18. . --visualize:是否可视化特征图,默认为False 19. --update:如果为True,则对所有模型进行strip_optimizer操作,去除pt文件中的优化器等信息,默认为F alse 20. --project:结果保存的项目目录路径,默认为’ROOT/runs/detect’ 21. --name:结果保存的子目录名称,默认为’exp’ 22. --exist-ok:是否覆盖已有...
cudnn as cudnn from numpy import random from models.experimental import attempt_load from utils.datasets import LoadStreams, LoadImages from utils.general import check_img_size, non_max_suppression, apply_classifier, scale_coords, xyxy2xywh, \ strip_optimizer, set_logging, increment_path from ...
from utils.general import (LOGGER, check_file, check_img_size, check_imshow, check_requirements, colorstr, cv2, increment_path, non_max_suppression, print_args, scale_coords, strip_optimizer, xyxy2xywh) from utils.plots import Annotator, colors, save_one_box from utils.torch_utils import sel...
parser.add_argument('--augment', action='store_true', help='augmented inference') # 如果为True,则对所有模型进行strip_optimizer操作,去除pt文件中的优化器等信息,默认为False parser.add_argument('--update', action='store_true', help='update all models') # 检测结果所存放的路径,默认为runs/detec...
\strip_optimizer, set_logging, increment_pathfrom utils.plots import plot_one_boxfrom utils.torch_utils import select_device, load_classifier, time_synchronizeddef detect(save_img=False):source, weights, view_img, save_txt, imgsz = opt.source, opt.weights, opt.view_img, opt.save_txt, opt...
(self):"""Evaluate trained model and save validation results."""# 遍历最后和最佳模型文件forfinself.last, self.best:# 检查文件是否存在iff.exists():# 去除模型文件中的优化器信息strip_optimizer(f)# strip optimizers# 如果当前文件是最佳模型文件iffisself.best:# 记录信息:正在验证最佳模型文件LOGGER....
strip_optimizer函数用于从训练好的模型中去除优化器信息,以便于保存和部署模型。profile函数用于对模型进行速度、内存和FLOPs的分析,帮助用户评估模型的性能。最后,EarlyStopping类实现了早停机制,用于在训练过程中监控模型的性能,并在若干个epoch内没有改进时停止训练,以避免过拟合。
coords = label_line.strip().split()[1:] # Remove the class label (assuming it's always 0) # Convert normalized coordinates to pixel coordinates width, height = 256, 256 # Set the dimensions of the output image coordinates = [(float(coords[i]) * width, float(coords[i+1]) * height...
(per RANK if DDP) project: # project name name: # experiment name, results saved to 'project/name' directory exist_ok: False # whether to overwrite existing experiment pretrained: False # whether to use a pretrained model optimizer: SGD # optimizer to use, choices=['SGD', 'Adam', '...
Returns: None Example: ```py from pathlib import Path from ultralytics.utils.torch_utils import strip_optimizer for f in Path('path/to/model/checkpoints').rglob('*.pt'): strip_optimizer(f) ``` Note: Use `ultralytics.nn.torch_safe_load` for missing modules with `x = torch_safe_...