diff --git a/codes/data/image_folder_dataset.py b/codes/data/image_folder_dataset.py
index 4279842d..9817cb62 100644
--- a/codes/data/image_folder_dataset.py
+++ b/codes/data/image_folder_dataset.py
@@ -134,7 +134,7 @@ if __name__ == '__main__':
         'corrupt_before_downsize': True,
         'labeler': {
             'type': 'patch_labels',
-            'label_file': 'F:\\4k6k\\datasets\\ns_images\\512_unsupervised\\categories.json'
+            'label_file': 'F:\\4k6k\\datasets\\ns_images\\512_unsupervised\\categories_new.json'
         }
     }
 
diff --git a/codes/data/image_label_parser.py b/codes/data/image_label_parser.py
index f8d55c84..bc9a7a28 100644
--- a/codes/data/image_label_parser.py
+++ b/codes/data/image_label_parser.py
@@ -1,4 +1,5 @@
 import os
+from collections import OrderedDict
 
 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
@@ -9,39 +10,40 @@ 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'])
+        if not isinstance(label_file, list):
+            label_file = [label_file]
+        self.labeled_images = {}
+        for lfil in label_file:
+            with open(lfil, "r") as read_file:
+                self.label_file = label_file
+                # Format of JSON file:
+                # "key_binding" {
+                #    "label": "<label>"
+                #    "index": <num>
+                #    "keyBinding": "key_binding"
+                #    "labeledImages": [
+                #        { "path", "label", "patch_top", "patch_left", "patch_height", "patch_width" }
+                #    ]
+                # }
+                categories = json.loads(read_file.read())
+                available_labels = {}
+                label_value_dict = {}
+                for cat in categories.values():
+                    available_labels[cat['index']] = cat['label']
+                    label_value_dict[cat['label']] = cat['index']
+                    for lbli in cat['labeledImages']:
+                        pth = lbli['path']
+                        if pth not in self.labeled_images.keys():
+                            self.labeled_images[pth] = []
+                        self.labeled_images[pth].append(lbli)
 
-            # Build the label values, from [1,inf]
-            label_value_dict = {}
-            for i, l in enumerate(available_labels):
-                label_value_dict[l] = i
+                # Insert "labelValue" for each entry.
+                for v in self.labeled_images.values():
+                    for l in v:
+                        l['labelValue'] = label_value_dict[l['label']]
 
-            # 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
-            self.str_labels = available_labels
+        self.categories = categories
+        self.str_labels = available_labels
 
     def get_labeled_paths(self, base_path):
         return [os.path.join(base_path, pth) for pth in self.labeled_images]
@@ -57,4 +59,13 @@ class VsNetImageLabeler:
             val = patch_lbl['labelValue']
             labels[:,t:t+h,l:l+w] = val
             mask[:,t:t+h,l:l+w] = 1.0
-        return labels, mask, self.str_labels
\ No newline at end of file
+        return labels, mask, self.str_labels
+
+    def add_label(self, binding, img_name, top, left, dim):
+        lbl = {"path": img_name, "label": self.categories[binding]['label'], "patch_top": top, "patch_left": left,
+               "patch_height": dim, "patch_width": dim}
+        self.categories[binding]['labeledImages'].append(lbl)
+
+    def save(self):
+        with open(self.label_file[0], "wb") as file:
+            file.write(json.dumps(self.categories))
diff --git a/codes/scripts/ui/image_labeler/image_labeler_ui.py b/codes/scripts/ui/image_labeler/image_labeler_ui.py
new file mode 100644
index 00000000..63c3dfc9
--- /dev/null
+++ b/codes/scripts/ui/image_labeler/image_labeler_ui.py
@@ -0,0 +1,181 @@
+# Script that builds and launches a tkinter UI for directly interacting with a pytorch model that performs
+# patch-level image classification.
+#
+# This script is a bit rough around the edges. I threw it together quickly to ascertain its usefulness. If I end up
+# using it a lot, I may re-visit it.
+
+import os
+import tkinter as tk
+
+import torch
+import torchvision
+from PIL import ImageTk
+
+from data.image_label_parser import VsNetImageLabeler
+from scripts.ui.image_labeler.pretrained_image_patch_classifier import PretrainedImagePatchClassifier
+
+# Globals used to define state that event handlers might operate on.
+classifier = None
+gen = None
+labeler = None
+to_pil = torchvision.transforms.ToPILImage()
+widgets = None
+batch_gen = None
+cur_img = 0
+batch_sz = 0
+mode = 0
+cur_path, cur_top, cur_left, cur_dim = None, None, None, None
+pending_labels = []
+
+
+def update_mode_label():
+    global widgets
+    image_widget, primary_label, secondary_label, mode_label = widgets
+    mode_label.config(text="Current mode: %s; Saved images: %i" % (labeler.str_labels[mode], len(pending_labels)))
+
+
+# Handles the "change mode" hotkey. Changes the classification label being targeted.
+def change_mode(event):
+    global mode, pending_labels
+    mode += 1
+    update_mode_label()
+
+
+# Handles key presses, which are interpreted as requests to categorize a currently active image patch.
+def key_press(event):
+    global batch_gen, labeler, pending_labels
+
+    if event.char != '\t':
+        if event.char not in labeler.categories.keys():
+            print("Specified category doesn't exist.")
+            return
+        cat = labeler.categories[event.char]
+        img_name = os.path.basename(cur_path)
+        print(cat['label'], img_name, cur_top, cur_left, cur_dim)
+        pending_labels.append([event.char, img_name, cur_top, cur_left, cur_dim])
+
+    if not next(batch_gen):
+        next_image(None)
+    update_mode_label()
+
+
+# Pop the most recent label off of the stack.
+def undo(event):
+    global pending_labels
+    c, nm, t, l, d = pending_labels.pop()
+    print("Removed pending label", c, nm, t, l, d)
+    update_mode_label()
+
+
+# Save the stack of pending labels to the underlying label file.
+def save(event):
+    global pending_labels, labeler
+    print("Saving %i labels", len(pending_labels,))
+    for l in pending_labels:
+        labeler.add_label(*l)
+    labeler.save()
+    pending_labels = []
+    update_mode_label()
+
+
+# This is the main controller for the state machine that this UI attaches to. At its core, it performs inference on
+# a batch of images, then repetitively yields patches of images that fit within a confidence bound for the currently
+# active label.
+def next_batch():
+    global gen, widgets, cur_img, batch_sz, cur_top, cur_left, cur_dim, mode, labeler, cur_path
+    image_widget, primary_label, secondary_label, mode_label = widgets
+    hq, res, data = next(gen)
+    scale = hq.shape[-1] // res.shape[-1]
+
+    # These are the confidence bounds. They apply to a post-softmax output. They are currently fixed.
+    conf_lower = .8
+    conf_upper = 1
+    valid_res = ((res > conf_lower) * (res < conf_upper)) * 1.0  # This results in a tensor of 0's where tensors are outside of the confidence bound, 1's where inside.
+
+    cur_img = 0
+    batch_sz = hq.shape[0]
+    while cur_img < batch_sz:  # Note: cur_img can (intentionally) be changed outside of this loop.
+        # Build a random permutation for every image patch in the image. We will search for patches that fall within the confidence bound and yield them.
+        #permutation = torch.randperm(res.shape[-1] * res.shape[-2])
+        for p in range(res.shape[-1]*res.shape[-2]):
+            # Reconstruct a top & left coordinate.
+            t = p // res.shape[-1]
+            l = p % res.shape[-1]
+            if not valid_res[cur_img,mode,t,l]:
+                continue
+
+            # Build a mask that shows the user the underlying construction of the image.
+            # - Areas that don't fit the current labeling restrictions get a mask value of .25.
+            # - Areas that do fit but are not the current patch, get a mask value of .5
+            # - The current patch gets a mask value of 1.0
+            # Expected output shape is (1,h,w) so it can be multiplied into the output image.
+            mask = (valid_res[cur_img,mode,:,:].clone()*.25 + .25).unsqueeze(0)
+            mask[:,t,l] = 1.0
+
+            # Interpolate the mask so that it can be directly multiplied against the HQ image.
+            masked = hq[cur_img,:,:,:].clone() * torch.nn.functional.interpolate(mask.unsqueeze(0), scale_factor=scale, mode="nearest").squeeze(0)
+
+            # Update the image widget to show the new masked image.
+            tk_picture = ImageTk.PhotoImage(to_pil(masked))
+            image_widget.image = tk_picture
+            image_widget.configure(image=tk_picture)
+
+            # Fill in the labels
+            probs = res[cur_img, :, t, l]
+            probs, lblis = torch.topk(probs, k=2)
+            primary_label.config(text="%s (p=%f)" % (labeler.str_labels[lblis[0].item()], probs[0]))
+            secondary_label.config(text="%s (p=%f)" % (labeler.str_labels[lblis[1].item()], probs[1]))
+
+            # Update state variables so that the key handlers can save the current patch as needed.
+            cur_top, cur_left, cur_dim = (t*scale), (l*scale), scale
+            cur_path = os.path.basename(data['HQ_path'][cur_img])
+            yield True
+        cur_img += 1
+    cur_top, cur_left, cur_dim = None, None, None
+    return False
+
+
+def next_image(event):
+    global batch_gen, batch_sz, cur_img
+    cur_img += 1
+    if cur_img >= batch_sz:
+        cur_img = 0
+        batch_gen = next_batch()
+    next(batch_gen)
+
+
+if __name__ == '__main__':
+    classifier = PretrainedImagePatchClassifier('../options/train_imgset_structural_classifier.yml')
+    gen = classifier.get_next_sample()
+    labeler = VsNetImageLabeler('F:\\4k6k\\datasets\\ns_images\\512_unsupervised\\categories_new.json')
+
+    window = tk.Tk()
+    window.title("Image labeler UI")
+    window.geometry('512x620+100+100')
+
+    # Photo view.
+    image_widget = tk.Label(window)
+    image_widget.place(x=0, y=0, width=512, height=512)
+
+    # Labels
+    primary_label = tk.Label(window, text="xxxx (p=1.0)", anchor="w")
+    primary_label.place(x=20, y=510, width=400, height=20)
+    secondary_label = tk.Label(window, text="yyyy (p=0.0)", anchor="w")
+    secondary_label.place(x=20, y=530, width=400, height=20)
+    help = tk.Label(window, text="Next: ctrl+f, Mode: ctrl+x, Undo: ctrl+z, Save: ctrl+s", anchor="w")
+    help.place(x=20, y=550, width=400, height=20)
+    help2 = tk.Label(window, text=','.join(list(labeler.categories.keys())), anchor="w")
+    help2.place(x=20, y=570, width=400, height=20)
+    mode_label = tk.Label(window, text="Current mode: %s; Saved images: %i" % (labeler.str_labels[mode], 0), anchor="w")
+    mode_label.place(x=20, y=590, width=400, height=20)
+
+    widgets = (image_widget, primary_label, secondary_label, mode_label)
+
+    window.bind("<Control-x>", change_mode)
+    window.bind("<Control-z>", undo)
+    window.bind("<Control-s>", save)
+    window.bind("<Control-f>", next_image)
+    for kb in labeler.categories.keys():
+        window.bind("%s" % (kb,), key_press)
+    window.bind("<Tab>", key_press)  # Skip current patch
+    window.mainloop()
diff --git a/codes/scripts/ui/image_labeler/pretrained_image_patch_classifier.py b/codes/scripts/ui/image_labeler/pretrained_image_patch_classifier.py
new file mode 100644
index 00000000..69741f10
--- /dev/null
+++ b/codes/scripts/ui/image_labeler/pretrained_image_patch_classifier.py
@@ -0,0 +1,52 @@
+import logging
+import os.path as osp
+
+import utils
+import utils.options as option
+import utils.util as util
+from data import create_dataset, create_dataloader
+from models.ExtensibleTrainer import ExtensibleTrainer
+
+
+class PretrainedImagePatchClassifier:
+    def __init__(self, cfg):
+        self.cfg = cfg
+
+        opt = option.parse(cfg, is_train=False)
+        opt = option.dict_to_nonedict(opt)
+        utils.util.loaded_options = opt
+
+        util.mkdirs(
+            (path for key, path in opt['path'].items()
+             if not key == 'experiments_root' and 'pretrain_model' not in key and 'resume' not in key))
+        util.setup_logger('base', opt['path']['log'], 'test_' + opt['name'], level=logging.INFO,
+                          screen=True, tofile=True)
+        logger = logging.getLogger('base')
+        logger.info(option.dict2str(opt))
+
+        #### Create test dataset and dataloader
+        dataset_opt = list(opt['datasets'].values())[0]
+        # Remove labeling features from the dataset config and wrappers.
+        if 'dataset' in dataset_opt.keys():
+            if 'labeler' in dataset_opt['dataset'].keys():
+                dataset_opt['dataset']['includes_labels'] = False
+                del dataset_opt['dataset']['labeler']
+            test_set = create_dataset(dataset_opt)
+            if hasattr(test_set, 'wrapped_dataset'):
+                test_set = test_set.wrapped_dataset
+        else:
+            test_set = create_dataset(dataset_opt)
+        logger.info('Number of test images: {:d}'.format(len(test_set)))
+        self.test_loader = create_dataloader(test_set, dataset_opt, opt)
+        self.model = ExtensibleTrainer(opt)
+        self.gen = self.model.netsG['generator']
+        self.dataset_dir = osp.join(opt['path']['results_root'], opt['name'])
+        util.mkdir(self.dataset_dir)
+
+    def get_next_sample(self):
+
+        for data in self.test_loader:
+            hq = data['hq'].to('cuda')
+            res = self.gen(hq)
+            yield hq, res, data
+
diff --git a/codes/scripts/ui/image_labeler/test_image_patch_classifier.py b/codes/scripts/ui/image_labeler/test_image_patch_classifier.py
new file mode 100644
index 00000000..89858b1a
--- /dev/null
+++ b/codes/scripts/ui/image_labeler/test_image_patch_classifier.py
@@ -0,0 +1,26 @@
+import argparse
+import os
+
+import torch
+import torchvision
+
+import utils.options as option
+from scripts.ui.image_labeler.pretrained_image_patch_classifier import PretrainedImagePatchClassifier
+
+if __name__ == "__main__":
+    #### options
+    torch.backends.cudnn.benchmark = True
+    want_metrics = False
+    parser = argparse.ArgumentParser()
+    parser.add_argument('-opt', type=str, help='Path to options YAML file.', default='../options/train_imgset_structural_classifier.yml')
+
+    classifier = PretrainedImagePatchClassifier(parser.parse_args().opt)
+    label_to_search_for = 4
+    step = 1
+    for hq, res in classifier.get_next_sample():
+        res = torch.nn.functional.interpolate(res, size=hq.shape[2:], mode="nearest")
+        res_lbl = res[:, label_to_search_for, :, :].unsqueeze(1)
+        res_lbl_mask = (1.0 * (res_lbl > .5))*.5 + .5
+        hq = hq * res_lbl_mask
+        torchvision.utils.save_image(hq, os.path.join(classifier.dataset_dir, "%i.png" % (step,)))
+        step += 1