diff --git a/recognition/UNet3D_47473289/README.md b/recognition/UNet3D_47473289/README.md
new file mode 100644
index 000000000..b51351991
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+# Improved 3D Unet for Prostate MRI Segmentation
+
+## Overview
+
+The implementation of an improved 3D UNet segmenting the (downsampled) Prostate 3D data set with all labels having a minimum Dice similarity coefficient of 0.7 on the test set.The data is augmented using the appropriate transforms in PyTorch and is loaded from a Nifti file format ".nii.gz"
+
+
+## Improved UNet3D Model
+
+The Improved 3D UNet model is a CNN for 3D image segmentation.
+The model is based on the keras brats UNet model using an encoder and decoder with a bottleneck inbetween. The encoder applies convolutions to the input images and reduces its dimensions to identify patterns in the images. The decoder applies up-convolutions to the downsampled images while concatenating at each step  with the relative encoder convolution in its respective layer. The bottleneck is the connection between the encoder and the decoder.
+
+The dataset is split into training data, validation data and testing data. 
+These datasets are a subset of the total data loaded, split into 70% training, 20% validation and 10% testing.
+
+Training the model has the goal of minimising the DSC loss on the training data, used to maximise
+the DSC in evaluation mode on the validation set. Appropriate transformations are applied to the 
+data to maximise the speed of training.
+
+## Dependencies
+
+PyTorch version: 2.4.0
+Numpy version: 1.26.4
+Tqdm version: 4.66.4
+Nibabel version: 5.3.1
+Python version: 3.12.3
+Matplotlib version: 3.8.4
+
+## Results
+
+Average DSC across classes = 0.6122
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diff --git a/recognition/UNet3D_47473289/dataset.py b/recognition/UNet3D_47473289/dataset.py
new file mode 100644
index 000000000..d8f9fb3f9
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+++ b/recognition/UNet3D_47473289/dataset.py
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+# Data loader for leading and preprocessing data.
+from torch.utils.data import DataLoader, TensorDataset, Dataset
+import torch
+import nibabel as nib
+import numpy as np
+import tqdm as tqdm
+#from sklearn.model_selection import train_test_split
+#from scipy.ndimage import zoom
+import torch.nn as nn
+import glob
+
+
+def to_channels ( arr : np . ndarray , dtype = np . uint8 ) -> np . ndarray :
+    channels = np . unique ( arr )
+    res = np . zeros ( arr . shape + ( len ( channels ) ,) , dtype = dtype )
+    for c in channels :
+        c = int ( c )
+        res [... , c : c +1][ arr == c ] = 1
+
+    return res
+
+
+def load_data_3D(imageNames, normImage=False , categorical=False, dtype=np.float32, 
+                    getAffines=False, orient=False, early_stop=False):
+    '''
+    Load medical image data from names , cases list provided into a list for each .
+
+    This function pre - allocates 5 D arrays for conv3d to avoid excessive memory ↘
+    usage .
+
+    normImage : bool ( normalise the image 0.0 -1.0)
+    orient : Apply orientation and resample image ? Good for images with large slice ↘
+    thickness or anisotropic resolution
+    dtype : Type of the data . If dtype = np . uint8 , it is assumed that the data is ↘
+    labels
+    early_stop : Stop loading pre - maturely ? Leaves arrays mostly empty , for quick ↘
+    loading and testing scripts .
+    '''
+    affines = []
+
+    # ~ interp = ' continuous '
+    interp = 'linear'
+    if dtype == np.uint8: # assume labels
+        interp = 'nearest'
+
+    # get fixed size
+    num = len(imageNames)
+
+        # ~ testResultName = "oriented.nii.gz"
+        # ~ niftiImage.to_filename(testResultName)
+    first_case = nib.load(imageNames[0]).get_fdata(caching='unchanged')
+
+    if len(first_case.shape) == 4:
+        first_case = first_case [:,:,:,0] # sometimes extra dims , remove
+    if categorical:
+        first_case = to_channels(first_case, dtype=dtype)
+        rows, cols, depth, channels = first_case.shape
+        images = np.zeros((num, rows, cols, depth, channels) , dtype=dtype)
+
+    else:
+        rows, cols, depth = first_case.shape
+        images = np.zeros((num, rows, cols, depth), dtype=dtype)
+
+
+    for i ,inName in enumerate(tqdm.tqdm(imageNames)):
+        niftiImage = nib.load(inName)
+        if len(first_case.shape) == 3:
+            first_case = np.expand_dims(first_case, axis=0)
+
+        inImage = niftiImage.get_fdata(caching='unchanged') # read disk only
+        affine = niftiImage.affine
+        
+        if len (inImage.shape) == 4:
+            inImage = inImage [:,:,:,0] # sometimes extra dims in HipMRI_study data
+
+        inImage = inImage [:,:,:depth] # clip slices
+        inImage = inImage.astype(dtype)
+        
+        if normImage:
+            # ~ inImage = inImage / np . linalg . norm ( inImage )
+            # ~ inImage = 255. * inImage / inImage . max ()
+            inImage = (inImage - inImage . mean () ) / inImage . std ()
+        if categorical :
+            inImage = to_channels(inImage, dtype=dtype)
+            # ~ images [i,:,:,:,:] = inImage
+            images [i,:inImage.shape[0],:inImage.shape[1],:inImage.shape [2],:inImage.shape[3]] = inImage # with pad
+      
+        else :
+            # ~ images [i,:,:,:] = inImage
+            images [i,:inImage.shape[0],:inImage.shape[1],:inImage.shape[2]] = inImage # with pad
+            
+        affines.append(affine)
+        if i > 20 and early_stop:
+            print("STOPPED EARly")
+            break
+
+    if getAffines:
+        return images, affines
+    else:
+        print("Returned images")
+        return images
+
+class MyCustomDataset(Dataset):
+    def __init__(self):
+        # load all nii handle in a list
+        #self.image_paths = glob.glob(f'{"/Users/charl/Documents/3710Report/PatternAnalysis-2024/recognition/semantic_MRs_anon"}/**/*.nii.gz', recursive=True)
+        #self.label_paths = glob.glob(f'{"/Users/charl/Documents/3710Report/PatternAnalysis-2024/recognition/semantic_labels_only"}/**/*.nii.gz', recursive=True)
+        self.label_paths = glob.glob(f'{"/home/groups/comp3710/HipMRI_Study_open/semantic_labels_only"}/**/*.nii.gz', recursive=True)
+        self.image_paths = glob.glob(f'{"/home/groups/comp3710/HipMRI_Study_open/semantic_MRs"}/**/*.nii.gz', recursive=True)
+  
+        self.up = torch.nn.Upsample(size=(128,128,128))
+
+        self.classes = {0: 0, 1: 1, 2: 2, 3: 3, 4: 4, 5: 5}
+    def __len__(self):
+        return len(self.image_paths)
+
+    def __getitem__(self, i):
+        image = load_data_3D([self.image_paths[i]],early_stop=True)
+        label = load_data_3D([self.label_paths[i]],early_stop=True)
+        print(image.shape)
+        image = torch.tensor(image).float().unsqueeze(1)
+        label = torch.tensor([self.classes.get(cla.item(), 0) for cla in label.flatten()]).reshape(label.shape)
+        label = nn.functional.one_hot(label.squeeze(0), num_classes=6).permute(3,1,0,2).float()
+        label = label.unsqueeze(0)
+        image = self.up(image).squeeze(0)
+        label = self.up(label).squeeze(0)
+        print(image.shape)
+        print(label.shape)
+
+        return image, label
+
+
+
+dataset = MyCustomDataset()
+print(dataset[10])
+print(dataset[10][1].shape)
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diff --git a/recognition/UNet3D_47473289/modules.py b/recognition/UNet3D_47473289/modules.py
new file mode 100644
index 000000000..a3e95be2f
--- /dev/null
+++ b/recognition/UNet3D_47473289/modules.py
@@ -0,0 +1,118 @@
+# Source code of the components of the UNet3D Model.
+
+import torch
+import torch.nn as nn
+
+class ConvBlock(nn.Module):
+    def __init__(self, in_channels, out_channels):
+        super(ConvBlock, self).__init__()
+        self.block = nn.Sequential(
+            nn.Conv3d(in_channels, out_channels, kernel_size=3, padding=1),
+            nn.BatchNorm3d(out_channels),
+            nn.ReLU(),
+            nn.Conv3d(out_channels, out_channels, kernel_size=3, padding=1),
+            nn.BatchNorm3d(out_channels),
+            nn.ReLU()
+        )
+
+    def forward(self, x):
+        return self.block(x)
+
+class UpConv(nn.Module):
+    def __init__(self, in_channels, out_channels):
+        super(UpConv, self).__init__()
+        self.up = nn.ConvTranspose3d(in_channels, out_channels, kernel_size=2, stride=2)
+
+    def forward(self, x):
+        return self.up(x)
+
+class ImprovedUNet3D(nn.Module):
+    def __init__(self, in_channels=1, out_channels=1):
+        super(ImprovedUNet3D, self).__init__()
+        print("UNET INIT")
+        # Contracting path
+        self.conv1 = ConvBlock(in_channels, 64)
+        self.pool1 = nn.MaxPool3d(2)
+
+        self.conv2 = ConvBlock(64, 128)
+        self.pool2 = nn.MaxPool3d(2)
+
+        self.conv3 = ConvBlock(128, 256)
+        self.pool3 = nn.MaxPool3d(2)
+
+        self.conv4 = ConvBlock(256, 512)
+        self.pool4 = nn.MaxPool3d(2)
+
+        self.bottleneck = ConvBlock(512, 1024)
+
+        # Expansive path
+        self.upconv4 = UpConv(1024, 512)
+        self.conv_up4 = ConvBlock(1024, 512)
+
+        self.upconv3 = UpConv(512, 256)
+        self.conv_up3 = ConvBlock(512, 256)
+
+        self.upconv2 = UpConv(256, 128)
+        self.conv_up2 = ConvBlock(256, 128)
+
+        self.upconv1 = UpConv(128, 64)
+        self.conv_up1 = ConvBlock(128, 64)
+
+        self.final_conv = nn.Conv3d(64, out_channels, kernel_size=1)
+
+    def forward(self, x):
+        x1 = self.conv1(x)
+        p1 = self.pool1(x1)
+
+        x2 = self.conv2(p1)
+        p2 = self.pool2(x2)
+
+        x3 = self.conv3(p2)
+        p3 = self.pool3(x3)
+
+        x4 = self.conv4(p3)
+        p4 = self.pool4(x4)
+
+        bn = self.bottleneck(p4)
+
+        u4 = self.upconv4(bn)
+        u4 = torch.cat([u4, x4], dim=1)
+        u4 = self.conv_up4(u4)
+
+
+        u3 = self.upconv3(u4)
+        u3 = torch.cat([u3, x3], dim=1)
+        u3 = self.conv_up3(u3)
+
+        u2 = self.upconv2(u3)
+        u2 = torch.cat([u2, x2], dim=1)
+        u2 = self.conv_up2(u2)
+
+        u1 = self.upconv1(u2)
+        u1 = torch.cat([u1, x1], dim=1)
+        u1 = self.conv_up1(u1)
+
+        return self.final_conv(u1)
+    
+class DSC(nn.Module):
+    """
+        Implementation of Dice-Sorensen Coefficient loss
+        2 * |predict * target|/|predict|+|target|
+    """
+    def __init__(self):
+        super(DSC, self).__init__()
+        # Smooth to avoid 0 division
+        self.smooth = 1.0 
+
+    def forward(self, y_pred, y_true):
+        assert y_pred.size() == y_true.size()
+
+        intersection = (y_pred * y_true).sum(dim=[2,3,4])
+        # Calculate DSC
+        dsc = (2. * intersection + self.smooth) / (
+            y_pred.sum(dim=[2,3,4]) + y_true.sum(dim=[2,3,4]) + self.smooth
+        )
+
+        dsc = dsc.mean()
+        print(1. - dsc)
+        return 1. - dsc
diff --git a/recognition/UNet3D_47473289/predict.py b/recognition/UNet3D_47473289/predict.py
new file mode 100644
index 000000000..a489fa2c6
--- /dev/null
+++ b/recognition/UNet3D_47473289/predict.py
@@ -0,0 +1,64 @@
+# Example usage of the trained UNet3D model.
+import torch
+import matplotlib.pyplot as plt
+
+from skimage.util import montage
+import numpy as np
+
+
+#dataset = MyCustomDataset()
+#_, test = torch.utils.data.random_split(cust_dataset, [0.9, 0.1])
+#test_loader = DataLoader(test)
+#model = ImprovedUNet3D(in_channels=1, out_channels=6).cuda()
+#model.load_state_dict(torch.load('./new_folder/model.pth'))
+def predict(model, test_loader):
+    model.eval()
+    with torch.no_grad():
+        for test_images, test_masks in test_loader:
+            # moves test_images to be on gpu
+            vis = Visulizer()
+            vis.visualize(test_images, test_masks)
+            
+            test_images = test_images.cuda()
+            test_outputs = model(test_images)
+            predicted_masks = torch.argmax(test_outputs, dim=1)
+
+            plt.figure(figsize=(10, 5))
+            # The base input image
+            plt.subplot(1, 3, 1)
+            plt.imshow(test_images[0].cpu().squeeze(), cmap='gray')
+            plt.title("Input Image")
+
+            # The segmentation mask
+            plt.subplot(1, 3, 2)
+            plt.imshow(test_masks[0].cpu().argmax(dim=0).squeeze(), cmap='gray')
+            plt.title("Segmentation Mask")
+
+            # The predicted Mask
+            plt.subplot(1, 3, 3)
+            plt.imshow(predicted_masks[0].cpu().squeeze(), cmap='gray')
+            plt.title("Predicted Mask")
+
+            # Save image
+            output_path = "output.png"
+            plt.savefig(output_path)
+            plt.show()  
+            break
+            
+
+class Visulizer:
+    def montage_nd(self, image):
+        if len(image.shape)>3:
+            return montage(np.stack([self.montage_nd(img) for img in image],0))
+        elif len(image.shape)==3:
+            return montage(image)
+        else:
+            print('Input less than 3d image, returning original')
+            return image
+
+    def visualize(self, image, mask):
+        fig, axs = plt.subplots(1, 2, figsize = (20, 15 * 2))
+        axs[0].imshow(self.montage_nd(image[...,0]), cmap = 'bone')
+        axs[1].imshow(self.montage_nd(mask[...,0]), cmap = 'bone')
+        plt.show()
+        
diff --git a/recognition/UNet3D_47473289/train.py b/recognition/UNet3D_47473289/train.py
new file mode 100644
index 000000000..bac5c9c68
--- /dev/null
+++ b/recognition/UNet3D_47473289/train.py
@@ -0,0 +1,84 @@
+# Source code for training, validating, testing and saving the UNet3D Model.
+from modules import *
+from dataset import *
+from predict import *
+import torch
+import torch.optim as optim
+from sklearn.model_selection import train_test_split
+import time
+import glob
+import os
+torch.cuda.empty_cache()
+
+
+cust_dataset = MyCustomDataset()
+train, val, test = torch.utils.data.random_split(cust_dataset, [0.7, 0.2, 0.1])
+train = DataLoader(train)
+val = DataLoader(val)
+test = DataLoader(test)
+model = ImprovedUNet3D(in_channels=1, out_channels=6).cuda()
+optimizer = optim.Adam(model.parameters(), lr=1e-4)
+
+
+criterion = DSC()
+
+learn_rate = 0.1
+# Alternates minimum and maximum learning rate
+l_sched_1 = optim.lr_scheduler.CyclicLR(optimizer, base_lr=0.005, max_lr = learn_rate, step_size_up=15, step_size_down=15, mode='triangular', verbose= False)
+# Linearly reduces learning rate
+l_sched_2 = optim.lr_scheduler.LinearLR(optimizer , start_factor=0.005/learn_rate, end_factor=0.005/5, verbose=False)
+# Sequentially applies sched 1 and 2 
+scheduler = optim.lr_scheduler.SequentialLR(optimizer, schedulers=[l_sched_1, l_sched_2], milestones=[30])
+
+
+# No. of Epochs (Number of times all training data is run)
+n_epochs = 5
+train_losses = []
+print(">> Training <<")
+start = time.time()
+for epoch in range(n_epochs):
+    epstart = time.time()
+    # Set training mode
+    model.train()
+    running_loss = 0.0
+    print(len(train))
+    for i, (inputs, labels) in enumerate(train):
+        inputs = inputs.cuda()
+        labels = labels.cuda()
+
+        optimizer.zero_grad()
+        output = model(inputs)
+
+        loss = criterion(output, labels)
+
+        loss.backward()
+
+        optimizer.step()
+
+        running_loss += loss.item()
+    scheduler.step()
+    # Set evaluation mode for validation
+    train_loss = running_loss / len(train)
+    train_losses.append(train_loss)
+    model.eval()
+    scores = []
+    with torch.no_grad():
+        for i, (val_inputs, val_labels, ) in enumerate(val):
+            val_inputs = val_inputs.cuda()
+            val_labels = val_labels.cuda()
+            output = model(val_inputs)
+            # Calculating Dice score for each pass
+            score = 1 - criterion(output, val_labels)
+            scores.append(score)
+    # Average validation score for all batches
+    mean_score = sum(scores) / len(scores)
+    epend = time.time()
+    print(f"Epoch {epoch + 1}/{n_epochs}, Training Loss: {loss.item()}, Validation DSC: {mean_score}")
+    print(f"Epoch {epoch + 1}/{n_epochs} took {epend - epstart} seconds")
+
+
+predict(model, test)
+
+end = time.time()
+total_time = end - start
+print(f"Training and Validation took {total_time} seconds or {total_time/60} minutes.")