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TensorFlow Lite Metadata Writer API

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TensorFlow Lite Model Metadata is a standard model description format. It contains rich semantics for general model information, inputs/outputs, and associated files, which makes the model self-descriptive and exchangeable.

Model Metadata is currently used in the following two primary use cases:

  1. Enable easy model inference using TensorFlow Lite Task Library and codegen tools. Model Metadata contains the mandatory information required during inference, such as label files in image classification, sampling rate of the audio input in audio classification, and tokenizer type to process input string in Natural Language models.

  2. Enable model creators to include documentation, such as description of model inputs/outputs or how to use the model. Model users can view these documentation via visualization tools such as Netron.

TensorFlow Lite Metadata Writer API provides an easy-to-use API to create Model Metadata for popular ML tasks supported by the TFLite Task Library. This notebook shows examples on how the metadata should be populated for the following tasks below:

Metadata writers for BERT natural language classifiers and BERT question answerers are coming soon.

If you want to add metadata for use cases that are not supported, please use the Flatbuffers Python API. See the tutorials here.

Prerequisites

Install the TensorFlow Lite Support Pypi package.

pip install tflite-support-nightly

Create Model Metadata for Task Library and Codegen

Image classifiers

See the image classifier model compatibility requirements for more details about the supported model format.

Step 1: Import the required packages.

from tflite_support.metadata_writers import image_classifier
from tflite_support.metadata_writers import writer_utils

Step 2: Download the example image classifier, mobilenet_v2_1.0_224.tflite, and the label file.

curl -L https://github.com/tensorflow/tflite-support/raw/master/tensorflow_lite_support/metadata/python/tests/testdata/image_classifier/mobilenet_v2_1.0_224.tflite -o mobilenet_v2_1.0_224.tflite
curl -L https://github.com/tensorflow/tflite-support/raw/master/tensorflow_lite_support/metadata/python/tests/testdata/image_classifier/labels.txt -o mobilenet_labels.txt
% Total    % Received % Xferd  Average Speed   Time    Time     Time  Current
                                 Dload  Upload   Total   Spent    Left  Speed
100   232  100   232    0     0    678      0 --:--:-- --:--:-- --:--:--   678
100 13.3M  100 13.3M    0     0  7950k      0  0:00:01  0:00:01 --:--:-- 81.7M
  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current
                                 Dload  Upload   Total   Spent    Left  Speed
100   215  100   215    0     0    581      0 --:--:-- --:--:-- --:--:--   581
100 10484  100 10484    0     0  13204      0 --:--:-- --:--:-- --:--:-- 2671k

Step 3: Create metadata writer and populate.

ImageClassifierWriter = image_classifier.MetadataWriter
_MODEL_PATH = "mobilenet_v2_1.0_224.tflite"
# Task Library expects label files that are in the same format as the one below.
_LABEL_FILE = "mobilenet_labels.txt"
_SAVE_TO_PATH = "mobilenet_v2_1.0_224_metadata.tflite"
# Normalization parameters is required when reprocessing the image. It is
# optional if the image pixel values are in range of [0, 255] and the input
# tensor is quantized to uint8. See the introduction for normalization and
# quantization parameters below for more details.
# https://www.tensorflow.org/lite/convert/metadata#normalization_and_quantization_parameters)
_INPUT_NORM_MEAN = 127.5
_INPUT_NORM_STD = 127.5

# Create the metadata writer.
writer = ImageClassifierWriter.create_for_inference(
    writer_utils.load_file(_MODEL_PATH), [_INPUT_NORM_MEAN], [_INPUT_NORM_STD],
    [_LABEL_FILE])

# Verify the metadata generated by metadata writer.
print(writer.get_metadata_json())

# Populate the metadata into the model.
writer_utils.save_file(writer.populate(), _SAVE_TO_PATH)
{
  "name": "ImageClassifier",
  "description": "Identify the most prominent object in the image from a known set of categories.",
  "subgraph_metadata": [
    {
      "input_tensor_metadata": [
        {
          "name": "image",
          "description": "Input image to be classified.",
          "content": {
            "content_properties_type": "ImageProperties",
            "content_properties": {
              "color_space": "RGB"
            }
          },
          "process_units": [
            {
              "options_type": "NormalizationOptions",
              "options": {
                "mean": [
                  127.5
                ],
                "std": [
                  127.5
                ]
              }
            }
          ],
          "stats": {
            "max": [
              1.0
            ],
            "min": [
              -1.0
            ]
          }
        }
      ],
      "output_tensor_metadata": [
        {
          "name": "probability",
          "description": "Probabilities of the labels respectively.",
          "content": {
            "content_properties_type": "FeatureProperties",
            "content_properties": {
            }
          },
          "stats": {
            "max": [
              1.0
            ],
            "min": [
              0.0
            ]
          },
          "associated_files": [
            {
              "name": "mobilenet_labels.txt",
              "description": "Labels for categories that the model can recognize.",
              "type": "TENSOR_AXIS_LABELS"
            }
          ]
        }
      ]
    }
  ]
}

Object detectors

See the object detector model compatibility requirements for more details about the supported model format.

Step 1: Import the required packages.

from tflite_support.metadata_writers import object_detector
from tflite_support.metadata_writers import writer_utils

Step 2: Download the example object detector, ssd_mobilenet_v1.tflite, and the label file.

curl -L https://github.com/tensorflow/tflite-support/raw/master/tensorflow_lite_support/metadata/python/tests/testdata/object_detector/ssd_mobilenet_v1.tflite -o ssd_mobilenet_v1.tflite
curl -L https://github.com/tensorflow/tflite-support/raw/master/tensorflow_lite_support/metadata/python/tests/testdata/object_detector/labelmap.txt -o ssd_mobilenet_labels.txt
% Total    % Received % Xferd  Average Speed   Time    Time     Time  Current
                                 Dload  Upload   Total   Spent    Left  Speed
100   227  100   227    0     0    238      0 --:--:-- --:--:-- --:--:--   238
100 4085k  100 4085k    0     0  2465k      0  0:00:01  0:00:01 --:--:-- 44.2M
  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current
                                 Dload  Upload   Total   Spent    Left  Speed
100   216  100   216    0     0    640      0 --:--:-- --:--:-- --:--:--   640
100   661  100   661    0     0    882      0 --:--:-- --:--:-- --:--:--     0

Step 3: Create metadata writer and populate.

ObjectDetectorWriter = object_detector.MetadataWriter
_MODEL_PATH = "ssd_mobilenet_v1.tflite"
# Task Library expects label files that are in the same format as the one below.
_LABEL_FILE = "ssd_mobilenet_labels.txt"
_SAVE_TO_PATH = "ssd_mobilenet_v1_metadata.tflite"
# Normalization parameters is required when reprocessing the image. It is
# optional if the image pixel values are in range of [0, 255] and the input
# tensor is quantized to uint8. See the introduction for normalization and
# quantization parameters below for more details.
# https://www.tensorflow.org/lite/convert/metadata#normalization_and_quantization_parameters)
_INPUT_NORM_MEAN = 127.5
_INPUT_NORM_STD = 127.5

# Create the metadata writer.
writer = ObjectDetectorWriter.create_for_inference(
    writer_utils.load_file(_MODEL_PATH), [_INPUT_NORM_MEAN], [_INPUT_NORM_STD],
    [_LABEL_FILE])

# Verify the metadata generated by metadata writer.
print(writer.get_metadata_json())

# Populate the metadata into the model.
writer_utils.save_file(writer.populate(), _SAVE_TO_PATH)
{
  "name": "ObjectDetector",
  "description": "Identify which of a known set of objects might be present and provide information about their positions within the given image or a video stream.",
  "subgraph_metadata": [
    {
      "input_tensor_metadata": [
        {
          "name": "image",
          "description": "Input image to be detected.",
          "content": {
            "content_properties_type": "ImageProperties",
            "content_properties": {
              "color_space": "RGB"
            }
          },
          "process_units": [
            {
              "options_type": "NormalizationOptions",
              "options": {
                "mean": [
                  127.5
                ],
                "std": [
                  127.5
                ]
              }
            }
          ],
          "stats": {
            "max": [
              255.0
            ],
            "min": [
              0.0
            ]
          }
        }
      ],
      "output_tensor_metadata": [
        {
          "name": "location",
          "description": "The locations of the detected boxes.",
          "content": {
            "content_properties_type": "BoundingBoxProperties",
            "content_properties": {
              "index": [
                1,
                0,
                3,
                2
              ],
              "type": "BOUNDARIES"
            },
            "range": {
              "min": 2,
              "max": 2
            }
          },
          "stats": {
          }
        },
        {
          "name": "category",
          "description": "The categories of the detected boxes.",
          "content": {
            "content_properties_type": "FeatureProperties",
            "content_properties": {
            },
            "range": {
              "min": 2,
              "max": 2
            }
          },
          "stats": {
          },
          "associated_files": [
            {
              "name": "ssd_mobilenet_labels.txt",
              "description": "Labels for categories that the model can recognize.",
              "type": "TENSOR_VALUE_LABELS"
            }
          ]
        },
        {
          "name": "score",
          "description": "The scores of the detected boxes.",
          "content": {
            "content_properties_type": "FeatureProperties",
            "content_properties": {
            },
            "range": {
              "min": 2,
              "max": 2
            }
          },
          "stats": {
          }
        },
        {
          "name": "number of detections",
          "description": "The number of the detected boxes.",
          "content": {
            "content_properties_type": "FeatureProperties",
            "content_properties": {
            }
          },
          "stats": {
          }
        }
      ],
      "output_tensor_groups": [
        {
          "name": "detection_result",
          "tensor_names": [
            "location",
            "category",
            "score"
          ]
        }
      ]
    }
  ]
}

Image segmenters

See the image segmenter model compatibility requirements for more details about the supported model format.

Step 1: Import the required packages.

from tflite_support.metadata_writers import image_segmenter
from tflite_support.metadata_writers import writer_utils

Step 2: Download the example image segmenter, deeplabv3.tflite, and the label file.

curl -L https://github.com/tensorflow/tflite-support/raw/master/tensorflow_lite_support/metadata/python/tests/testdata/image_segmenter/deeplabv3.tflite -o deeplabv3.tflite
curl -L https://github.com/tensorflow/tflite-support/raw/master/tensorflow_lite_support/metadata/python/tests/testdata/image_segmenter/labelmap.txt -o deeplabv3_labels.txt
% Total    % Received % Xferd  Average Speed   Time    Time     Time  Current
                                 Dload  Upload   Total   Spent    Left  Speed
100   220  100   220    0     0    331      0 --:--:-- --:--:-- --:--:--   331
100 2714k  100 2714k    0     0  1564k      0  0:00:01  0:00:01 --:--:-- 33.4M
  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current
                                 Dload  Upload   Total   Spent    Left  Speed
100   216  100   216    0     0    650      0 --:--:-- --:--:-- --:--:--   650
100   141  100   141    0     0    199      0 --:--:-- --:--:-- --:--:--     0

Step 3: Create metadata writer and populate.

ImageSegmenterWriter = image_segmenter.MetadataWriter
_MODEL_PATH = "deeplabv3.tflite"
# Task Library expects label files that are in the same format as the one below.
_LABEL_FILE = "deeplabv3_labels.txt"
_SAVE_TO_PATH = "deeplabv3_metadata.tflite"
# Normalization parameters is required when reprocessing the image. It is
# optional if the image pixel values are in range of [0, 255] and the input
# tensor is quantized to uint8. See the introduction for normalization and
# quantization parameters below for more details.
# https://www.tensorflow.org/lite/convert/metadata#normalization_and_quantization_parameters)
_INPUT_NORM_MEAN = 127.5
_INPUT_NORM_STD = 127.5

# Create the metadata writer.
writer = ImageSegmenterWriter.create_for_inference(
    writer_utils.load_file(_MODEL_PATH), [_INPUT_NORM_MEAN], [_INPUT_NORM_STD],
    [_LABEL_FILE])

# Verify the metadata generated by metadata writer.
print(writer.get_metadata_json())

# Populate the metadata into the model.
writer_utils.save_file(writer.populate(), _SAVE_TO_PATH)
{
  "name": "ImageSegmenter",
  "description": "Semantic image segmentation predicts whether each pixel of an image is associated with a certain class.",
  "subgraph_metadata": [
    {
      "input_tensor_metadata": [
        {
          "name": "image",
          "description": "Input image to be segmented.",
          "content": {
            "content_properties_type": "ImageProperties",
            "content_properties": {
              "color_space": "RGB"
            }
          },
          "process_units": [
            {
              "options_type": "NormalizationOptions",
              "options": {
                "mean": [
                  127.5
                ],
                "std": [
                  127.5
                ]
              }
            }
          ],
          "stats": {
            "max": [
              1.0
            ],
            "min": [
              -1.0
            ]
          }
        }
      ],
      "output_tensor_metadata": [
        {
          "name": "segmentation_masks",
          "description": "Masks over the target objects with high accuracy.",
          "content": {
            "content_properties_type": "ImageProperties",
            "content_properties": {
              "color_space": "GRAYSCALE"
            },
            "range": {
              "min": 1,
              "max": 2
            }
          },
          "stats": {
          },
          "associated_files": [
            {
              "name": "deeplabv3_labels.txt",
              "description": "Labels for categories that the model can recognize.",
              "type": "TENSOR_AXIS_LABELS"
            }
          ]
        }
      ]
    }
  ]
}

Natural language classifiers

See the natural language classifier model compatibility requirements for more details about the supported model format.

Step 1: Import the required packages.

from tflite_support.metadata_writers import nl_classifier
from tflite_support.metadata_writers import metadata_info
from tflite_support.metadata_writers import writer_utils

Step 2: Download the example natural language classifier, movie_review.tflite, the label file, and the vocab file.

curl -L https://github.com/tensorflow/tflite-support/raw/master/tensorflow_lite_support/metadata/python/tests/testdata/nl_classifier/movie_review.tflite -o movie_review.tflite
curl -L https://github.com/tensorflow/tflite-support/raw/master/tensorflow_lite_support/metadata/python/tests/testdata/nl_classifier/labels.txt -o movie_review_labels.txt
curl -L https://storage.googleapis.com/download.tensorflow.org/models/tflite_support/nl_classifier/vocab.txt -o movie_review_vocab.txt
% Total    % Received % Xferd  Average Speed   Time    Time     Time  Current
                                 Dload  Upload   Total   Spent    Left  Speed
100   221  100   221    0     0    520      0 --:--:-- --:--:-- --:--:--   520
100  628k  100  628k    0     0   633k      0 --:--:-- --:--:-- --:--:-- 8061k
  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current
                                 Dload  Upload   Total   Spent    Left  Speed
100   212  100   212    0     0    636      0 --:--:-- --:--:-- --:--:--   636
100    17  100    17    0     0     21      0 --:--:-- --:--:-- --:--:--     0
  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current
                                 Dload  Upload   Total   Spent    Left  Speed
100   223  100   223    0     0   1037      0 --:--:-- --:--:-- --:--:--  1037

Step 3: Create metadata writer and populate.

NLClassifierWriter = nl_classifier.MetadataWriter
_MODEL_PATH = "movie_review.tflite"
# Task Library expects label files and vocab files that are in the same formats
# as the ones below.
_LABEL_FILE = "movie_review_labels.txt"
_VOCAB_FILE = "movie_review_vocab.txt"
# NLClassifier supports tokenize input string using the regex tokenizer. See
# more details about how to set up RegexTokenizer below:
# https://github.com/tensorflow/tflite-support/blob/master/tensorflow_lite_support/metadata/python/metadata_writers/metadata_info.py#L130
_DELIM_REGEX_PATTERN = r"[^\w\']+"
_SAVE_TO_PATH = "moview_review_metadata.tflite"

# Create the metadata writer.
writer = nl_classifier.MetadataWriter.create_for_inference(
    writer_utils.load_file(_MODEL_PATH),
    metadata_info.RegexTokenizerMd(_DELIM_REGEX_PATTERN, _VOCAB_FILE),
    [_LABEL_FILE])

# Verify the metadata generated by metadata writer.
print(writer.get_metadata_json())

# Populate the metadata into the model.
writer_utils.save_file(writer.populate(), _SAVE_TO_PATH)
{
  "name": "NLClassifier",
  "description": "Classify the input text into a set of known categories.",
  "subgraph_metadata": [
    {
      "input_tensor_metadata": [
        {
          "name": "input_text",
          "description": "Embedding vectors representing the input text to be classified.",
          "content": {
            "content_properties_type": "FeatureProperties",
            "content_properties": {
            }
          },
          "process_units": [
            {
              "options_type": "RegexTokenizerOptions",
              "options": {
                "delim_regex_pattern": "[^\\w\\']+",
                "vocab_file": [
                  {
                    "name": "movie_review_vocab.txt",
                    "description": "Vocabulary file to convert natural language words to embedding vectors.",
                    "type": "VOCABULARY"
                  }
                ]
              }
            }
          ],
          "stats": {
          }
        }
      ],
      "output_tensor_metadata": [
        {
          "name": "probability",
          "description": "Probabilities of the labels respectively.",
          "content": {
            "content_properties_type": "FeatureProperties",
            "content_properties": {
            }
          },
          "stats": {
            "max": [
              1.0
            ],
            "min": [
              0.0
            ]
          },
          "associated_files": [
            {
              "name": "movie_review_labels.txt",
              "description": "Labels for categories that the model can recognize.",
              "type": "TENSOR_AXIS_LABELS"
            }
          ]
        }
      ]
    }
  ]
}

Audio classifiers

See the audio classifier model compatibility requirements for more details about the supported model format.

Step 1: Import the required packages.

from tflite_support.metadata_writers import audio_classifier
from tflite_support.metadata_writers import metadata_info
from tflite_support.metadata_writers import writer_utils

Step 2: Download the example audio classifier, yamnet.tflite, and the label file.

curl -L https://github.com/tensorflow/tflite-support/raw/master/tensorflow_lite_support/metadata/python/tests/testdata/audio_classifier/yamnet_wavin_quantized_mel_relu6.tflite -o yamnet.tflite
curl -L https://github.com/tensorflow/tflite-support/raw/master/tensorflow_lite_support/metadata/python/tests/testdata/audio_classifier/yamnet_521_labels.txt -o yamnet_labels.txt
% Total    % Received % Xferd  Average Speed   Time    Time     Time  Current
                                 Dload  Upload   Total   Spent    Left  Speed
100   244  100   244    0     0    290      0 --:--:-- --:--:-- --:--:--   289
100 4022k  100 4022k    0     0  2478k      0  0:00:01  0:00:01 --:--:-- 31.3M
  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current
                                 Dload  Upload   Total   Spent    Left  Speed
100   226  100   226    0     0    656      0 --:--:-- --:--:-- --:--:--   656
100  6230  100  6230    0     0   8049      0 --:--:-- --:--:-- --:--:-- 27688

Step 3: Create metadata writer and populate.

AudioClassifierWriter = audio_classifier.MetadataWriter
_MODEL_PATH = "yamnet.tflite"
# Task Library expects label files that are in the same format as the one below.
_LABEL_FILE = "yamnet_labels.txt"
# Expected sampling rate of the input audio buffer.
_SAMPLE_RATE = 16000
# Expected number of channels of the input audio buffer. Note, Task library only
# support single channel so far.
_CHANNELS = 1
_SAVE_TO_PATH = "yamnet_metadata.tflite"

# Create the metadata writer.
writer = AudioClassifierWriter.create_for_inference(
    writer_utils.load_file(_MODEL_PATH), _SAMPLE_RATE, _CHANNELS, [_LABEL_FILE])

# Verify the metadata generated by metadata writer.
print(writer.get_metadata_json())

# Populate the metadata into the model.
writer_utils.save_file(writer.populate(), _SAVE_TO_PATH)
{
  "name": "AudioClassifier",
  "description": "Identify the most prominent type in the audio clip from a known set of categories.",
  "subgraph_metadata": [
    {
      "input_tensor_metadata": [
        {
          "name": "audio_clip",
          "description": "Input audio clip to be classified.",
          "content": {
            "content_properties_type": "AudioProperties",
            "content_properties": {
              "sample_rate": 16000,
              "channels": 1
            }
          },
          "stats": {
          }
        }
      ],
      "output_tensor_metadata": [
        {
          "name": "probability",
          "description": "Scores of the labels respectively.",
          "content": {
            "content_properties_type": "FeatureProperties",
            "content_properties": {
            }
          },
          "stats": {
            "max": [
              1.0
            ],
            "min": [
              0.0
            ]
          },
          "associated_files": [
            {
              "name": "yamnet_labels.txt",
              "description": "Labels for categories that the model can recognize.",
              "type": "TENSOR_AXIS_LABELS"
            }
          ]
        }
      ]
    }
  ]
}

Create Model Metadata with semantic information

You can fill in more descriptive information about the model and each tensor through the Metadata Writer API to help improve model understanding. It can be done through the 'create_from_metadata_info' method in each metadata writer. In general, you can fill in data through the parameters of 'create_from_metadata_info', i.e. general_md, input_md, and output_md. See the example below to create a rich Model Metadata for image classifers.

Step 1: Import the required packages.

from tflite_support.metadata_writers import image_classifier
from tflite_support.metadata_writers import metadata_info
from tflite_support.metadata_writers import writer_utils
from tflite_support import metadata_schema_py_generated as _metadata_fb

Step 2: Download the example image classifier, mobilenet_v2_1.0_224.tflite, and the label file.

curl -L https://github.com/tensorflow/tflite-support/raw/master/tensorflow_lite_support/metadata/python/tests/testdata/image_classifier/mobilenet_v2_1.0_224.tflite -o mobilenet_v2_1.0_224.tflite
curl -L https://github.com/tensorflow/tflite-support/raw/master/tensorflow_lite_support/metadata/python/tests/testdata/image_classifier/labels.txt -o mobilenet_labels.txt
% Total    % Received % Xferd  Average Speed   Time    Time     Time  Current
                                 Dload  Upload   Total   Spent    Left  Speed
100   232  100   232    0     0   1798      0 --:--:-- --:--:-- --:--:--  1798
100 13.3M  100 13.3M    0     0  33.8M      0 --:--:-- --:--:-- --:--:-- 33.8M
  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current
                                 Dload  Upload   Total   Spent    Left  Speed
100   215  100   215    0     0   1720      0 --:--:-- --:--:-- --:--:--  1720
100 10484  100 10484    0     0  57922      0 --:--:-- --:--:-- --:--:-- 57922

Step 3: Create model and tensor information.

model_buffer = writer_utils.load_file("mobilenet_v2_1.0_224.tflite")

# Create general model information.
general_md = metadata_info.GeneralMd(
    name="ImageClassifier",
    version="v1",
    description=("Identify the most prominent object in the image from a "
                 "known set of categories."),
    author="TensorFlow Lite",
    licenses="Apache License. Version 2.0")

# Create input tensor information.
input_md = metadata_info.InputImageTensorMd(
    name="input image",
    description=("Input image to be classified. The expected image is "
                 "128 x 128, with three channels (red, blue, and green) per "
                 "pixel. Each element in the tensor is a value between min and "
                 "max, where (per-channel) min is [0] and max is [255]."),
    norm_mean=[127.5],
    norm_std=[127.5],
    color_space_type=_metadata_fb.ColorSpaceType.RGB,
    tensor_type=writer_utils.get_input_tensor_types(model_buffer)[0])

# Create output tensor information.
output_md = metadata_info.ClassificationTensorMd(
    name="probability",
    description="Probabilities of the 1001 labels respectively.",
    label_files=[
        metadata_info.LabelFileMd(file_path="mobilenet_labels.txt",
                                  locale="en")
    ],
    tensor_type=writer_utils.get_output_tensor_types(model_buffer)[0])

Step 4: Create metadata writer and populate.

ImageClassifierWriter = image_classifier.MetadataWriter
# Create the metadata writer.
writer = ImageClassifierWriter.create_from_metadata_info(
    model_buffer, general_md, input_md, output_md)

# Verify the metadata generated by metadata writer.
print(writer.get_metadata_json())

# Populate the metadata into the model.
writer_utils.save_file(writer.populate(), _SAVE_TO_PATH)
{
  "name": "ImageClassifier",
  "description": "Identify the most prominent object in the image from a known set of categories.",
  "version": "v1",
  "subgraph_metadata": [
    {
      "input_tensor_metadata": [
        {
          "name": "input image",
          "description": "Input image to be classified. The expected image is 128 x 128, with three channels (red, blue, and green) per pixel. Each element in the tensor is a value between min and max, where (per-channel) min is [0] and max is [255].",
          "content": {
            "content_properties_type": "ImageProperties",
            "content_properties": {
              "color_space": "RGB"
            }
          },
          "process_units": [
            {
              "options_type": "NormalizationOptions",
              "options": {
                "mean": [
                  127.5
                ],
                "std": [
                  127.5
                ]
              }
            }
          ],
          "stats": {
            "max": [
              1.0
            ],
            "min": [
              -1.0
            ]
          }
        }
      ],
      "output_tensor_metadata": [
        {
          "name": "probability",
          "description": "Probabilities of the 1001 labels respectively.",
          "content": {
            "content_properties_type": "FeatureProperties",
            "content_properties": {
            }
          },
          "stats": {
            "max": [
              1.0
            ],
            "min": [
              0.0
            ]
          },
          "associated_files": [
            {
              "name": "mobilenet_labels.txt",
              "description": "Labels for categories that the model can recognize.",
              "type": "TENSOR_AXIS_LABELS",
              "locale": "en"
            }
          ]
        }
      ]
    }
  ],
  "author": "TensorFlow Lite",
  "license": "Apache License. Version 2.0"
}

Read the metadata populated to your model.

You can display the metadata and associated files in a TFLite model through the following code:

from tflite_support import metadata

displayer = metadata.MetadataDisplayer.with_model_file("mobilenet_v2_1.0_224_metadata.tflite")
print("Metadata populated:")
print(displayer.get_metadata_json())

print("Associated file(s) populated:")
for file_name in displayer.get_packed_associated_file_list():
  print("file name: ", file_name)
  print("file content:")
  print(displayer.get_associated_file_buffer(file_name))
Metadata populated:
{
  "name": "ImageClassifier",
  "description": "Identify the most prominent object in the image from a known set of categories.",
  "subgraph_metadata": [
    {
      "input_tensor_metadata": [
        {
          "name": "image",
          "description": "Input image to be classified.",
          "content": {
            "content_properties_type": "ImageProperties",
            "content_properties": {
              "color_space": "RGB"
            }
          },
          "process_units": [
            {
              "options_type": "NormalizationOptions",
              "options": {
                "mean": [
                  127.5
                ],
                "std": [
                  127.5
                ]
              }
            }
          ],
          "stats": {
            "max": [
              1.0
            ],
            "min": [
              -1.0
            ]
          }
        }
      ],
      "output_tensor_metadata": [
        {
          "name": "probability",
          "description": "Probabilities of the labels respectively.",
          "content": {
            "content_properties_type": "FeatureProperties",
            "content_properties": {
            }
          },
          "stats": {
            "max": [
              1.0
            ],
            "min": [
              0.0
            ]
          },
          "associated_files": [
            {
              "name": "mobilenet_labels.txt",
              "description": "Labels for categories that the model can recognize.",
              "type": "TENSOR_AXIS_LABELS"
            }
          ]
        }
      ]
    }
  ],
  "min_parser_version": "1.0.0"
}

Associated file(s) populated:
file name:  mobilenet_labels.txt
file content:
b"background\ntench\ngoldfish\ngreat white shark\ntiger shark\nhammerhead\nelectric ray\nstingray\ncock\nhen\nostrich\nbrambling\ngoldfinch\nhouse finch\njunco\nindigo bunting\nrobin\nbulbul\njay\nmagpie\nchickadee\nwater ouzel\nkite\nbald eagle\nvulture\ngreat grey owl\nEuropean fire salamander\ncommon newt\neft\nspotted salamander\naxolotl\nbullfrog\ntree frog\ntailed frog\nloggerhead\nleatherback turtle\nmud turtle\nterrapin\nbox turtle\nbanded gecko\ncommon iguana\nAmerican chameleon\nwhiptail\nagama\nfrilled lizard\nalligator lizard\nGila monster\ngreen lizard\nAfrican chameleon\nKomodo dragon\nAfrican crocodile\nAmerican alligator\ntriceratops\nthunder snake\nringneck snake\nhognose snake\ngreen snake\nking snake\ngarter snake\nwater snake\nvine snake\nnight snake\nboa constrictor\nrock python\nIndian cobra\ngreen mamba\nsea snake\nhorned viper\ndiamondback\nsidewinder\ntrilobite\nharvestman\nscorpion\nblack and gold garden spider\nbarn spider\ngarden spider\nblack widow\ntarantula\nwolf spider\ntick\ncentipede\nblack grouse\nptarmigan\nruffed grouse\nprairie chicken\npeacock\nquail\npartridge\nAfrican grey\nmacaw\nsulphur-crested cockatoo\nlorikeet\ncoucal\nbee eater\nhornbill\nhummingbird\njacamar\ntoucan\ndrake\nred-breasted merganser\ngoose\nblack swan\ntusker\nechidna\nplatypus\nwallaby\nkoala\nwombat\njellyfish\nsea anemone\nbrain coral\nflatworm\nnematode\nconch\nsnail\nslug\nsea slug\nchiton\nchambered nautilus\nDungeness crab\nrock crab\nfiddler crab\nking crab\nAmerican lobster\nspiny lobster\ncrayfish\nhermit crab\nisopod\nwhite stork\nblack stork\nspoonbill\nflamingo\nlittle blue heron\nAmerican egret\nbittern\ncrane\nlimpkin\nEuropean gallinule\nAmerican coot\nbustard\nruddy turnstone\nred-backed sandpiper\nredshank\ndowitcher\noystercatcher\npelican\nking penguin\nalbatross\ngrey whale\nkiller whale\ndugong\nsea lion\nChihuahua\nJapanese spaniel\nMaltese dog\nPekinese\nShih-Tzu\nBlenheim spaniel\npapillon\ntoy terrier\nRhodesian ridgeback\nAfghan hound\nbasset\nbeagle\nbloodhound\nbluetick\nblack-and-tan coonhound\nWalker hound\nEnglish foxhound\nredbone\nborzoi\nIrish wolfhound\nItalian greyhound\nwhippet\nIbizan hound\nNorwegian elkhound\notterhound\nSaluki\nScottish deerhound\nWeimaraner\nStaffordshire bullterrier\nAmerican Staffordshire terrier\nBedlington terrier\nBorder terrier\nKerry blue terrier\nIrish terrier\nNorfolk terrier\nNorwich terrier\nYorkshire terrier\nwire-haired fox terrier\nLakeland terrier\nSealyham terrier\nAiredale\ncairn\nAustralian terrier\nDandie Dinmont\nBoston bull\nminiature schnauzer\ngiant schnauzer\nstandard schnauzer\nScotch terrier\nTibetan terrier\nsilky terrier\nsoft-coated wheaten terrier\nWest Highland white terrier\nLhasa\nflat-coated retriever\ncurly-coated retriever\ngolden retriever\nLabrador retriever\nChesapeake Bay retriever\nGerman short-haired pointer\nvizsla\nEnglish setter\nIrish setter\nGordon setter\nBrittany spaniel\nclumber\nEnglish springer\nWelsh springer spaniel\ncocker spaniel\nSussex spaniel\nIrish water spaniel\nkuvasz\nschipperke\ngroenendael\nmalinois\nbriard\nkelpie\nkomondor\nOld English sheepdog\nShetland sheepdog\ncollie\nBorder collie\nBouvier des Flandres\nRottweiler\nGerman shepherd\nDoberman\nminiature pinscher\nGreater Swiss Mountain dog\nBernese mountain dog\nAppenzeller\nEntleBucher\nboxer\nbull mastiff\nTibetan mastiff\nFrench bulldog\nGreat Dane\nSaint Bernard\nEskimo dog\nmalamute\nSiberian husky\ndalmatian\naffenpinscher\nbasenji\npug\nLeonberg\nNewfoundland\nGreat Pyrenees\nSamoyed\nPomeranian\nchow\nkeeshond\nBrabancon griffon\nPembroke\nCardigan\ntoy poodle\nminiature poodle\nstandard poodle\nMexican hairless\ntimber wolf\nwhite wolf\nred wolf\ncoyote\ndingo\ndhole\nAfrican hunting dog\nhyena\nred fox\nkit fox\nArctic fox\ngrey fox\ntabby\ntiger cat\nPersian cat\nSiamese cat\nEgyptian cat\ncougar\nlynx\nleopard\nsnow leopard\njaguar\nlion\ntiger\ncheetah\nbrown bear\nAmerican black bear\nice bear\nsloth bear\nmongoose\nmeerkat\ntiger beetle\nladybug\nground beetle\nlong-horned beetle\nleaf beetle\ndung beetle\nrhinoceros beetle\nweevil\nfly\nbee\nant\ngrasshopper\ncricket\nwalking stick\ncockroach\nmantis\ncicada\nleafhopper\nlacewing\ndragonfly\ndamselfly\nadmiral\nringlet\nmonarch\ncabbage butterfly\nsulphur butterfly\nlycaenid\nstarfish\nsea urchin\nsea cucumber\nwood rabbit\nhare\nAngora\nhamster\nporcupine\nfox squirrel\nmarmot\nbeaver\nguinea pig\nsorrel\nzebra\nhog\nwild boar\nwarthog\nhippopotamus\nox\nwater buffalo\nbison\nram\nbighorn\nibex\nhartebeest\nimpala\ngazelle\nArabian camel\nllama\nweasel\nmink\npolecat\nblack-footed ferret\notter\nskunk\nbadger\narmadillo\nthree-toed sloth\norangutan\ngorilla\nchimpanzee\ngibbon\nsiamang\nguenon\npatas\nbaboon\nmacaque\nlangur\ncolobus\nproboscis monkey\nmarmoset\ncapuchin\nhowler monkey\ntiti\nspider monkey\nsquirrel monkey\nMadagascar cat\nindri\nIndian elephant\nAfrican elephant\nlesser panda\ngiant panda\nbarracouta\neel\ncoho\nrock beauty\nanemone fish\nsturgeon\ngar\nlionfish\npuffer\nabacus\nabaya\nacademic gown\naccordion\nacoustic guitar\naircraft carrier\nairliner\nairship\naltar\nambulance\namphibian\nanalog clock\napiary\napron\nashcan\nassault rifle\nbackpack\nbakery\nbalance beam\nballoon\nballpoint\nBand Aid\nbanjo\nbannister\nbarbell\nbarber chair\nbarbershop\nbarn\nbarometer\nbarrel\nbarrow\nbaseball\nbasketball\nbassinet\nbassoon\nbathing cap\nbath towel\nbathtub\nbeach wagon\nbeacon\nbeaker\nbearskin\nbeer bottle\nbeer glass\nbell cote\nbib\nbicycle-built-for-two\nbikini\nbinder\nbinoculars\nbirdhouse\nboathouse\nbobsled\nbolo tie\nbonnet\nbookcase\nbookshop\nbottlecap\nbow\nbow tie\nbrass\nbrassiere\nbreakwater\nbreastplate\nbroom\nbucket\nbuckle\nbulletproof vest\nbullet train\nbutcher shop\ncab\ncaldron\ncandle\ncannon\ncanoe\ncan opener\ncardigan\ncar mirror\ncarousel\ncarpenter's kit\ncarton\ncar wheel\ncash machine\ncassette\ncassette player\ncastle\ncatamaran\nCD player\ncello\ncellular telephone\nchain\nchainlink fence\nchain mail\nchain saw\nchest\nchiffonier\nchime\nchina cabinet\nChristmas stocking\nchurch\ncinema\ncleaver\ncliff dwelling\ncloak\nclog\ncocktail shaker\ncoffee mug\ncoffeepot\ncoil\ncombination lock\ncomputer keyboard\nconfectionery\ncontainer ship\nconvertible\ncorkscrew\ncornet\ncowboy boot\ncowboy hat\ncradle\ncrane\ncrash helmet\ncrate\ncrib\nCrock Pot\ncroquet ball\ncrutch\ncuirass\ndam\ndesk\ndesktop computer\ndial telephone\ndiaper\ndigital clock\ndigital watch\ndining table\ndishrag\ndishwasher\ndisk brake\ndock\ndogsled\ndome\ndoormat\ndrilling platform\ndrum\ndrumstick\ndumbbell\nDutch oven\nelectric fan\nelectric guitar\nelectric locomotive\nentertainment center\nenvelope\nespresso maker\nface powder\nfeather boa\nfile\nfireboat\nfire engine\nfire screen\nflagpole\nflute\nfolding chair\nfootball helmet\nforklift\nfountain\nfountain pen\nfour-poster\nfreight car\nFrench horn\nfrying pan\nfur coat\ngarbage truck\ngasmask\ngas pump\ngoblet\ngo-kart\ngolf ball\ngolfcart\ngondola\ngong\ngown\ngrand piano\ngreenhouse\ngrille\ngrocery store\nguillotine\nhair slide\nhair spray\nhalf track\nhammer\nhamper\nhand blower\nhand-held computer\nhandkerchief\nhard disc\nharmonica\nharp\nharvester\nhatchet\nholster\nhome theater\nhoneycomb\nhook\nhoopskirt\nhorizontal bar\nhorse cart\nhourglass\niPod\niron\njack-o'-lantern\njean\njeep\njersey\njigsaw puzzle\njinrikisha\njoystick\nkimono\nknee pad\nknot\nlab coat\nladle\nlampshade\nlaptop\nlawn mower\nlens cap\nletter opener\nlibrary\nlifeboat\nlighter\nlimousine\nliner\nlipstick\nLoafer\nlotion\nloudspeaker\nloupe\nlumbermill\nmagnetic compass\nmailbag\nmailbox\nmaillot\nmaillot\nmanhole cover\nmaraca\nmarimba\nmask\nmatchstick\nmaypole\nmaze\nmeasuring cup\nmedicine chest\nmegalith\nmicrophone\nmicrowave\nmilitary uniform\nmilk can\nminibus\nminiskirt\nminivan\nmissile\nmitten\nmixing bowl\nmobile home\nModel T\nmodem\nmonastery\nmonitor\nmoped\nmortar\nmortarboard\nmosque\nmosquito net\nmotor scooter\nmountain bike\nmountain tent\nmouse\nmousetrap\nmoving van\nmuzzle\nnail\nneck brace\nnecklace\nnipple\nnotebook\nobelisk\noboe\nocarina\nodometer\noil filter\norgan\noscilloscope\noverskirt\noxcart\noxygen mask\npacket\npaddle\npaddlewheel\npadlock\npaintbrush\npajama\npalace\npanpipe\npaper towel\nparachute\nparallel bars\npark bench\nparking meter\npassenger car\npatio\npay-phone\npedestal\npencil box\npencil sharpener\nperfume\nPetri dish\nphotocopier\npick\npickelhaube\npicket fence\npickup\npier\npiggy bank\npill bottle\npillow\nping-pong ball\npinwheel\npirate\npitcher\nplane\nplanetarium\nplastic bag\nplate rack\nplow\nplunger\nPolaroid camera\npole\npolice van\nponcho\npool table\npop bottle\npot\npotter's wheel\npower drill\nprayer rug\nprinter\nprison\nprojectile\nprojector\npuck\npunching bag\npurse\nquill\nquilt\nracer\nracket\nradiator\nradio\nradio telescope\nrain barrel\nrecreational vehicle\nreel\nreflex camera\nrefrigerator\nremote control\nrestaurant\nrevolver\nrifle\nrocking chair\nrotisserie\nrubber eraser\nrugby ball\nrule\nrunning shoe\nsafe\nsafety pin\nsaltshaker\nsandal\nsarong\nsax\nscabbard\nscale\nschool bus\nschooner\nscoreboard\nscreen\nscrew\nscrewdriver\nseat belt\nsewing machine\nshield\nshoe shop\nshoji\nshopping basket\nshopping cart\nshovel\nshower cap\nshower curtain\nski\nski mask\nsleeping bag\nslide rule\nsliding door\nslot\nsnorkel\nsnowmobile\nsnowplow\nsoap dispenser\nsoccer ball\nsock\nsolar dish\nsombrero\nsoup bowl\nspace bar\nspace heater\nspace shuttle\nspatula\nspeedboat\nspider web\nspindle\nsports car\nspotlight\nstage\nsteam locomotive\nsteel arch bridge\nsteel drum\nstethoscope\nstole\nstone wall\nstopwatch\nstove\nstrainer\nstreetcar\nstretcher\nstudio couch\nstupa\nsubmarine\nsuit\nsundial\nsunglass\nsunglasses\nsunscreen\nsuspension bridge\nswab\nsweatshirt\nswimming trunks\nswing\nswitch\nsyringe\ntable lamp\ntank\ntape player\nteapot\nteddy\ntelevision\ntennis ball\nthatch\ntheater curtain\nthimble\nthresher\nthrone\ntile roof\ntoaster\ntobacco shop\ntoilet seat\ntorch\ntotem pole\ntow truck\ntoyshop\ntractor\ntrailer truck\ntray\ntrench coat\ntricycle\ntrimaran\ntripod\ntriumphal arch\ntrolleybus\ntrombone\ntub\nturnstile\ntypewriter keyboard\numbrella\nunicycle\nupright\nvacuum\nvase\nvault\nvelvet\nvending machine\nvestment\nviaduct\nviolin\nvolleyball\nwaffle iron\nwall clock\nwallet\nwardrobe\nwarplane\nwashbasin\nwasher\nwater bottle\nwater jug\nwater tower\nwhiskey jug\nwhistle\nwig\nwindow screen\nwindow shade\nWindsor tie\nwine bottle\nwing\nwok\nwooden spoon\nwool\nworm fence\nwreck\nyawl\nyurt\nweb site\ncomic book\ncrossword puzzle\nstreet sign\ntraffic light\nbook jacket\nmenu\nplate\nguacamole\nconsomme\nhot pot\ntrifle\nice cream\nice lolly\nFrench loaf\nbagel\npretzel\ncheeseburger\nhotdog\nmashed potato\nhead cabbage\nbroccoli\ncauliflower\nzucchini\nspaghetti squash\nacorn squash\nbutternut squash\ncucumber\nartichoke\nbell pepper\ncardoon\nmushroom\nGranny Smith\nstrawberry\norange\nlemon\nfig\npineapple\nbanana\njackfruit\ncustard apple\npomegranate\nhay\ncarbonara\nchocolate sauce\ndough\nmeat loaf\npizza\npotpie\nburrito\nred wine\nespresso\ncup\neggnog\nalp\nbubble\ncliff\ncoral reef\ngeyser\nlakeside\npromontory\nsandbar\nseashore\nvalley\nvolcano\nballplayer\ngroom\nscuba diver\nrapeseed\ndaisy\nyellow lady's slipper\ncorn\nacorn\nhip\nbuckeye\ncoral fungus\nagaric\ngyromitra\nstinkhorn\nearthstar\nhen-of-the-woods\nbolete\near\ntoilet tissue\n"