Fix and add missing infos about available CLI TASK commands in docs and code comments (#16697)

ultralytics/cfg/default.yaml

@@ -1,7 +1,7 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
 # Default training settings and hyperparameters for medium-augmentation COCO training
 
-task: detect # (str) YOLO task, i.e. detect, segment, classify, pose
+task: detect # (str) YOLO task, i.e. detect, segment, classify, pose, obb
 mode: train # (str) YOLO mode, i.e. train, val, predict, export, track, benchmark
 
 # Train settings -------------------------------------------------------------------------------------------------------

ultralytics/data/utils.py

@@ -452,12 +452,12 @@ class HUBDatasetStats:
         path = Path(path).resolve()
         LOGGER.info(f"Starting HUB dataset checks for {path}....")
 
-        self.task = task  # detect, segment, pose, classify
+        self.task = task  # detect, segment, pose, classify, obb
         if self.task == "classify":
             unzip_dir = unzip_file(path)
             data = check_cls_dataset(unzip_dir)
             data["path"] = unzip_dir
-        else:  # detect, segment, pose
+        else:  # detect, segment, pose, obb
             _, data_dir, yaml_path = self._unzip(Path(path))
             try:
                 # Load YAML with checks

ultralytics/utils/__init__.py

@@ -61,8 +61,8 @@ HELP_MSG = """
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO("yolov8n.yaml")  # build a new model from scratch
-        model = YOLO("yolov8n.pt")  # load a pretrained model (recommended for training)
+        model = YOLO("yolo11n.yaml")  # build a new model from scratch
+        model = YOLO("yolo11n.pt")  # load a pretrained model (recommended for training)
 
         # Use the model
         results = model.train(data="coco8.yaml", epochs=3)  # train the model
@@ -77,21 +77,21 @@ HELP_MSG = """
             yolo TASK MODE ARGS
 
             Where   TASK (optional) is one of [detect, segment, classify, pose, obb]
-                    MODE (required) is one of [train, val, predict, export, benchmark]
+                    MODE (required) is one of [train, val, predict, export, track, benchmark]
                     ARGS (optional) are any number of custom "arg=value" pairs like "imgsz=320" that override defaults.
                         See all ARGS at https://docs.ultralytics.com/usage/cfg or with "yolo cfg"
 
         - Train a detection model for 10 epochs with an initial learning_rate of 0.01
-            yolo detect train data=coco8.yaml model=yolov8n.pt epochs=10 lr0=0.01
+            yolo detect train data=coco8.yaml model=yolo11n.pt epochs=10 lr0=0.01
 
         - Predict a YouTube video using a pretrained segmentation model at image size 320:
-            yolo segment predict model=yolov8n-seg.pt source='https://youtu.be/LNwODJXcvt4' imgsz=320
+            yolo segment predict model=yolo11n-seg.pt source='https://youtu.be/LNwODJXcvt4' imgsz=320
 
         - Val a pretrained detection model at batch-size 1 and image size 640:
-            yolo detect val model=yolov8n.pt data=coco8.yaml batch=1 imgsz=640
+            yolo detect val model=yolo11n.pt data=coco8.yaml batch=1 imgsz=640
 
-        - Export a YOLOv8n classification model to ONNX format at image size 224 by 128 (no TASK required)
-            yolo export model=yolov8n-cls.pt format=onnx imgsz=224,128
+        - Export a YOLO11n classification model to ONNX format at image size 224 by 128 (no TASK required)
+            yolo export model=yolo11n-cls.pt format=onnx imgsz=224,128
 
         - Run special commands:
             yolo help