增加obb

ultralytics_yolo11-main/obb_data.yaml

@@ -0,0 +1,16 @@
+# Ultralytics YOLO 🚀, AGPL-3.0 license
+# DOTA8 dataset 8 images from split DOTAv1 dataset by Ultralytics
+# Documentation: https://docs.ultralytics.com/datasets/obb/dota8/
+# Example usage: yolo train model=yolov8n-obb.pt data=dota8.yaml
+# parent
+# ├── ultralytics
+# └── datasets
+#     └── dota8  ← downloads here (1MB)
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+train: /home/hx/桌面/image/images/train
+val: /home/hx/桌面/image/images/val
+
+nc: 1
+names: ['clamp']
+

ultralytics_yolo11-main/train_obb_main.py

@@ -0,0 +1,18 @@
+from ultralytics import YOLO
+
+if __name__ == '__main__':
+    model = YOLO(r'/home/hx/yolo/ultralytics_yolo11-main/ultralytics/cfg/models/11/yolo11-obb.yaml')
+    results = model.train(
+        data='obb_data.yaml',
+        epochs=1000,
+        imgsz=1280,
+        batch=4,
+        workers=10,
+        device='0',
+        project='runs/train',
+        name='exp_obb',
+        exist_ok=False,
+        optimizer='AdamW',
+        lr0=0.001,
+        patience=20,
+    )

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

@@ -2,7 +2,7 @@
 # YOLO11 Oriented Bounding Boxes (OBB) model with P3-P5 outputs. For Usage examples see https://docs.ultralytics.com/tasks/obb
 
 # Parameters
-nc: 80 # number of classes
+nc: 1 # number of classes
 scales: # model compound scaling constants, i.e. 'model=yolo11n-obb.yaml' will call yolo11-obb.yaml with scale 'n'
   # [depth, width, max_channels]
   n: [0.50, 0.25, 1024] # summary: 344 layers, 2695747 parameters, 2695731 gradients, 6.9 GFLOPs

ultralytics_yolo11-main/ultralytics/data/converter.py

@@ -455,24 +455,7 @@ def convert_dota_to_yolo_obb(dota_root_path: str):
 
     # Class names to indices mapping
     class_mapping = {
-        "plane": 0,
-        "ship": 1,
-        "storage-tank": 2,
-        "baseball-diamond": 3,
-        "tennis-court": 4,
-        "basketball-court": 5,
-        "ground-track-field": 6,
-        "harbor": 7,
-        "bridge": 8,
-        "large-vehicle": 9,
-        "small-vehicle": 10,
-        "helicopter": 11,
-        "roundabout": 12,
-        "soccer-ball-field": 13,
-        "swimming-pool": 14,
-        "container-crane": 15,
-        "airport": 16,
-        "helipad": 17,
+        "clamp": 0,
     }
 
     def convert_label(image_name, image_width, image_height, orig_label_dir, save_dir):
@@ -504,7 +487,7 @@ def convert_dota_to_yolo_obb(dota_root_path: str):
 
         image_paths = list(image_dir.iterdir())
         for image_path in TQDM(image_paths, desc=f"Processing {phase} images"):
-            if image_path.suffix != ".png":
+            if image_path.suffix not in [".png" ,".jpg" , ".jpeg"]:
                 continue
             image_name_without_ext = image_path.stem
             img = cv2.imread(str(image_path))