chore: 更新最新代码

ultralytics_yolo11-main/point_data.yaml

@@ -0,0 +1,50 @@
+path: /home/hx/yolo/ultralytics_yolo11-main/dataset2 # 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: 1 # number of classes
+kpt_shape: [4, 3] # number of keypoints, number of dims (2 for x,y or 3 for x,y,visible)
+scales: # model compound scaling constants, i.e. 'model=yolo11n-pose.yaml' will call yolo11.yaml with scale 'n'
+  # [depth, width, max_channels]
+  n: [0.50, 0.25, 1024] # summary: 344 layers, 2908507 parameters, 2908491 gradients, 7.7 GFLOPs
+  s: [0.50, 0.50, 1024] # summary: 344 layers, 9948811 parameters, 9948795 gradients, 23.5 GFLOPs
+  m: [0.50, 1.00, 512] # summary: 434 layers, 20973273 parameters, 20973257 gradients, 72.3 GFLOPs
+  l: [1.00, 1.00, 512] # summary: 656 layers, 26230745 parameters, 26230729 gradients, 91.4 GFLOPs
+  x: [1.00, 1.50, 512] # summary: 656 layers, 58889881 parameters, 58889865 gradients, 204.3 GFLOPs
+
+# YOLO11n backbone
+backbone:
+  # [from, repeats, module, args]
+  - [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
+  - [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
+  - [-1, 2, C3k2, [256, False, 0.25]]
+  - [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
+  - [-1, 2, C3k2, [512, False, 0.25]]
+  - [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
+  - [-1, 2, C3k2, [512, True]]
+  - [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
+  - [-1, 2, C3k2, [1024, True]]
+  - [-1, 1, SPPF, [1024, 5]] # 9
+  - [-1, 2, C2PSA, [1024]] # 10
+
+# YOLO11n head
+head:
+  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+  - [[-1, 6], 1, Concat, [1]] # cat backbone P4
+  - [-1, 2, C3k2, [512, False]] # 13
+
+  - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+  - [[-1, 4], 1, Concat, [1]] # cat backbone P3
+  - [-1, 2, C3k2, [256, False]] # 16 (P3/8-small)
+
+  - [-1, 1, Conv, [256, 3, 2]]
+  - [[-1, 13], 1, Concat, [1]] # cat head P4
+  - [-1, 2, C3k2, [512, False]] # 19 (P4/16-medium)
+
+  - [-1, 1, Conv, [512, 3, 2]]
+  - [[-1, 10], 1, Concat, [1]] # cat head P5
+  - [-1, 2, C3k2, [1024, True]] # 22 (P5/32-large)
+
+  - [[16, 19, 22], 1, Pose, [nc, kpt_shape]] # Detect(P3, P4, P5)

ultralytics_yolo11-main/train_point_main.py

@@ -0,0 +1,22 @@
+from ultralytics import YOLO
+
+if __name__ == '__main__':
+    # ✅ 使用官方支持的关键点模型（YOLOv11）
+    model = YOLO(r'ultralytics/cfg/models/11/yolo11-pose.yaml')  # 或
+
+    results = model.train(
+        data='point_data.yaml',
+        imgsz=1280,
+        epochs=100,
+        batch=4,
+        workers=10,
+        device='0',
+        project='runs/train',
+        name='exp_pose',
+        exist_ok=False,
+        optimizer='AdamW',
+        lr0=0.001,
+        patience=20,
+        # ✅ 关键：指定关键点数量
+        kpt_shape=[4, 2],  # [数量, 坐标维度]，您的任务是 4 个关键点 (x,y)
+    )

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

@@ -2,8 +2,8 @@
 # YOLO11-pose keypoints/pose estimation model. For Usage examples see https://docs.ultralytics.com/tasks/pose
 
 # Parameters
-nc: 80 # number of classes
-kpt_shape: [17, 3] # number of keypoints, number of dims (2 for x,y or 3 for x,y,visible)
+nc: 1 # number of classes
+kpt_shape: [4, 3] # number of keypoints, number of dims (2 for x,y or 3 for x,y,visible)
 scales: # model compound scaling constants, i.e. 'model=yolo11n-pose.yaml' will call yolo11.yaml with scale 'n'
   # [depth, width, max_channels]
   n: [0.50, 0.25, 1024] # summary: 344 layers, 2908507 parameters, 2908491 gradients, 7.7 GFLOPs