| { |
| "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": { |
| } |
| }, |
| "process_units": [ |
| { |
| "options_type": "ScoreCalibrationOptions", |
| "options": { |
| "score_transformation": "LOG", |
| "default_score": 0.2 |
| } |
| } |
| ], |
| "stats": { |
| "max": [ |
| 1.0 |
| ], |
| "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" |
| } |
| ] |
| } |
| ] |
| } |
| ] |
| } |