Uniqueness
quality metric is used to identify duplicate and near-duplicate images.
Uniqueness
metric evaluates all images within the dataset and assigns a uniqueness score to each, indicating their distinctiveness.
Near-duplicate image
and are presented side by side in the Explorer’s grid view. This setup simplifies the decision-making process when selecting which image to keep and which one to remove.
1: Duplicates Shortcut
Uniqueness
value of 0 to 0.0001 are highlighted as duplicates. You can adjust this value from the Filter tab.2: Sorting by `Uniqueness`
Uniqueness
. Sort by ascending order to display duplicates first.3: Filtering by `Uniqueness`
Uniqueness
.Go to Filter tab > Add Filter > Data Quality Metrics > Uniqueness. A small histogram diagram appears above the filter.You can then change the filter settings to specify a range closer to 0.Uniqueness
quality metric for the Metric Distribution section.
Uniqueness
scores.
To remove duplicate images from your Project:
Uniqueness
filter to all images in the Project. The Uniqueness
filter returns images with a Uniqueness
value between 0 and 0.0001.
Uniqueness
.
Uniqueness
filter from the default value to find all the duplicate images in the Project.
As you adjust the filter the images that appear in the Explorer workspace change.
Duplicates
.
All selected images have the tag Duplicates
applied to them.
Duplicates
.
Duplicates
.
Uniqueness
filter to all images in the Project. The Uniqueness
filter returns images with a Uniqueness
value between 0 and 0.0001.
Uniqueness
.
Uniqueness
filter from the default value to 0 to 0.05.
Duplicates
, add the images to the existing Collection and go to step 11.Duplicates
.
All selected images have the tag Duplicates
applied to them.
Duplicates
.
Duplicates
.