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

docs/en/usage/cfg.md

@@ -41,8 +41,8 @@ Ultralytics commands use the following syntax:
 
 Where:
 
-- `TASK` (optional) is one of ([detect](../tasks/detect.md), [segment](../tasks/segment.md), [classify](../tasks/classify.md), [pose](../tasks/pose.md))
-- `MODE` (required) is one of ([train](../modes/train.md), [val](../modes/val.md), [predict](../modes/predict.md), [export](../modes/export.md), [track](../modes/track.md))
+- `TASK` (optional) is one of ([detect](../tasks/detect.md), [segment](../tasks/segment.md), [classify](../tasks/classify.md), [pose](../tasks/pose.md), [obb](../tasks/obb.md))
+- `MODE` (required) is one of ([train](../modes/train.md), [val](../modes/val.md), [predict](../modes/predict.md), [export](../modes/export.md), [track](../modes/track.md), [benchmark](../modes/benchmark.md))
 - `ARGS` (optional) are `arg=value` pairs like `imgsz=640` that override defaults.
 
 Default `ARG` values are defined on this page from the `cfg/defaults.yaml` [file](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/default.yaml).
@@ -59,7 +59,7 @@ YOLO models can be used for a variety of tasks, including detection, segmentatio
 
 | Argument | Default    | Description                                                                                                                                                                                                                                                                                                                                                                  |
 | -------- | ---------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
-| `task`   | `'detect'` | Specifies the YOLO task to be executed. Options include `detect` for [object detection](https://www.ultralytics.com/glossary/object-detection), `segment` for segmentation, `classify` for classification, `pose` for pose estimation and `OBB` for oriented bounding boxes. Each task is tailored to specific types of output and problems within image and video analysis. |
+| `task`   | `'detect'` | Specifies the YOLO task to be executed. Options include `detect` for [object detection](https://www.ultralytics.com/glossary/object-detection), `segment` for segmentation, `classify` for classification, `pose` for pose estimation and `obb` for oriented bounding boxes. Each task is tailored to specific types of output and problems within image and video analysis. |
 
 [Tasks Guide](../tasks/index.md){ .md-button }
 

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