用专业英语翻译下面这段话：使用AdamW19优化器对模型进行训练初始学习率为1e−3权值衰减因子为0批量大小为30个epoch其中前15个epoch为冻结训练即将主干网络中不包含cbam的前两层冻结进行训练后15个epoch则正常训练学习速率衰减策略是余弦衰减每13个时代学习速率将是前一个时代的09倍。此外由于该数据集图像数量较少因此采用数据增强技术对训练样本进行扩展包括旋转、裁剪、缩放翻转等变换

The model was trained using the AdamW[19] optimizer with an initial learning rate of 1e−3, weight decay factor of 0, and a batch size of 30 epochs. The first 15 epochs were frozen training, where the first two layers of the backbone network that did not include cbam were frozen. The next 15 epochs were normal training with a cosine learning rate decay strategy, where the learning rate decreased by 0.9 every 1.3 epochs. Data augmentation techniques such as rotation, cropping, scaling, and flipping were used to expand the training samples due to the limited number of images in the dataset. To ensure that the experimental results only reflected the performance of the network structure and verify the effectiveness of the proposed method, the same data augmentation settings were used during the training process. The hyperparameter settings are shown in Table 2