Table of Contents
Contents

fiftyone.brain

Module contents

The brains behind FiftyOne: a powerful package for dataset curation, analysis, and visualization.

See https://github.com/voxel51/fiftyone for more information.

Copyright 2017-2021, Voxel51, Inc.
voxel51.com

Functions

compute_hardness(samples, label_field[, …])

Adds a hardness field to each sample scoring the difficulty that the specified label field observed in classifying the sample.

compute_mistakenness(samples, pred_field[, …])

Computes the mistakenness of the labels in the specified label_field, scoring the chance that the labels are incorrect.

compute_uniqueness(samples[, …])

Adds a uniqueness field to each sample scoring how unique it is with respect to the rest of the samples.

compute_visualization(samples[, …])

Computes a low-dimensional representation of the samples’ media or their patches that can be interactively visualized and manipulated via the returned VisualizationResults object.
fiftyone.brain.compute_hardness(samples, label_field, hardness_field='hardness')

Adds a hardness field to each sample scoring the difficulty that the specified label field observed in classifying the sample.

Hardness is a measure computed based on model prediction output (through logits) that summarizes a measure of the uncertainty the model had with the sample. This makes hardness quantitative and can be used to detect things like hard samples, annotation errors during noisy training, and more.

Note

Runs of this method can be referenced later via brain key hardness_field.

Parameters
fiftyone.brain.compute_mistakenness(samples, pred_field, label_field='ground_truth', mistakenness_field='mistakenness', missing_field='possible_missing', spurious_field='possible_spurious', use_logits=False, copy_missing=False)

Computes the mistakenness of the labels in the specified label_field, scoring the chance that the labels are incorrect.

Mistakenness is computed based on the predictions in the pred_field, through either its confidence or logits attributes. This measure can be used to detect things like annotation errors and unusually hard samples.

This method supports both classifications and detections.

For classifications, a mistakenness_field field is populated on each sample that quantifies the likelihood that the label in the label_field of that sample is incorrect.

For detections, the mistakenness of each detection in label_field is computed, using fiftyone.core.collections.SampleCollection.evaluate_detections() to locate corresponding detections in pred_field. Three types of mistakes are identified:

  • (Mistakes) Detections in label_field with a match in pred_field are assigned a mistakenness value in their mistakenness_field that captures the likelihood that the class label of the detection in label_field is a mistake. A mistakenness_field + "_loc" field is also populated that captures the likelihood that the detection in label_field is a mistake due to its localization (bounding box).

  • (Missing) Detections in pred_field with no matches in label_field but which are likely to be correct will have their missing_field attribute set to True. In addition, if copy_missing is True, copies of these detections are added to the ground truth detections label_field.

  • (Spurious) Detections in label_field with no matches in pred_field but which are likely to be incorrect will have their spurious_field attribute set to True.

In addition, for detections only, the following sample-level fields are populated:

  • (Mistakes) The mistakenness_field of each sample is populated with the maximum mistakenness of the detections in label_field

  • (Missing) The missing_field of each sample is populated with the number of missing detections that were deemed missing from label_field.

  • (Spurious) The spurious_field of each sample is populated with the number of detections in label_field that were given deemed spurious.

Note

Runs of this method can be referenced later via brain key mistakenness_field.

Parameters

  • samples – a fiftyone.core.collections.SampleCollection

  • pred_field – the name of the predicted label field to use from each sample. Can be of type fiftyone.core.labels.Classification, fiftyone.core.labels.Classifications, or fiftyone.core.labels.Detections

  • label_field ("ground_truth") – the name of the “ground truth” label field that you want to test for mistakes with respect to the predictions in pred_field. Must have the same type as pred_field

  • mistakenness_field ("mistakenness") – the field name to use to store the mistakenness value for each sample

  • missing_field ("possible_missing) – the field in which to store per-sample counts of potential missing detections. Only applicable for fiftyone.core.labels.Detections labels

  • spurious_field ("possible_spurious) – the field in which to store per-sample counts of potential spurious detections. Only applicable for fiftyone.core.labels.Detections labels

  • use_logits (False) – whether to use logits (True) or confidence (False) to compute mistakenness. Logits typically yield better results, when they are available

  • copy_missing (False) – whether to copy predicted detections that were deemed to be missing into label_field. Only applicable for fiftyone.core.labels.Detections labels

fiftyone.brain.compute_uniqueness(samples, uniqueness_field='uniqueness', roi_field=None, embeddings=None, model=None, batch_size=None, force_square=False, alpha=None)

Adds a uniqueness field to each sample scoring how unique it is with respect to the rest of the samples.

This function only uses the pixel data and can therefore process labeled or unlabeled samples.

You can provide your own embeddings to seed this method by specifying either the embeddings or model arguments.

Note

Runs of this method can be referenced later via brain key uniqueness_field.

Parameters

  • samples – a fiftyone.core.collections.SampleCollection

  • uniqueness_field ("uniqueness") – the field name to use to store the uniqueness value for each sample

  • roi_field (None) – an optional fiftyone.core.labels.Detection, fiftyone.core.labels.Detections, fiftyone.core.labels.Polyline, or fiftyone.core.labels.Polylines field defining a region of interest within each image to use to compute uniqueness

  • embeddings (None) –

    pre-computed embeddings to use. Can be any of the following:

    • a num_samples x num_dims array of embeddings

    • if roi_field is specified, a dict mapping sample IDs to num_patches x num_dims arrays of patch embeddings

    • the name of a dataset field containing the embeddings to use

  • model (None) – a fiftyone.core.models.Model or the name of a model from the FiftyOne Model Zoo to use to generate embeddings. The model must expose embeddings (model.has_embeddings = True)

  • batch_size (None) – a batch size to use when computing embeddings. Only applicable when a model is provided

  • force_square (False) – whether to minimally manipulate the patch bounding boxes into squares prior to extraction. Only applicable when a model and roi_field are specified

  • alpha (None) – an optional expansion/contraction to apply to the patches before extracting them, in [-1, \infty). If provided, the length and width of the box are expanded (or contracted, when alpha < 0) by (100 * alpha)%. For example, set alpha = 1.1 to expand the boxes by 10%, and set alpha = 0.9 to contract the boxes by 10%. Only applicable when a model and roi_field are specified

fiftyone.brain.compute_visualization(samples, patches_field=None, embeddings=None, brain_key=None, num_dims=2, method='umap', config=None, model=None, batch_size=None, force_square=False, alpha=None, **kwargs)

Computes a low-dimensional representation of the samples’ media or their patches that can be interactively visualized and manipulated via the returned VisualizationResults object.

If no embeddings or model is provided, a default model is used to generate embeddings.

Parameters

  • samples – a fiftyone.core.collections.SampleCollection

  • patches_field (None) – a sample field defining the image patches in each sample that have been/will be embedded

  • embeddings (None) –

    pre-computed embeddings to use. Can be any of the following:

    • a num_samples x num_dims array of embeddings

    • if patches_field is specified, a dict mapping sample IDs to num_patches x num_dims arrays of patch embeddings

    • the name of a dataset field containing the embeddings to use

  • brain_key (None) – a brain key under which to store the results of this visualization

  • num_dims (2) – the dimension of the visualization space

  • method ("umap") – the dimensionality-reduction method to use. Supported values are ("umap", "tsne", "pca")

  • config (None) – a fiftyone.brain.visualization.VisualizationConfig specifying the parameters to use. If provided, takes precedence over other parameters

  • model (None) –

    a fiftyone.core.models.Model or the name of a model from the FiftyOne Model Zoo to use to generate embeddings. The model must expose embeddings (model.has_embeddings = True)

  • batch_size (None) – an optional batch size to use when computing embeddings. Only applicable when a model is provided

  • force_square (False) – whether to minimally manipulate the patch bounding boxes into squares prior to extraction. Only applicable when a model and patches_field are specified

  • alpha (None) – an optional expansion/contraction to apply to the patches before extracting them, in [-1, \infty). If provided, the length and width of the box are expanded (or contracted, when alpha < 0) by (100 * alpha)%. For example, set alpha = 1.1 to expand the boxes by 10%, and set alpha = 0.9 to contract the boxes by 10%. Only applicable when a model and patches_field are specified

  • **kwargs – optional keyword arguments for the constructor of the fiftyone.brain.visualization.VisualizationConfig being used

Returns

a fiftyone.brain.visualization.VisualizationResults