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Update YOLOv8n to YOLO11n in args (#16873)

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Signed-off-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
This commit is contained in:
Glenn Jocher 2024-10-12 18:09:44 +02:00 • committed by GitHub
parent 60dbee2839
commit a9d0cf66cb
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15 changed files with 53 additions and 53 deletions
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6
examples/YOLOv8-Action-Recognition/action_recognition.py
View file

@ -263,7 +263,7 @@ def crop_and_pad(frame, box, margin_percent):
def run(
weights: str = "yolov8n.pt",
weights: str = "yolo11n.pt",
device: str = "",
source: str = "https://www.youtube.com/watch?v=dQw4w9WgXcQ",
output_path: Optional[str] = None,
@ -279,7 +279,7 @@ def run(
Run action recognition on a video source using YOLO for object detection and a video classifier.
Args:
weights (str): Path to the YOLO model weights. Defaults to "yolov8n.pt".
weights (str): Path to the YOLO model weights. Defaults to "yolo11n.pt".
device (str): Device to run the model on. Use 'cuda' for NVIDIA GPU, 'mps' for Apple Silicon, or 'cpu'. Defaults to auto-detection.
source (str): Path to mp4 video file or YouTube URL. Defaults to a sample YouTube video.
output_path (Optional[str], optional): Path to save the output video. Defaults to None.
@ -421,7 +421,7 @@ def run(
def parse_opt():
"""Parse command line arguments."""
parser = argparse.ArgumentParser()
parser.add_argument("--weights", type=str, default="yolov8n.pt", help="ultralytics detector model path")
parser.add_argument("--weights", type=str, default="yolo11n.pt", help="ultralytics detector model path")
parser.add_argument("--device", default="", help='cuda device, i.e. 0 or 0,1,2,3 or cpu/mps, "" for auto-detection')
parser.add_argument(
"--source",

2
ultralytics/data/annotator.py
View file

@ -21,7 +21,7 @@ def auto_annotate(data, det_model="yolov8x.pt", sam_model="sam_b.pt", device="",
Examples:
>>> from ultralytics.data.annotator import auto_annotate
>>> auto_annotate(data="ultralytics/assets", det_model="yolov8n.pt", sam_model="mobile_sam.pt")
>>> auto_annotate(data="ultralytics/assets", det_model="yolo11n.pt", sam_model="mobile_sam.pt")
Notes:
- The function creates a new directory for output if not specified.

66
ultralytics/engine/model.py
View file

@ -72,16 +72,16 @@ class Model(nn.Module):
Examples:
>>> from ultralytics import YOLO
>>> model = YOLO("yolov8n.pt")
>>> model = YOLO("yolo11n.pt")
>>> results = model.predict("image.jpg")
>>> model.train(data="coco128.yaml", epochs=3)
>>> model.train(data="coco8.yaml", epochs=3)
>>> metrics = model.val()
>>> model.export(format="onnx")
"""
def __init__(
self,
model: Union[str, Path] = "yolov8n.pt",
model: Union[str, Path] = "yolo11n.pt",
task: str = None,
verbose: bool = False,
) -> None:
@ -106,7 +106,7 @@ class Model(nn.Module):
ImportError: If required dependencies for specific model types (like HUB SDK) are not installed.
Examples:
>>> model = Model("yolov8n.pt")
>>> model = Model("yolo11n.pt")
>>> model = Model("path/to/model.yaml", task="detect")
>>> model = Model("hub_model", verbose=True)
"""
@ -168,7 +168,7 @@ class Model(nn.Module):
Results object.
Examples:
>>> model = YOLO("yolov8n.pt")
>>> model = YOLO("yolo11n.pt")
>>> results = model("https://ultralytics.com/images/bus.jpg")
>>> for r in results:
... print(f"Detected {len(r)} objects in image")
@ -192,7 +192,7 @@ class Model(nn.Module):
Examples:
>>> Model.is_triton_model("http://localhost:8000/v2/models/yolov8n")
True
>>> Model.is_triton_model("yolov8n.pt")
>>> Model.is_triton_model("yolo11n.pt")
False
"""
from urllib.parse import urlsplit
@ -217,7 +217,7 @@ class Model(nn.Module):
Examples:
>>> Model.is_hub_model("https://hub.ultralytics.com/models/MODEL")
True
>>> Model.is_hub_model("yolov8n.pt")
>>> Model.is_hub_model("yolo11n.pt")
False
"""
return model.startswith(f"{HUB_WEB_ROOT}/models/")
@ -274,7 +274,7 @@ class Model(nn.Module):
Examples:
>>> model = Model()
>>> model._load("yolov8n.pt")
>>> model._load("yolo11n.pt")
>>> model._load("path/to/weights.pth", task="detect")
"""
if weights.lower().startswith(("https://", "http://", "rtsp://", "rtmp://", "tcp://")):
@ -307,7 +307,7 @@ class Model(nn.Module):
information about supported model formats and operations.
Examples:
>>> model = Model("yolov8n.pt")
>>> model = Model("yolo11n.pt")
>>> model._check_is_pytorch_model() # No error raised
>>> model = Model("yolov8n.onnx")
>>> model._check_is_pytorch_model() # Raises TypeError
@ -338,7 +338,7 @@ class Model(nn.Module):
AssertionError: If the model is not a PyTorch model.
Examples:
>>> model = Model("yolov8n.pt")
>>> model = Model("yolo11n.pt")
>>> model.reset_weights()
"""
self._check_is_pytorch_model()
@ -349,7 +349,7 @@ class Model(nn.Module):
p.requires_grad = True
return self
def load(self, weights: Union[str, Path] = "yolov8n.pt") -> "Model":
def load(self, weights: Union[str, Path] = "yolo11n.pt") -> "Model":
"""
Loads parameters from the specified weights file into the model.
@ -367,7 +367,7 @@ class Model(nn.Module):
Examples:
>>> model = Model()
>>> model.load("yolov8n.pt")
>>> model.load("yolo11n.pt")
>>> model.load(Path("path/to/weights.pt"))
"""
self._check_is_pytorch_model()
@ -391,7 +391,7 @@ class Model(nn.Module):
AssertionError: If the model is not a PyTorch model.
Examples:
>>> model = Model("yolov8n.pt")
>>> model = Model("yolo11n.pt")
>>> model.save("my_model.pt")
"""
self._check_is_pytorch_model()
@ -428,7 +428,7 @@ class Model(nn.Module):
TypeError: If the model is not a PyTorch model.
Examples:
>>> model = Model("yolov8n.pt")
>>> model = Model("yolo11n.pt")
>>> model.info() # Prints model summary
>>> info_list = model.info(detailed=True, verbose=False) # Returns detailed info as a list
"""
@ -451,7 +451,7 @@ class Model(nn.Module):
TypeError: If the model is not a PyTorch nn.Module.
Examples:
>>> model = Model("yolov8n.pt")
>>> model = Model("yolo11n.pt")
>>> model.fuse()
>>> # Model is now fused and ready for optimized inference
"""
@ -483,7 +483,7 @@ class Model(nn.Module):
AssertionError: If the model is not a PyTorch model.
Examples:
>>> model = YOLO("yolov8n.pt")
>>> model = YOLO("yolo11n.pt")
>>> image = "https://ultralytics.com/images/bus.jpg"
>>> embeddings = model.embed(image)
>>> print(embeddings[0].shape)
@ -520,7 +520,7 @@ class Model(nn.Module):
Results object.
Examples:
>>> model = YOLO("yolov8n.pt")
>>> model = YOLO("yolo11n.pt")
>>> results = model.predict(source="path/to/image.jpg", conf=0.25)
>>> for r in results:
... print(r.boxes.data) # print detection bounding boxes
@ -581,7 +581,7 @@ class Model(nn.Module):
AttributeError: If the predictor does not have registered trackers.
Examples:
>>> model = YOLO("yolov8n.pt")
>>> model = YOLO("yolo11n.pt")
>>> results = model.track(source="path/to/video.mp4", show=True)
>>> for r in results:
... print(r.boxes.id) # print tracking IDs
@ -624,8 +624,8 @@ class Model(nn.Module):
AssertionError: If the model is not a PyTorch model.
Examples:
>>> model = YOLO("yolov8n.pt")
>>> results = model.val(data="coco128.yaml", imgsz=640)
>>> model = YOLO("yolo11n.pt")
>>> results = model.val(data="coco8.yaml", imgsz=640)
>>> print(results.box.map) # Print mAP50-95
"""
custom = {"rect": True} # method defaults
@ -666,7 +666,7 @@ class Model(nn.Module):
AssertionError: If the model is not a PyTorch model.
Examples:
>>> model = YOLO("yolov8n.pt")
>>> model = YOLO("yolo11n.pt")
>>> results = model.benchmark(data="coco8.yaml", imgsz=640, half=True)
>>> print(results)
"""
@ -716,7 +716,7 @@ class Model(nn.Module):
RuntimeError: If the export process fails due to errors.
Examples:
>>> model = YOLO("yolov8n.pt")
>>> model = YOLO("yolo11n.pt")
>>> model.export(format="onnx", dynamic=True, simplify=True)
'path/to/exported/model.onnx'
"""
@ -771,8 +771,8 @@ class Model(nn.Module):
ModuleNotFoundError: If the HUB SDK is not installed.
Examples:
>>> model = YOLO("yolov8n.pt")
>>> results = model.train(data="coco128.yaml", epochs=3)
>>> model = YOLO("yolo11n.pt")
>>> results = model.train(data="coco8.yaml", epochs=3)
"""
self._check_is_pytorch_model()
if hasattr(self.session, "model") and self.session.model.id: # Ultralytics HUB session with loaded model
@ -836,7 +836,7 @@ class Model(nn.Module):
AssertionError: If the model is not a PyTorch model.
Examples:
>>> model = YOLO("yolov8n.pt")
>>> model = YOLO("yolo11n.pt")
>>> results = model.tune(use_ray=True, iterations=20)
>>> print(results)
"""
@ -871,7 +871,7 @@ class Model(nn.Module):
AssertionError: If the model is not a PyTorch model.
Examples:
>>> model = Model("yolov8n.pt")
>>> model = Model("yolo11n.pt")
>>> model = model._apply(lambda t: t.cuda()) # Move model to GPU
"""
self._check_is_pytorch_model()
@ -896,7 +896,7 @@ class Model(nn.Module):
AttributeError: If the model or predictor does not have a 'names' attribute.
Examples:
>>> model = YOLO("yolov8n.pt")
>>> model = YOLO("yolo11n.pt")
>>> print(model.names)
{0: 'person', 1: 'bicycle', 2: 'car', ...}
"""
@ -924,7 +924,7 @@ class Model(nn.Module):
AttributeError: If the model is not a PyTorch nn.Module instance.
Examples:
>>> model = YOLO("yolov8n.pt")
>>> model = YOLO("yolo11n.pt")
>>> print(model.device)
device(type='cuda', index=0) # if CUDA is available
>>> model = model.to("cpu")
@ -946,7 +946,7 @@ class Model(nn.Module):
(object | None): The transform object of the model if available, otherwise None.
Examples:
>>> model = YOLO("yolov8n.pt")
>>> model = YOLO("yolo11n.pt")
>>> transforms = model.transforms
>>> if transforms:
... print(f"Model transforms: {transforms}")
@ -975,9 +975,9 @@ class Model(nn.Module):
Examples:
>>> def on_train_start(trainer):
... print("Training is starting!")
>>> model = YOLO("yolov8n.pt")
>>> model = YOLO("yolo11n.pt")
>>> model.add_callback("on_train_start", on_train_start)
>>> model.train(data="coco128.yaml", epochs=1)
>>> model.train(data="coco8.yaml", epochs=1)
"""
self.callbacks[event].append(func)
@ -994,7 +994,7 @@ class Model(nn.Module):
recognized by the Ultralytics callback system.
Examples:
>>> model = YOLO("yolov8n.pt")
>>> model = YOLO("yolo11n.pt")
>>> model.add_callback("on_train_start", lambda: print("Training started"))
>>> model.clear_callback("on_train_start")
>>> # All callbacks for 'on_train_start' are now removed
@ -1024,7 +1024,7 @@ class Model(nn.Module):
modifications, ensuring consistent behavior across different runs or experiments.
Examples:
>>> model = YOLO("yolov8n.pt")
>>> model = YOLO("yolo11n.pt")
>>> model.add_callback("on_train_start", custom_function)
>>> model.reset_callbacks()
# All callbacks are now reset to their default functions

2
ultralytics/engine/results.py
View file

@ -676,7 +676,7 @@ class Results(SimpleClass):
Examples:
>>> from ultralytics import YOLO
>>> model = YOLO("yolov8n.pt")
>>> model = YOLO("yolo11n.pt")
>>> results = model("path/to/image.jpg")
>>> for result in results:
... result.save_txt("output.txt")

6
ultralytics/engine/tuner.py
View file

@ -12,7 +12,7 @@ Example:
```python
from ultralytics import YOLO
model = YOLO("yolov8n.pt")
model = YOLO("yolo11n.pt")
model.tune(data="coco8.yaml", epochs=10, iterations=300, optimizer="AdamW", plots=False, save=False, val=False)
```
"""
@ -54,7 +54,7 @@ class Tuner:
```python
from ultralytics import YOLO
model = YOLO("yolov8n.pt")
model = YOLO("yolo11n.pt")
model.tune(data="coco8.yaml", epochs=10, iterations=300, optimizer="AdamW", plots=False, save=False, val=False)
```
@ -62,7 +62,7 @@ class Tuner:
```python
from ultralytics import YOLO
model = YOLO("yolov8n.pt")
model = YOLO("yolo11n.pt")
model.tune(space={key1: val1, key2: val2}) # custom search space dictionary
```
"""

2
ultralytics/models/yolo/detect/predict.py
View file

@ -14,7 +14,7 @@ class DetectionPredictor(BasePredictor):
from ultralytics.utils import ASSETS
from ultralytics.models.yolo.detect import DetectionPredictor
args = dict(model="yolov8n.pt", source=ASSETS)
args = dict(model="yolo11n.pt", source=ASSETS)
predictor = DetectionPredictor(overrides=args)
predictor.predict_cli()
```

2
ultralytics/models/yolo/detect/train.py
View file

@ -24,7 +24,7 @@ class DetectionTrainer(BaseTrainer):
```python
from ultralytics.models.yolo.detect import DetectionTrainer
args = dict(model="yolov8n.pt", data="coco8.yaml", epochs=3)
args = dict(model="yolo11n.pt", data="coco8.yaml", epochs=3)
trainer = DetectionTrainer(overrides=args)
trainer.train()
```

2
ultralytics/models/yolo/detect/val.py
View file

@ -22,7 +22,7 @@ class DetectionValidator(BaseValidator):
```python
from ultralytics.models.yolo.detect import DetectionValidator
args = dict(model="yolov8n.pt", data="coco8.yaml")
args = dict(model="yolo11n.pt", data="coco8.yaml")
validator = DetectionValidator(args=args)
validator()
```

2
ultralytics/models/yolo/model.py
View file

@ -11,7 +11,7 @@ from ultralytics.utils import ROOT, yaml_load
class YOLO(Model):
"""YOLO (You Only Look Once) object detection model."""
def __init__(self, model="yolov8n.pt", task=None, verbose=False):
def __init__(self, model="yolo11n.pt", task=None, verbose=False):
"""Initialize YOLO model, switching to YOLOWorld if model filename contains '-world'."""
path = Path(model)
if "-world" in path.stem and path.suffix in {".pt", ".yaml", ".yml"}: # if YOLOWorld PyTorch model

2
ultralytics/nn/autobackend.py
View file

@ -82,7 +82,7 @@ class AutoBackend(nn.Module):
@torch.no_grad()
def __init__(
self,
weights="yolov8n.pt",
weights="yolo11n.pt",
device=torch.device("cpu"),
dnn=False,
data=None,

4
ultralytics/utils/benchmarks.py
View file

@ -47,7 +47,7 @@ from ultralytics.utils.torch_utils import get_cpu_info, select_device
def benchmark(
model=WEIGHTS_DIR / "yolov8n.pt",
model=WEIGHTS_DIR / "yolo11n.pt",
data=None,
imgsz=160,
half=False,
@ -76,7 +76,7 @@ def benchmark(
Examples:
Benchmark a YOLO model with default settings:
>>> from ultralytics.utils.benchmarks import benchmark
>>> benchmark(model="yolov8n.pt", imgsz=640)
>>> benchmark(model="yolo11n.pt", imgsz=640)
"""
import pandas as pd # scope for faster 'import ultralytics'

2
ultralytics/utils/checks.py
View file

@ -458,7 +458,7 @@ def check_torchvision():
)
def check_suffix(file="yolov8n.pt", suffix=".pt", msg=""):
def check_suffix(file="yolo11n.pt", suffix=".pt", msg=""):
"""Check file(s) for acceptable suffix."""
if file and suffix:
if isinstance(suffix, str):

2
ultralytics/utils/downloads.py
View file

@ -425,7 +425,7 @@ def attempt_download_asset(file, repo="ultralytics/assets", release="v8.3.0", **
Example:
```python
file_path = attempt_download_asset("yolov8n.pt", repo="ultralytics/assets", release="latest")
file_path = attempt_download_asset("yolo11n.pt", repo="ultralytics/assets", release="latest")
```
"""
from ultralytics.utils import SETTINGS # scoped for circular import

4
ultralytics/utils/files.py
View file

@ -183,7 +183,7 @@ def get_latest_run(search_dir="."):
return max(last_list, key=os.path.getctime) if last_list else ""
def update_models(model_names=("yolov8n.pt",), source_dir=Path("."), update_names=False):
def update_models(model_names=("yolo11n.pt",), source_dir=Path("."), update_names=False):
"""
Updates and re-saves specified YOLO models in an 'updated_models' subdirectory.
@ -195,7 +195,7 @@ def update_models(model_names=("yolov8n.pt",), source_dir=Path("."), update_name
Examples:
Update specified YOLO models and save them in 'updated_models' subdirectory:
>>> from ultralytics.utils.files import update_models
>>> model_names = ("yolov8n.pt", "yolov8s.pt")
>>> model_names = ("yolo11n.pt", "yolov8s.pt")
>>> update_models(model_names, source_dir=Path("/models"), update_names=True)
"""
from ultralytics import YOLO

2
ultralytics/utils/tuner.py
View file

@ -28,7 +28,7 @@ def run_ray_tune(
from ultralytics import YOLO
# Load a YOLOv8n model
model = YOLO("yolov8n.pt")
model = YOLO("yolo11n.pt")
# Start tuning hyperparameters for YOLOv8n training on the COCO8 dataset
result_grid = model.tune(data="coco8.yaml", use_ray=True)