diff --git a/codes/data/__init__.py b/codes/data/__init__.py
index 5d33a206..60bfc90a 100644
--- a/codes/data/__init__.py
+++ b/codes/data/__init__.py
@@ -55,6 +55,8 @@ def create_dataset(dataset_opt):
         from data.byol_attachment import DatasetRandomAugWrapper as D
     elif mode == 'random_dataset':
         from data.random_dataset import RandomDataset as D
+    elif mode == 'zipfile':
+        from data.zip_file_dataset import ZipFileDataset as D
     else:
         raise NotImplementedError('Dataset [{:s}] is not recognized.'.format(mode))
     dataset = D(dataset_opt)
diff --git a/codes/data/zip_file_dataset.py b/codes/data/zip_file_dataset.py
new file mode 100644
index 00000000..513391c3
--- /dev/null
+++ b/codes/data/zip_file_dataset.py
@@ -0,0 +1,64 @@
+import PIL.Image
+import zipfile
+import torch
+import torchvision
+from torch.utils.data import DataLoader
+from torchvision.transforms import Compose, ToTensor, Normalize, Resize
+
+
+class ZipFileDataset(torch.utils.data.Dataset):
+    def __init__(self, opt):
+        self.path = opt['path']
+        zip = zipfile.ZipFile(self.path)
+        self.all_files = list(zip.namelist())
+        self.resolution = opt['resolution']
+        self.paired_mode = opt['paired_mode']
+        self.transforms = Compose([ToTensor(),
+                                 Resize(self.resolution),
+                                 Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
+                                  ])
+        self.zip = None
+
+    def __len__(self):
+        return len(self.all_files)
+
+    # Loaded on the fly because ZipFile does not tolerate pickling.
+    def get_zip(self):
+        if self.zip is None:
+            self.zip = zipfile.ZipFile(self.path)
+        return self.zip
+
+    def load_image(self, path):
+        file = self.get_zip().open(path, 'r')
+        pilimg = PIL.Image.open(file)
+        tensor = self.transforms(pilimg)
+        return tensor
+
+    def __getitem__(self, i):
+        fname = self.all_files[i]
+        out = {
+            'hq': self.load_image(fname),
+            'HQ_path': fname,
+            'has_alt': self.paired_mode
+        }
+        if self.paired_mode:
+            if fname.endswith('0.jpg'):
+                aname = fname.replace('0.jpg', '1.jpg')
+            else:
+                aname = fname.replace('1.jpg', '0.jpg')
+            out['alt_hq'] = self.load_image(aname)
+        return out
+
+if __name__ == '__main__':
+    opt = {
+        'path': 'E:\\4k6k\\datasets\\images\\youtube-imagenet-paired\\output.zip',
+        'resolution': 224,
+        'paired_mode': True
+    }
+    dataset = ZipFileDataset(opt)
+    print(len(dataset))
+    loader = DataLoader(dataset, shuffle=True)
+    for i, d in enumerate(loader):
+        torchvision.utils.save_image(d['hq'], f'{i}_hq.png')
+        torchvision.utils.save_image(d['alt_hq'], f'{i}_althq.png')
+