diff --git a/xml_2_yolo.py b/xml_2_yolo.py
new file mode 100644
index 00000000..9e948407
--- /dev/null
+++ b/xml_2_yolo.py
@@ -0,0 +1,204 @@
+# %%
+import torch
+from IPython.display import Image  # for displaying images
+import os 
+import random
+import shutil
+from sklearn.model_selection import train_test_split
+import xml.etree.ElementTree as ET
+from xml.dom import minidom
+from tqdm import tqdm
+from PIL import Image, ImageDraw
+import numpy as np
+import matplotlib.pyplot as plt
+
+import string
+
+
+import pathlib
+
+random.seed(108)
+
+# %%
+def extract_info_from_xml(xml_file):
+    root = ET.parse(xml_file).getroot()
+    a=xml_file.split("/")[-1].replace("xml","jpg")
+    
+    # Initialise the info dict 
+    info_dict = {}
+    info_dict['bboxes'] = []
+    info_dict['filename']=a
+    if root.attrib == {'verified': 'yes'}:
+
+        # Parse the XML Tree
+        for elem in root:
+            # # Get the file name 
+            # if elem.tag == "filename":
+            #     info_dict['filename'] = elem.text  
+            # Get the image size
+            if elem.tag == "size":
+                image_size = []
+                for subelem in elem:
+                    image_size.append(int(subelem.text))
+                
+                info_dict['image_size'] = tuple(image_size)
+            
+            # Get details of the bounding box 
+            elif elem.tag == "object":
+                bbox = {}
+                for subelem in elem:
+                    if subelem.tag == "name":
+                        bbox["class"] = subelem.text
+                        
+                    elif subelem.tag == "bndbox":
+                        for subsubelem in subelem:
+                            bbox[subsubelem.tag] = int(subsubelem.text)            
+                info_dict['bboxes'].append(bbox)
+        
+    return info_dict
+
+
+
+
+
+class_name_to_id_mapping=dict(zip(list("1234567890"+string.ascii_uppercase),list(range(len(list("1234567890"+string.ascii_uppercase))))))
+
+
+# Convert the info dict to the required yolo format and write it to disk
+def convert_to_yolov5(info_dict):
+    print_buffer = []
+    
+    # For each bounding box
+    for b in info_dict["bboxes"]:
+        try:
+            class_id = class_name_to_id_mapping[b["class"]]
+        except KeyError:
+            print("Invalid Class. Must be one from ", class_name_to_id_mapping.keys())
+        
+        # Transform the bbox co-ordinates as per the format required by YOLO v5
+        b_center_x = (b["xmin"] + b["xmax"]) / 2 
+        b_center_y = (b["ymin"] + b["ymax"]) / 2
+        b_width    = (b["xmax"] - b["xmin"])
+        b_height   = (b["ymax"] - b["ymin"])
+        
+        # Normalise the co-ordinates by the dimensions of the image
+        image_w, image_h, image_c = info_dict["image_size"]  
+        b_center_x /= image_w 
+        b_center_y /= image_h 
+        b_width    /= image_w 
+        b_height   /= image_h 
+        
+        #Write the bbox details to the file 
+        print_buffer.append("{} {:.3f} {:.3f} {:.3f} {:.3f}".format(class_id, b_center_x, b_center_y, b_width, b_height))
+    # print(print_buffer)
+    # Name of the file which we have to save 
+    save_file_name = os.path.join("unval_add_anno_txt", info_dict["filename"].replace("jpg", "txt"))
+
+    # Save the annotation to disk
+    print("\n".join(print_buffer), file= open(save_file_name, "w"))
+
+
+
+
+
+
+for a,b,c in os.walk("/workspaces/internship_YOLO/yolov5/unval_add_anno/"):
+    annotations=[a+i for i in c]
+
+# Convert and save the annotations
+for ann in tqdm(annotations):
+    info_dict = extract_info_from_xml(ann)
+    # print(info_dict)
+    convert_to_yolov5(info_dict)
+
+
+annotations = [os.path.join('annotations', x) for x in os.listdir('annotations') if x[-3:] == "txt"]
+
+
+
+random.seed(0)
+
+class_id_to_name_mapping = dict(zip(class_name_to_id_mapping.values(), class_name_to_id_mapping.keys()))
+
+def plot_bounding_box(image, annotation_list):
+    annotations = np.array(annotation_list)
+    w, h = image.size
+    
+    plotted_image = ImageDraw.Draw(image)
+
+    transformed_annotations = np.copy(annotations)
+    transformed_annotations[:,[1,3]] = annotations[:,[1,3]] * w
+    transformed_annotations[:,[2,4]] = annotations[:,[2,4]] * h 
+    
+    transformed_annotations[:,1] = transformed_annotations[:,1] - (transformed_annotations[:,3] / 2)
+    transformed_annotations[:,2] = transformed_annotations[:,2] - (transformed_annotations[:,4] / 2)
+    transformed_annotations[:,3] = transformed_annotations[:,1] + transformed_annotations[:,3]
+    transformed_annotations[:,4] = transformed_annotations[:,2] + transformed_annotations[:,4]
+    
+    for ann in transformed_annotations:
+        obj_cls, x0, y0, x1, y1 = ann
+        plotted_image.rectangle(((x0,y0), (x1,y1)))
+        
+        plotted_image.text((x0, y0 - 10), class_id_to_name_mapping[(int(obj_cls))])
+    
+    plt.imshow(np.array(image))
+    plt.show()
+
+# Get any random annotation file 
+annotation_file = random.choice(annotations)
+with open(annotation_file, "r") as file:
+    annotation_list = file.read().split("\n")[:-1]
+    annotation_list = [x.split(" ") for x in annotation_list]
+    annotation_list = [[float(y) for y in x ] for x in annotation_list]
+
+#Get the corresponding image file
+image_file = annotation_file.replace("annotations", "images").replace("txt", "jpg")
+print(image_file)
+assert os.path.exists(image_file)
+
+#Load the image
+image = Image.open(image_file)
+
+#Plot the Bounding Box
+plot_bounding_box(image, annotation_list)
+
+# Read images and annotations
+# images = [os.path.join('images', x) for x in os.listdir('images')]
+annotations = [os.path.join('annotations', x) for x in os.listdir('annotations') if x[-3:] == "txt"]
+images=[a.replace("annotations","images").replace("txt","jpg") for a in annotations]
+
+images.sort()
+annotations.sort()
+
+# Split the dataset into train-valid-test splits 
+train_images, val_images, train_annotations, val_annotations = train_test_split(images, annotations, test_size = 0.2, random_state = 1)
+val_images, test_images, val_annotations, test_annotations = train_test_split(val_images, val_annotations, test_size = 0.5, random_state = 1)
+
+len(train_annotations)
+
+# !mkdir images/train images/val images/test annotations/train annotations/val annotations/test
+
+
+#Utility function to move images 
+def move_files_to_folder(list_of_files, destination_folder):
+    for f in list_of_files:
+        try:
+            shutil.move(f, destination_folder)
+        except:
+            print(f)
+            assert False
+
+# Move the splits into their folders
+move_files_to_folder(train_images, 'images/train')
+move_files_to_folder(val_images, 'images/val/')
+move_files_to_folder(test_images, 'images/test/')
+move_files_to_folder(train_annotations, 'annotations/train/')
+move_files_to_folder(val_annotations, 'annotations/val/')
+move_files_to_folder(test_annotations, 'annotations/test/')
+
+
+
+os.listdir("/workspaces/internship_YOLO/yolov5/unval_add_anno_txt")
+
+
+