smart_optimizer: def smart_optimizer(model, name='Adam', lr=0.001, momentum=0.9, decay=1e-5): # YOLOv5 3-param group optimizer: 0) weights with decay, 1) weights no decay, 2) biases no decay g = [], [], [] # optimizer parameter groups bn = tuple(v for k, v in nn.__dic...
fromutils.oneflow_utilsimportEarlyStopping,ModelEMA,de_parallel,select_device,smart_DDP,smart_optimizer,smart_resume#导入早停机制模块,模型滑动平均更新模块,解分布式模块,智能选择设备,智能优化器以及智能断点续训模块等 fromutils.plotsimportplot_evolve,plot_labels #LOCAL_RANK:当前进程对应的GPU号。 LOCAL_RANK...
LOGGER.warning(f'WARNING ⚠️ label smoothing{label_smoothing}requires torch>=1.10.0')returnnn.CrossEntropyLoss() 优化器: defsmart_optimizer(model, name='Adam', lr=0.001, momentum=0.9, decay=1e-5):# YOLOv5 3-param group optimizer: 0) weights with decay, 1) weights no decay, 2) b...
view_img= check_imshow(warn=True) dataset=LoadStreams(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=vid_stride) bs=len(dataset)elifscreenshot: dataset= LoadScreenshots(source, img_size=imgsz, stride=stride, auto=pt)else: dataset= LoadImages(source, img_size=imgsz, stride=st...
strip_optimizer, xyxy2xywh) from utils.plots import Annotator, colors, save_one_box from utils.torch_utils import select_device, smart_inference_mode @smart_inference_mode() def run( weights=ROOT / 'yolov5s.pt', # model.pt path(s) source=ROOT / 'data/images', # file/dir/URL/glob,...
但是需要在optimizer之后 defsmart_DDP(model):# Model DDP creation with checksassertnotcheck_version(torch.__version__,'1.12.0', pinned=True), \'torch==1.12.0 torchvision==0.13.0 DDP training is not supported due to a known issue. '\'Please upgrade or downgrade torch to use DDP. See ht...
Fix missing attr model.model when loading custom yolov model (ultraly… Aug 2, 2022 requirements.txt ClearML experiment tracking integration (ultralytics#8620) Aug 6, 2022 setup.cfg Update setup.cfg to description_file field (ultralytics#7248) Apr 3, 2022 train.py smart_optimizer() improved...
This experiment first used the YOLOv5l baseline model for experiments, the training parameter epoch was set to 300, the training optimizer selected SGD, and the initial learning rate was 1e-2. The GPU memory is 24GB, so the batch-size was set to 48. When the training reached 134th, t...
master .github classify data models segment utils .dockerignore .gitattributes .gitignore CITATION.cff CONTRIBUTING.md LICENSE README.md README.zh-CN.md benchmarks.py detect.py export.py hubconf.py pyproject.toml requirements.txt train.py
increment_path, non_max_suppression, print_args, scale_boxes, strip_optimizer, xyxy2xywh) from utils.plots import Annotator, colors, save_one_box from utils.torch_utils import select_device, smart_inference_mode 代码执行跳到下方main函数: ...