diff --git a/learn/Darknet2Pytorch2Onnx b/learn/Darknet2Pytorch2Onnx
index 1d4cf92..549f9a6 160000
--- a/learn/Darknet2Pytorch2Onnx
+++ b/learn/Darknet2Pytorch2Onnx
@@ -1 +1 @@
-Subproject commit 1d4cf9249f723166ceaea3573becbba4a7726916
+Subproject commit 549f9a60c2e04771fd45ed8420e012cad28a4094
diff --git a/learn/templates/home.html b/learn/templates/home.html
index 4f2bffb..3aa7918 100644
--- a/learn/templates/home.html
+++ b/learn/templates/home.html
@@ -40,7 +40,7 @@
       $("#class").change(function () {
           $("#model").empty();//清空回调列表;
          var val = this.value;
-          console.log("选择的检测类别：", val);
+          console.log("选择的检测类别：", Object.keys(dic_class)[val-1]);
          $.each(dic_class, function (i, n) { //each()方法为每个匹配元素规定要运行的函数;
               if (Object.keys(dic_class)[val-1] == i) {
                   $.each(dic_class[i], function (j,m) {
@@ -58,10 +58,10 @@
 
 <script>
   var dic_class={};//创建一个字典;
-  dic_class["外破"]=["外破（ljp4500张）四类v5v3", "外破（ljp4500张）四类v5v7", "外破（ljp4500张）四类v5v7m", "烟火与塔吊", "外破（ljp4500张）四类v5v7m6", "yolov3-tiny四类(正式使用)", "yolov3-tiny两类(正式使用)"]
-  dic_class["山火"]=["小模型", "大模型", "泛模型"]
-  dic_class["绝缘子"]=["绝缘子缺陷（ljp2000张）两类v5v7"]
-  dic_class["一般物体80类"]=["v5sv7"]
+  dic_class["外破"]=["所有模型", "外破（ljp4500张）四类v5v3", "外破（ljp4500张）四类v5v7", "外破（ljp4500张）四类v5v7m", "烟火与塔吊", "外破（ljp4500张）四类v5v7m6", "yolov3-tiny四类(正式使用)", "yolov3-tiny两类(正式使用)"]
+  dic_class["山火"]=["所有模型","小模型", "大模型", "泛模型"]
+  dic_class["绝缘子"]=["所有模型","绝缘子缺陷（ljp2000张）两类v5v7"]
+  dic_class["一般物体80类"]=["所有模型","v5sv7"]
 
 $(document).ready(function(){
           $("#model").empty();//清空回调列表;
@@ -90,8 +90,35 @@ document.addEventListener("paste", function () {
         // 字典 dict
         var model = $('#model option:selected').val();
         var model_class = $('#class option:selected').val();
-        console.log("检测类别与使用模型：", Object.keys(dic_class)[model_class-1], ',',dic_class[Object.keys(dic_class)[model_class-1]][model-1])
-        $.getJSON('/dlurl/', {'url':val, 'model':model, 'class':model_class,'model_name':dic_class[Object.keys(dic_class)[model_class-1]][model-1],'class_name':Object.keys(dic_class)[model_class-1]},function(res){ShowResult(res);});
+        if(model==1) {
+          console.log("检测类别所有模型：",Object.keys(dic_class)[model_class-1])
+          var date = new Date(); //时间对象
+          var str = date.getTime(); //转换成时间戳
+          var num = dic_class[Object.keys(dic_class)[model_class-1]].length-1
+          var table = $("<table id='" + str + "' class='main_css'></table>").prependTo($("#img"))
+          console.log("检测总数：", num)
+          for(var i=0; i<Math.ceil(num/3);i++) {
+            var tr = $("<tr id='" + i + "'></tr>").appendTo(table)
+            var last_num 
+
+            if((num-i*3)>2){last_num=3}else{last_num=num-i*3}
+            
+            for(var j=0;j<last_num;j++){
+              var td = $("<td id='" + j + "'></td>").appendTo(tr)
+            }
+          }
+          $.each(dic_class[Object.keys(dic_class)[model_class-1]], function (j,m) {
+            var model_num_count = j+1;
+            if(model_num_count==1) {
+              return true;
+            }
+            $.getJSON('/dlurl/', {'url':val, 'model':model_num_count, 'class':model_class,'model_name':dic_class[Object.keys(dic_class)[model_class-1]][model_num_count-1],'class_name':Object.keys(dic_class)[model_class-1]},function(res){ShowResult(res, num, j-1, table);});
+          })
+        }
+        else {
+          console.log("检测类别与使用模型：", Object.keys(dic_class)[model_class-1], ',',dic_class[Object.keys(dic_class)[model_class-1]][model-1])
+          $.getJSON('/dlurl/', {'url':val, 'model':model, 'class':model_class,'model_name':dic_class[Object.keys(dic_class)[model_class-1]][model-1],'class_name':Object.keys(dic_class)[model_class-1]},function(res){ShowResult(res,1);});
+        }
     }
 });
 </script>
@@ -108,28 +135,40 @@ layui.use('upload', function(){
         'csrfmiddlewaretoken':function(){
             return $(':input:first').val()
         }
-        , 'model':function(){
-          var model = $('#model option:selected').val();
-          return model;
-        }
-        , 'class':function(){
-          var model_class = $('#class option:selected').val();
-          return model_class;
-        }
-        , 'model_name':function(){
-          var model_class = $('#class option:selected').val();
-          var model = $('#model option:selected').val();
-          var model_name=dic_class[Object.keys(dic_class)[model_class-1]][model-1];
-          return model_name;
-        }
-        , 'class_name':function(){
-          var model_class = $('#class option:selected').val();
-          var class_name=Object.keys(dic_class)[model_class-1];
-          return class_name;
-        }
       }
       ,done: function(res){
-      ShowResult(res);
+        // 字典 dict
+        var model = $('#model option:selected').val();
+        var model_class = $('#class option:selected').val();
+        if(model==1) {
+          console.log("检测类别所有模型：",Object.keys(dic_class)[model_class-1])
+          var date = new Date(); //时间对象
+          var str = date.getTime(); //转换成时间戳
+          var num = dic_class[Object.keys(dic_class)[model_class-1]].length-1
+          var table = $("<table id='" + str + "'></table>").prependTo($("#img"))
+          console.log("检测总数：", num)
+          for(var i=0; i<Math.ceil(num/3);i++) {
+            var tr = $("<tr id='" + i + "'></tr>").appendTo(table)
+            var last_num 
+
+            if((num-i*3)>2){last_num=3}else{last_num=num-i*3}
+            
+            for(var j=0;j<last_num;j++){
+              var td = $("<td id='" + j + "'></td>").appendTo(tr)
+            }
+          }
+          $.each(dic_class[Object.keys(dic_class)[model_class-1]], function (j,m) {
+            var model_num_count = j+1;
+            if(model_num_count==1) {
+              return true;
+            }
+            $.getJSON('/dlurl/', {'url':res.upload_img_path, 'filename':res.source_image_name,'model':model_num_count, 'class':model_class,'model_name':dic_class[Object.keys(dic_class)[model_class-1]][model_num_count-1],'class_name':Object.keys(dic_class)[model_class-1]},function(res){ShowResult(res, num, j-1, table);});
+          })
+        }
+        else {
+          console.log("检测类别与使用模型：", Object.keys(dic_class)[model_class-1], ',',dic_class[Object.keys(dic_class)[model_class-1]][model-1])
+          $.getJSON('/dlurl/', {'url':res.upload_img_path, 'filename':res.source_image_name,'model':model, 'class':model_class,'model_name':dic_class[Object.keys(dic_class)[model_class-1]][model-1],'class_name':Object.keys(dic_class)[model_class-1]},function(res){ShowResult(res, 1);});
+        }
     }
     ,error: function(){
     }
@@ -144,7 +183,7 @@ layui.use('upload', function(){
   }  
 </script>
 <script type="text/javascript">
-  function ShowResult(res){
+  function ShowResult(res, num, curnum, table){
             if(res.flag == false)
             {
                 alert("图片地址错误。")
@@ -167,9 +206,20 @@ layui.use('upload', function(){
             }
             };  
             }
-        $("#img").prepend(i);
-        $("#img").prepend('<br>' +'<br>' + '<b>'+'<font size="4">'+"检测信息："+res.code+'</font> '+"</b>"+'<br>');
+        if(num==1) {
+          $("#img").prepend(i);
+          $("#img").prepend('<br>' + '<b>'+'<font size="4">'+"检测信息："+res.code+res.msg+'</font> '+"</b>"+'<br>');
+        }
+        else {
+          var tr_num = Math.floor(curnum/3)
+          var td_num = curnum-Math.floor(curnum/3)*3
+          var w = $(window).width()
+          var img = $('<img class="img1" alt="" src="' + res.img_name + '" onload="DrawImg(' + Math.floor(w/3) + ',600);" />')
+          table.find("tr:eq('" + tr_num + "') td:eq('" + td_num + "')").prepend(img);
+          table.find("tr:eq('" + tr_num + "') td:eq('" + td_num + "')").prepend('<b>'+'<font size="2">'+"检测信息："+res.code+'</font>'+"</b>");
+          //$('.main_css').css('width','1000px')
         }
+  }
     function AutoResizeImage(maxWidth,maxHeight,objImg){
     var img = new Image();
     img.src = objImg.src;
@@ -196,6 +246,31 @@ layui.use('upload', function(){
     objImg.height = h;
     objImg.width = w;
     }
+    function DrawImg(boxWidth,boxHeight)
+{
+    var imgWidth=$(".img1").width();
+    var imgHeight=$(".img1").height();
+    //比较imgBox的长宽比与img的长宽比大小
+    if((boxWidth/boxHeight)>=(imgWidth/imgHeight))
+    {
+        //重新设置img的width和height
+        $(".img1").width((boxHeight*imgWidth)/imgHeight);
+          $(".img1").height(boxHeight);
+          //让图片居中显示
+          var margin=(boxWidth-$(".img1").width())/2;
+          $(".img1").css("margin-left",margin);
+      }
+      else
+      {
+          //重新设置img的width和height
+          $(".img1").width(boxWidth);
+          $(".img1").height((boxWidth*imgHeight)/imgWidth);
+          //让图片居中显示
+          //var margin=(boxHeight-$(".img1").height())/2;
+          var margin=10
+         $(".img1").css("margin-top",margin);
+      }
+  }
 </script>
 <div id="resContainer">
 </div>
diff --git a/learn/views.py b/learn/views.py
index 0f514a4..11b176e 100644
--- a/learn/views.py
+++ b/learn/views.py
@@ -10,6 +10,7 @@ from pathlib import PosixPath
 import requests, os, json
 import cv2
 import numpy as np
+from shutil import copyfile
 
 def adb_shell(cmd):
     result = subprocess.getstatusoutput(cmd)
@@ -44,24 +45,15 @@ def detect_brightness():
 def upload(request):
     if request.method == 'POST':
         file = request.FILES.get('file') #获取前端上传的文件
-        m_model=request.POST.get('model')
-        m_class=request.POST.get('class')
-        model_name = request.POST.get("model_name")
-        class_name = request.POST.get("class_name")
-        print(request.POST["model_name"])
-        print(request.POST["class_name"])
-        fix = datetime.now().strftime('%Y%m%d%H%M%S%f')+'1' #给文件加前缀防止文件名重复
 		#以下用绝对路径存储文件，之前我用相对路径一直写不对
         curPath = os.path.abspath( os.path.dirname( __file__ ) )
-        img_path = os.path.abspath(curPath+'/static/upload/'+fix+file.name)
-        #返回给前端的图片路径用相对路径，前端用绝对路径反而加载不了图片
-        img_path_res = '/static/detected/'+fix+file.name
+        img_path = os.path.abspath(curPath+'/static/upload/'+file.name)
         print(img_path)
         f = open(img_path,'wb')
         for i in file.chunks():
             f.write(i)
         f.close()
-        return inference(m_class, m_model, curPath, img_path_res, img_path, model_name, class_name)
+        return JsonResponse({'upload_img_path':img_path, 'source_image_name':file.name})
 
 
 def dlurl(request):
@@ -75,67 +67,31 @@ def dlurl(request):
         print(request.GET["class_name"])
         print("m_model:",m_model)
         print("url:",url)
-        name = url.split(r'/')[-1]
-        if not (name.endswith(".jpg") or name.endswith(".jpeg") or name.endswith(".png") or name.endswith(".bmp")):
-            return JsonResponse({'flag': False})
+        if(("filename") in request.GET):
+            print("上传图片模式")
+            fix = datetime.now().strftime('%Y%m%d%H%M%S%f')+'1' #给文件加前缀防止文件名重复
+            source_img_path=request.GET["url"]
+            img_path = os.path.join(os.path.dirname(source_img_path),fix+request.GET["filename"])
+            copyfile(source_img_path,img_path)   
+            img_path_res = '/static/detected/'+fix+request.GET["filename"]
+            curPath = os.path.abspath( os.path.dirname( __file__ ) )
+        else:
+            print("下载URL模式")
+            name = url.split(r'/')[-1]
+            if not (name.endswith(".jpg") or name.endswith(".jpeg") or name.endswith(".png") or name.endswith(".bmp")):
+                return JsonResponse({'flag': False})
 
-        fix = datetime.now().strftime('%Y%m%d%H%M%S%f')+'1' #给文件加前缀防止文件名重复
-        curPath = os.path.abspath( os.path.dirname( __file__ ) )
-        img_path = os.path.abspath(curPath+'/static/download/'+fix+name)
-        r = requests.get(url)
-        # 保存
-        with open (img_path, 'wb') as f:
-            f.write(r.content)
-            f.close
-        #返回给前端的图片路径用相对路径，前端用绝对路径反而加载不了图片
-        img_path_res = '/static/detected/'+fix+name
+            fix = datetime.now().strftime('%Y%m%d%H%M%S%f')+'1' #给文件加前缀防止文件名重复
+            curPath = os.path.abspath( os.path.dirname( __file__ ) )
+            img_path = os.path.abspath(curPath+'/static/download/'+fix+name)
+            r = requests.get(url)
+            # 保存
+            with open (img_path, 'wb') as f:
+                f.write(r.content)
+                f.close
+            #返回给前端的图片路径用相对路径，前端用绝对路径反而加载不了图片
+            img_path_res = '/static/detected/'+fix+name
         return inference(m_class, m_model, curPath, img_path_res, img_path, model_name, class_name)
-        '''
-        result = adb_shell("ImageDetect/build/ImageDetect {}".format(img_path))
-        if result[0]==0:
-            cast = result[1].split("||")[0]
-            da = result[1].split("||")[1]
-            satu = result[1].split("||")[2]
-            CON = result[1].split("||")[3]
-            ASM = result[1].split("||")[4]
-            ENT = result[1].split("||")[5]
-            MEAN = result[1].split("||")[6]
-            sb = result[1].split("||")[7]
-            lap = result[1].split("||")[8]
-            dev = result[1].split("||")[9]
-        print(cast, da, satu)
-        mesg = "颜色分布(0-1合理):{}&nbsp;&nbsp;&nbsp;分布方差(负数偏暗):{}&nbsp;&nbsp;&nbsp;色彩饱和度(零为补光):{}<br/>对比度:{}&nbsp;&nbsp;&nbsp;角度方向二阶矩:{}&nbsp;&nbsp;&nbsp;熵:{}&nbsp;&nbsp;&nbsp;平均值:{}&nbsp;&nbsp;&nbsp;Sobel:{}&nbsp;&nbsp;&nbsp;Lap:{}&nbsp;&nbsp;&nbsp;dev:{}".format(cast, da, satu, CON, ASM, ENT, MEAN,sb,lap,dev)
-        if(int(m_class)==1):
-            if(int(m_model)==1): #小模型
-                RR = DL.run(weights=(curPath+"/yolov5/v3s.pt"), source=img_path, project=(curPath+"/static/detected"))
-                return JsonResponse({'img_name':img_path_res,'code':"【烟火 小模型】"+' <br/> '+RR+' <br/> '+mesg, 'flag': True})
-            if(int(m_model)==2): #大模型
-                RR = DL.run(weights=(curPath+"/yolov5/v6m6.pt"), source=img_path, imgsz=(1280, 1280), project=(curPath+"/static/detected"))
-                return JsonResponse({'img_name':img_path_res,'code':"【烟火 大模型】"+' <br/> '+RR+' <br/> '+mesg, 'flag': True})
-            if(int(m_model)==3): #泛模型
-                RR = DL.run(weights=(curPath+"/yolov5/best.pt"), source=img_path, project=(curPath+"/static/detected"))
-                return JsonResponse({'img_name':img_path_res,'code':"【烟火 泛模型】"+' <br/> '+RR+' <br/> '+mesg, 'flag': True})
-        if(int(m_class)==2):
-            RR = DL.run(weights=(curPath+"/yolov5/yolov5s.pt"), source=img_path, project=(curPath+"/static/detected"))
-            return JsonResponse({'img_name':img_path_res,'code':"【一般物体检测】"+' <br/> '+RR+' <br/> '+mesg, 'flag': True})
-        if(int(m_class)==3):
-            RR = DL.run(weights=(curPath+"/yolov5/fire_smoke_towercrane.pt"), source=img_path, project=(curPath+"/static/detected"))
-            return JsonResponse({'img_name':img_path_res,'code':"【烟火+塔吊】"+' <br/> '+RR+' <br/> '+mesg, 'flag': True})
-        if(int(m_class)==4):
-            RR = DL.run(weights=(curPath+"/yolov5/vehicle_4class_ljp_v3.0.pt"), source=img_path, project=(curPath+"/static/detected"))
-            return JsonResponse({'img_name':img_path_res,'code':"【外破四类】"+' <br/> '+RR+' <br/> '+mesg, 'flag': True})
-        if(int(m_class)==5):
-            RR = DL.run(weights=(curPath+"/yolov5/vehicle_4class_ljp_v7.0.pt"), source=img_path, project=(curPath+"/static/detected"))
-            return JsonResponse({'img_name':img_path_res,'code':"【外破四类】"+' <br/> '+RR+' <br/> '+mesg, 'flag': True})
-        if(int(m_class)==6):
-            RR = DL.run(weights=(curPath+"/yolov5/vehicle_4class_ljp_v7.0m.pt"), source=img_path, project=(curPath+"/static/detected"))
-            return JsonResponse({'img_name':img_path_res,'code':"【外破四类】"+' <br/> '+RR+' <br/> '+mesg, 'flag': True})
-        if(int(m_class)==7):
-            RR = DL.run(weights=(curPath+"/yolov5/birdpecked_unnormal_version1.pt"), source=img_path, project=(curPath+"/static/detected"))
-            return JsonResponse({'img_name':img_path_res,'code':"【绝缘子两类】"+' <br/> '+RR+' <br/> '+mesg, 'flag': True})
-        '''
-
-
 
 def home(request):
     return render(request, 'home.html')
@@ -158,45 +114,53 @@ def inference(m_class, m_model, curPath, img_path_res, img_path, model_name, cla
     
     if(int(m_class)==1): #外破
         if(int(m_model)==1):
-            RR = DL.run(weights=(curPath+"/yolov5/vehicle_4class_ljp_v3.0.pt"), source=img_path, project=(curPath+"/static/detected"))
-            return JsonResponse({'img_name':img_path_res,'code':class_name+":"+model_name+",模型: "+"vehicle_4class_ljp_v3.0.pt"+' <br/> '+RR+' <br/> '+mesg, 'flag': True})
+            print("使用所有模型检测")
         if(int(m_model)==2):
+            RR = DL.run(weights=(curPath+"/yolov5/vehicle_4class_ljp_v3.0.pt"), source=img_path, project=(curPath+"/static/detected"))
+            return JsonResponse({'img_name':img_path_res,'code':class_name+":"+model_name+",模型: "+"vehicle_4class_ljp_v3.0.pt"+' <br/> '+RR+' <br/> ',"msg":mesg, 'flag': True})
+        if(int(m_model)==3):
             RR = DL.run(weights=(curPath+"/yolov5/vehicle_4class_ljp_v7.0.pt"), source=img_path, project=(curPath+"/static/detected"))
-            return JsonResponse({'img_name':img_path_res,'code':class_name+":"+model_name+",模型: "+"vehicle_4class_ljp_v7.0.pt"+' <br/> '+RR+' <br/> '+mesg, 'flag': True})  
-        if(int(m_model)==3): 
-            RR = DL.run(weights=(curPath+"/yolov5/vehicle_4class_ljp_v7.0m.pt"), source=img_path, project=(curPath+"/static/detected"))
-            return JsonResponse({'img_name':img_path_res,'code':class_name+":"+model_name+",模型: "+"vehicle_4class_ljp_v7.0m.pt"+' <br/> '+RR+' <br/> '+mesg, 'flag': True}) 
+            return JsonResponse({'img_name':img_path_res,'code':class_name+":"+model_name+",模型: "+"vehicle_4class_ljp_v7.0.pt"+' <br/> '+RR+' <br/> ',"msg":mesg, 'flag': True})  
         if(int(m_model)==4): 
-            RR = DL.run(weights=(curPath+"/yolov5/fire_smoke_towercrane.pt"), source=img_path, project=(curPath+"/static/detected"))
-            return JsonResponse({'img_name':img_path_res,'code':class_name+":"+model_name+",模型: "+"fire_smoke_towercrane.pt"+' <br/> '+RR+' <br/> '+mesg, 'flag': True})
+            RR = DL.run(weights=(curPath+"/yolov5/vehicle_4class_ljp_v7.0m.pt"), source=img_path, project=(curPath+"/static/detected"))
+            return JsonResponse({'img_name':img_path_res,'code':class_name+":"+model_name+",模型: "+"vehicle_4class_ljp_v7.0m.pt"+' <br/> '+RR+' <br/> ',"msg":mesg, 'flag': True}) 
         if(int(m_model)==5): 
-            RR = DL.run(weights=(curPath+"/yolov5/vehicle_4class_ljp_v7.0m6.pt"), source=img_path, project=(curPath+"/static/detected"),imgsz=(1280,1280))
-            return JsonResponse({'img_name':img_path_res,'code':class_name+":"+model_name+",模型: "+"vehicle_4class_ljp_v7.0m6.pt"+' <br/> '+RR+' <br/> '+mesg, 'flag': True})
+            RR = DL.run(weights=(curPath+"/yolov5/fire_smoke_towercrane.pt"), source=img_path, project=(curPath+"/static/detected"))
+            return JsonResponse({'img_name':img_path_res,'code':class_name+":"+model_name+",模型: "+"fire_smoke_towercrane.pt"+' <br/> '+RR+' <br/> ',"msg":mesg, 'flag': True})
         if(int(m_model)==6): 
-            RR=RONNX.main(curPath+"/Darknet2Pytorch2Onnx/hy_vehicle_4class.onnx", curPath+"/Darknet2Pytorch2Onnx/hy_vehicle_4class.names", img_path, (curPath+"/static/detected"))
-            return JsonResponse({'img_name':img_path_res,'code':class_name+":"+model_name+",模型: "+"hy_vehicle_4class.onnx"+' <br/> '+RR+' <br/> '+mesg, 'flag': True})
+            RR = DL.run(weights=(curPath+"/yolov5/vehicle_4class_ljp_v7.0m6.pt"), source=img_path, project=(curPath+"/static/detected"),imgsz=(1280,1280))
+            return JsonResponse({'img_name':img_path_res,'code':class_name+":"+model_name+",模型: "+"vehicle_4class_ljp_v7.0m6.pt"+' <br/> '+RR+' <br/> ',"msg":mesg, 'flag': True})
         if(int(m_model)==7): 
+            RR=RONNX.main(curPath+"/Darknet2Pytorch2Onnx/hy_vehicle_4class.onnx", curPath+"/Darknet2Pytorch2Onnx/hy_vehicle_4class.names", img_path, (curPath+"/static/detected"))
+            return JsonResponse({'img_name':img_path_res,'code':class_name+":"+model_name+",模型: "+"hy_vehicle_4class.onnx"+' <br/> '+RR+' <br/> ',"msg":mesg, 'flag': True})
+        if(int(m_model)==8): 
             RR=RONNX.main(curPath+"/Darknet2Pytorch2Onnx/hy_vehicle_2class.onnx", curPath+"/Darknet2Pytorch2Onnx/hy_vehicle_2class.names", img_path, (curPath+"/static/detected"))
-            return JsonResponse({'img_name':img_path_res,'code':class_name+":"+model_name+",模型: "+"hy_vehicle_2class.onnx"+' <br/> '+RR+' <br/> '+mesg, 'flag': True})
+            return JsonResponse({'img_name':img_path_res,'code':class_name+":"+model_name+",模型: "+"hy_vehicle_2class.onnx"+' <br/> '+RR+' <br/> ',"msg":mesg, 'flag': True})
         
     if(int(m_class)==2): #山火
-        if(int(m_model)==1): # 小
+        if(int(m_model)==1):
+            print("使用所有模型检测")
+        if(int(m_model)==2): # 小
             RR = DL.run(weights=(curPath+"/yolov5/v3s.pt"), source=img_path, project=(curPath+"/static/detected"))
-            return JsonResponse({'img_name':img_path_res,'code':class_name+":"+model_name+",模型: "+"v3s.pt"+' <br/> '+RR+' <br/> '+mesg, 'flag': True})
-        if(int(m_model)==2): # 大
+            return JsonResponse({'img_name':img_path_res,'code':class_name+":"+model_name+",模型: "+"v3s.pt"+' <br/> '+RR+' <br/> ',"msg":mesg, 'flag': True})
+        if(int(m_model)==3): # 大
             RR = DL.run(weights=(curPath+"/yolov5/v6m6.pt"), source=img_path, imgsz=(1280, 1280), project=(curPath+"/static/detected"))
-            return JsonResponse({'img_name':img_path_res,'code':class_name+":"+model_name+",模型: "+"v6m6.pt"+' <br/> '+RR+' <br/> '+mesg, 'flag': True})
-        if(int(m_model)==3): # 范模型
+            return JsonResponse({'img_name':img_path_res,'code':class_name+":"+model_name+",模型: "+"v6m6.pt"+' <br/> '+RR+' <br/> ',"msg":mesg, 'flag': True})
+        if(int(m_model)==4): # 范模型
             RR = DL.run(weights=(curPath+"/yolov5/best.pt"), source=img_path, project=(curPath+"/static/detected"))
-            return JsonResponse({'img_name':img_path_res,'code':class_name+":"+model_name+",模型: "+"best.pt"+' <br/> '+RR+' <br/> '+mesg, 'flag': True})
+            return JsonResponse({'img_name':img_path_res,'code':class_name+":"+model_name+",模型: "+"best.pt"+' <br/> '+RR+' <br/> ',"msg":mesg, 'flag': True})
         
     if(int(m_class)==3): # 绝缘子
         if(int(m_model)==1):
+            print("使用所有模型检测")
+        if(int(m_model)==2):
             RR = DL.run(weights=(curPath+"/yolov5/birdpecked_unnormal_version1.pt"), source=img_path, project=(curPath+"/static/detected"))
-            return JsonResponse({'img_name':img_path_res,'code':class_name+":"+model_name+",模型: "+"birdpecked_unnormal_version1.pt"+' <br/> '+RR+' <br/> '+mesg, 'flag': True})
+            return JsonResponse({'img_name':img_path_res,'code':class_name+":"+model_name+",模型: "+"birdpecked_unnormal_version1.pt"+' <br/> '+RR+' <br/> ',"msg":mesg, 'flag': True})
         
     if(int(m_class)==4): # 一般物体coco
-        if(int(m_model)==1): #yolov5s
+        if(int(m_model)==1):
+            print("使用所有模型检测")
+        if(int(m_model)==2): #yolov5s
             RR = DL.run(weights=(curPath+"/yolov5/yolov5s.pt"), source=img_path, project=(curPath+"/static/detected"))
-            return JsonResponse({'img_name':img_path_res,'code':class_name+":"+model_name+",模型: "+"yolov5s.pt"+' <br/> '+RR+' <br/> '+mesg, 'flag': True})
+            return JsonResponse({'img_name':img_path_res,'code':class_name+":"+model_name+",模型: "+"yolov5s.pt"+' <br/> '+RR+' <br/> ',"msg":mesg, 'flag': True})
             
\ No newline at end of file
diff --git a/learn/yolov5 b/learn/yolov5
index aded494..fc08e6c 160000
--- a/learn/yolov5
+++ b/learn/yolov5
@@ -1 +1 @@
-Subproject commit aded49418b7c23dc1dcf7e62fcdaeef7f3feea5b
+Subproject commit fc08e6c13803803ded26c0e1d2664da396fa115e