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Default to YOLO11 models (#16561)

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Glenn Jocher 2024-09-30 13:12:20 +02:00 • committed by GitHub
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ultralytics/cfg/__init__.py
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@ -42,11 +42,11 @@ TASK2DATA = {
"obb": "dota8.yaml",
}
TASK2MODEL = {
"detect": "yolov8n.pt",
"segment": "yolov8n-seg.pt",
"classify": "yolov8n-cls.pt",
"pose": "yolov8n-pose.pt",
"obb": "yolov8n-obb.pt",
"detect": "yolo11n.pt",
"segment": "yolo11n-seg.pt",
"classify": "yolo11n-cls.pt",
"pose": "yolo11n-pose.pt",
"obb": "yolo11n-obb.pt",
}
TASK2METRIC = {
"detect": "metrics/mAP50-95(B)",
@ -69,19 +69,19 @@ CLI_HELP_MSG = f"""
See all ARGS at https://docs.ultralytics.com/usage/cfg or with 'yolo cfg'
1. Train a detection model for 10 epochs with an initial learning_rate of 0.01
yolo train data=coco8.yaml model=yolov8n.pt epochs=10 lr0=0.01
yolo train data=coco8.yaml model=yolo11n.pt epochs=10 lr0=0.01
2. Predict a YouTube video using a pretrained segmentation model at image size 320:
yolo predict model=yolov8n-seg.pt source='https://youtu.be/LNwODJXcvt4' imgsz=320
yolo predict model=yolo11n-seg.pt source='https://youtu.be/LNwODJXcvt4' imgsz=320
3. Val a pretrained detection model at batch-size 1 and image size 640:
yolo val model=yolov8n.pt data=coco8.yaml batch=1 imgsz=640
yolo val model=yolo11n.pt data=coco8.yaml batch=1 imgsz=640
4. 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
4. 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
5. Explore your datasets using semantic search and SQL with a simple GUI powered by Ultralytics Explorer API
yolo explorer data=data.yaml model=yolov8n.pt
yolo explorer data=data.yaml model=yolo11n.pt
6. Streamlit real-time webcam inference GUI
yolo streamlit-predict
@ -517,7 +517,7 @@ def handle_yolo_settings(args: List[str]) -> None:
Examples:
>>> handle_yolo_settings(["reset"]) # Reset YOLO settings
>>> handle_yolo_settings(["default_cfg_path=yolov8n.yaml"]) # Update a specific setting
>>> handle_yolo_settings(["default_cfg_path=yolo11n.yaml"]) # Update a specific setting
Notes:
- If no arguments are provided, the function will display the current settings.
@ -557,7 +557,7 @@ def handle_explorer(args: List[str]):
Examples:
```bash
yolo explorer data=data.yaml model=yolov8n.pt
yolo explorer data=data.yaml model=yolo11n.pt
```
Notes:
@ -611,9 +611,9 @@ def parse_key_value_pair(pair: str = "key=value"):
AssertionError: If the value is missing or empty.
Examples:
>>> key, value = parse_key_value_pair("model=yolov8n.pt")
>>> key, value = parse_key_value_pair("model=yolo11n.pt")
>>> print(f"Key: {key}, Value: {value}")
Key: model, Value: yolov8n.pt
Key: model, Value: yolo11n.pt
>>> key, value = parse_key_value_pair("epochs=100")
>>> print(f"Key: {key}, Value: {value}")
@ -686,13 +686,13 @@ def entrypoint(debug=""):
Examples:
Train a detection model for 10 epochs with an initial learning_rate of 0.01:
>>> entrypoint("train data=coco8.yaml model=yolov8n.pt epochs=10 lr0=0.01")
>>> entrypoint("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:
>>> entrypoint("predict model=yolov8n-seg.pt source='https://youtu.be/LNwODJXcvt4' imgsz=320")
>>> entrypoint("predict model=yolo11n-seg.pt source='https://youtu.be/LNwODJXcvt4' imgsz=320")
Validate a pretrained detection model at batch-size 1 and image size 640:
>>> entrypoint("val model=yolov8n.pt data=coco8.yaml batch=1 imgsz=640")
>>> entrypoint("val model=yolo11n.pt data=coco8.yaml batch=1 imgsz=640")
Notes:
- If no arguments are passed, the function will display the usage help message.
@ -782,7 +782,7 @@ def entrypoint(debug=""):
# Model
model = overrides.pop("model", DEFAULT_CFG.model)
if model is None:
model = "yolov8n.pt"
model = "yolo11n.pt"
LOGGER.warning(f"WARNING ⚠️ 'model' argument is missing. Using default 'model={model}'.")
overrides["model"] = model
stem = Path(model).stem.lower()
@ -869,5 +869,5 @@ def copy_default_cfg():
if __name__ == "__main__":
# Example: entrypoint(debug='yolo predict model=yolov8n.pt')
# Example: entrypoint(debug='yolo predict model=yolo11n.pt')
entrypoint(debug="")