diff --git a/BANK_atm/xml-dimension.py b/BANK_atm/xml-dimension.py
new file mode 100644
index 0000000..d28316f
--- /dev/null
+++ b/BANK_atm/xml-dimension.py
@@ -0,0 +1,110 @@
+import time
+import torch
+from ultralytics import YOLO
+import numpy as np
+import cv2
+import os
+from lxml.etree import Element, SubElement, tostring
+
+
+def create_xml(boxs,img_shape,xml_path):
+    """
+    创建xml文件，依次写入xml文件必备关键字
+    :param boxs:   txt文件中的box
+    :param img_shape:   图片信息，xml中需要写入WHC
+    :return:
+    """
+    node_root = Element('annotation')
+    node_folder = SubElement(node_root, 'folder')
+    node_folder.text = 'Images'
+    node_filename = SubElement(node_root, 'filename')
+    node_filename.text = str(img_shape[3])
+    node_size = SubElement(node_root, 'size')
+    node_width = SubElement(node_size, 'width')
+    node_width.text = str(img_shape[1])
+    node_height = SubElement(node_size, 'height')
+    node_height.text = str(img_shape[0])
+    node_depth = SubElement(node_size, 'depth')
+    node_depth.text = str(img_shape[2])
+
+    if len(boxs)>=1:        # 循环写入box
+        for box in boxs:
+            node_object = SubElement(node_root, 'object')
+            node_name = SubElement(node_object, 'name')
+            # if str(list_[4]) == "person":                # 根据条件筛选需要标注的标签,例如这里只标记person这类，不符合则直接跳过
+            #     node_name.text = str(list_[4])
+            # else:
+            #     continue
+            node_name.text = str(box[4])
+            node_difficult = SubElement(node_object, 'difficult')
+            node_difficult.text = '0'
+            node_bndbox = SubElement(node_object, 'bndbox')
+            node_xmin = SubElement(node_bndbox, 'xmin')
+            node_xmin.text = str(box[0])
+            node_ymin = SubElement(node_bndbox, 'ymin')
+            node_ymin.text = str(box[1])
+            node_xmax = SubElement(node_bndbox, 'xmax')
+            node_xmax.text = str(box[2])
+            node_ymax = SubElement(node_bndbox, 'ymax')
+            node_ymax.text = str(box[3])
+
+    xml = tostring(node_root, pretty_print=True)   # 格式化显示，该换行的换行
+
+    file_name = img_shape[3].split(".")[0]
+    filename = xml_path+"/{}.xml".format(file_name)
+
+    f = open(filename, "wb")
+    f.write(xml)
+    f.close()
+
+
+def draw_bounding_box(img, class_name, confidence, x, y, x_plus_w, y_plus_h,color):
+    label = f'{class_name} ({confidence:.2f})'
+    cv2.rectangle(img, (x, y), (x_plus_w, y_plus_h), color, 2)
+    cv2.putText(img, label, (x - 10, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
+
+
+def main(weights_path,img_path,xml_path,outputs_path):
+    model = YOLO(weights_path)
+    arr = torch.ones(1,3,224,224)
+    result_init = model(arr)[0]                    
+    colors = np.random.uniform(0, 255, size=(len(result_init.names), 3))       # 初始化画图颜色
+
+    for name in os.listdir(img_path):
+        t0 = time.time()
+        original_image = cv2.imread(os.path.join(img_path,name))
+        img_shape = (original_image.shape[0], original_image.shape[1], original_image.shape[2], name)
+
+        # Use the model
+        results = model(original_image)[0]  # predict on an image
+        boxes = results.boxes.cpu().numpy()
+        CLASSES = results.names
+        xyxy_cls = []
+
+        for i in range(len(boxes.xyxy)):
+            xyxy_cls.append([int(boxes.xyxy[i][0]),int(boxes.xyxy[i][1]),int(boxes.xyxy[i][2]),int(boxes.xyxy[i][3]),CLASSES[boxes.cls[i]]])
+            draw_bounding_box(original_image,CLASSES[boxes.cls[i]],boxes.conf[i],int(boxes.xyxy[i][0]),int(boxes.xyxy[i][1]),int(boxes.xyxy[i][2]),int(boxes.xyxy[i][3]),colors[int(boxes.cls[i])])
+
+        if len(xyxy_cls) >0:
+            create_xml(xyxy_cls,img_shape,xml_path)      # 创建xmls
+        t1 = time.time()
+
+        print("img name: {}  infer:{:4f} ms".format(name,(t1-t0)*1000))
+        cv2.imwrite(os.path.join(outputs_path, name), original_image)          # 保留输出结果图
+
+        # cv2.imshow('image', original_image)
+        # cv2.waitKey(0)
+        # cv2.destroyAllWindows()
+
+
+
+if __name__ =="__main__":
+
+    # weights_path =r"C:\Users\Administrator\Desktop\train24\weights\best.pt"
+    weights_path =r"C:\Users\Administrator\Desktop\ultralytics-main\yolov8s.pt"        #权重地址
+    imgs_path = r"C:\Users\Administrator\Desktop\yolov5-label-xml-main\inference\images"  #图片地址
+    xmls_path = r"C:\Users\Administrator\Desktop\yolov5-label-xml-main\inference\xmlss"  #输出xml文件地址
+    outputs_path = "./"
+
+
+    main(weights_path,imgs_path,xmls_path,outputs_path)