diff --git a/train.py b/train.py
index 626bbcc0..76d96efb 100644
--- a/train.py
+++ b/train.py
@@ -90,7 +90,7 @@ def main(opt):
     modelinfo(model)
     t0, t1 = time.time(), time.time()
     print('%10s' * 16 % (
-        'Epoch', 'Batch', 'x', 'y', 'w', 'h', 'conf', 'cls', 'total', 'P', 'R', 'nGT', 'TP', 'FP', 'FN', 'time'))
+        'Epoch', 'Batch', 'x', 'y', 'w', 'h', 'conf', 'cls', 'total', 'P', 'R', 'nTargets', 'TP', 'FP', 'FN', 'time'))
     for epoch in range(opt.epochs):
         epoch += start_epoch
 
@@ -115,56 +115,49 @@ def main(opt):
         metrics = torch.zeros(4, num_classes)
         for i, (imgs, targets) in enumerate(dataloader):
 
-            n = opt.batch_size  # number of pictures at a time
-            for j in range(int(len(imgs) / n)):
-                targets_j = targets[j * n:j * n + n]
-                nGT = sum([len(x) for x in targets_j])
-                if nGT < 1:
-                    continue
+            if sum([len(x) for x in targets]) < 1:  # if no targets continue
+                continue
 
-                loss = model(imgs[j * n:j * n + n].to(device), targets_j, requestPrecision=True, epoch=epoch)
-                optimizer.zero_grad()
-                loss.backward()
-                optimizer.step()
+            loss = model(imgs.to(device), targets, requestPrecision=True, epoch=epoch)
+            optimizer.zero_grad()
+            loss.backward()
+            optimizer.step()
 
-                ui += 1
-                metrics += model.losses['metrics']
-                for key, val in model.losses.items():
-                    rloss[key] = (rloss[key] * ui + val) / (ui + 1)
+            ui += 1
+            metrics += model.losses['metrics']
+            for key, val in model.losses.items():
+                rloss[key] = (rloss[key] * ui + val) / (ui + 1)
 
-                # Precision
-                precision = metrics[0] / (metrics[0] + metrics[1] + 1e-16)
-                k = (metrics[0] + metrics[1]) > 0
-                if k.sum() > 0:
-                    mean_precision = precision[k].mean()
-                else:
-                    mean_precision = 0
+            # Precision
+            precision = metrics[0] / (metrics[0] + metrics[1] + 1e-16)
+            k = (metrics[0] + metrics[1]) > 0
+            if k.sum() > 0:
+                mean_precision = precision[k].mean()
+            else:
+                mean_precision = 0
 
-                # Recall
-                recall = metrics[0] / (metrics[0] + metrics[2] + 1e-16)
-                k = (metrics[0] + metrics[2]) > 0
-                if k.sum() > 0:
-                    mean_recall = recall[k].mean()
-                else:
-                    mean_recall = 0
+            # Recall
+            recall = metrics[0] / (metrics[0] + metrics[2] + 1e-16)
+            k = (metrics[0] + metrics[2]) > 0
+            if k.sum() > 0:
+                mean_recall = recall[k].mean()
+            else:
+                mean_recall = 0
 
-                s = ('%10s%10s' + '%10.3g' * 14) % (
-                    '%g/%g' % (epoch, opt.epochs - 1), '%g/%g' % (i, len(dataloader) - 1), rloss['x'],
-                    rloss['y'], rloss['w'], rloss['h'], rloss['conf'], rloss['cls'],
-                    rloss['loss'], mean_precision, mean_recall, model.losses['nGT'], model.losses['TP'],
-                    model.losses['FP'], model.losses['FN'], time.time() - t1)
-                t1 = time.time()
-                print(s)
-
-            # if i == 1:
-            #    return
+            s = ('%10s%10s' + '%10.3g' * 14) % (
+                 '%g/%g' % (epoch, opt.epochs - 1), '%g/%g' % (i, len(dataloader) - 1), rloss['x'],
+                rloss['y'], rloss['w'], rloss['h'], rloss['conf'], rloss['cls'],
+                rloss['loss'], mean_precision, mean_recall, model.losses['nT'], model.losses['TP'],
+                model.losses['FP'], model.losses['FN'], time.time() - t1)
+            t1 = time.time()
+            print(s)
 
         # Write epoch results
         with open('results.txt', 'a') as file:
             file.write(s + '\n')
 
         # Update best loss
-        loss_per_target = rloss['loss'] / rloss['nGT']
+        loss_per_target = rloss['loss'] / rloss['nT']
         if loss_per_target < best_loss:
             best_loss = loss_per_target