这取决于您使用预先训练的权重的方式:

It depends on how you use the pre trained weights:

1. 用于迁移学习(我认为您发送的链接就是他们的工作)
2. 使用为整个模型的拟合提供了起点.

第一个选项仅训练检测头，而不训练网络的主干-这意味着主干权重位于模型和原始模型之间.

The first option only trains the detection head and not the backbone of the network - This means that the backbone weights are sherd between your model and the original model.

在第二个选项中，您训练所有网络，主干+检测头-这意味着您有两种不同的模型

In the second option you train all the network, backbone + detection head- This means that you have two different models

如果在您的情况下使用第二个选项，则要做的唯一方法是加载两个网络，并在图像上对原始网络运行一次推理，然后对新网络运行推理.然后，您可以合并结果.

If in your case you use the second option then the only way to do what you want is to load both networks and run inference on the image once with the original network and second with your new network. Then you combine your results.

如果使用第一个选项，则可以执行以下操作:

If you use the first option then you could do the following:

1. 训练网络上的数据并保存新的检测头权重.
2. 创建一个具有相同主干但有两个检测头的新网络:一个带有原始砝码，另一个带有新砝码.

这个想法是因为因为两者的主干都相同，所以我们可以使用主干提取图像的特征，然后为每个检测头提供特征.

The idea is that because the backbone is the same for both we can use the backbone to extract the features for the image and then feed each detection head with the features.

这是一本有关如何从一个图形中提取权重并将其合并为一个新的权重的教程(这是针对TF1的).

This is a tutorial on how to extract weights from one graph and combine them in a new one (This is for TF1) TensorFlow: saving/restoring and mixing multiple models

在这里您可以阅读如何保存和还原模型的一部分-

Here you can read on how to save and restore part of a model - save-and-restore-a-subset-of-variables