METADATA EXAMPLE HOW TO
METADATA EXAMPLE INSTALL
METADATA EXAMPLE ZIP FILE

In terms of preprocessing and post-processing, normalization and quantizationĪre two independent steps. Optionally, activations for both storage and computation. Is a technique that allows for reduced precision representations of weights and Goal of normalization is to change the values to a common scale, withoutĭistorting differences in the ranges of values. Normalization is a common data preprocessing technique in machine learning. Normalization and quantization parameters May apply corresponding pre/post processing automatically to the object. The TensorFlow Lite Android code generator The file type and where the file is attached to (i.e. The associated file information can be recorded in the metadata. Pack metadata and associated files into the model Is compatible with existing TFLite framework and Interpreter. This new model format keeps using the same file extension.

METADATA EXAMPLE ZIP FILE

The new TensorFlow Lite model becomes a zip file that containsīoth the model and the associated files. The associated files can now be bundled with the model through the metadata Without the associated files (if there are), a model will not function well.

IDs classification models may have label files that indicate object categories. Natural language models usually have vocab files that map word pieces to word

TensorFlow Lite models may come with different associated files. Bounding box - Rectangular shape bounding boxes.Image - Metadata currently supports RGB and greyscale images.Feature - Numbers which are unsigned integers or float32.The following or a combination of the following, it is supported by TensorFlow The model functionally does, as long as the input and output types consists of Model types in mind but rather input and output types. TensorFlow Lite metadata for input and output are not designed with specific SubGraphMetadata.name and scription, when displaying Will use ModelMetadata.name and scription, instead of Since TensorFlow Lite only supports single subgraph at this point, the Output information - Description of the output and post-processing.Input information - Description of the inputs and pre-processing.Model information - Overall description of the model as well as items.There are three parts to the model metadata in the Adding metadata using Flatbuffers Python API Note: to create metadata for the popular ML tasks supported in TensorFlow Lite metadata tooling supports Python 3.

METADATA EXAMPLE INSTALL

There is a detailed guide on how toĪfter setup the Python programming environment, you will need to installĪdditional tooling: pip install tflite-support Setup the metadata toolsīefore adding metadata to your model, you will need to a Python programmingĮnvironment setup for running TensorFlow.

METADATA EXAMPLE HOW TO

See the instruction below about how to populate, visualize, and read metadata. TFLite Interpreter can consume the new file format in the same way as before.

These files are concatenated to the end of the original model file as a ZIP Some models may come with associated files, As shown in Figure 1, it is stored in the TFLite model with metadata and associated files.įile.

machine readable parts that can be leveraged by code generators, such as the.

human readable parts which convey the best practice when using the model,.

Metadata is an important source of knowledge about what the model does and its TensorFlow Lite metadata provides a standard for model descriptions.

Metadata example