diff --git a/.gitignore b/.gitignore
index 95d67c8..f8bb4cb 100644
--- a/.gitignore
+++ b/.gitignore
@@ -16,6 +16,8 @@ wan/
 label_imgs/
 pretrained_model/
 cfg1/
+test_imgs/
+weixian/
 # 不忽略下面指定的文件类型
 !*.cpp
 !*.h
diff --git a/detect_rec_plate.py b/detect_rec_plate.py
index 3e009cd..28099ff 100644
--- a/detect_rec_plate.py
+++ b/detect_rec_plate.py
@@ -133,7 +133,7 @@ def draw_result(orgimg,dict_list):
         result = result['plate_no']
         result_str+=result+" "
         cv2.rectangle(orgimg,(rect_area[0],rect_area[1]),(rect_area[2],rect_area[3]),(0,0,255),2) #画框
-        if len(result)>6:
+        if len(result)>1:
             for i in range(4):  #关键点
                 cv2.circle(orgimg, (int(landmarks[i][0]), int(landmarks[i][1])), 5, clors[i], -1)
             
@@ -146,7 +146,7 @@ if __name__ == '__main__':
     parser = argparse.ArgumentParser()
     parser.add_argument('--detect_model', nargs='+', type=str, default='weights/yolov7-lite-s.pt', help='model.pt path(s)')
     parser.add_argument('--rec_model', type=str, default='weights/plate_rec.pth', help='model.pt path(s)')
-    parser.add_argument('--source', type=str, default='wan', help='source')  # file/folder, 0 for webcam
+    parser.add_argument('--source', type=str, default='imgs', help='source')  # file/folder, 0 for webcam
     # parser.add_argument('--img-size', nargs= '+', type=int, default=640, help='inference size (pixels)')
     parser.add_argument('--img_size', type=int, default=640, help='inference size (pixels)')
     parser.add_argument('--output', type=str, default='result', help='source') 
diff --git a/onnx/yolov7_plate_onnx_infer.py b/onnx/yolov7_plate_onnx_infer.py
index caa0fa2..04ee241 100644
--- a/onnx/yolov7_plate_onnx_infer.py
+++ b/onnx/yolov7_plate_onnx_infer.py
@@ -140,6 +140,14 @@ def restore_box(boxes,r,left,top):  #返回原图上面的坐标
     boxes[:,[1,3,6,8,10,12]]/=r
     return boxes
 
+def restore_box_2(boxes,r,left,top):  #返回原图上面的坐标
+    boxes[:,[0,2,5,7]]-=left
+    boxes[:,[1,3,6,8]]-=top
+
+    boxes[:,[0,2,5,7]]/=r
+    boxes[:,[1,3,6,8]]/=r
+    return boxes
+
 def detect_pre_precessing(img,img_size):  #检测前处理
     img,r,left,top=my_letter_box(img,img_size)
     # cv2.imwrite("1.jpg",img)
@@ -159,10 +167,12 @@ def post_precessing(dets,r,left,top,conf_thresh=0.3,iou_thresh=0.45):#检测后
     index = np.argmax(dets[:,5:5+num_cls],axis=-1).reshape(-1,1)
     kpt_b=5+num_cls
     landmarks=dets[:,[kpt_b,kpt_b+1,kpt_b+3,kpt_b+4,kpt_b+6,kpt_b+7,kpt_b+9,kpt_b+10]]              #yolov7关键有三个数，x,y,score，这里我们只需要x,y
+    # landmarks=dets[:,[kpt_b,kpt_b+1,kpt_b+3,kpt_b+4]] 
     output = np.concatenate((boxes,score,landmarks,index),axis=1) 
     reserve_=my_nms(output,iou_thresh) 
     output=output[reserve_] 
     output = restore_box(output,r,left,top)
+    # output = restore_box_2(output,r,left,top)
     return output
 
 def rec_plate(outputs,img0,session_rec):  #识别车牌
@@ -214,7 +224,7 @@ def draw_result(orgimg,dict_list):
         for i in range(4):  #关键点
             cv2.circle(orgimg, (int(landmarks[i][0]), int(landmarks[i][1])), 5, clors[i], -1)
         cv2.rectangle(orgimg,(rect_area[0],rect_area[1]),(rect_area[2],rect_area[3]),(0,0,255),2) #画框
-        if len(result)<0:
+        if len(result)>6:
             orgimg=cv2ImgAddText(orgimg,result,rect_area[0]-height_area,rect_area[1]-height_area-10,(255,0,0),height_area)
     print(result_str)
     return orgimg
diff --git a/utils/loss.py b/utils/loss.py
index 1cdf1f2..e9693d2 100644
--- a/utils/loss.py
+++ b/utils/loss.py
@@ -213,9 +213,9 @@ class ComputeLoss:
         na, nt = self.na, targets.shape[0]  # number of anchors, targets
         tcls, tbox, tkpt, indices, anch = [], [], [], [], []
         if self.kpt_label:
-            gain = torch.ones(self.kpt_label*2+7, device=targets.device)  # normalized to gridspace gain
+            gain = torch.ones(self.kpt_label*2+7, device=targets.device).long()  # normalized to gridspace gain
         else:
-            gain = torch.ones(7, device=targets.device)  # normalized to gridspace gain
+            gain = torch.ones(7, device=targets.device).long()  # normalized to gridspace gain
         ai = torch.arange(na, device=targets.device).float().view(na, 1).repeat(1, nt)  # same as .repeat_interleave(nt)
         targets = torch.cat((targets.repeat(na, 1, 1), ai[:, :, None]), 2)  # append anchor indices