A short update on the current state of my implementation. I heard there is interest to use it to parse tensor flow models. 0.) Load the BaselineOfProtobuf from https://github.com/zecke/pharo-protobuf 1.) Generate code Use the Google protoc to generate a descriptor set: $ protoc -o tf.pb --include_imports tensorflow/core/framework/graph.proto And then use this descriptor to generate code: | descriptor nameTable generator | descriptor := GPBFileDescriptorSet materializeFrom: 'tf.pb' asFileReference binaryReadStream. nameTable := GPBTypeNamesVisitor new. nameTable customPrefix: 'TF_'. generator := GPBGeneratingVisitor new typeNames: nameTable; targetPackage: 'Tensorflow-Definitions'. descriptor visit: nameTable. generator visit: descriptor. 2.) Parse a model (e.g. the inception v3 model) TF_GraphDef materializeFrom: 'inception_v3_2016_08_28_frozen.pb' asFileReference binaryReadStream 3.) ??? I guess load it into tensorflow? I am not sure if the endianness for the Float is correct (if not the weights are wrong so please be careful and have a look). There are still plenty of TODOs left. JSON and TextProto parsing needs to be implemented. Working on the official regression suite is needed as well. Strict/Non-strict modes for parsing are needed as well.