问题描述
我是新来的.我最近开始研究对象检测并决定使用 Tensorflow 对象检测 API.但是，当我开始训练模型时，它并没有像它应该的那样显示全局步骤，尽管它仍在后台进行训练.
I am new here. I recently started working with object detection and decided to use the Tensorflow object detection API. But, when I start training the model, it does not display the global step like it should, although it's still training in the background.
详情:我正在服务器上进行培训并在 Windows 上使用 OpenSSH 访问它.我通过收集图片并标记它们来训练自定义数据集.我使用model_main.py 训练它.此外，直到几个月前，API 还有些不同，直到最近才更改为最新版本.例如，之前它曾经使用 train.py 进行训练，而不是 model_main.py.我能找到的所有在线教程都使用train.py，所以它可能是最新提交的问题.但我发现没有其他人解决这个问题.
Details:I am training on a server and accessing it using OpenSSH on Windows. I trained a custom dataset, by collecting pictures and labeling them. I trained it using model_main.py. Also, until a couple of months back, the API was a little different, and only recently they changed to the latest version. For instance, earlier it used to use train.py for training, instead of model_main.py. All the online tutorial I can find use train.py, so it might be a problem with the latest commit. But I don't find anyone else fining this problem.
提前致谢！
推荐答案
在model_main.pytf.logging.set_verbosity(tf.logging.INFO)/代码> 脚本.它将在每 100 步后显示一个摘要.
Add tf.logging.set_verbosity(tf.logging.INFO) after the import section of the model_main.py script. It will display a summary after every 100th step.
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