diff --git a/ieemoo-ai-isempty.py b/ieemoo-ai-isempty.py
index 7bba370..25593a7 100755
--- a/ieemoo-ai-isempty.py
+++ b/ieemoo-ai-isempty.py
@@ -45,12 +45,13 @@ print(torch.__version__)
 
 def parse_args():
     parser = argparse.ArgumentParser()
-    parser.add_argument("--img_size", default=320, type=int, help="Resolution size")
+    parser.add_argument("--img_size", default=600, type=int, help="Resolution size")
     parser.add_argument('--split', type=str, default='overlap', help="Split method")
     parser.add_argument('--slide_step', type=int, default=2, help="Slide step for overlap split")
     parser.add_argument('--smoothing_value', type=float, default=0.0, help="Label smoothing value")
-    parser.add_argument("--pretrained_model", type=str, default="../module/ieemoo-ai-isempty/model/now/emptyjudge5_checkpoint.bin", help="load pretrained model")
+    #parser.add_argument("--pretrained_model", type=str, default="output/emptyjudge5_checkpoint.bin", help="load pretrained model")
     #parser.add_argument("--pretrained_model", type=str, default="output/ieemooempty_vit_checkpoint.pth", help="load pretrained model") #使用自定义VIT
+    parser.add_argument("--pretrained_model", type=str, default="../module/ieemoo-ai-isempty/model/new/ieemooempty_vit_checkpoint.pth", help="load pretrained model")
     opt, unknown = parser.parse_known_args()
     return opt
 
@@ -65,7 +66,7 @@ class Predictor(object):
         self.num_classes = 0
         self.model = None
         self.prepare_model()
-        self.test_transform = transforms.Compose([transforms.Resize((320, 320), Image.BILINEAR),
+        self.test_transform = transforms.Compose([transforms.Resize((600, 600), Image.BILINEAR),
                                              transforms.ToTensor(),
                                              transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])])
 
@@ -84,7 +85,8 @@ class Predictor(object):
         #         self.model = torch.load(self.args.pretrained_model,map_location='cpu')
         self.model = torch.load(self.args.pretrained_model,map_location=torch.device('cpu'))
         self.model.eval()
-        self.model.to("cuda")
+        if torch.cuda.is_available():
+            self.model.to("cuda")
         
     def normal_predict(self, img_data, result):
         # img = Image.open(img_path)
@@ -95,8 +97,8 @@ class Predictor(object):
         else:
             with torch.no_grad():
                 x = self.test_transform(img_data)
-                # if torch.cuda.is_available():
-                #     x = x.cuda()
+                if torch.cuda.is_available():
+                    x = x.cuda()
                 part_logits = self.model(x.unsqueeze(0))
                 probs = torch.nn.Softmax(dim=-1)(part_logits)
                 topN = torch.argsort(probs, dim=-1, descending=True).tolist()