diff --git a/bob/ip/binseg/modeling/backbones/mobilenetv2.py b/bob/ip/binseg/modeling/backbones/mobilenetv2.py
index 2f4b88519a961115c06b096330a213800b3bcb70..9e6cd245a00bc5a95fef118acc26b87f136e437b 100644
--- a/bob/ip/binseg/modeling/backbones/mobilenetv2.py
+++ b/bob/ip/binseg/modeling/backbones/mobilenetv2.py
@@ -83,7 +83,7 @@ class MobileNetV2(torch.nn.Module):
         self.m2u = m2u
         block = InvertedResidual
         input_channel = 32
-        last_channel = 1280
+        #last_channel = 1280
         interverted_residual_setting = [
             # t, c, n, s
             [1, 16, 1, 1],
diff --git a/bob/ip/binseg/modeling/losses.py b/bob/ip/binseg/modeling/losses.py
index e2aa27b892d84ce6a3762ca998d27ce2391395ff..2f435c14cbaa3d33453a6c3b4c6d82a6d0588821 100644
--- a/bob/ip/binseg/modeling/losses.py
+++ b/bob/ip/binseg/modeling/losses.py
@@ -239,7 +239,7 @@ class HEDSoftJaccardBCELogitsLoss(_Loss):
             loss = self.alpha * bceloss + (1 - self.alpha) * (1 - softjaccard)
             loss_over_all_inputs.append(loss.unsqueeze(0))
         final_loss = torch.cat(loss_over_all_inputs).mean()
-        return loss
+        return final_loss
 
 
 class MixJacLoss(_Loss):
diff --git a/bob/ip/binseg/utils/evaluate.py b/bob/ip/binseg/utils/evaluate.py
index 5015e5b35eb7b7a883894c82edfdc27b7e552591..11cb4c51af922350ab9c9038278897b834cabb01 100644
--- a/bob/ip/binseg/utils/evaluate.py
+++ b/bob/ip/binseg/utils/evaluate.py
@@ -124,10 +124,8 @@ def do_eval(
 
     # Collect overall metrics
     metrics = []
-    num_images = len(data_loader)
     for samples in tqdm(data_loader):
         names = samples[0]
-        images = samples[1]
         ground_truths = samples[2]
 
         if prediction_extension is None:
diff --git a/bob/ip/binseg/utils/plot.py b/bob/ip/binseg/utils/plot.py
index ba85ba3dd1679a257a033055ae46a2a3d0ac18b1..caf75c65d74faabbe50fc56d6cfcb53bc90fca7d 100644
--- a/bob/ip/binseg/utils/plot.py
+++ b/bob/ip/binseg/utils/plot.py
@@ -354,7 +354,6 @@ def plot_overview(outputfolders, title):
     re_ups = []
     re_lows = []
     names = []
-    params = []
     for folder in outputfolders:
         # metrics
         metrics_path = os.path.join(folder, "results/Metrics.csv")