diff --git a/codes/data/image_folder_dataset.py b/codes/data/image_folder_dataset.py
index 1ee85dab..1c21c920 100644
--- a/codes/data/image_folder_dataset.py
+++ b/codes/data/image_folder_dataset.py
@@ -10,6 +10,7 @@ import os
 from data import util
 # Builds a dataset created from a simple folder containing a list of training/test/validation images.
 from data.image_corruptor import ImageCorruptor
+from data.image_label_parser import VsNetImageLabeler
 
 
 class ImageFolderDataset:
@@ -28,21 +29,29 @@ class ImageFolderDataset:
         else:
             self.weights = opt['weights']
 
-        # Just scan the given directory for images of standard types.
-        supported_types = ['jpg', 'jpeg', 'png', 'gif']
-        self.image_paths = []
-        for path, weight in zip(self.paths, self.weights):
-            cache_path = os.path.join(path, 'cache.pth')
-            if os.path.exists(cache_path):
-                imgs = torch.load(cache_path)
-            else:
-                print("Building image folder cache, this can take some time for large datasets..")
-                imgs = []
-                for ext in supported_types:
-                    imgs.extend(glob.glob(os.path.join(path, "*." + ext)))
-                torch.save(imgs, cache_path)
-            for w in range(weight):
-                self.image_paths.extend(imgs)
+        if 'labeler' in opt.keys():
+            if opt['labeler']['type'] == 'patch_labels':
+                self.labeler = VsNetImageLabeler(opt['labeler']['label_file'])
+            assert len(self.paths) == 1   # Only a single base-path is supported for labeled images.
+            self.image_paths = self.labeler.get_labeled_paths(self.paths[0])
+        else:
+            self.labeler = None
+
+            # Just scan the given directory for images of standard types.
+            supported_types = ['jpg', 'jpeg', 'png', 'gif']
+            self.image_paths = []
+            for path, weight in zip(self.paths, self.weights):
+                cache_path = os.path.join(path, 'cache.pth')
+                if os.path.exists(cache_path):
+                    imgs = torch.load(cache_path)
+                else:
+                    print("Building image folder cache, this can take some time for large datasets..")
+                    imgs = []
+                    for ext in supported_types:
+                        imgs.extend(glob.glob(os.path.join(path, "*." + ext)))
+                    torch.save(imgs, cache_path)
+                for w in range(weight):
+                    self.image_paths.extend(imgs)
         self.len = len(self.image_paths)
 
     def get_paths(self):
@@ -74,6 +83,7 @@ class ImageFolderDataset:
         h, w, _ = hs[0].shape
         ls = []
         if self.corrupt_before_downsize:
+            hs = [h.copy() for h in hs]
             hs = self.corruptor.corrupt_images(hs)
         for hq in hs:
             ls.append(cv2.resize(hq, (h // self.scale, w // self.scale), interpolation=cv2.INTER_AREA))
@@ -87,6 +97,9 @@ class ImageFolderDataset:
 
     def __getitem__(self, item):
         hq = util.read_img(None, self.image_paths[item], rgb=True)
+        if self.labeler:
+            assert hq.shape[0] == hq.shape[1]  # This just has not been accomodated yet.
+            dim = hq.shape[0]
 
         hs = self.resize_hq([hq])
         ls = self.synthesize_lq(hs)
@@ -95,13 +108,19 @@ class ImageFolderDataset:
         hq = torch.from_numpy(np.ascontiguousarray(np.transpose(hs[0], (2, 0, 1)))).float()
         lq = torch.from_numpy(np.ascontiguousarray(np.transpose(ls[0], (2, 0, 1)))).float()
 
-        return {'lq': lq, 'hq': hq, 'LQ_path': self.image_paths[item], 'HQ_path': self.image_paths[item]}
-
+        out_dict = {'lq': lq, 'hq': hq, 'LQ_path': self.image_paths[item], 'HQ_path': self.image_paths[item]}
+        if self.labeler:
+            base_file = self.image_paths[item].replace(self.paths[0], "")
+            assert dim % hq.shape[1] == 0
+            lbls, lbl_masks = self.labeler.get_labels_as_tensor(hq, base_file, dim // hq.shape[1])
+            out_dict['labels'] = lbls
+            out_dict['labels_mask'] = lbl_masks
+        return out_dict
 
 if __name__ == '__main__':
     opt = {
         'name': 'amalgam',
-        'paths': ['F:\\4k6k\\datasets\\ns_images\\imagesets\\random_100_1024px'],
+        'paths': ['F:\\4k6k\\datasets\\ns_images\\512_unsupervised\\'],
         'weights': [1],
         'target_size': 128,
         'force_multiple': 32,
@@ -110,20 +129,19 @@ if __name__ == '__main__':
         'random_corruptions': ['noise-5', 'none'],
         'num_corrupts_per_image': 1,
         'corrupt_before_downsize': True,
+        'labeler': {
+            'type': 'patch_labels',
+            'label_file': 'F:\\4k6k\\datasets\\ns_images\\512_unsupervised\\categories.json'
+        }
     }
 
     ds = ImageFolderDataset(opt)
     import os
     os.makedirs("debug", exist_ok=True)
     for i in range(0, len(ds)):
-        o = ds[random.randint(0, len(ds))]
-        #for k, v in o.items():
-        k = 'lq'
-        v = o[k]
-        #if 'LQ' in k and 'path' not in k and 'center' not in k:
-        #if 'full' in k:
-            #masked = v[:3, :, :] * v[3]
-            #torchvision.utils.save_image(masked.unsqueeze(0), "debug/%i_%s_masked.png" % (i, k))
-            #v = v[:3, :, :]
+        o = ds[random.randint(0, len(ds)-1)]
+        hq = o['hq']
+        masked = (o['labels_mask'] * .5 + .5) * hq
         import torchvision
-        torchvision.utils.save_image(v.unsqueeze(0), "debug/%i_%s.png" % (i, k))
\ No newline at end of file
+        torchvision.utils.save_image(hq.unsqueeze(0), "debug/%i_hq.png" % (i,))
+        torchvision.utils.save_image(masked.unsqueeze(0), "debug/%i_masked.png" % (i,))
\ No newline at end of file
diff --git a/codes/data/image_label_parser.py b/codes/data/image_label_parser.py
new file mode 100644
index 00000000..241746c6
--- /dev/null
+++ b/codes/data/image_label_parser.py
@@ -0,0 +1,58 @@
+import os
+
+import orjson as json
+# Given a JSON file produced by the VS.net image labeler utility, produces a dict where the keys are image file names
+# and the values are a list of object with the following properties:
+# [patch_top, patch_left, patch_height, patch_width, label]
+import torch
+
+
+class VsNetImageLabeler:
+    def __init__(self, label_file):
+        with open(label_file, "r") as read_file:
+            # Format of JSON file:
+            # "<nonsense>" {
+            #    "label": "<label>"
+            #    "keyBinding": "<nonsense>"
+            #    "labeledImages": [
+            #        { "path", "label", "patch_top", "patch_left", "patch_height", "patch_width" }
+            #    ]
+            # }
+            categories = json.loads(read_file.read())
+            labeled_images = {}
+            available_labels = []
+            for cat in categories.values():
+                for lbli in cat['labeledImages']:
+                    pth = lbli['path']
+                    if pth not in labeled_images.keys():
+                        labeled_images[pth] = []
+                    labeled_images[pth].append(lbli)
+                    if lbli['label'] not in available_labels:
+                        available_labels.append(lbli['label'])
+
+            # Build the label values, from [1,inf]
+            label_value_dict = {}
+            for i, l in enumerate(available_labels):
+                label_value_dict[l] = i+1
+
+            # Insert "labelValue" for each entry.
+            for v in labeled_images.values():
+                for l in v:
+                    l['labelValue'] = label_value_dict[l['label']]
+
+            self.labeled_images = labeled_images
+
+    def get_labeled_paths(self, base_path):
+        return [os.path.join(base_path, pth) for pth in self.labeled_images]
+
+    def get_labels_as_tensor(self, hq, img_key, resize_factor):
+        labels = torch.zeros(hq.shape, dtype=torch.long)
+        mask = torch.zeros_like(hq)
+        lbl_list = self.labeled_images[img_key]
+        for patch_lbl in lbl_list:
+            t, l, h, w = patch_lbl['patch_top'] // resize_factor, patch_lbl['patch_left'] // resize_factor, \
+                         patch_lbl['patch_height'] // resize_factor, patch_lbl['patch_width'] // resize_factor
+            val = patch_lbl['labelValue']
+            labels[:,t:t+h,l:l+w] = val
+            mask[:,t:t+h,l:l+w] = 1.0
+        return labels, mask
\ No newline at end of file