The target of the search query

The search element represents the target of the search query. This can be a class, an annotation, or a specific dataset. Search elements can be combined with other search elements by logic to refine the search query.

Can image like the query block equals to the where-clause of SQL.


<Example> Find all the data in dataset 001

dataset = 'dataset 001'

# Expression 1
dataset IN ('dataset 001', 'dataset 002')

# Expression 2
dataset = 'dataset 001' OR dataset = 'dataset 002'


<Example> Find all the data in dataslice 001 or data slice 002

dataslice = 'dataslice 001'

# Expression 1
dataslice IN ('dataslice 001', 'dataslice 002')

# Expression 2
dataslice = 'dataslice 001' OR dataslice = 'dataslice 002'


The image quality assessment.

There are 7 types of IQA items: lightness, contrast, colorfulness, max color Kurtosis, BRISQUE, Laplacian-blur, and FFT-Blur.sql

<Example> Find all the data iqa lightness > 30

iqa.lightness > 30


The class follows the ontology settings. In the context of your search syntax, the element “class.name” specifically refers to the classification of an object (ex.bounding box) in an image or a set of images.


The class.name does not indicate whether an image or a dataset contains a certain class of objects as a whole. Instead, it is used to find specific instances of objects within the bounding boxes in the data.

<Example> Find all the data with object class car.

class.name = "car"

# Expression 1
class.name IN ('car', 'people')

# Expression 2
class.name = 'car' OR class.name = 'people'

# Expression 3
class.name = 'car' and class.name = 'people'  # Note: There will be no result. A bounding box cannot be classified as both a car and a truck.


Use 'class IS NULL' in your search query to find items that do not have a specified object class.

Class with Attribute

The class attributes follow the ontology settings.

<Example> Find the person age between 28~30, year in 1995, 1996, 1997.

# search class is person who's attribute contains age that is between 28 - 30
(class.name = 'person' AND class.attribute.name='age' AND (class.attribute.value BETWEEN 28 AND 30))

# search all datarows whos attribute year is either 1995, 1996 or 1997
class.attribute.name = 'year' AND class.attribute.value IN (1995, 1996, 1997)

# search class is person or car where both attribute names contains year
(class.name = 'person' AND class.attribute.name = 'year') AND (class.name = 'car' AND class.attribute.name 'year')

# search class is person or (class is car but containes year attribute)
class.name = 'person' OR (class.name = car AND class.attribute.name = year)


The target annotations result. It can be ground truth or model predictions.

<Example> Find all the data with ground truth, model-a annotations.

annotation = 'ground_truth'
annotation = 'model-a'

# Expression 1
annotation IN ('ground_truth', 'model-a')

# Expression 2
annotation = 'ground_truth' OR dataset = 'model-a'

<Example> Find all the data with confidence score > 50% annotations.

annotation.confidence > 0.5


The object information includes height, width, length, area, and the cuboid object distance and the points included.


Find all the data with object size height < 100px.

object_size.height < 100

Find all the data with object size area < 40px^2.

object_size.area < 40

Find all the data with cuboid center distance to origin > 10m

object_cuboid.distance > 10

Find all the data with cuboid containing < 5 pcd points

object_cuboid.point < 5


The project taggings.

<Example> Find the weather of sunny or rainy.

tag.name = 'weather'
tag.name != 'weather'
tag.name = 'weather' and tag.value IN ('sunny', 'rainy')

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