nucleus.model_run¶

Model Runs are deprecated and will be removed in a future version of the python client. It is now possible to upload model predictions without a need for creating a model run

For example:

import nucleus
client = nucleus.NucleusClient(YOUR_SCALE_API_KEY)
prediction_1 = nucleus.BoxPrediction(label="label", x=0, y=0, width=10, height=10, reference_id="1", confidence=0.9, class_pdf={'label': 0.9, 'other_label': 0.1})
prediction_2 = nucleus.BoxPrediction(label="label", x=0, y=0, width=10, height=10, reference_id="2", confidence=0.2, class_pdf={'label': 0.2, 'other_label': 0.8})
model = client.create_model(name="My Model", reference_id="My-CNN", metadata={"timestamp": "121012401"})
response = dataset.upload_predictions(model, [prediction_1, prediction_2])

ModelRun

This class is deprecated and will be removed from the python client.

class nucleus.model_run.ModelRun(model_run_id, dataset_id, client)¶

This class is deprecated and will be removed from the python client.

Parameters:

model_run_id (str)
dataset_id (str)

commit(payload=None)¶

Commits the model run. Starts matching algorithm defined by payload.

Parameters:

payload (Optional[dict]) –

Dict with optional keys:

class_agnostic: A flag to specify if matching algorithm should be class-agnostic or not. Default value: True.
allowed_label_matches: An optional list of AllowedMatch objects to specify allowed matches for ground truth and model predictions. If specified, class_agnostic flag is assumed to be False. Each AllowedMatch has ground_truth_label and model_prediction_label.

Example response format:
```
[
    {
        "ground_truth_label": "car",
        "model_prediction_label": "car",
    }
]
```

Returns:

Dict with model_run_id.

Example response format:

{
    "model_run_id": "run_123",
}

Return type:

dict

iloc(i)¶

Returns Model Run Info For Dataset Item by its number.

Parameters:: i (int) – Absolute number of Dataset Item for a dataset corresponding to the model run.

Returns:

List[Union[BoxPrediction, PolygonPrediction, CuboidPrediction, SegmentationPrediction]]

info()¶

Provides information about the Model Run.

model_id: Model Id corresponding to the run.

name: A human-readable name of the model project.

status: Status of the Model Run.

metadata: An arbitrary metadata blob specified for the run.

Returns:

model_id, name, status, and metadata.

Example response format:

{
    "model_id": "prj_123",
    "name": "example-model-project",
    "status": "Completed",
    "metadata": Dict[str, Any],
}

Return type:

Dict with the following keys

loc(dataset_item_id)¶

Returns Model Run Info For Dataset Item by its id.

Parameters:: dataset_item_id (str) – Internally controlled id for dataset item.

Returns:

{
    "annotations": List[Box2DPrediction],
}

predict(annotations, update=DEFAULT_ANNOTATION_UPDATE_MODE, asynchronous=False, batch_size=5000, remote_files_per_upload_request=20, local_files_per_upload_request=10)¶

Uploads model outputs as predictions for a model_run.

Parameters:

annotations (List[Union[nucleus.prediction.BoxPrediction, nucleus.prediction.PolygonPrediction, nucleus.prediction.CuboidPrediction, nucleus.prediction.SegmentationPrediction]]) – Predictions to upload for this model run.
update (bool) – If True, existing predictions for the same (reference_id, annotation_id) will be overwritten. If False, existing predictions will be skipped.
asynchronous (bool) – Whether or not to process the upload asynchronously (and return an AsyncJob object). Default is False.
batch_size (int) – Number of predictions processed in each concurrent batch. Default is 5000. If you get timeouts when uploading geometric annotations, you can try lowering this batch size. This is only relevant for asynchronous=False.
remote_files_per_upload_request (int) – Number of remote files to upload in each request. Segmentations have either local or remote files, if you are getting timeouts while uploading segmentations with remote urls, you should lower this value from its default of 20. This is only relevant for asynchronous=False.
local_files_per_upload_request (int) – Number of local files to upload in each request. Segmentations have either local or remote files, if you are getting timeouts while uploading segmentations with local files, you should lower this value from its default of 10. The maximum is 10. This is only relevant for asynchronous=False

Return type:

Union[dict, nucleus.async_job.AsyncJob]

Returns:

{
    "model_run_id": str,
    "predictions_processed": int,
    "predictions_ignored": int,
}

prediction_loc(reference_id, annotation_id)¶

Returns info for single Prediction by its reference id and annotation id.

Parameters:

reference_id (str) – The user specified id for the image.
annotation_id (str) – The user specified id for the prediction, or if one was not provided, the Scale internally generated id for the prediction.

Returns:

BoxPrediction | PolygonPrediction | CuboidPrediction

refloc(reference_id)¶

Returns Model Run Info For Dataset Item by its reference_id.

Parameters:: reference_id (str) – reference_id of a dataset item.

Returns:

List[Union[BoxPrediction, PolygonPrediction, CuboidPrediction, SegmentationPrediction]]