diff --git a/predict/predict_yolo11seg.py b/predict/predict_yolo11seg.py
index 99746c7..b4c5247 100644
--- a/predict/predict_yolo11seg.py
+++ b/predict/predict_yolo11seg.py
@@ -2,7 +2,6 @@ import logging
 import shutil
 import zipfile
 from os.path import exists
-
 import torch
 import gc
 import os
@@ -14,9 +13,7 @@ import glob
 from typing import List, Tuple, Optional, Dict
 from ultralytics import YOLO
 import matplotlib.pyplot as plt
-
 from ultralytics import YOLO
-
 from middleware.recognition_task import RecognitionTaskDAO, RecognitionTask
 from middleware.minio_util import upload_file, downFile, check_zip_size, upload_folder
 from util.yolo2pix_new import *
@@ -91,7 +88,6 @@ class InferenceResult:
         self.scores = []  # 置信度列表
         self.class_names = []  # 类别名称列表
         self.inference_time = 0.0  # 推理时间（秒）
-        self.temp_label_path = None  # YOLO临时输出目录路径
 
 
 class YOLOSegmentationInference:
@@ -227,7 +223,7 @@ class YOLOSegmentationInference:
 
             # 使用YOLO模型进行推理
             # predictions = self.model(image_path, conf=conf_threshold, iou=iou_threshold)[0]
-            predictions = self.model(original_image_rgb, conf=conf_threshold, iou=iou_threshold)[0]
+            predictions = self.model(original_image_rgb, conf=conf_threshold, iou=iou_threshold, imgsz=640)[0]
 
             result.inference_time = time.time() - start_time
 
@@ -291,12 +287,8 @@ class YOLOSegmentationInference:
             print(f"正在处理图片: {os.path.basename(image_path)}")
             start_time = time.time()
 
-            # 使用YOLO模型进行推理（启用save_txt自动生成标签）
-            temp_output_dir = os.path.join(os.path.dirname(image_path), ".yolo_temp")
-            predictions = self.model(image_path, conf=conf_threshold, iou=iou_threshold, 
-                                     save=False, save_txt=True, project=temp_output_dir, 
-                                     name="labels", exist_ok=True)[0]
-            result.temp_label_path = temp_output_dir
+            # 使用YOLO模型进行推理
+            predictions = self.model(image_path, conf=conf_threshold, iou=iou_threshold, imgsz=640)[0]
 
             result.inference_time = time.time() - start_time
 
@@ -398,7 +390,7 @@ class YOLOSegmentationInference:
             result: 推理结果
             output_dir: 输出目录
             save_mask: 是否保存单独的掩码文件
-            save_label: 是否保存YOLO格式的标签文件（使用YOLO原生格式）
+            save_label: 是否保存YOLO格式的标签文件
         """
         if result.result_image is None:
             return
@@ -437,26 +429,50 @@ class YOLOSegmentationInference:
 
                 print(f"共保存 {len(result.masks)} 个掩码文件到: {mask_dir}")
 
-            # 保存YOLO格式的标签文件（直接从YOLO输出复制）
-            if save_label and result.masks is not None and len(result.masks) > 0:
+            # 保存YOLO格式的标签文件
+            if save_label and result.masks is not None and len(result.masks) > 0 and len(result.boxes) > 0:
+                # label_dir = os.path.join(output_dir, "labels")
                 label_dir = output_dir
                 os.makedirs(label_dir, exist_ok=True)
 
                 label_path = os.path.join(label_dir, f"{base_name}.txt")
-                
-                # 从YOLO临时输出目录复制txt文件
-                if hasattr(result, 'temp_label_path') and result.temp_label_path:
-                    yolo_label_path = os.path.join(result.temp_label_path, "labels", f"{base_name}.txt")
-                    if os.path.exists(yolo_label_path):
-                        shutil.copy(yolo_label_path, label_path)
-                        print(f"标签文件已保存: {label_path}")
-                        redir_obj["label"] = label_path
-                    else:
-                        print(f"警告: YOLO生成的标签文件不存在: {yolo_label_path}")
-                else:
-                    print(f"警告: 未找到YOLO临时输出目录")
+
+                with open(label_path, 'w') as f:
+                    for i in range(len(result.masks)):
+                        class_id = result.classes[i]
+                        score = result.scores[i]
+                        mask = result.masks[i]
+
+                        # 获取掩码的多边形轮廓
+                        contours, _ = cv2.findContours((mask > 0.5).astype(np.uint8),
+                                                       cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+
+                        if contours:
+                            # 取最大的轮廓
+                            contour = max(contours, key=cv2.contourArea)
+
+                            # 简化轮廓
+                            epsilon = 0.001 * cv2.arcLength(contour, True)
+                            approx = cv2.approxPolyDP(contour, epsilon, True)
+
+                            # 归一化坐标
+                            h, w = mask.shape
+                            points = []
+                            for point in approx:
+                                x = point[0][0] / w
+                                y = point[0][1] / h
+                                points.extend([x, y])
+
+                            # 写入标签文件
+                            if len(points) >= 6:  # 至少3个点
+                                line = f"{class_id} {' '.join(map(lambda x: f'{x:.6f}', points))} {score:.6f}\n"
+                                f.write(line)
+
+                print(f"标签文件已保存: {label_path}")
+                redir_obj["label"] = label_path
             result_save.append(redir_obj)
-            
+
+
         except PermissionError:
             print(f"权限错误: 无法写入到目录 {output_dir}")
         except Exception as e:
@@ -1008,8 +1024,4 @@ def predict_images_share_dir(task_id, pt_name, zip_url, user_name, pwd, output_d
         else :
             print(f"错误: 输入 {zip_url} 不是有效的文件或目录")
 
-    return standardized_path(f"{target_path}_识别/{task_id}/{current_time}/{task_id}.zip"), "success" 
-
-if __name__ == '__main__':
-    predict_images
-    #predict_images_share_dir(1, "road_crack", "smb://192.168.1.100/share/ai_train_platform/train.zip", "admin", "admin.123", "predictions", 0.25, True)
\ No newline at end of file
+    return standardized_path(f"{target_path}_识别/{task_id}/{current_time}/{task_id}.zip"), "success"   
\ No newline at end of file