Tensorflow Fine-tuning a model from my own checkpoint Assume that I am going to do more training with a similar data set in the future, is there any benefit to me using a fine tune checkpoint from a model that I created from my own training as opposed to the original SSD_Mobilenet_V1 version (for example 5000 images and 50000 steps).  Does it improve any future training or am I just better off using the original one every time I train?  I'm probably searching for the wrong thing, but I cannot find anything that suggests improvement.  In my reading, I thought that the fine tune only helped with the final layer which leads me to believe its a pointless exercise no matter how good the resulting model was.  Can anyone confirm this point and any useful reading for reference?
Yes, you can. After the below lines everything else will be same as what you have done previously to save the checkpoint.

with tf.Session() as sess:
  saver = tf.train.Saver()
  saver.restore(sess, tf.train.latest_checkpoint('./')) # checkpoint file path


Please refer to the below links:

https://nathanbrixius.wordpress.com/2016/05/24/checkpointing-and-reusing-tensorflow-models/

https://github.com/tensorflow/nmt/issues/51

https://blog.metaflow.fr/tensorflow-saving-restoring-and-mixing-multiple-models-c4c94d5d7125