diff --git a/tac_ssl_test.py b/tac_ssl_test.py
new file mode 100644
index 0000000..ce78701
--- /dev/null
+++ b/tac_ssl_test.py
@@ -0,0 +1,135 @@
+import torch
+from torchvision import transforms
+from torch.utils.data import random_split
+from torch.utils.data import DataLoader, Dataset
+
+import os
+import random
+import pickle
+import numpy as np
+from PIL import Image
+import matplotlib.pyplot as plt
+from sklearn.manifold import TSNE
+from train_mm_moco import MultiModalMoCo
+from sklearn.neighbors import NearestNeighbors
+
+
+def denormalize(tensor, mean, std):
+    for t, m, s in zip(tensor, mean, std):
+        t.mul_(s).add_(m)
+    return tensor
+
+def compute_tsne(model, test_dataloader):
+    with torch.no_grad():
+        test_data_list = list(test_dataloader)
+        x_vision_test, x_tactile_test = random.choice(test_data_list)
+        random_indices = random.sample(range(x_vision_test.shape[0]), 100)
+        x_vision_test = x_vision_test[random_indices].to('cuda')
+        x_tactile_test = x_tactile_test[random_indices].to('cuda')
+        vision_base_q = model.vision_base_q(x_vision_test)
+        tactile_base_q = model.tactile_base_q(x_tactile_test)
+    
+    x_vision_test = denormalize(x_vision_test, [0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+    x_tactile_test = denormalize(x_tactile_test, [0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+
+    vision_base_q = vision_base_q.cpu().numpy()
+    tactile_base_q = tactile_base_q.cpu().numpy()
+
+    image_data = np.concatenate((x_vision_test.cpu().numpy(), x_tactile_test.cpu().numpy()), axis=0)
+
+    tsne = TSNE(n_components=2, random_state=0, perplexity=75,n_iter=50000)
+
+    # Create pairs of corresponding representations and labels
+    num_samples = min(vision_base_q.shape[0], tactile_base_q.shape[0])
+    data = np.concatenate((vision_base_q[:num_samples], tactile_base_q[:num_samples]), axis=0)
+    labels = np.arange(1, 2*(num_samples)+1)
+
+    tsne_data = tsne.fit_transform(data)
+    nn_all = find_knn(tsne_data, labels)
+    plot_images_by_labels(image_data, nn_all[0])
+    print(nn_all[:5])
+
+    fig = plt.figure(figsize=(10, 10))
+    
+    for i, (x, y) in enumerate(tsne_data):
+        plt.scatter(x, y, color='blue')
+        plt.text(x, y, f"{labels[i]}", fontsize=12, ha='center', va='bottom')
+    plt.savefig('temp_figure.png')
+    plt.close(fig)
+
+    image = Image.open('temp_figure.png')
+    image = np.array(image)  # Convert image to a NumPy array
+    image_rgb = image[:, :, :3] # Extract RGB channels and change format to CHW
+    plt.imshow(image_rgb)
+    plt.title('t-SNE plot')
+    plt.axis('off')
+    plt.show()
+
+def find_knn(tsne_data, labels):
+    neigh = NearestNeighbors(n_neighbors=8)
+    neigh.fit(tsne_data)
+    knn = neigh.kneighbors(tsne_data, return_distance=False)
+    return labels[knn]
+
+def plot_images_by_labels(image_data, labels_to_plot):
+    fig, axes = plt.subplots(1, len(labels_to_plot), figsize=(15, 5))
+    for i, label in enumerate(labels_to_plot):
+        axes[i].imshow(image_data[label].transpose(1, 2, 0))
+        axes[i].set_title(label)
+        axes[i].axis('off')
+    plt.show()
+
+if __name__ == "__main__":
+    class CustomMultiModalDataset(Dataset):
+        def __init__(self, vision_folder, tactile_folder, transform=None):
+            self.vision_folder = vision_folder
+            self.tactile_folder = tactile_folder
+            self.transform = transform
+
+            self.vision_files = sorted(os.listdir(vision_folder))
+            self.tactile_files = sorted(os.listdir(tactile_folder))
+
+        def __len__(self):
+            return len(self.vision_files)
+
+        def __getitem__(self, idx):
+            vision_path = os.path.join(self.vision_folder, self.vision_files[idx])
+            tactile_path = os.path.join(self.tactile_folder, self.tactile_files[idx])
+
+            vision_image = Image.open(vision_path).convert("RGB")
+            tactile_image = Image.open(tactile_path).convert("RGB")
+
+            if self.transform:
+                vision_image = self.transform(vision_image)
+                tactile_image = self.transform(tactile_image)
+
+            return vision_image, tactile_image
+
+    # Initialize augmentation
+    simple_transforms = transforms.Compose([
+        transforms.CenterCrop(500),
+        transforms.ToTensor(),
+        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+    ])
+
+    # Load the indices from disk
+    with open('indices/train_indices.pkl', 'rb') as f:
+        train_indices = pickle.load(f)
+        
+    with open('indices/test_indices.pkl', 'rb') as f:
+        test_indices = pickle.load(f)
+
+    # Initialize dataset and dataloader
+    vision_folder = "/home/vedant/Downloads/ssvtp_data/images_rgb"
+    tactile_folder = "/home/vedant/Downloads/ssvtp_data/images_tac"
+    dataset = CustomMultiModalDataset(vision_folder, tactile_folder, transform=simple_transforms)
+
+    # Create subset datasets and DataLoaders
+    test_subset = torch.utils.data.Subset(dataset, test_indices)
+    test_dataloader = DataLoader(test_subset, batch_size=150, shuffle=False)
+
+    from torch.utils.tensorboard import SummaryWriter
+    writer = SummaryWriter('runs/mmssl1')
+    model = MultiModalMoCo(writer).to('cuda')
+    model.load_state_dict(torch.load('/home/vedant/TacSSL/models/model.pth'))
+    compute_tsne(model, test_dataloader)
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