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...
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) biases no decayg = [], [], []# optimizer parameter groupsbn =tuple(vfork, vinnn.__dict__.items()if'Norm'ink...
fromutils.metricsimportfitness 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...
increment_path, non_max_suppression, print_args, scale_boxes, strip_optimizer, xyxy2xywh)fromutils.plotsimportAnnotator, colors, save_one_boxfromutils.torch_utilsimportselect_device, smart_inference_mode@smart_inference_mode()defrun(weights=ROOT /'yolov5s.pt',# model path or triton URLsource=...
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,...
increment_path, non_max_suppression, print_args, scale_boxes, strip_optimizer, xyxy2xywh)fromutils.plotsimportAnnotator, colors, save_one_boxfromutils.torch_utilsimportselect_device, smart_inference_mode 包导入完成之后,执行最下面的这段代码: ...
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...
The basic training parameters of the MLP-YOLOv5 model include: the maximum number of training epochs was 200; the image input size was 640 × 640; the batch size was set to 16; the momentum was set to 0.937; the initial learning rate was set to 0.01, and the optimizer used was SGD....
.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 tutorial.ipynb ...
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 1. 2. 3. 4. 5. 6. ...