基础训练

data.yaml

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+names:
+  0: Abdomen
+  1: Hips
+  2: Chest
+  3: vulva
+  4: back
+  5: penis
+  6: Horror
+path: D:\Train\data\images
+test:
+- test
+train:
+- train
+val:
+- val

path.py

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+from pathlib import Path
+
+import yaml
+
+
+def update_data_yml_path():
+    root_dir = Path(__file__).parent.absolute()
+    images_dir = root_dir / "data" / "images"
+    data_yml_path = root_dir / "data.yaml"
+    with open(data_yml_path, "r", encoding="utf-8") as f:
+        yaml_data = yaml.safe_load(f)
+    if yaml_data is None:
+        yaml_data = {}
+    yaml_data["path"] = str(images_dir.absolute())
+    with open(data_yml_path, "w", encoding="utf-8") as f:
+        yaml.safe_dump(
+            yaml_data,
+            f,
+            allow_unicode=True,
+            default_flow_style=False
+        )

train.py

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+import warnings
+from path import update_data_yml_path
+warnings.filterwarnings('ignore')
+from ultralytics import YOLO
+
+
+if __name__ == '__main__':
+    # 加载并设置配置文件数据集绝对路径
+    update_data_yml_path()
+    # 加载模型配置文件
+    model = YOLO(model=r'yolo11.yml')
+    # 加载预训练权重
+    model.load(r'yolo11n.pt')
+    model.train(
+        data=r'data.yaml',
+        imgsz=640,
+        epochs=200,
+        batch=16,
+        workers=4,
+        close_mosaic=30,
+        device='0',
+        optimizer='SGD',
+        resume=False,
+        project='runs/train',
+        name='result',
+        single_cls=False,
+        cache=False
+    )
+
+

yolo11.yml

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+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Ultralytics YOLO11 object detection model with P3/8 - P5/32 outputs
+# Model docs: https://docs.ultralytics.com/models/yolo11
+# Task docs: https://docs.ultralytics.com/tasks/detect
+
+# Parameters
+nc: 80 # 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: 181 layers, 2624080 parameters, 2624064 gradients, 6.6 GFLOPs
+  s: [0.50, 0.50, 1024] # summary: 181 layers, 9458752 parameters, 9458736 gradients, 21.7 GFLOPs
+  m: [0.50, 1.00, 512] # summary: 231 layers, 20114688 parameters, 20114672 gradients, 68.5 GFLOPs
+  l: [1.00, 1.00, 512] # summary: 357 layers, 25372160 parameters, 25372144 gradients, 87.6 GFLOPs
+  x: [1.00, 1.50, 512] # summary: 357 layers, 56966176 parameters, 56966160 gradients, 196.0 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, Detect, [nc]] # Detect(P3, P4, P5)