From 1797506c9f6a36c1638f6e7ea3347f2add8d3edc Mon Sep 17 00:00:00 2001
From: Glenn Jocher <glenn.jocher@ultralytics.com>
Date: Mon, 30 Sep 2024 13:12:20 +0200
Subject: [PATCH] Default to YOLO11 models (#16561)

---
 ultralytics/cfg/__init__.py | 40 ++++++++++++++++++-------------------
 1 file changed, 20 insertions(+), 20 deletions(-)

diff --git a/ultralytics/cfg/__init__.py b/ultralytics/cfg/__init__.py
index 06356e75..7058c3d4 100644
--- a/ultralytics/cfg/__init__.py
+++ b/ultralytics/cfg/__init__.py
@@ -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="")