updates

train.py

@@ -77,7 +77,7 @@ def train(
 
     cutoff = -1  # backbone reaches to cutoff layer
     start_epoch = 0
-    best_map = 0.
+    best_fitness = 0.0
     nf = int(model.module_defs[model.yolo_layers[0] - 1]['filters'])  # yolo layer size (i.e. 255)
     if opt.resume or opt.transfer:  # Load previously saved model
         if opt.transfer:  # Transfer learning
@@ -94,7 +94,7 @@ def train(
         start_epoch = chkpt['epoch'] + 1
         if chkpt['optimizer'] is not None:
             optimizer.load_state_dict(chkpt['optimizer'])
-            best_loss = chkpt['best_loss']
+            best_fitness = chkpt['best_fitness']
         del chkpt
 
     else:  # Initialize model with backbone (optional)
@@ -257,16 +257,16 @@ def train(
             file.write(s + '%11.3g' * 5 % results + '\n')  # P, R, mAP, F1, test_loss
 
         # Update best map
-        test_map = results[2]
-        if test_map > best_map:
-            best_map = test_map
+        fitness = results[2]
+        if fitness > best_fitness:
+            best_fitness = fitness
 
         # Save training results
         save = (not opt.nosave) or (epoch == epochs - 1)
         if save:
             # Create checkpoint
             chkpt = {'epoch': epoch,
-                     'best_map': best_map,
+                     'best_fitness': best_fitness,
                      'model': model.module.state_dict() if type(
                          model) is nn.parallel.DistributedDataParallel else model.state_dict(),
                      'optimizer': optimizer.state_dict()}
@@ -275,7 +275,7 @@ def train(
             torch.save(chkpt, latest)
 
             # Save best checkpoint
-            if best_loss == test_loss:
+            if best_fitness == fitness:
                 torch.save(chkpt, best)
 
             # Save backup every 10 epochs (optional)