diff --git a/breeding_date_set.py b/breeding_date_set.py
new file mode 100644
index 0000000..2f39933
--- /dev/null
+++ b/breeding_date_set.py
@@ -0,0 +1,119 @@
+import math
+import datetime
+import cv2
+import albumentations as A
+import numpy as np
+import os
+def file_magnification(folder_path):
+    file_txt = []
+    file_jpg = []
+    for foldername, subfolders, filenames in os.walk(folder_path):
+        for filename in filenames:
+            f = filename.split('.')
+
+            if f[-1] == 'txt':
+                file_txt.append(filename)
+            elif f[-1] == 'jpg':
+                file_jpg.append(filename)
+
+    for k in range(len(file_jpg)):
+        image = cv2.imread(f'{folder_path}\\{file_jpg[k]}')
+        annotations = []
+        with open(f'{folder_path}\\{file_txt[k]}', 'r') as file:
+            lines = file.readlines()
+            for line in lines:
+                annotations.append(line)
+
+
+        main_fillet_yolo_conversion = []
+        fillet_yolo_bboxes = []
+        fillet_yolo_class = []
+        print(annotations)
+        for ii in range(len(annotations)):
+            a = annotations[ii].split(' ')
+            for i in range(len(a)):
+                try:
+                    main_fillet_yolo_conversion.append(int(a[i]))
+                except ValueError:
+                    main_fillet_yolo_conversion.append(float(a[i]))
+                print(main_fillet_yolo_conversion)
+            fillet_yolo_class.append(main_fillet_yolo_conversion[0])
+            del main_fillet_yolo_conversion[0]
+            fillet_yolo_bboxes.append(main_fillet_yolo_conversion)
+            main_fillet_yolo_conversion = []
+
+        for o in range(10):
+            bboxes = fillet_yolo_bboxes
+            category_ids = fillet_yolo_class
+
+            transform = A.Compose([
+                A.HorizontalFlip(p=0.5),
+                A.RandomBrightnessContrast(p=0.2),
+                A.ShiftScaleRotate(shift_limit=0.1, scale_limit=0.2, rotate_limit=15, p=0.5),
+            ], bbox_params=A.BboxParams(format='yolo', label_fields=['category_ids']))
+
+            transformed = transform(image=image, bboxes=bboxes, category_ids=category_ids)
+            transformed_image = transformed['image']
+            transformed_bboxes = transformed['bboxes']
+            transformed_category_ids = transformed['category_ids']
+
+            cv2.imwrite(f'{folder_path}\\{o}_{file_jpg[k]}', transformed_image)
+            with open(f'{folder_path}\\{o}_{file_txt[k]}', 'w') as f:
+                for bbox, category_id in zip(transformed_bboxes, transformed_category_ids):
+                    x_center, y_center, width, height = bbox
+                    cla = category_id
+                    f.write(f"{cla} {x_center} {y_center} {width} {height}\n")
+file_txt_1 = []
+file_jpg_1 = []
+file_start = 'Zombobase-'+str(datetime.date.today())
+folder_path = ('train')
+for foldername, subfolders, filenames in os.walk(folder_path):
+    for subfolder in subfolders:
+        folder_path = (f'train\\{subfolder}')
+        for foldername, subfolders, filenames in os.walk(folder_path):
+            for filename in filenames:
+
+                f = filename.split('.')
+
+                if f[-1] == 'txt':
+                    file_txt_1.append(filename)
+                elif f[-1] == 'jpg':
+                    file_jpg_1.append(filename)
+
+annotations = []
+os.makedirs(file_start)
+file = ['test', 'train']
+percent_fille = [0.20,0.10]
+for fi, p_f in zip(file, percent_fille):
+    os.makedirs(f'{file_start}\\{fi}')
+
+    for i in range(math.ceil(len(file_txt_1)* p_f)):
+        image = cv2.imread(f'D:\\train\\images\\{file_jpg_1[i]}')
+        with open(f'D:\\train\\labels\\{file_txt_1[i]}', 'r') as file:
+            lines = file.readlines()
+            for line in lines:
+                annotations.append(line)
+        cv2.imwrite(f'{file_start}\\{fi}\\{file_jpg_1[i]}', image)
+        with open(f'{file_start}\\{fi}\\{file_txt_1[i]}', 'w') as f:
+            for iii in range(len(annotations)):
+                f.write(annotations[iii])
+
+        annotations = []
+        del file_txt_1[i]
+        del file_jpg_1[i]
+
+os.makedirs(f'{file_start}\\validation')
+for a, j in zip(file_txt_1, file_jpg_1):
+    image = cv2.imread(f'D:\\train\\images\\{j}')
+    with open(f'D:\\train\\labels\\{a}', 'r') as file:
+        lines = file.readlines()
+        for line in lines:
+            annotations.append(line)
+    cv2.imwrite(f'{file_start}\\Validation\\{j}', image)
+    with open(f'{file_start}\\Validation\\{a}', 'w') as f:
+        for iii in range(len(annotations)):
+            f.write(annotations[iii])
+    annotations = []
+file = ['test', 'train','validation']
+for i in file:
+    file_magnification(f'{file_start}\\{i}')
\ No newline at end of file
diff --git a/images/Himars_vs_Tulpan_000091.jpg b/images/Himars_vs_Tulpan_000091.jpg
new file mode 100644
index 0000000..afc1e60
Binary files /dev/null and b/images/Himars_vs_Tulpan_000091.jpg differ
diff --git a/labels/Himars_vs_Tulpan_000091.txt b/labels/Himars_vs_Tulpan_000091.txt
new file mode 100644
index 0000000..50d774d
--- /dev/null
+++ b/labels/Himars_vs_Tulpan_000091.txt
@@ -0,0 +1,3 @@
+3 0.41877 0.64332 0.06107 0.07926
+7 0.40442 0.78827 0.06779 0.05212
+7 0.19860 0.90662 0.05314 0.05429
\ No newline at end of file