data_root="./datasets/DOTAv1.5/", save_dir="./datasets/DOTAv1.5-split/", rates=[0.5, 1.0, 1.5], # multiscale gap=500, ) # split test set, without labels. split_test( data_root="./datasets/DOTAv1.5/", save_dir="./datasets/DOTAv1.5-split/", rates=[0.5, 1.0, 1.5], # mult...
Is there a way to modify the save_dir in validation mode in YOLOv8? I have tried "save_dir=" but it raise error "'save_dir' is not a valid YOLO argument. Similar arguments are ['save_crop', 'save', 'save_hybrid']." Additional ...
save_path = str(save_dir / p.name) # im.jpg # 坐标txt存储路径 txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}') # im.txt # s图片尺寸信息 s += '%gx%g ' % im.shape[2:] # print string # 原图片宽和高信息 gn = tor...
from ultralytics.data.split_dotaimportsplit_test,split_trainval # split train and val set,withlabels.split_trainval(data_root="./datasets/DOTAv1.5/",save_dir="./datasets/DOTAv1.5-split/",rates=[0.5,1.0,1.5],# multiscale gap=500,)# split test set,without labels.split_test(data_root=...
save_model(compressed_model, optimized_save_dir, model_config["model_name"] + "_int8") pot_int8_path = f"{optimized_save_dir}/{MODEL_NAME}_int8.xml" 1. 2. 3. 4. 5. 6. 7. 8. 2. PTQ with NNCF 2.1 准备数据 nncf不需要再实现一个Dataloader类,它只需要将yolov5中的val_dataloader...
self.save_dir = increment_path(Path(project) / name, exist_ok=self.args.exist_ok) self.done_warmup = False if self.args.show: self.args.show = check_imshow(warn=True) # GUI args self.used_model_name = None # The detection model name to use ...
"""self.loss_names ="giou_loss","cls_loss","l1_loss"returnRTDETRValidator(self.test_loader, save_dir=self.save_dir, args=copy(self.args))# 继承父类方法,预处理图像批次。将图像缩放并转换为浮点格式。defpreprocess_batch(self, batch):""" ...
WRITER = SummaryWriter(str(trainer.save_dir))# 记录日志,指示如何启动 TensorBoard 并查看日志LOGGER.info(f"{PREFIX}Start with 'tensorboard --logdir{trainer.save_dir}', view at http://localhost:6006/")# 捕获可能发生的异常exceptExceptionase:# 记录警告日志,指示 TensorBoard 初始化失败,当前运行未记录...
ndimage import gaussian_filter1d # 导入 scipy 库中的高斯滤波函数 # 确定保存图片的目录 save_dir = Path(file).parent if file else Path(dir) # 根据不同的数据类型和设置,选择合适的子图布局和指数索引 if classify: fig, ax = plt.subplots(2, 2, figsize=(6, 6), tight_layout=True) # 分类...
results = model(source="path/to/video.mp4", show=True, conf=0.25, save=True) # video file results = model(source="path/to/dir", show=True, conf=0.25, save=True) # all images and videos within directory results = model(source="path/to/dir/**/*.jpg", show=True, conf=0.25, save...