The script retrain.py described in How to Retrain an Image Classifier for New Categories was run as

python retrain.py --tfhub_module https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/2 --image_dir /tmp/test

and produced the output file /tmp/output_graph.pb. Converting this with

tensorflowjs_converter --input_format=tf_saved_model --output_format=tfjs_graph_model /tmp/output_graph.pb /tmp/model

failed with

IOError: SavedModel file does not exist at: /tmp/output_graph.pb/{saved_model.pbtxt|saved_model.pb}

If the file output_graph.pb is renamed to saved_model.pb (by @edkeveked), the error changes to

RuntimeError: MetaGraphDef associated with tags 'serve' could not be found in SavedModel. To inspect available tag-sets in the SavedModel, please use the SavedModel CLI: saved_model_cli

saved_model_cli show --dir . reports an empty tag set.

How can this be fixed?

2 Answers

1
serv-inc On Best Solutions

As hinted by @Ping Yu in Retrain image detection with MobileNet, you can use

python retrain.py --tfhub_module https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/2 \
    --image_dir /tmp/flower_photos --saved_model_dir /tmp/saved_retrained_model
tensorflowjs_converter --input_format=tf_saved_model \
    --output_format=tfjs_graph_model \
    --saved_model_tags=serve \
    /tmp/saved_retrained_model/ /tmp/converted_model/

This saves the model using the saved model format.

2
edkeveked On

The input path is the path of the folder and not of the file. Consider the following:

tensorflowjs_converter --input_format=tf_saved_model --output_format=tfjs_graph_model /tmp /tmp/model