diff --git a/README.md b/README.md index dfeabbe..406be6f 100644 --- a/README.md +++ b/README.md @@ -52,26 +52,6 @@ To request an Enterprise License please complete the form at [Ultralytics Licens -##
Ultralytics Live Session
- -
- -[Ultralytics Live Session 3](https://youtu.be/IPcpYO5ITa8) ✨ is here! Join us on January 24th at 18 CET as we dive into -the latest advancements in YOLOv8, and demonstrate how to use this cutting-edge, SOTA model to improve your object -detection, instance segmentation, and image classification projects. See firsthand how YOLOv8's speed, accuracy, and -ease of use make it a top choice for professionals and researchers alike. - -In addition to learning about the exciting new features and improvements of Ultralytics YOLOv8, you will also have the -opportunity to ask questions and interact with our team during the live Q&A session. We encourage you to come prepared -with any questions you may have. - -To join the webinar, visit our YouTube [Channel](https://www.youtube.com/@Ultralytics/streams) and turn on your -notifications! - - - -
- ##
Documentation
See below for a quickstart installation and usage example, and see the [YOLOv8 Docs](https://docs.ultralytics.com) for diff --git a/tests/test_cli.py b/tests/test_cli.py index 5239fad..bcdf74e 100644 --- a/tests/test_cli.py +++ b/tests/test_cli.py @@ -50,6 +50,8 @@ def test_val_classify(): def test_predict_detect(): run(f"yolo predict detect model={MODEL}.pt source={ROOT / 'assets'} imgsz=32") run(f"yolo predict detect model={MODEL}.pt source=https://ultralytics.com/images/bus.jpg imgsz=32") + run(f"yolo predict detect model={MODEL}.pt source=https://ultralytics.com/assets/decelera_landscape.mov imgsz=32") + run(f"yolo predict detect model={MODEL}.pt source=https://ultralytics.com/assets/decelera_portrait.mov imgsz=32") def test_predict_segment(): diff --git a/ultralytics/__init__.py b/ultralytics/__init__.py index ae0c295..64b32a2 100644 --- a/ultralytics/__init__.py +++ b/ultralytics/__init__.py @@ -1,6 +1,6 @@ # Ultralytics YOLO 🚀, GPL-3.0 license -__version__ = "8.0.21" +__version__ = "8.0.22" from ultralytics.yolo.engine.model import YOLO from ultralytics.yolo.utils import ops diff --git a/ultralytics/nn/modules.py b/ultralytics/nn/modules.py index 7847593..d24d225 100644 --- a/ultralytics/nn/modules.py +++ b/ultralytics/nn/modules.py @@ -4,7 +4,6 @@ Common modules """ import math -import warnings import torch import torch.nn as nn @@ -155,7 +154,7 @@ class C3(nn.Module): self.cv1 = Conv(c1, c_, 1, 1) self.cv2 = Conv(c1, c_, 1, 1) self.cv3 = Conv(2 * c_, c2, 1) # optional act=FReLU(c2) - self.m = nn.Sequential(*(Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n))) + self.m = nn.Sequential(*(Bottleneck(c_, c_, shortcut, g, k=((1, 1), (3, 3)), e=1.0) for _ in range(n))) def forward(self, x): return self.cv3(torch.cat((self.m(self.cv1(x)), self.cv2(x)), 1)) @@ -274,9 +273,7 @@ class SPP(nn.Module): def forward(self, x): x = self.cv1(x) - with warnings.catch_warnings(): - warnings.simplefilter('ignore') # suppress torch 1.9.0 max_pool2d() warning - return self.cv2(torch.cat([x] + [m(x) for m in self.m], 1)) + return self.cv2(torch.cat([x] + [m(x) for m in self.m], 1)) class SPPF(nn.Module): @@ -290,11 +287,9 @@ class SPPF(nn.Module): def forward(self, x): x = self.cv1(x) - with warnings.catch_warnings(): - warnings.simplefilter('ignore') # suppress torch 1.9.0 max_pool2d() warning - y1 = self.m(x) - y2 = self.m(y1) - return self.cv2(torch.cat((x, y1, y2, self.m(y2)), 1)) + y1 = self.m(x) + y2 = self.m(y1) + return self.cv2(torch.cat((x, y1, y2, self.m(y2)), 1)) class Focus(nn.Module): diff --git a/ultralytics/yolo/cfg/__init__.py b/ultralytics/yolo/cfg/__init__.py index 2081888..f00b39b 100644 --- a/ultralytics/yolo/cfg/__init__.py +++ b/ultralytics/yolo/cfg/__init__.py @@ -88,6 +88,11 @@ def get_cfg(cfg: Union[str, Path, Dict, SimpleNamespace] = DEFAULT_CFG, override check_cfg_mismatch(cfg, overrides) cfg = {**cfg, **overrides} # merge cfg and overrides dicts (prefer overrides) + # Type checks + for k in 'project', 'name': + if isinstance(cfg[k], (int, float)): + cfg[k] = str(cfg[k]) + # Return instance return IterableSimpleNamespace(**cfg) @@ -211,12 +216,15 @@ def entrypoint(debug=False): else: raise argument_error(a) + # Defaults + task2model = dict(detect='yolov8n.pt', segment='yolov8n-seg.pt', classify='yolov8n-cls.pt') + task2data = dict(detect='coco128.yaml', segment='coco128-seg.yaml', classify='mnist160') + # Mode - mode = overrides.pop('mode', None) - model = overrides.pop('model', None) + mode = overrides['mode'] if mode is None: mode = DEFAULT_CFG.mode or 'predict' - LOGGER.warning(f"WARNING ⚠️ 'mode' is missing. Valid modes are {modes}. Using default 'mode={mode}'.") + LOGGER.warning(f"WARNING ⚠️ 'mode=' is missing. Valid modes are {modes}. Using default 'mode={mode}'.") elif mode not in modes: if mode != 'checks': raise ValueError(emojis(f"ERROR ❌ Invalid 'mode={mode}'. Valid modes are {modes}.")) @@ -225,27 +233,33 @@ def entrypoint(debug=False): return # Model + model = overrides.pop('model', DEFAULT_CFG.model) + task = overrides.pop('task', None) if model is None: - model = DEFAULT_CFG.model or 'yolov8n.pt' - LOGGER.warning(f"WARNING ⚠️ 'model' is missing. Using default 'model={model}'.") + model = task2model.get(task, 'yolov8n.pt') + LOGGER.warning(f"WARNING ⚠️ 'model=' is missing. Using default 'model={model}'.") from ultralytics.yolo.engine.model import YOLO + overrides['model'] = model model = YOLO(model) - task = model.task # Task + if task and task != model.task: + LOGGER.warning(f"WARNING ⚠️ 'task={task}' conflicts with {model.task} model {overrides['model']}. " + f"Inheriting 'task={model.task}' from {overrides['model']} and ignoring 'task={task}'.") + task = model.task + overrides['task'] = task if mode == 'predict' and 'source' not in overrides: overrides['source'] = DEFAULT_CFG.source or ROOT / "assets" if (ROOT / "assets").exists() \ else "https://ultralytics.com/images/bus.jpg" - LOGGER.warning(f"WARNING ⚠️ 'source' is missing. Using default 'source={overrides['source']}'.") + LOGGER.warning(f"WARNING ⚠️ 'source=' is missing. Using default 'source={overrides['source']}'.") elif mode in ('train', 'val'): if 'data' not in overrides: - overrides['data'] = DEFAULT_CFG.data or 'mnist160' if task == 'classify' \ - else 'coco128-seg.yaml' if task == 'segment' else 'coco128.yaml' - LOGGER.warning(f"WARNING ⚠️ 'data' is missing. Using default 'data={overrides['data']}'.") + overrides['data'] = task2data.get(task, DEFAULT_CFG.data) + LOGGER.warning(f"WARNING ⚠️ 'data=' is missing. Using {model.task} default 'data={overrides['data']}'.") elif mode == 'export': if 'format' not in overrides: overrides['format'] = DEFAULT_CFG.format or 'torchscript' - LOGGER.warning(f"WARNING ⚠️ 'format' is missing. Using default 'format={overrides['format']}'.") + LOGGER.warning(f"WARNING ⚠️ 'format=' is missing. Using default 'format={overrides['format']}'.") # Run command in python getattr(model, mode)(**overrides) diff --git a/ultralytics/yolo/data/dataloaders/stream_loaders.py b/ultralytics/yolo/data/dataloaders/stream_loaders.py index 78ca282..5338eef 100644 --- a/ultralytics/yolo/data/dataloaders/stream_loaders.py +++ b/ultralytics/yolo/data/dataloaders/stream_loaders.py @@ -186,6 +186,7 @@ class LoadImages: self.transforms = transforms # optional self.vid_stride = vid_stride # video frame-rate stride if any(videos): + self.orientation = None # rotation degrees self._new_video(videos[0]) # new video else: self.cap = None @@ -243,8 +244,10 @@ class LoadImages: self.frame = 0 self.cap = cv2.VideoCapture(path) self.frames = int(self.cap.get(cv2.CAP_PROP_FRAME_COUNT) / self.vid_stride) - self.orientation = int(self.cap.get(cv2.CAP_PROP_ORIENTATION_META)) # rotation degrees - # self.cap.set(cv2.CAP_PROP_ORIENTATION_AUTO, 0) # disable https://github.com/ultralytics/yolov5/issues/8493 + if hasattr(cv2, 'CAP_PROP_ORIENTATION_META'): # cv2<4.6.0 compatibility + self.orientation = int(self.cap.get(cv2.CAP_PROP_ORIENTATION_META)) # rotation degrees + # Disable auto-orientation due to known issues in https://github.com/ultralytics/yolov5/issues/8493 + # self.cap.set(cv2.CAP_PROP_ORIENTATION_AUTO, 0) def _cv2_rotate(self, im): # Rotate a cv2 video manually diff --git a/ultralytics/yolo/data/utils.py b/ultralytics/yolo/data/utils.py index 598fa6f..64193a8 100644 --- a/ultralytics/yolo/data/utils.py +++ b/ultralytics/yolo/data/utils.py @@ -16,7 +16,7 @@ from PIL import ExifTags, Image, ImageOps from ultralytics.yolo.utils import DATASETS_DIR, LOGGER, ROOT, colorstr, emojis, yaml_load from ultralytics.yolo.utils.checks import check_file, check_font, is_ascii -from ultralytics.yolo.utils.downloads import download +from ultralytics.yolo.utils.downloads import download, safe_download from ultralytics.yolo.utils.files import unzip_file from ultralytics.yolo.utils.ops import segments2boxes @@ -238,8 +238,7 @@ def check_det_dataset(dataset, autodownload=True): t = time.time() if s.startswith('http') and s.endswith('.zip'): # URL f = Path(s).name # filename - LOGGER.info(f'Downloading {s} to {f}...') - torch.hub.download_url_to_file(s, f) + safe_download(file=f, url=s) Path(DATASETS_DIR).mkdir(parents=True, exist_ok=True) # create root unzip_file(f, path=DATASETS_DIR) # unzip Path(f).unlink() # remove zip diff --git a/ultralytics/yolo/engine/predictor.py b/ultralytics/yolo/engine/predictor.py index 5bf1f77..b83fd2a 100644 --- a/ultralytics/yolo/engine/predictor.py +++ b/ultralytics/yolo/engine/predictor.py @@ -233,7 +233,7 @@ class BasePredictor: device = select_device(self.args.device) model = model or self.args.model self.args.half &= device.type != 'cpu' # half precision only supported on CUDA - self.model = AutoBackend(model, device=device, dnn=self.args.dnn, fp16=self.args.half) + self.model = AutoBackend(model, device=device, dnn=self.args.dnn, data=self.args.data, fp16=self.args.half) self.device = device self.model.eval() diff --git a/ultralytics/yolo/utils/callbacks/clearml.py b/ultralytics/yolo/utils/callbacks/clearml.py index 4053f06..4c8c8db 100644 --- a/ultralytics/yolo/utils/callbacks/clearml.py +++ b/ultralytics/yolo/utils/callbacks/clearml.py @@ -6,7 +6,7 @@ try: import clearml from clearml import Task - assert hasattr(clearml, '__version__') + assert clearml.__version__ # verify package is not directory except (ImportError, AssertionError): clearml = None diff --git a/ultralytics/yolo/utils/callbacks/comet.py b/ultralytics/yolo/utils/callbacks/comet.py index 6c62f2d..a1b4a36 100644 --- a/ultralytics/yolo/utils/callbacks/comet.py +++ b/ultralytics/yolo/utils/callbacks/comet.py @@ -5,7 +5,7 @@ from ultralytics.yolo.utils.torch_utils import get_flops, get_num_params try: import comet_ml -except (ModuleNotFoundError, ImportError): +except ImportError: comet_ml = None @@ -35,7 +35,7 @@ def on_fit_epoch_end(trainer): def on_train_end(trainer): experiment = comet_ml.get_global_experiment() - experiment.log_model("YOLOv8", file_or_folder=trainer.best, file_name="best.pt", overwrite=True) + experiment.log_model("YOLOv8", file_or_folder=str(trainer.best), file_name="best.pt", overwrite=True) callbacks = { diff --git a/ultralytics/yolo/utils/checks.py b/ultralytics/yolo/utils/checks.py index 2045062..cffdcac 100644 --- a/ultralytics/yolo/utils/checks.py +++ b/ultralytics/yolo/utils/checks.py @@ -18,8 +18,8 @@ import psutil import torch from IPython import display -from ultralytics.yolo.utils import (AUTOINSTALL, FONT, LOGGER, ROOT, USER_CONFIG_DIR, TryExcept, colorstr, emojis, - is_colab, is_docker, is_jupyter) +from ultralytics.yolo.utils import (AUTOINSTALL, FONT, LOGGER, ROOT, USER_CONFIG_DIR, TryExcept, colorstr, downloads, + emojis, is_colab, is_docker, is_jupyter) def is_ascii(s) -> bool: @@ -123,9 +123,7 @@ def check_font(font: str = FONT, progress: bool = False) -> None: # Check if font file exists at the source or destination path if not font.exists() and not file.exists(): # Download font file - url = f'https://ultralytics.com/assets/{font.name}' - LOGGER.info(f'Downloading {url} to {file}...') - torch.hub.download_url_to_file(url, str(file), progress=progress) + downloads.safe_download(file=file, url=f'https://ultralytics.com/assets/{font.name}', progress=progress) def check_online() -> bool: @@ -215,9 +213,7 @@ def check_file(file, suffix=''): if Path(file).is_file(): LOGGER.info(f'Found {url} locally at {file}') # file already exists else: - LOGGER.info(f'Downloading {url} to {file}...') - torch.hub.download_url_to_file(url, file) - assert Path(file).exists() and Path(file).stat().st_size > 0, f'File download failed: {url}' # check + downloads.safe_download(file=file, url=url) return file else: # search files = [] diff --git a/ultralytics/yolo/utils/downloads.py b/ultralytics/yolo/utils/downloads.py index 99026da..c86e24b 100644 --- a/ultralytics/yolo/utils/downloads.py +++ b/ultralytics/yolo/utils/downloads.py @@ -12,16 +12,16 @@ from zipfile import ZipFile import requests import torch -from ultralytics.yolo.utils import LOGGER, SETTINGS +from ultralytics.yolo.utils import LOGGER -def safe_download(file, url, url2=None, min_bytes=1E0, error_msg=''): +def safe_download(file, url, url2=None, min_bytes=1E0, error_msg='', progress=True): # Attempts to download file from url or url2, checks and removes incomplete downloads < min_bytes file = Path(file) assert_msg = f"Downloaded file '{file}' does not exist or size is < min_bytes={min_bytes}" try: # url1 LOGGER.info(f'Downloading {url} to {file}...') - torch.hub.download_url_to_file(url, str(file), progress=LOGGER.level <= logging.INFO) + torch.hub.download_url_to_file(url, str(file), progress=progress and LOGGER.level <= logging.INFO) assert file.exists() and file.stat().st_size > min_bytes, assert_msg # check except Exception as e: # url2 if file.exists(): @@ -32,7 +32,7 @@ def safe_download(file, url, url2=None, min_bytes=1E0, error_msg=''): if not file.exists() or file.stat().st_size < min_bytes: # check if file.exists(): file.unlink() # remove partial downloads - LOGGER.info(f"ERROR: {assert_msg}\n{error_msg}") + LOGGER.warning(f"ERROR: {assert_msg}\n{error_msg}") LOGGER.info('') @@ -49,6 +49,7 @@ def is_url(url, check=True): def attempt_download(file, repo='ultralytics/assets', release='v0.0.0'): # Attempt file download from GitHub release assets if not found locally. release = 'latest', 'v6.2', etc. + from ultralytics.yolo.utils import SETTINGS def github_assets(repository, version='latest'): # Return GitHub repo tag and assets (i.e. ['yolov8n.pt', 'yolov5m.pt', ...]) @@ -76,7 +77,6 @@ def attempt_download(file, repo='ultralytics/assets', release='v0.0.0'): return file # GitHub assets - assets = [f'yolov5{size}{suffix}.pt' for size in 'nsmlx' for suffix in ('', '6', '-cls', '-seg')] # default assets = [f'yolov8{size}{suffix}.pt' for size in 'nsmlx' for suffix in ('', '6', '-cls', '-seg')] # default try: tag, assets = github_assets(repo, release) @@ -110,13 +110,12 @@ def download(url, dir=Path.cwd(), unzip=True, delete=True, curl=False, threads=1 f = dir / Path(url).name LOGGER.info(f'Downloading {url} to {f}...') for i in range(retry + 1): - if curl: - s = 'sS' if threads > 1 else '' # silent - r = os.system( - f'curl -# -{s}L "{url}" -o "{f}" --retry 9 -C -') # curl download with retry, continue + if curl: # curl download with retry, continue + s = 'sS' * (threads > 1) # silent + r = os.system(f'curl -# -{s}L "{url}" -o "{f}" --retry 9 -C -') success = r == 0 - else: - torch.hub.download_url_to_file(url, f, progress=threads == 1) # torch download + else: # torch download + torch.hub.download_url_to_file(url, f, progress=threads == 1) success = f.is_file() if success: break diff --git a/ultralytics/yolo/utils/torch_utils.py b/ultralytics/yolo/utils/torch_utils.py index 5e93103..87987b4 100644 --- a/ultralytics/yolo/utils/torch_utils.py +++ b/ultralytics/yolo/utils/torch_utils.py @@ -67,9 +67,19 @@ def select_device(device='', batch=0, newline=False): if cpu or mps: os.environ['CUDA_VISIBLE_DEVICES'] = '-1' # force torch.cuda.is_available() = False elif device: # non-cpu device requested + visible = os.environ.get('CUDA_VISIBLE_DEVICES', None) os.environ['CUDA_VISIBLE_DEVICES'] = device # set environment variable - must be before assert is_available() if not (torch.cuda.is_available() and torch.cuda.device_count() >= len(device.replace(',', ''))): - raise ValueError(f"Invalid CUDA 'device={device}' requested, use 'device=cpu' or pass valid CUDA device(s)") + LOGGER.info(s) + install = "See https://pytorch.org/get-started/locally/ for up-to-date torch install instructions if no " \ + "CUDA devices are seen by torch.\n" if torch.cuda.device_count() == 0 else "" + raise ValueError(f"Invalid CUDA 'device={device}' requested." + f" Use 'device=cpu' or pass valid CUDA device(s) if available," + f" i.e. 'device=0' or 'device=0,1,2,3' for Multi-GPU.\n" + f"\ntorch.cuda.is_available(): {torch.cuda.is_available()}" + f"\ntorch.cuda.device_count(): {torch.cuda.device_count()}" + f"\nos.environ['CUDA_VISIBLE_DEVICES']: {visible}\n" + f"{install}") if not cpu and not mps and torch.cuda.is_available(): # prefer GPU if available devices = device.split(',') if device else '0' # range(torch.cuda.device_count()) # i.e. 0,1,6,7 diff --git a/ultralytics/yolo/v8/classify/predict.py b/ultralytics/yolo/v8/classify/predict.py index f6c05cf..d50bdba 100644 --- a/ultralytics/yolo/v8/classify/predict.py +++ b/ultralytics/yolo/v8/classify/predict.py @@ -69,7 +69,7 @@ def predict(cfg=DEFAULT_CFG, use_python=False): source = cfg.source if cfg.source is not None else ROOT / "assets" if (ROOT / "assets").exists() \ else "https://ultralytics.com/images/bus.jpg" - args = dict(model=model, source=source, verbose=True) + args = dict(model=model, source=source) if use_python: from ultralytics import YOLO YOLO(model)(**args) diff --git a/ultralytics/yolo/v8/classify/train.py b/ultralytics/yolo/v8/classify/train.py index c26524d..f4ff404 100644 --- a/ultralytics/yolo/v8/classify/train.py +++ b/ultralytics/yolo/v8/classify/train.py @@ -141,7 +141,7 @@ def train(cfg=DEFAULT_CFG, use_python=False): data = cfg.data or "mnist160" # or yolo.ClassificationDataset("mnist") device = cfg.device if cfg.device is not None else '' - args = dict(model=model, data=data, device=device, verbose=True) + args = dict(model=model, data=data, device=device) if use_python: from ultralytics import YOLO YOLO(model).train(**args) diff --git a/ultralytics/yolo/v8/classify/val.py b/ultralytics/yolo/v8/classify/val.py index 21cb159..34bbdea 100644 --- a/ultralytics/yolo/v8/classify/val.py +++ b/ultralytics/yolo/v8/classify/val.py @@ -50,7 +50,7 @@ def val(cfg=DEFAULT_CFG, use_python=False): model = cfg.model or "yolov8n-cls.pt" # or "resnet18" data = cfg.data or "mnist160" - args = dict(model=model, data=data, verbose=True) + args = dict(model=model, data=data) if use_python: from ultralytics import YOLO YOLO(model).val(**args) diff --git a/ultralytics/yolo/v8/detect/predict.py b/ultralytics/yolo/v8/detect/predict.py index 2131b83..802e69b 100644 --- a/ultralytics/yolo/v8/detect/predict.py +++ b/ultralytics/yolo/v8/detect/predict.py @@ -87,7 +87,7 @@ def predict(cfg=DEFAULT_CFG, use_python=False): source = cfg.source if cfg.source is not None else ROOT / "assets" if (ROOT / "assets").exists() \ else "https://ultralytics.com/images/bus.jpg" - args = dict(model=model, source=source, verbose=True) + args = dict(model=model, source=source) if use_python: from ultralytics import YOLO YOLO(model)(**args) diff --git a/ultralytics/yolo/v8/detect/train.py b/ultralytics/yolo/v8/detect/train.py index 719568b..78c1f13 100644 --- a/ultralytics/yolo/v8/detect/train.py +++ b/ultralytics/yolo/v8/detect/train.py @@ -199,7 +199,7 @@ def train(cfg=DEFAULT_CFG, use_python=False): data = cfg.data or "coco128.yaml" # or yolo.ClassificationDataset("mnist") device = cfg.device if cfg.device is not None else '' - args = dict(model=model, data=data, device=device, verbose=True) + args = dict(model=model, data=data, device=device) if use_python: from ultralytics import YOLO YOLO(model).train(**args) diff --git a/ultralytics/yolo/v8/detect/val.py b/ultralytics/yolo/v8/detect/val.py index ab6e465..09826b1 100644 --- a/ultralytics/yolo/v8/detect/val.py +++ b/ultralytics/yolo/v8/detect/val.py @@ -129,7 +129,7 @@ class DetectionValidator(BaseValidator): f'WARNING ⚠️ no labels found in {self.args.task} set, can not compute metrics without labels') # Print results per class - if (self.args.verbose or not self.training) and self.nc > 1 and len(self.stats): + if self.args.verbose and not self.training and self.nc > 1 and len(self.stats): for i, c in enumerate(self.metrics.ap_class_index): self.logger.info(pf % (self.names[c], self.seen, self.nt_per_class[c], *self.metrics.class_result(i))) @@ -237,7 +237,7 @@ def val(cfg=DEFAULT_CFG, use_python=False): model = cfg.model or "yolov8n.pt" data = cfg.data or "coco128.yaml" - args = dict(model=model, data=data, verbose=True) + args = dict(model=model, data=data) if use_python: from ultralytics import YOLO YOLO(model).val(**args) diff --git a/ultralytics/yolo/v8/segment/predict.py b/ultralytics/yolo/v8/segment/predict.py index 76bd0eb..fd52a18 100644 --- a/ultralytics/yolo/v8/segment/predict.py +++ b/ultralytics/yolo/v8/segment/predict.py @@ -105,7 +105,7 @@ def predict(cfg=DEFAULT_CFG, use_python=False): source = cfg.source if cfg.source is not None else ROOT / "assets" if (ROOT / "assets").exists() \ else "https://ultralytics.com/images/bus.jpg" - args = dict(model=model, source=source, verbose=True) + args = dict(model=model, source=source) if use_python: from ultralytics import YOLO YOLO(model)(**args) diff --git a/ultralytics/yolo/v8/segment/train.py b/ultralytics/yolo/v8/segment/train.py index adebb3b..2a0d11d 100644 --- a/ultralytics/yolo/v8/segment/train.py +++ b/ultralytics/yolo/v8/segment/train.py @@ -145,7 +145,7 @@ def train(cfg=DEFAULT_CFG, use_python=False): data = cfg.data or "coco128-seg.yaml" # or yolo.ClassificationDataset("mnist") device = cfg.device if cfg.device is not None else '' - args = dict(model=model, data=data, device=device, verbose=True) + args = dict(model=model, data=data, device=device) if use_python: from ultralytics import YOLO YOLO(model).train(**args) diff --git a/ultralytics/yolo/v8/segment/val.py b/ultralytics/yolo/v8/segment/val.py index 492a682..fe46b3f 100644 --- a/ultralytics/yolo/v8/segment/val.py +++ b/ultralytics/yolo/v8/segment/val.py @@ -247,7 +247,7 @@ def val(cfg=DEFAULT_CFG, use_python=False): model = cfg.model or "yolov8n-seg.pt" data = cfg.data or "coco128-seg.yaml" - args = dict(model=model, data=data, verbose=True) + args = dict(model=model, data=data) if use_python: from ultralytics import YOLO YOLO(model).val(**args)