增加cvat反向上传

ultralytics_yolo11-main/train_xiantiao_detect.py

@@ -0,0 +1,19 @@
+from ultralytics import YOLO
+
+if __name__ == '__main__':
+    #model = YOLO(r'/home/hx/yolo/ultralytics_yolo11-main/ultralytics/cfg/models/11/yolo11-obb.yaml')
+    model = YOLO(r'/home/hx/yolo/ultralytics_yolo11-main/ultralytics/cfg/models/11/yolo11.yaml')
+    results = model.train(
+        data='xiantiaodata.yaml',
+        epochs=2000,
+        imgsz=640,
+        batch=4,
+        workers=10,
+        device='0',
+        project='runs/train',
+        name='exp_detect',
+        exist_ok=False,
+        optimizer='AdamW',
+        lr0=0.0005,
+        patience=0,
+    )

ultralytics_yolo11-main/ultralytics/cfg/models/11/yolo11.yaml

@@ -2,7 +2,7 @@
 # YOLO11 object detection model with P3-P5 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect
 
 # Parameters
-nc: 1 # number of classes
+nc: 2 # number of classes
 scales: # model compound scaling constants, i.e. 'model=yolo11n.yaml' will call yolo11.yaml with scale 'n'
   # [depth, width, max_channels]
   n: [0.50, 0.25, 1024] # summary: 319 layers, 2624080 parameters, 2624064 gradients, 6.6 GFLOPs

ultralytics_yolo11-main/xiantiaodata.yaml

@@ -0,0 +1,7 @@
+path: /home/hx/开发/ML_xiantiao/class_xiantiao_pc/test_image # 数据集所在路径
+train: train  # 数据集路径下的train.txt
+val: val  # 数据集路径下的val.txt
+test: test  # 数据集路径下的test.txt
+
+nc: 2
+names: ['hole','crack']