diff --git a/codes/data/image_pair_with_corresponding_points_dataset.py b/codes/data/image_pair_with_corresponding_points_dataset.py
new file mode 100644
index 00000000..c6650080
--- /dev/null
+++ b/codes/data/image_pair_with_corresponding_points_dataset.py
@@ -0,0 +1,84 @@
+import glob
+import itertools
+import random
+
+import cv2
+import kornia
+import numpy as np
+import pytorch_ssim
+import torch
+import os
+
+import torchvision
+from PIL import Image
+from torch.utils.data import DataLoader, Dataset
+from torchvision import transforms
+from torchvision.transforms import Normalize
+from tqdm import tqdm
+
+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
+from utils.util import opt_get
+
+
+class ImagePairWithCorrespondingPointsDataset(Dataset):
+    def __init__(self, opt):
+        self.opt = opt
+        self.path = opt['path']
+        self.pairs = list(filter(lambda f: not os.path.isdir(f), os.listdir(self.path)))
+        self.transforms = transforms.Compose([transforms.ToTensor(),
+                                              transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),
+                                              ])
+        self.size = opt['size']
+
+
+    def __getitem__(self, item):
+        dir = self.pairs[item]
+        img1 = self.transforms(Image.open(os.path.join(self.path, dir, "1.jpg")))
+        img2 = self.transforms(Image.open(os.path.join(self.path, dir, "2.jpg")))
+        coords1, coords2 = torch.load(os.path.join(self.path, dir, "coords.pth"))
+        assert img1.shape[-2] == img1.shape[-1]
+        assert img2.shape[-2] == img2.shape[-1]
+        if img1.shape[-1] != self.size:
+            scale = img1.shape[-1] / self.size
+            assert(int(scale) == scale)  # We will only downsample to even resolutions.
+            scale = 1 / scale
+            img1 = torch.nn.functional.interpolate(img1.unsqueeze(0), scale_factor=scale, mode='bilinear', align_corners=False).squeeze(0)
+            coords1 = [int(c * scale) for c in coords1]
+        if img2.shape[-1] != self.size:
+            scale = img2.shape[-1] / self.size
+            assert(int(scale) == scale)  # We will only downsample to even resolutions.
+            scale = 1 / scale
+            img2 = torch.nn.functional.interpolate(img2.unsqueeze(0), scale_factor=scale, mode='bilinear', align_corners=False).squeeze(0)
+            coords2 = [int(c * scale) for c in coords2]
+        coords1 = (coords1[1], coords1[0])  # The UI puts these out backwards (x,y). Flip them.
+        coords2 = (coords2[1], coords2[0])
+        return {
+            'img1': img1,
+            'img2': img2,
+            'coords1': coords1,
+            'coords2': coords2
+        }
+
+    def __len__(self):
+        return len(self.pairs)
+
+if __name__ == '__main__':
+    opt = {
+        'path': 'F:\\dlas\\codes\\scripts\\ui\\image_pair_labeler\\results',
+        'size': 256
+    }
+    output_path = '.'
+
+    ds = DataLoader(ImagePairWithCorrespondingPointsDataset(opt), shuffle=True, num_workers=0)
+    for i, d in tqdm(enumerate(ds)):
+        i1 = d['img1']
+        i2 = d['img2']
+        c1 = d['coords1']
+        c2 = d['coords2']
+        i1[:,:,c1[0]-3:c1[0]+3,c1[1]-3:c1[1]+3] = 0
+        i2[:,:,c2[0]-3:c2[0]+3,c2[1]-3:c2[1]+3] = 0
+        torchvision.utils.save_image(i1, f'{output_path}\\{i}_1.png')
+        torchvision.utils.save_image(i2, f'{output_path}\\{i}_2.png')
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diff --git a/codes/scripts/ui/image_pair_labeler/image_pair_ui.py b/codes/scripts/ui/image_pair_labeler/image_pair_ui.py
new file mode 100644
index 00000000..186df7d9
--- /dev/null
+++ b/codes/scripts/ui/image_pair_labeler/image_pair_ui.py
@@ -0,0 +1,104 @@
+# Script that builds and launches a tkinter UI for labeling similar points between two images.
+import os
+import tkinter as tk
+from glob import glob
+from random import choices
+
+import torch
+from PIL import ImageTk, Image
+
+# Globals used to define state that event handlers might operate on.
+imgs_list = []
+widgets = None
+cur_img_1, cur_img_2 = None, None
+pil_img_1, pil_img_2 = None, None
+pending_labels = []
+mode_select_image_1 = True
+img_count = 1
+img_loc_1 = None
+output_location = "results"
+
+
+def update_mode_label():
+    global widgets, mode_select_image_1, img_count
+    image_widget_1, image_widget_2, mode_label = widgets
+    mode_str = "Select point in image 1" if mode_select_image_1 else "Select point in image 2"
+    mode_label.config(text="%s; Saved images: %i" % (mode_str, img_count))
+
+
+# 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':
+        next_images()
+
+    update_mode_label()
+
+
+def click(event):
+    global img_loc_1, mode_select_image_1, pil_img_1, pil_img_2, img_count
+    x, y = event.x, event.y
+    if x > 512 or y > 512:
+        print(f"Bounds error {x} {y}")
+        return
+
+    print(f"Detected click. {x} {y}")
+    if mode_select_image_1:
+        img_loc_1 = x, y
+        mode_select_image_1 = False
+    else:
+        ofolder = f'{output_location}/{img_count}'
+        os.makedirs(ofolder)
+        pil_img_1.save(os.path.join(ofolder, "1.jpg"))
+        pil_img_2.save(os.path.join(ofolder, "2.jpg"))
+        torch.save([img_loc_1, (x,y)], os.path.join(ofolder, "coords.pth"))
+        img_count = img_count + 1
+        mode_select_image_1 = True
+        next_images()
+    update_mode_label()
+
+
+def load_image_into_pane(img_path, pane, size=512):
+    pil_img = Image.open(img_path)
+    pil_img = pil_img.resize((size,size))
+    tk_picture = ImageTk.PhotoImage(pil_img)
+    pane.image = tk_picture
+    pane.configure(image=tk_picture)
+    return pil_img
+
+def next_images():
+    global imgs_list, widgets, cur_img_1, cur_img_2, pil_img_1, pil_img_2
+    image_widget_1, image_widget_2, mode_label = widgets
+
+    cur_img_1, cur_img_2 = choices(imgs_list, k=2)
+    pil_img_1 = load_image_into_pane(cur_img_1, image_widget_1)
+    pil_img_2 = load_image_into_pane(cur_img_2, image_widget_2)
+
+if __name__ == '__main__':
+    os.makedirs(output_location, exist_ok=True)
+
+    window = tk.Tk()
+    window.title("Image pair labeler UI")
+    window.geometry('1024x620+100+100')
+
+    # Load images
+    imgs_list = glob("E:\\4k6k\\datasets\\ns_images\\imagesets\\imageset_1024_square_with_new\\*.jpg")
+
+    # Photo view.
+    image_widget_1 = tk.Label(window)
+    image_widget_1.place(x=0, y=0, width=512, height=512)
+    image_widget_2 = tk.Label(window)
+    image_widget_2.place(x=512, y=0, width=512, height=512)
+
+    # Labels
+    mode_label = tk.Label(window, text="", anchor="w")
+    mode_label.place(x=20, y=590, width=400, height=20)
+
+    widgets = (image_widget_1, image_widget_2, mode_label)
+
+    window.bind("<Tab>", key_press)  # Skip current patch
+    window.bind("<Button-1>", click)
+    next_images()
+    update_mode_label()
+    window.mainloop()
diff --git a/codes/trainer/eval/single_point_pair_contrastive_eval.py b/codes/trainer/eval/single_point_pair_contrastive_eval.py
new file mode 100644
index 00000000..fccebbd5
--- /dev/null
+++ b/codes/trainer/eval/single_point_pair_contrastive_eval.py
@@ -0,0 +1,48 @@
+import os
+
+import torch
+import os.path as osp
+import torchvision
+from torch.nn import MSELoss
+from torch.utils.data import DataLoader
+from tqdm import tqdm
+
+import trainer.eval.evaluator as evaluator
+from pytorch_fid import fid_score
+
+from data.image_pair_with_corresponding_points_dataset import ImagePairWithCorrespondingPointsDataset
+from utils.util import opt_get
+
+# Uses two datasets: a "similar" and "dissimilar" dataset, each of which contains pairs of images and similar/dissimilar
+# points in those datasets. Uses the provided network to compute a latent vector for both similar and dissimilar.
+# Reports a score for the l2 distance of both. A properly trained network will show similar points getting closer while
+# dissimilar points remain constant or get further apart.
+class SinglePointPairContrastiveEval(evaluator.Evaluator):
+    def __init__(self, model, opt_eval, env):
+        super().__init__(model, opt_eval, env)
+        self.batch_sz = opt_eval['batch_size']
+        self.eval_qty = opt_eval['quantity']
+        assert self.eval_qty % self.batch_sz == 0
+        self.similar_set = DataLoader(ImagePairWithCorrespondingPointsDataset(**opt_eval['similar_set_args']), shuffle=False, batch_size=self.batch_sz)
+        self.dissimilar_set = DataLoader(ImagePairWithCorrespondingPointsDataset(**opt_eval['dissimilar_set_args']), shuffle=False, batch_size=self.batch_sz)
+
+    def get_l2_score(self, dl):
+        distances = []
+        l2 = MSELoss()
+        for i, data in tqdm(enumerate(dl)):
+            latent1 = self.model(data['img1'], data['coords1'])
+            latent2 = self.model(data['img2'], data['coords2'])
+            distances.append(l2(latent1, latent2))
+            if i * self.batch_sz >= self.eval_qty:
+                break
+
+        return torch.stack(distances).mean()
+
+    def perform_eval(self):
+        self.model.eval()
+        print("Computing contrastive eval on similar set")
+        similars = self.get_l2_score(self.similar_set)
+        print("Computing contrastive eval on dissimilar set")
+        dissimilars = self.get_l2_score(self.dissimilar_set)
+        print(f"Eval done. val_similar_lq: {similars.item()}; val_dissimilar_l2: {dissimilars.item()}")
+        return {"val_similar_l2": similars.item(), "val_dissimilar_l2": dissimilars.item()}
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