diff --git a/README.md b/README.md
index 48ae9ce..9cd1b0d 100644
--- a/README.md
+++ b/README.md
@@ -4,7 +4,6 @@
 
 **车牌颜色和车牌识别一起训练看这里： [车牌识别+车牌颜色](https://github.com/we0091234/crnn_plate_recognition/tree/plate_color)**
 
-
 | 模型   | 准确率 | 速度(ms) | 模型大小(MB) | link                                                 |
 | ------ | ------ | -------- | ------------ | ---------------------------------------------------- |
 | small  | 96.82% | 1.2ms    | 0.67         | [ezhe](https://pan.baidu.com/s/1IsQNPSRuW7bXNWc2ULfFLg) |
@@ -101,9 +100,6 @@ python export.py --weights saved_model/best.pth --save_path saved_model/best.onn
 
 ```
 
-导出onnx文件为 saved_model/best.onnx
-
-如果需要onnx支持trt的话，支持[这里推理](https://github.com/we0091234/chinese_plate_tensorrt)，则加上--trt
 
 #### onnx 推理
 
diff --git a/export.py b/export.py
index 95c60af..a6fc159 100644
--- a/export.py
+++ b/export.py
@@ -14,7 +14,7 @@ if __name__=="__main__":
     parser.add_argument('--batch_size', type=int, default=1, help='batch size')
     parser.add_argument('--dynamic', action='store_true', default=False, help='enable dynamic axis in onnx model')
     parser.add_argument('--simplify', action='store_true', default=False, help='simplified onnx')
-    parser.add_argument('--trt', action='store_true', default=False, help='support trt')
+    # parser.add_argument('--trt', action='store_true', default=False, help='support trt')
 
 
     
@@ -22,7 +22,7 @@ if __name__=="__main__":
     print(opt)
     checkpoint = torch.load(opt.weights)
     cfg = checkpoint['cfg']
-    model = myNet_ocr(num_classes=len(plate_chr),cfg=cfg,export=True,trt=opt.trt)
+    model = myNet_ocr(num_classes=len(plate_chr),cfg=cfg,export=True)
     model.load_state_dict(checkpoint['state_dict'])
     model.eval()
     
diff --git a/plateNet.py b/plateNet.py
index 63faa93..134d272 100644
--- a/plateNet.py
+++ b/plateNet.py
@@ -3,14 +3,13 @@ import torch
 import torch.nn.functional as F
 
 class myNet_ocr(nn.Module):
-    def __init__(self,cfg=None,num_classes=78,export=False,trt=False):
+    def __init__(self,cfg=None,num_classes=78,export=False):
         super(myNet_ocr, self).__init__()
         if cfg is None:
             cfg =[32,32,64,64,'M',128,128,'M',196,196,'M',256,256]
             # cfg =[32,32,'M',64,64,'M',128,128,'M',256,256]
         self.feature = self.make_layers(cfg, True)
         self.export = export
-        self.trt= trt
         # self.classifier = nn.Linear(cfg[-1], num_classes)
         # self.loc =  nn.MaxPool2d((2, 2), (5, 1), (0, 1),ceil_mode=True)
         # self.loc =  nn.AvgPool2d((2, 2), (5, 2), (0, 1),ceil_mode=False)
@@ -48,9 +47,6 @@ class myNet_ocr(nn.Module):
         if self.export:
             conv = x.squeeze(2) # b *512 * width
             conv = conv.transpose(2,1)  # [w, b, c]
-            if self.trt:
-                conv =conv.argmax(dim=2)
-                conv = conv.float()
             return conv
         else:
             b, c, h, w = x.size()