diff --git a/TSNE/animals_dataset.py b/TSNE/animals_dataset.py
new file mode 100644
index 000000000..2a6605e95
--- /dev/null
+++ b/TSNE/animals_dataset.py
@@ -0,0 +1,93 @@
+from os import path, listdir
+import torch
+from torchvision import transforms
+import random
+
+from PIL import Image, ImageFile
+ImageFile.LOAD_TRUNCATED_IMAGES = True
+
+
+colors_per_class = {
+    'dog' : [254, 202, 87],
+    'horse' : [255, 107, 107],
+    'elephant' : [10, 189, 227],
+    'butterfly' : [255, 159, 243],
+    'chicken' : [16, 172, 132],
+    'cat' : [128, 80, 128],
+    'cow' : [87, 101, 116],
+    'sheep' : [52, 31, 151],
+    'spider' : [0, 0, 0],
+    'squirrel' : [100, 100, 255],
+}
+
+
+# processes Animals10 dataset: https://www.kaggle.com/alessiocorrado99/animals10
+class AnimalsDataset(torch.utils.data.Dataset):
+    def __init__(self, data_path, num_images=1000):
+        translation = {'cane' : 'dog',
+                       'cavallo' : 'horse',
+                       'elefante' : 'elephant',
+                       'farfalla' : 'butterfly',
+                       'gallina' : 'chicken',
+                       'gatto' : 'cat',
+                       'mucca' : 'cow',
+                       'pecora' : 'sheep',
+                       'ragno' : 'spider',
+                       'scoiattolo' : 'squirrel'}
+
+        self.classes = translation.values()
+
+        if not path.exists(data_path):
+            raise Exception(data_path + ' does not exist!')
+
+        self.data = []
+
+        folders = listdir(data_path)
+        for folder in folders:
+            label = translation[folder]
+
+            full_path = path.join(data_path, folder)
+            images = listdir(full_path)
+
+            current_data = [(path.join(full_path, image), label) for image in images]
+            self.data += current_data
+
+        num_images = min(num_images, len(self.data))
+        self.data = random.sample(self.data, num_images) # only use num_images images
+
+        # We use the transforms described in official PyTorch ResNet inference example:
+        # https://pytorch.org/hub/pytorch_vision_resnet/.
+        self.transform = transforms.Compose([
+            transforms.Resize(256),
+            transforms.CenterCrop(224),
+            transforms.ToTensor(),
+            transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+        ])
+
+
+    def __len__(self):
+        return len(self.data)
+
+
+    def __getitem__(self, index):
+        image_path, label = self.data[index]
+
+        image = Image.open(image_path)
+
+        try:
+            image = self.transform(image) # some images in the dataset cannot be processed - we'll skip them
+        except Exception:
+            return None
+
+        dict_data = {
+            'image' : image,
+            'label' : label,
+            'image_path' : image_path
+        }
+        return dict_data
+
+
+# Skips empty samples in a batch
+def collate_skip_empty(batch):
+    batch = [sample for sample in batch if sample] # check that sample is not None
+    return torch.utils.data.dataloader.default_collate(batch)
diff --git a/TSNE/requirements.txt b/TSNE/requirements.txt
new file mode 100644
index 000000000..eabd6e3ec
--- /dev/null
+++ b/TSNE/requirements.txt
@@ -0,0 +1,6 @@
+torch==1.4
+torchvision==0.5.0
+scikit-learn==0.22.2.post1
+opencv-python>=3.4.1.15
+matplotlib==3.2.1
+tqdm==4.44.1
diff --git a/TSNE/resnet.py b/TSNE/resnet.py
new file mode 100644
index 000000000..dbdcebe3a
--- /dev/null
+++ b/TSNE/resnet.py
@@ -0,0 +1,37 @@
+import torch
+from torchvision import models
+from torch.hub import load_state_dict_from_url
+
+
+# Define the architecture by modifying resnet.
+# Original code is here
+# https://github.com/pytorch/vision/blob/b2e95657cd5f389e3973212ba7ddbdcc751a7878/torchvision/models/resnet.py
+class ResNet101(models.ResNet):
+    def __init__(self, num_classes=1000, pretrained=True, **kwargs):
+        # Start with standard resnet101 defined here
+        # https://github.com/pytorch/vision/blob/b2e95657cd5f389e3973212ba7ddbdcc751a7878/torchvision/models/resnet.py
+        super().__init__(block=models.resnet.Bottleneck, layers=[3, 4, 23, 3], num_classes=num_classes, **kwargs)
+        if pretrained:
+            state_dict = load_state_dict_from_url(models.resnet.model_urls['resnet101'], progress=True)
+            self.load_state_dict(state_dict)
+
+    # Reimplementing forward pass.
+    # Replacing the following code
+    # https://github.com/pytorch/vision/blob/b2e95657cd5f389e3973212ba7ddbdcc751a7878/torchvision/models/resnet.py#L197-L213
+    def _forward_impl(self, x):
+        # Standard forward for resnet
+        x = self.conv1(x)
+        x = self.bn1(x)
+        x = self.relu(x)
+        x = self.maxpool(x)
+
+        x = self.layer1(x)
+        x = self.layer2(x)
+        x = self.layer3(x)
+        x = self.layer4(x)
+
+        # Notice there is no forward pass through the original classifier.
+        x = self.avgpool(x)
+        x = torch.flatten(x, 1)
+
+        return x
diff --git a/TSNE/tsne.py b/TSNE/tsne.py
new file mode 100644
index 000000000..0b72bd061
--- /dev/null
+++ b/TSNE/tsne.py
@@ -0,0 +1,212 @@
+import argparse
+from tqdm import tqdm
+import cv2
+import torch
+import random
+import numpy as np
+from sklearn.manifold import TSNE
+import matplotlib.pyplot as plt
+
+from animals_dataset import AnimalsDataset, collate_skip_empty, colors_per_class
+from resnet import ResNet101
+
+
+def fix_random_seeds():
+    seed = 10
+    random.seed(seed)
+    torch.manual_seed(seed)
+    np.random.seed(seed)
+
+
+def get_features(dataset, batch, num_images):
+    # move the input and model to GPU for speed if available
+    if torch.cuda.is_available():
+        device = 'cuda'
+    else:
+        device = 'cpu'
+
+    # initialize our implementation of ResNet
+    model = ResNet101(pretrained=True)
+    model.eval()
+    model.to(device)
+
+    # read the dataset and initialize the data loader
+    dataset = AnimalsDataset(dataset, num_images)
+    dataloader = torch.utils.data.DataLoader(dataset, batch_size=batch, collate_fn=collate_skip_empty, shuffle=True)
+
+    # we'll store the features as NumPy array of size num_images x feature_size
+    features = None
+
+    # we'll also store the image labels and paths to visualize them later
+    labels = []
+    image_paths = []
+
+    for batch in tqdm(dataloader, desc='Running the model inference'):
+        images = batch['image'].to(device)
+        labels += batch['label']
+        image_paths += batch['image_path']
+
+        with torch.no_grad():
+            output = model.forward(images)
+
+        current_features = output.cpu().numpy()
+        if features is not None:
+            features = np.concatenate((features, current_features))
+        else:
+            features = current_features
+
+    return features, labels, image_paths
+
+
+# scale and move the coordinates so they fit [0; 1] range
+def scale_to_01_range(x):
+    # compute the distribution range
+    value_range = (np.max(x) - np.min(x))
+
+    # move the distribution so that it starts from zero
+    # by extracting the minimal value from all its values
+    starts_from_zero = x - np.min(x)
+
+    # make the distribution fit [0; 1] by dividing by its range
+    return starts_from_zero / value_range
+
+
+def scale_image(image, max_image_size):
+    image_height, image_width, _ = image.shape
+
+    scale = max(1, image_width / max_image_size, image_height / max_image_size)
+    image_width = int(image_width / scale)
+    image_height = int(image_height / scale)
+
+    image = cv2.resize(image, (image_width, image_height))
+    return image
+
+
+def draw_rectangle_by_class(image, label):
+    image_height, image_width, _ = image.shape
+
+    # get the color corresponding to image class
+    color = colors_per_class[label]
+    image = cv2.rectangle(image, (0, 0), (image_width - 1, image_height - 1), color=color, thickness=5)
+
+    return image
+
+
+def compute_plot_coordinates(image, x, y, image_centers_area_size, offset):
+    image_height, image_width, _ = image.shape
+
+    # compute the image center coordinates on the plot
+    center_x = int(image_centers_area_size * x) + offset
+
+    # in matplotlib, the y axis is directed upward
+    # to have the same here, we need to mirror the y coordinate
+    center_y = int(image_centers_area_size * (1 - y)) + offset
+
+    # knowing the image center, compute the coordinates of the top left and bottom right corner
+    tl_x = center_x - int(image_width / 2)
+    tl_y = center_y - int(image_height / 2)
+
+    br_x = tl_x + image_width
+    br_y = tl_y + image_height
+
+    return tl_x, tl_y, br_x, br_y
+
+
+def visualize_tsne_images(tx, ty, images, labels, plot_size=1000, max_image_size=100):
+    # we'll put the image centers in the central area of the plot
+    # and use offsets to make sure the images fit the plot
+    offset = max_image_size // 2
+    image_centers_area_size = plot_size - 2 * offset
+
+    tsne_plot = 255 * np.ones((plot_size, plot_size, 3), np.uint8)
+
+    # now we'll put a small copy of every image to its corresponding T-SNE coordinate
+    for image_path, label, x, y in tqdm(
+            zip(images, labels, tx, ty),
+            desc='Building the T-SNE plot',
+            total=len(images)
+    ):
+        image = cv2.imread(image_path)
+
+        # scale the image to put it to the plot
+        image = scale_image(image, max_image_size)
+
+        # draw a rectangle with a color corresponding to the image class
+        image = draw_rectangle_by_class(image, label)
+
+        # compute the coordinates of the image on the scaled plot visualization
+        tl_x, tl_y, br_x, br_y = compute_plot_coordinates(image, x, y, image_centers_area_size, offset)
+
+        # put the image to its TSNE coordinates using numpy subarray indices
+        tsne_plot[tl_y:br_y, tl_x:br_x, :] = image
+
+    cv2.imshow('T-SNE', tsne_plot)
+    cv2.waitKey()
+
+
+def visualize_tsne_points(tx, ty, labels):
+    # initialize matplotlib plot
+    fig = plt.figure()
+    ax = fig.add_subplot(111)
+
+    # for every class, we'll add a scatter plot separately
+    for label in colors_per_class:
+        # find the samples of the current class in the data
+        indices = [i for i, l in enumerate(labels) if l == label]
+
+        # extract the coordinates of the points of this class only
+        current_tx = np.take(tx, indices)
+        current_ty = np.take(ty, indices)
+
+        # convert the class color to matplotlib format:
+        # BGR -> RGB, divide by 255, convert to np.array
+        color = np.array([colors_per_class[label][::-1]], dtype=np.float) / 255
+
+        # add a scatter plot with the correponding color and label
+        ax.scatter(current_tx, current_ty, c=color, label=label)
+
+    # build a legend using the labels we set previously
+    ax.legend(loc='best')
+
+    # finally, show the plot
+    plt.show()
+
+
+def visualize_tsne(tsne, images, labels, plot_size=1000, max_image_size=100):
+    # extract x and y coordinates representing the positions of the images on T-SNE plot
+    tx = tsne[:, 0]
+    ty = tsne[:, 1]
+
+    # scale and move the coordinates so they fit [0; 1] range
+    tx = scale_to_01_range(tx)
+    ty = scale_to_01_range(ty)
+
+    # visualize the plot: samples as colored points
+    visualize_tsne_points(tx, ty, labels)
+
+    # visualize the plot: samples as images
+    visualize_tsne_images(tx, ty, images, labels, plot_size=plot_size, max_image_size=max_image_size)
+
+
+def main():
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument('--path', type=str, default='data/raw-img')
+    parser.add_argument('--batch', type=int, default=64)
+    parser.add_argument('--num_images', type=int, default=500)
+    args = parser.parse_args()
+
+    fix_random_seeds()
+
+    features, labels, image_paths = get_features(
+        dataset=args.path,
+        batch=args.batch,
+        num_images=args.num_images
+    )
+
+    tsne = TSNE(n_components=2).fit_transform(features)
+
+    visualize_tsne(tsne, image_paths, labels)
+
+if __name__ == '__main__':
+    main()