diff --git a/test.py b/eval.py
similarity index 99%
rename from test.py
rename to eval.py
index ed7e29c..4aa692e 100644
--- a/test.py
+++ b/eval.py
@@ -233,7 +233,7 @@ def test(data,
 
 
 if __name__ == '__main__':
-    parser = argparse.ArgumentParser(prog='test.py')
+    parser = argparse.ArgumentParser(prog='eval.py')
     parser.add_argument('--weights', nargs='+', type=str, default='yolov5s.pt', help='model.pt path(s)')
     parser.add_argument('--data', type=str, default='data/coco128.yaml', help='*.data path')
     parser.add_argument('--batch-size', type=int, default=32, help='size of each image batch')
diff --git a/train.py b/train.py
index 879bb2e..1c28fec 100644
--- a/train.py
+++ b/train.py
@@ -7,7 +7,7 @@ import torch.optim.lr_scheduler as lr_scheduler
 import torch.utils.data
 from torch.utils.tensorboard import SummaryWriter
 
-import test  # import test.py to get mAP after each epoch
+import eval  # import eval.py to get mAP after each epoch
 from models.yolo import Model
 from utils import google_utils
 from utils.datasets import *
@@ -291,7 +291,7 @@ def train(hyp):
         ema.update_attr(model, include=['md', 'nc', 'hyp', 'gr', 'names', 'stride'])
         final_epoch = epoch + 1 == epochs
         if not opt.notest or final_epoch:  # Calculate mAP
-            results, maps, times = test.test(opt.data,
+            results, maps, times = eval.test(opt.data,
                                              batch_size=batch_size,
                                              imgsz=imgsz_test,
                                              save_json=final_epoch and opt.data.endswith(os.sep + 'coco.yaml'),
diff --git a/utils/utils.py b/utils/utils.py
index ce1d910..209e884 100755
--- a/utils/utils.py
+++ b/utils/utils.py
@@ -1087,7 +1087,7 @@ def plot_targets_txt():  # from utils.utils import *; plot_targets_txt()
 
 
 def plot_study_txt(f='study.txt', x=None):  # from utils.utils import *; plot_study_txt()
-    # Plot study.txt generated by test.py
+    # Plot study.txt generated by eval.py
     fig, ax = plt.subplots(2, 4, figsize=(10, 6), tight_layout=True)
     ax = ax.ravel()