diff --git a/doctr/models/classification/textnet/pytorch.py b/doctr/models/classification/textnet/pytorch.py
index 5279f03888..cf8fc164ee 100644
--- a/doctr/models/classification/textnet/pytorch.py
+++ b/doctr/models/classification/textnet/pytorch.py
@@ -22,21 +22,21 @@
         "std": (0.299, 0.296, 0.301),
         "input_shape": (3, 32, 32),
         "classes": list(VOCABS["french"]),
-        "url": "https://doctr-static.mindee.com/models?id=v0.7.0/textnet_tiny-c23a1b9a.pt&src=0",
+        "url": "https://doctr-static.mindee.com/models?id=v0.7.0/textnet_tiny-c5970fe0.pt&src=0",
     },
     "textnet_small": {
         "mean": (0.694, 0.695, 0.693),
         "std": (0.299, 0.296, 0.301),
         "input_shape": (3, 32, 32),
         "classes": list(VOCABS["french"]),
-        "url": "https://doctr-static.mindee.com/models?id=v0.7.0/textnet_small-775169f7.pt&src=0",
+        "url": "https://doctr-static.mindee.com/models?id=v0.7.0/textnet_small-6e8ab0ce.pt&src=0",
     },
     "textnet_base": {
         "mean": (0.694, 0.695, 0.693),
         "std": (0.299, 0.296, 0.301),
         "input_shape": (3, 32, 32),
         "classes": list(VOCABS["french"]),
-        "url": "https://doctr-static.mindee.com/models?id=v0.7.0/textnet_base-6121c044.pt&src=0",
+        "url": "https://doctr-static.mindee.com/models?id=v0.7.0/textnet_base-8295dc85.pt&src=0",
     },
 }
 
@@ -66,13 +66,13 @@ def __init__(
             *conv_sequence_pt(
                 in_channels=3, out_channels=64, relu=True, bn=True, kernel_size=3, stride=2, padding=(1, 1)
             ),
-            nn.Sequential(*[
+            *[
                 nn.Sequential(*[
                     FASTConvLayer(**params)  # type: ignore[arg-type]
                     for params in [{key: stage[key][i] for key in stage} for i in range(len(stage["in_channels"]))]
                 ])
                 for stage in stages
-            ]),
+            ],
         ]
 
         if include_top:
@@ -167,7 +167,7 @@ def textnet_tiny(pretrained: bool = False, **kwargs: Any) -> TextNet:
                 "stride": [2, 1, 1, 1],
             },
         ],
-        ignore_keys=["4.2.weight", "4.2.bias"],
+        ignore_keys=["7.2.weight", "7.2.bias"],
         **kwargs,
     )
 
@@ -216,7 +216,7 @@ def textnet_small(pretrained: bool = False, **kwargs: Any) -> TextNet:
                 "stride": [2, 1, 1, 1, 1],
             },
         ],
-        ignore_keys=["4.2.weight", "4.2.bias"],
+        ignore_keys=["7.2.weight", "7.2.bias"],
         **kwargs,
     )
 
@@ -270,6 +270,6 @@ def textnet_base(pretrained: bool = False, **kwargs: Any) -> TextNet:
                 "stride": [2, 1, 1, 1, 1],
             },
         ],
-        ignore_keys=["4.2.weight", "4.2.bias"],
+        ignore_keys=["7.2.weight", "7.2.bias"],
         **kwargs,
     )
diff --git a/references/detection/train_pytorch.py b/references/detection/train_pytorch.py
index c2267d3d2e..6f01f6e9e3 100644
--- a/references/detection/train_pytorch.py
+++ b/references/detection/train_pytorch.py
@@ -270,10 +270,10 @@ def main(args):
             # Augmentations
             T.RandomApply(T.ColorInversion(), 0.1),
             T.RandomApply(T.GaussianNoise(mean=0.1, std=0.1), 0.1),
-            T.RandomApply(T.RandomShadow(), 0.4),
-            T.RandomApply(GaussianBlur(kernel_size=3), 0.3),
-            RandomPhotometricDistort(p=0.1),
-            RandomGrayscale(p=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(
             (
@@ -442,7 +442,7 @@ def parse_args():
         action="store_true",
         help="metrics evaluation with straight boxes instead of polygons to save time + memory",
     )
-    parser.add_argument("--sched", type=str, default="cosine", help="scheduler to use")
+    parser.add_argument("--sched", type=str, default="onecycle", help="scheduler to use")
     parser.add_argument("--amp", dest="amp", help="Use Automatic Mixed Precision", action="store_true")
     parser.add_argument("--find-lr", action="store_true", help="Gridsearch the optimal LR")
     parser.add_argument("--early-stop", action="store_true", help="Enable early stopping")
diff --git a/references/detection/train_tensorflow.py b/references/detection/train_tensorflow.py
index d6eff53464..a4652affe5 100644
--- a/references/detection/train_tensorflow.py
+++ b/references/detection/train_tensorflow.py
@@ -224,12 +224,12 @@ def main(args):
             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.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), 0.1),
+            T.RandomApply(T.ToGray(num_output_channels=3), 0.05),
         ]),
         sample_transforms=T.SampleCompose(
             (