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diff --git a/runs/detect/train36/train_batch0.jpg b/runs/detect/train36/train_batch0.jpg
new file mode 100644
index 0000000..8f43221
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diff --git a/runs/detect/train36/train_batch1.jpg b/runs/detect/train36/train_batch1.jpg
new file mode 100644
index 0000000..c6ac35d
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diff --git a/runs/detect/train36/train_batch2.jpg b/runs/detect/train36/train_batch2.jpg
new file mode 100644
index 0000000..8de88e2
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diff --git a/runs/detect/train36/weights/best.pt b/runs/detect/train36/weights/best.pt
new file mode 100644
index 0000000..0a878cf
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diff --git a/runs/detect/train36/weights/last.pt b/runs/detect/train36/weights/last.pt
new file mode 100644
index 0000000..c130353
Binary files /dev/null and b/runs/detect/train36/weights/last.pt differ
diff --git a/src/labels.py b/src/labels.py
new file mode 100644
index 0000000..011ce9e
--- /dev/null
+++ b/src/labels.py
@@ -0,0 +1,43 @@
+import torch
+from ultralytics import YOLO
+import os
+class Yolov8Detect():
+    def __init__(self, weights):
+        cuda = True if torch.cuda.is_available() else False
+        self.device = torch.device('cuda:0' if cuda else 'cpu')
+        self.detect_model = YOLO(weights)
+        self.detect_model.to(self.device)
+    def inferences(self, inputs):
+        results = self.detect_model(inputs)
+        for result in results:
+            label_text = []
+            boxes = result.boxes
+            for box in boxes:
+                cat_num = int(box.cls.cpu())
+                label_text.append([cat_num, box.xywhn.cpu().numpy()])
+                # 指定新的保存文件夹
+                save_folder = r'C:/workspace/le-yolo/data/labels/aut/'
+                filename = os.path.splitext(os.path.basename(inputs))[0] + '.txt'
+                save_path = os.path.join(save_folder, filename)
+
+                txt_construct(save_path, label_text=label_text)
+def txt_construct(save_path, label_text):
+    with open(save_path, 'w') as file:
+        file.truncate()
+    for label in label_text:
+        with open(save_path, 'a') as txt_file:
+            label_ = label[0]
+            size = label[1][0].tolist()
+            size_string = ' '.join(map(str, size))
+            result = f'{label_} {size_string}'
+            print('result', result)
+            txt_file.write(str(result))
+            txt_file.write('\n')
+
+if __name__ == '__main__':
+    model_path = r'C:\workspace\le-yolo\runs\detect\train34\weights\best.pt'
+    model = Yolov8Detect(model_path)
+    import glob
+    image_path = glob.glob('../data/images/test/*.jpg')
+    for img_path in image_path[:]:
+        model.inferences(img_path)
\ No newline at end of file
diff --git a/src/test.py b/src/test.py
index 1875b89..3ca46c7 100644
--- a/src/test.py
+++ b/src/test.py
@@ -1,3 +1,3 @@
 from ultralytics import YOLO
-model = YOLO(r"C:\workspace\le-yolo\runs\detect\train32\weights\best.pt")
-results = model.predict("../res/3.mp4", show=True, save=True)
+model = YOLO(r"C:\workspace\le-yolo\runs\detect\train35\weights\best.pt")
+results = model.predict("../res/4.mp4", show=True, save=True)
diff --git a/src/train.py b/src/train.py
index e52ae7c..34d9b42 100644
--- a/src/train.py
+++ b/src/train.py
@@ -1,5 +1,5 @@
 from ultralytics import YOLO
-model = YOLO(r"C:\workspace\le-yolo\runs\detect\train32\weights\last.pt")
+model = YOLO(r"C:\workspace\le-yolo\runs\detect\train35\weights\last.pt")
 model.train(data="data.yaml", epochs=100,  batch=8, device='cpu',  imgsz=640)
 model.val()
 print('训练完成')
\ No newline at end of file