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ultralytics 8.3.0 YOLO11 Models Release (#16539)

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Signed-off-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Laughing-q <1185102784@qq.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
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Glenn Jocher 2024-09-30 02:59:20 +02:00 • committed by GitHub
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18
tests/test_integrations.py
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@ -17,7 +17,7 @@ from ultralytics.utils.checks import check_requirements
@pytest.mark.skipif(not check_requirements("ray", install=False), reason="ray[tune] not installed")
def test_model_ray_tune():
"""Tune YOLO model using Ray for hyperparameter optimization."""
YOLO("yolov8n-cls.yaml").tune(
YOLO("yolo11n-cls.yaml").tune(
use_ray=True, data="imagenet10", grace_period=1, iterations=1, imgsz=32, epochs=1, plots=False, device="cpu"
)
@ -26,7 +26,7 @@ def test_model_ray_tune():
def test_mlflow():
"""Test training with MLflow tracking enabled (see https://mlflow.org/ for details)."""
SETTINGS["mlflow"] = True
YOLO("yolov8n-cls.yaml").train(data="imagenet10", imgsz=32, epochs=3, plots=False, device="cpu")
YOLO("yolo11n-cls.yaml").train(data="imagenet10", imgsz=32, epochs=3, plots=False, device="cpu")
SETTINGS["mlflow"] = False
@ -42,7 +42,7 @@ def test_mlflow_keep_run_active():
# Test with MLFLOW_KEEP_RUN_ACTIVE=True
os.environ["MLFLOW_KEEP_RUN_ACTIVE"] = "True"
YOLO("yolov8n-cls.yaml").train(data="imagenet10", imgsz=32, epochs=1, plots=False, device="cpu")
YOLO("yolo11n-cls.yaml").train(data="imagenet10", imgsz=32, epochs=1, plots=False, device="cpu")
status = mlflow.active_run().info.status
assert status == "RUNNING", "MLflow run should be active when MLFLOW_KEEP_RUN_ACTIVE=True"
@ -50,13 +50,13 @@ def test_mlflow_keep_run_active():
# Test with MLFLOW_KEEP_RUN_ACTIVE=False
os.environ["MLFLOW_KEEP_RUN_ACTIVE"] = "False"
YOLO("yolov8n-cls.yaml").train(data="imagenet10", imgsz=32, epochs=1, plots=False, device="cpu")
YOLO("yolo11n-cls.yaml").train(data="imagenet10", imgsz=32, epochs=1, plots=False, device="cpu")
status = mlflow.get_run(run_id=run_id).info.status
assert status == "FINISHED", "MLflow run should be ended when MLFLOW_KEEP_RUN_ACTIVE=False"
# Test with MLFLOW_KEEP_RUN_ACTIVE not set
os.environ.pop("MLFLOW_KEEP_RUN_ACTIVE", None)
YOLO("yolov8n-cls.yaml").train(data="imagenet10", imgsz=32, epochs=1, plots=False, device="cpu")
YOLO("yolo11n-cls.yaml").train(data="imagenet10", imgsz=32, epochs=1, plots=False, device="cpu")
status = mlflow.get_run(run_id=run_id).info.status
assert status == "FINISHED", "MLflow run should be ended by default when MLFLOW_KEEP_RUN_ACTIVE is not set"
SETTINGS["mlflow"] = False
@ -126,23 +126,23 @@ def test_pycocotools():
from ultralytics.models.yolo.segment import SegmentationValidator
# Download annotations after each dataset downloads first
url = "https://github.com/ultralytics/assets/releases/download/v8.2.0/"
url = "https://github.com/ultralytics/assets/releases/download/v0.0.0/"
args = {"model": "yolov8n.pt", "data": "coco8.yaml", "save_json": True, "imgsz": 64}
args = {"model": "yolo11n.pt", "data": "coco8.yaml", "save_json": True, "imgsz": 64}
validator = DetectionValidator(args=args)
validator()
validator.is_coco = True
download(f"{url}instances_val2017.json", dir=DATASETS_DIR / "coco8/annotations")
_ = validator.eval_json(validator.stats)
args = {"model": "yolov8n-seg.pt", "data": "coco8-seg.yaml", "save_json": True, "imgsz": 64}
args = {"model": "yolo11n-seg.pt", "data": "coco8-seg.yaml", "save_json": True, "imgsz": 64}
validator = SegmentationValidator(args=args)
validator()
validator.is_coco = True
download(f"{url}instances_val2017.json", dir=DATASETS_DIR / "coco8-seg/annotations")
_ = validator.eval_json(validator.stats)
args = {"model": "yolov8n-pose.pt", "data": "coco8-pose.yaml", "save_json": True, "imgsz": 64}
args = {"model": "yolo11n-pose.pt", "data": "coco8-pose.yaml", "save_json": True, "imgsz": 64}
validator = PoseValidator(args=args)
validator()
validator.is_coco = True