diff --git a/codes/data/GTLQ_dataset.py b/codes/data/GTLQ_dataset.py
index a52817eb..c9a47a4c 100644
--- a/codes/data/GTLQ_dataset.py
+++ b/codes/data/GTLQ_dataset.py
@@ -7,14 +7,14 @@ import torch.utils.data as data
 import data.util as util
 
 
-class LQGTDataset(data.Dataset):
+class GTLQDataset(data.Dataset):
     """
-    Read LQ (Low Quality, e.g. LR (Low Resolution), blurry, etc) and GT image pairs.
-    If only GT images are provided, generate LQ images on-the-fly.
+    Reads unpaired high-resolution and low resolution images. Downsampled, LR images matching the provided high res
+     images are produced and fed to the downstream model, which can be used in a pixel loss.
     """
 
     def __init__(self, opt):
-        super(LQGTDataset, self).__init__()
+        super(GTLQDataset, self).__init__()
         self.opt = opt
         self.data_type = self.opt['data_type']
         self.paths_LQ, self.paths_GT = None, None
@@ -43,7 +43,7 @@ class LQGTDataset(data.Dataset):
             self._init_lmdb()
         GT_path, LQ_path = None, None
         scale = self.opt['scale']
-        GT_size = self.opt['GT_size']
+        GT_size = self.opt['target_size']
 
         # get GT image
         GT_path = self.paths_GT[index]
diff --git a/codes/data/__init__.py b/codes/data/__init__.py
index 01bddc01..29b0dbbe 100644
--- a/codes/data/__init__.py
+++ b/codes/data/__init__.py
@@ -32,6 +32,8 @@ def create_dataset(dataset_opt):
         from data.LQ_dataset import LQDataset as D
     elif mode == 'LQGT':
         from data.LQGT_dataset import LQGTDataset as D
+    elif mode == 'GTLQ':
+        from data.GTLQ_dataset import GTLQDataset as D
     # datasets for video restoration
     elif mode == 'REDS':
         from data.REDS_dataset import REDSDataset as D