{
  "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 processed.",
          "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": "score",
          "description": "Score of the labels respectively.",
          "content": {
            "content_properties_type": "FeatureProperties",
            "content_properties": {
            }
          },
          "process_units": [
            {
              "options_type": "ScoreCalibrationOptions",
              "options": {
                "score_transformation": "LOG",
                "default_score": 0.2
              }
            }
          ],
          "stats": {
            "max": [
              255.0
            ],
            "min": [
              0.0
            ]
          },
          "associated_files": [
            {
              "name": "labels.txt",
              "description": "Labels for categories that the model can recognize.",
              "type": "TENSOR_AXIS_LABELS"
            },
            {
              "name": "score_calibration.txt",
              "description": "Contains sigmoid-based score calibration parameters. The main purposes of score calibration is to make scores across classes comparable, so that a common threshold can be used for all output classes.",
              "type": "TENSOR_AXIS_SCORE_CALIBRATION"
            }
          ]
        }
      ]
    }
  ],
  "min_parser_version": "1.0.0"
}
