Element

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.

Dataset

<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'

Dataslice

<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'

IQA

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

Class

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.

Note

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.

Hint

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)

Annotation

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

Object

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

Example:

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

Tag

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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