diff --git a/detect.py b/detect.py
index da63114f..b71d5d9d 100755
--- a/detect.py
+++ b/detect.py
@@ -37,7 +37,7 @@ def detect(
     model.to(device).eval()
 
     # Set Dataloader
-    dataloader = load_images(images, img_size=img_size)
+    dataloader = LoadImages(images, img_size=img_size)
 
     # Get classes and colors
     classes = load_classes(parse_data_cfg('cfg/coco.data')['names'])
diff --git a/test.py b/test.py
index 69c21db1..e0d5deb3 100644
--- a/test.py
+++ b/test.py
@@ -36,8 +36,8 @@ def test(
     model.to(device).eval()
 
     # Get dataloader
-    # dataloader = torch.utils.data.DataLoader(load_images_with_labels(test_path), batch_size=batch_size)  # pytorch
-    dataloader = load_images_and_labels(test_path, batch_size=batch_size, img_size=img_size)
+    # dataloader = torch.utils.data.DataLoader(LoadImagesAndLabels(test_path), batch_size=batch_size)  # pytorch
+    dataloader = LoadImagesAndLabels(test_path, batch_size=batch_size, img_size=img_size)
 
     mean_mAP, mean_R, mean_P = 0.0, 0.0, 0.0
     print('%11s' * 5 % ('Image', 'Total', 'P', 'R', 'mAP'))
diff --git a/train.py b/train.py
index 383b5c6c..e7290b13 100644
--- a/train.py
+++ b/train.py
@@ -43,7 +43,7 @@ def train(
     model = Darknet(cfg, img_size)
 
     # Get dataloader
-    dataloader = load_images_and_labels(train_path, batch_size, img_size, multi_scale=multi_scale, augment=True)
+    dataloader = LoadImagesAndLabels(train_path, batch_size, img_size, multi_scale=multi_scale, augment=True)
 
     lr0 = 0.001
     if resume:
diff --git a/utils/datasets.py b/utils/datasets.py
index 95e86fcd..ef64c4e9 100755
--- a/utils/datasets.py
+++ b/utils/datasets.py
@@ -12,7 +12,7 @@ import torch
 from utils.utils import xyxy2xywh
 
 
-class load_images():  # for inference
+class LoadImages:  # for inference
     def __init__(self, path, img_size=416):
         if os.path.isdir(path):
             image_format = ['.jpg', '.jpeg', '.png', '.tif']
@@ -55,7 +55,7 @@ class load_images():  # for inference
         return self.nF  # number of files
 
 
-class load_images_and_labels():  # for training
+class LoadImagesAndLabels:  # for training
     def __init__(self, path, batch_size=1, img_size=608, multi_scale=False, augment=False):
         self.path = path
         with open(path, 'r') as file: