openimages | downloading images and annotations from Google | Dataset library

I solved the problem!!

You need to call saver.restore (sess, FLAGS.checkpoint) after initializing the network with sess.run (tf.global_variables_initializer ()).

Important: The saver = tf_saver.Saver () must be instantiated before adding new layers to the graph.

This way, when the saver.restore(sess, FLAGS.checkpoint) is performed, it only knows the computation graph prior to creating new layers.

After spending a lot of time, I finally found this hack. Now I have the following code which works for me: File index.html:

In the repository you've provided there is a class-descriptions.csv file which is a list of all possible 19868 labels. This seems to be what you're looking for?

However there is no guarantee that this list is the same as the vision API!

Below I've included some draft documentation I'm working on that might be helpful. One other thing to look out for is that if you're using Slim, you'll need to run export_inference_graph.py to get a .pb GraphDef file initially.

In most situations, training a model with TensorFlow will give you a folder containing a GraphDef file (usually ending with the .pb or .pbtxt extension) and a set of checkpoint files. What you need for mobile or embedded deployment is a single GraphDef file that’s been ‘frozen’, or had its variables converted into inline constants so everything’s in one file. To handle the conversion, you’ll need the freeze_graph.py script, that’s held in tensorflow/pythons/tools/freeze_graph.py. You’ll run it like this:

bazel build tensorflow/tools:freeze_graph bazel-bin/tensorflow/tools/freeze_graph \ --input_graph=/tmp/model/my_graph.pb \ --input_checkpoint=/tmp/model/model.ckpt-1000 \ --output_graph=/tmp/frozen_graph.pb \ --input_node_names=input_node \ --output_node_names=output_node \

The input_graph argument should point to the GraphDef file that holds your model architecture. It’s possible that your GraphDef has been stored in a text format on disk, in which case it’s likely to end in ‘.pbtxt’ instead of ‘.pb’, and you should add an extra --input_binary=false flag to the command. The input_checkpoint should be the most recent saved checkpoint. As mentioned in the checkpoint section, you need to give the common prefix to the set of checkpoints here, rather than a full filename. output_graph defines where the resulting frozen GraphDef will be saved. Because it’s likely to contain a lot of weight values that take up a large amount of space in text format, it’s always saved as a binary protobuf. output_node_names is a list of the names of the nodes that you want to extract the results of your graph from. This is needed because the freezing process needs to understand which parts of the graph are actually needed, and which are artifacts of the training process, like summarization ops. Only ops that contribute to calculating the given output nodes will be kept. If you know how your graph is going to be used, these should just be the names of the nodes you pass into Session::Run() as your fetch targets. If you don’t have this information handy, you can get some suggestions on likely outputs by running the summarize_graph tool. Because the output format for TensorFlow has changed over time, there are a variety of other less commonly used flags available too, like input_saver, but hopefully you shouldn’t need these on graphs trained with modern versions of the framework.