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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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22
tests/test_python.py
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@ -211,7 +211,7 @@ def test_train_scratch():
def test_train_pretrained():
"""Test training of the YOLO model starting from a pre-trained checkpoint."""
model = YOLO(WEIGHTS_DIR / "yolov8n-seg.pt")
model = YOLO(WEIGHTS_DIR / "yolo11n-seg.pt")
model.train(data="coco8-seg.yaml", epochs=1, imgsz=32, cache="ram", copy_paste=0.5, mixup=0.5, name=0)
model(SOURCE)
@ -281,13 +281,13 @@ def test_results(model):
def test_labels_and_crops():
"""Test output from prediction args for saving YOLO detection labels and crops; ensures accurate saving."""
imgs = [SOURCE, ASSETS / "zidane.jpg"]
results = YOLO(WEIGHTS_DIR / "yolov8n.pt")(imgs, imgsz=160, save_txt=True, save_crop=True)
results = YOLO(WEIGHTS_DIR / "yolo11n.pt")(imgs, imgsz=160, save_txt=True, save_crop=True)
save_path = Path(results[0].save_dir)
for r in results:
im_name = Path(r.path).stem
cls_idxs = r.boxes.cls.int().tolist()
# Check correct detections
assert cls_idxs == ([0, 0, 5, 0, 7] if r.path.endswith("bus.jpg") else [0, 0]) # bus.jpg and zidane.jpg classes
assert cls_idxs == ([0, 7, 0, 0] if r.path.endswith("bus.jpg") else [0, 0, 0]) # bus.jpg and zidane.jpg classes
# Check label path
labels = save_path / f"labels/{im_name}.txt"
assert labels.exists()
@ -339,7 +339,7 @@ def test_data_annotator():
auto_annotate(
ASSETS,
det_model=WEIGHTS_DIR / "yolov8n.pt",
det_model=WEIGHTS_DIR / "yolo11n.pt",
sam_model=WEIGHTS_DIR / "mobile_sam.pt",
output_dir=TMP / "auto_annotate_labels",
)
@ -393,7 +393,7 @@ def test_utils_benchmarks():
"""Benchmark model performance using 'ProfileModels' from 'ultralytics.utils.benchmarks'."""
from ultralytics.utils.benchmarks import ProfileModels
ProfileModels(["yolov8n.yaml"], imgsz=32, min_time=1, num_timed_runs=3, num_warmup_runs=1).profile()
ProfileModels(["yolo11n.yaml"], imgsz=32, min_time=1, num_timed_runs=3, num_warmup_runs=1).profile()
def test_utils_torchutils():
@ -568,14 +568,14 @@ def test_classify_transforms_train(image, auto_augment, erasing, force_color_jit
@pytest.mark.skipif(not ONLINE, reason="environment is offline")
def test_model_tune():
"""Tune YOLO model for performance improvement."""
YOLO("yolov8n-pose.pt").tune(data="coco8-pose.yaml", plots=False, imgsz=32, epochs=1, iterations=2, device="cpu")
YOLO("yolov8n-cls.pt").tune(data="imagenet10", plots=False, imgsz=32, epochs=1, iterations=2, device="cpu")
YOLO("yolo11n-pose.pt").tune(data="coco8-pose.yaml", plots=False, imgsz=32, epochs=1, iterations=2, device="cpu")
YOLO("yolo11n-cls.pt").tune(data="imagenet10", plots=False, imgsz=32, epochs=1, iterations=2, device="cpu")
def test_model_embeddings():
"""Test YOLO model embeddings."""
model_detect = YOLO(MODEL)
model_segment = YOLO(WEIGHTS_DIR / "yolov8n-seg.pt")
model_segment = YOLO(WEIGHTS_DIR / "yolo11n-seg.pt")
for batch in [SOURCE], [SOURCE, SOURCE]: # test batch size 1 and 2
assert len(model_detect.embed(source=batch, imgsz=32)) == len(batch)
@ -585,11 +585,11 @@ def test_model_embeddings():
@pytest.mark.skipif(checks.IS_PYTHON_3_12, reason="YOLOWorld with CLIP is not supported in Python 3.12")
def test_yolo_world():
"""Tests YOLO world models with CLIP support, including detection and training scenarios."""
model = YOLO("yolov8s-world.pt") # no YOLOv8n-world model yet
model = YOLO("yolov8s-world.pt") # no YOLO11n-world model yet
model.set_classes(["tree", "window"])
model(SOURCE, conf=0.01)
model = YOLO("yolov8s-worldv2.pt") # no YOLOv8n-world model yet
model = YOLO("yolov8s-worldv2.pt") # no YOLO11n-world model yet
# Training from a pretrained model. Eval is included at the final stage of training.
# Use dota8.yaml which has fewer categories to reduce the inference time of CLIP model
model.train(
@ -603,7 +603,7 @@ def test_yolo_world():
# test WorWorldTrainerFromScratch
from ultralytics.models.yolo.world.train_world import WorldTrainerFromScratch
model = YOLO("yolov8s-worldv2.yaml") # no YOLOv8n-world model yet
model = YOLO("yolov8s-worldv2.yaml") # no YOLO11n-world model yet
model.train(
data={"train": {"yolo_data": ["dota8.yaml"]}, "val": {"yolo_data": ["dota8.yaml"]}},
epochs=1,