最新推送

ultralytics_yolo11-main/ailaidata.yaml

@@ -3,5 +3,5 @@ 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
-names: ['bag']
+nc: 2
+names: ['bag','bag35']

ultralytics_yolo11-main/data_seg60.yaml

@@ -0,0 +1,7 @@
+path: /media/hx/04e879fa-d697-4b02-ac7e-a4148876ebb0/dataset/60seg/60-seg
+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
+names: ['yemian']

ultralytics_yolo11-main/data_seg61.yaml

@@ -0,0 +1,7 @@
+path: /media/hx/04e879fa-d697-4b02-ac7e-a4148876ebb0/dataset/61_seg1
+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
+names: ['yemian']

ultralytics_yolo11-main/train_ailai_detect.py

@@ -1,11 +1,11 @@
 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/runs/train/exp_ailai_detect2/weights/best.pt')
     model = YOLO(r'/home/hx/yolo/ultralytics_yolo11-main/ultralytics/cfg/models/11/yolo11.yaml')
     results = model.train(
         data='ailaidata.yaml',
-        epochs=1000,
+        epochs=500,
         imgsz=640,
         batch=4,
         workers=10,
@@ -14,6 +14,6 @@ if __name__ == '__main__':
         name='exp_ailai_detect',
         exist_ok=False,
         optimizer='AdamW',
-        lr0=0.0001,
+        lr0=0.0005,
         patience=0,
-    )
+    )

ultralytics_yolo11-main/train_ailai_detect_n.py

@@ -26,3 +26,4 @@ results = model.train(
     close_mosaic=10,
     val=True,
 )
+

ultralytics_yolo11-main/train_ailai_main.py

@@ -2,10 +2,11 @@ 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/runs/train/exp_ailai2/weights/best.pt')
+    #model = YOLO(r'/home/hx/yolo/ultralytics_yolo11-main/runs/train/exp_ailai2/weights/best.pt')
+    model = YOLO(r'/home/hx/yolo/ultralytics_yolo11-main/ultralytics/cfg/models/11/yolo11-ailai.yaml')
     results = model.train(
         data='data_ailai.yaml',
-        epochs=1000,
+        epochs=300,
         imgsz=640,
         batch=4,
         workers=10,
@@ -14,6 +15,6 @@ if __name__ == '__main__':
         name='exp_ailai',
         exist_ok=False,
         optimizer='AdamW',
-        lr0=0.0001,
+        lr0=0.0005,
         patience=0,
     )

ultralytics_yolo11-main/train_cls_main.py

@@ -1,16 +1,17 @@
 from ultralytics import YOLO
 
 if __name__ == '__main__':
-    model = YOLO(r'/home/hx/yolo/ultralytics_yolo11-main/ultralytics/cfg/models/11/yolo11-cls-xiantiao.yaml')
+    model = YOLO(r'/home/hx/yolo/ultralytics_yolo11-main/ultralytics/cfg/models/11/yolo11-2cls.yaml')
     results = model.train(
-        data='/home/hx/开发/ML_xiantiao/image/datasetr1',
+        #data='/home/hx/开发/ML_xiantiao/image/datasetr1',
+        data='/media/hx/04e879fa-d697-4b02-ac7e-a4148876ebb0/dataset/1/charge',
         epochs=1000,
         imgsz=640,
         batch=4,
         workers=10,
         device='0',
         project='runs/train/cls',
-        name='exp_xiantiao_cls',
+        name='exp_zdb_cls',
         exist_ok=False,
         optimizer='AdamW',
         lr0=0.0005,

ultralytics_yolo11-main/train_resize_cls_muju.py

@@ -0,0 +1,19 @@
+from ultralytics import YOLO
+
+if __name__ == '__main__':
+    model = YOLO(r'/home/hx/yolo/muju_cls/yolo11-cls-muju-resize.yaml')
+    #model = YOLO(r'/home/hx/yolo/ultralytics_yolo11-main/runs/train/cls_resize/exp_cls6/weights/best.pt')
+    results = model.train(
+        data='/media/hx/04e879fa-d697-4b02-ac7e-a4148876ebb0/dataset/1/模具车分类resize',
+        epochs=500,
+        imgsz=640,
+        batch=4,
+        workers=10,
+        device='0',
+        project='runs/train/cls_resize_muju',
+        name='exp_cls',
+        exist_ok=False,
+        optimizer='AdamW',
+        lr0=0.0005,
+        patience=0,
+    )

ultralytics_yolo11-main/train_seg_r_main_60.py

@@ -2,20 +2,20 @@ from ultralytics import YOLO
 
 if __name__ == '__main__':
     # ✅ 推荐：使用官方预训练分割模型
-    #model = YOLO(r'/home/hx/yolo/ultralytics_yolo11-main/runs/train/seg_j/exp2/weights/best.pt')
+    #model = YOLO(r'/home/hx/yolo/ultralytics_yolo11-main/runs/train/61seg/exp2/weights/best.pt')
     model = YOLO(r'/home/hx/yolo/ultralytics_yolo11-main/ultralytics/cfg/models/11/yolo11-seg.yaml')
     # 开始训练
     results = model.train(
-        data='/home/hx/yolo/ultralytics_yolo11-main/resize_seg_data.yaml',           # 数据配置文件
-        epochs=100,               # 训练轮数
+        data='data_seg60.yaml',           # 数据配置文件
+        epochs=300,                 # 训练轮数
         imgsz=1280,
         batch=4,                   # 每批图像数量
         workers=10,                 # 数据加载线程数
         device='0',                 # 使用 GPU 0
-        project='runs/train/seg_r',       # 保存项目目录
+        project='runs/train/60seg',       # 保存项目目录
         name='exp',                 # 实验名称
         exist_ok=False,             # 不覆盖已有实验
         optimizer='AdamW',          # 可选优化器
-        lr0=0.0005,                  # 初始学习率
+        lr0=0.0003,                  # 初始学习率
         patience=0,                # 早停轮数
     )

ultralytics_yolo11-main/train_seg_r_main_61.py

@@ -2,20 +2,20 @@ from ultralytics import YOLO
 
 if __name__ == '__main__':
     # ✅ 推荐：使用官方预训练分割模型
-    #model = YOLO(r'/home/hx/yolo/ultralytics_yolo11-main/runs/train/seg_j/exp2/weights/best.pt')
+    #model = YOLO(r'/home/hx/yolo/ultralytics_yolo11-main/runs/train/61seg/exp2/weights/best.pt')
     model = YOLO(r'/home/hx/yolo/ultralytics_yolo11-main/ultralytics/cfg/models/11/yolo11-seg.yaml')
     # 开始训练
     results = model.train(
-        data='data.yaml',           # 数据配置文件
-        epochs=200,                 # 训练轮数
+        data='data_seg61.yaml',           # 数据配置文件
+        epochs=500,                # 训练轮数
         imgsz=1280,
         batch=4,                   # 每批图像数量
         workers=10,                 # 数据加载线程数
         device='0',                 # 使用 GPU 0
-        project='runs/train/seg_02',       # 保存项目目录
+        project='runs/train/61seg',       # 保存项目目录
         name='exp',                 # 实验名称
         exist_ok=False,             # 不覆盖已有实验
         optimizer='AdamW',          # 可选优化器
-        lr0=0.0005,                  # 初始学习率
-        patience=20,                # 早停轮数
+        lr0=0.0005,               # 初始学习率
+        patience=0,                # 早停轮数
     )

ultralytics_yolo11-main/ultralytics/cfg/models/11/yolo11-2.yaml

@@ -0,0 +1,47 @@
+# Ultralytics YOLO 🚀, AGPL-3.0 license
+# YOLO11 object detection model with P3-P5 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect
+
+# Parameters
+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
+  s: [0.50, 0.50, 1024] # summary: 319 layers, 9458752 parameters, 9458736 gradients, 21.7 GFLOPs
+  m: [0.50, 1.00, 512] # summary: 409 layers, 20114688 parameters, 20114672 gradients, 68.5 GFLOPs
+  l: [1.00, 1.00, 512] # summary: 631 layers, 25372160 parameters, 25372144 gradients, 87.6 GFLOPs
+  x: [1.00, 1.50, 512] # summary: 631 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)

ultralytics_yolo11-main/ultralytics/cfg/models/11/yolo11-2cls.yaml

@@ -0,0 +1,30 @@
+# Ultralytics YOLO 🚀, AGPL-3.0 license
+# YOLO11-cls image classification model. For Usage examples see https://docs.ultralytics.com/tasks/classify
+
+# Parameters
+nc: 5 # number of classes
+scales: # model compound scaling constants, i.e. 'model=yolo11n-cls.yaml' will call yolo11-cls.yaml with scale 'n'
+  # [depth, width, max_channels]
+  n: [0.50, 0.25, 1024] # summary: 151 layers, 1633584 parameters, 1633584 gradients, 3.3 GFLOPs
+  s: [0.50, 0.50, 1024] # summary: 151 layers, 5545488 parameters, 5545488 gradients, 12.2 GFLOPs
+  m: [0.50, 1.00, 512] # summary: 187 layers, 10455696 parameters, 10455696 gradients, 39.7 GFLOPs
+  l: [1.00, 1.00, 512] # summary: 309 layers, 12937104 parameters, 12937104 gradients, 49.9 GFLOPs
+  x: [1.00, 1.50, 512] # summary: 309 layers, 28458544 parameters, 28458544 gradients, 111.1 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, 2, C2PSA, [1024]] # 9
+
+# YOLO11n head
+head:
+  - [-1, 1, Classify, [nc]] # Classify

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: 2 # number of classes
+nc: 1 # 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