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Removing Duplicate Objects

This recipe demonstrates a simple workflow for finding and removing duplicate objects in your FiftyOne datasets using intersection over union (IoU).

Specificially, it covers:

• Using the compute_max_ious() utility to compute overlap between spatial objects

• Using the App’s tagging UI to review and delete duplicate labels

• Using FiftyOne’s CVAT integration to edit duplicate labels

• Using the find_duplicates() utility to automatically detect duplicate objects

Also, check out our blog post for more information about using IoU to evaluate your object detection models.

Setup

If you haven’t already, install FiftyOne:

!pip install fiftyone

Load a dataset

In this recipe, we’ll work with the validation split of the COCO dataset, which is conveniently available for download via the FiftyOne Dataset Zoo.

The snippet below downloads and loads a subset of the validation split into FiftyOne:

import fiftyone as fo
import fiftyone.zoo as foz

dataset = foz.load_zoo_dataset("coco-2017", split="validation", max_samples=1000)

Downloading split 'validation' to '/Users/Brian/fiftyone/coco-2017/validation' if necessary
Found annotations at '/Users/Brian/fiftyone/coco-2017/raw/instances_val2017.json'
Sufficient images already downloaded
Existing download of split 'validation' is sufficient
Loading 'coco-2017' split 'validation'
 100% |███████████████| 1000/1000 [4.9s elapsed, 0s remaining, 216.7 samples/s]
Dataset 'coco-2017-validation-1000' created

Let’s print the dataset to see what we downloaded:

print(dataset)

Name:        coco-2017-validation-1000
Media type:  image
Num samples: 1000
Persistent:  False
Tags:        ['validation']
Sample fields:
    id:           fiftyone.core.fields.ObjectIdField
    filepath:     fiftyone.core.fields.StringField
    tags:         fiftyone.core.fields.ListField(fiftyone.core.fields.StringField)
    metadata:     fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.metadata.Metadata)
    ground_truth: fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Detections)

Finding duplicate objects

Now let’s use the compute_max_ious() utility to compute the maximum IoU between each object in the ground_truth field with another object of the same class (classwise=True) within the same image.

The max IOU will be stored in a max_iou attribute of each object, and the idea here is that duplicate objects will necessarily have high IoU with another object.

import fiftyone.utils.iou as foui

foui.compute_max_ious(dataset, "ground_truth", iou_attr="max_iou", classwise=True)
print("Max IoU range: (%f, %f)" % dataset.bounds("ground_truth.detections.max_iou"))

 100% |███████████████| 1000/1000 [3.2s elapsed, 0s remaining, 348.2 samples/s]
Max IoU range: (0.000000, 0.951640)

Note that compute_max_ious() provides an optional other_field parameter if you would like to compute IoUs between objects in different fields instead.

In any case, let’s create a view that contains only labels with a max IoU > 0.75:

from fiftyone import ViewField as F

# Retrieve detections that overlap above a chosen threshold
dups_view = dataset.filter_labels("ground_truth", F("max_iou") > 0.75)
print(dups_view)

Dataset:     coco-2017-validation-1000
Media type:  image
Num samples: 7
Tags:        ['validation']
Sample fields:
    id:           fiftyone.core.fields.ObjectIdField
    filepath:     fiftyone.core.fields.StringField
    tags:         fiftyone.core.fields.ListField(fiftyone.core.fields.StringField)
    metadata:     fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.metadata.Metadata)
    ground_truth: fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Detections)
View stages:
    1. FilterLabels(field='ground_truth', filter={'$gt': ['$$this.max_iou', 0.75]}, only_matches=True, trajectories=False)

and load it in the App:

session = fo.launch_app(view=dups_view)

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