True, kernel_size=3, input_shape=output_shape[1:]),
-            layers.Conv2DTranspose(64, 2, strides=2, use_bias=False, kernel_initializer="he_normal"),
-            layers.BatchNormalization(),
-            layers.Activation("relu"),
-            layers.Conv2DTranspose(num_classes, 2, strides=2, kernel_initializer="he_normal"),
-        ])
+        self.probability_head = keras.Sequential(
+            [
+                *conv_sequence(64, "relu", True, kernel_size=3, input_shape=output_shape[1:]),
+                layers.Conv2DTranspose(64, 2, strides=2, use_bias=False, kernel_initializer="he_normal"),
+                layers.BatchNormalization(),
+                layers.Activation("relu"),
+                layers.Conv2DTranspose(num_classes, 2, strides=2, kernel_initializer="he_normal"),
+            ]
+        )
+        self.threshold_head = keras.Sequential(
+            [
+                *conv_sequence(64, "relu", True, kernel_size=3, input_shape=output_shape[1:]),
+                layers.Conv2DTranspose(64, 2, strides=2, use_bias=False, kernel_initializer="he_normal"),
+                layers.BatchNormalization(),
+                layers.Activation("relu"),
+                layers.Conv2DTranspose(num_classes, 2, strides=2, kernel_initializer="he_normal"),
+            ]
+        )
 
         self.postprocessor = DBPostProcessor(
             assume_straight_pages=assume_straight_pages, bin_thresh=bin_thresh, box_thresh=box_thresh