diff --git a/docs/en/usage/cfg.md b/docs/en/usage/cfg.md index 8f8ac602..a7fef9e2 100644 --- a/docs/en/usage/cfg.md +++ b/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 } diff --git a/ultralytics/cfg/default.yaml b/ultralytics/cfg/default.yaml index da616d65..7922f635 100644 --- a/ultralytics/cfg/default.yaml +++ b/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 ------------------------------------------------------------------------------------------------------- diff --git a/ultralytics/data/utils.py b/ultralytics/data/utils.py index e82d8bb7..b2d47b3b 100644 --- a/ultralytics/data/utils.py +++ b/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 diff --git a/ultralytics/utils/__init__.py b/ultralytics/utils/__init__.py index a7ab5d90..3edfac11 100644 --- a/ultralytics/utils/__init__.py +++ b/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