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Label data with advanced annotation tools
Labelbox interfaces are intuitive to use and have full hotkey support to increase labeler productivity. To get started, configure a labeling interface for your task in minutes using one or more of the segmentation and classification tools. Or, build your own custom interface to use with Labelbox.
Upload or connect to your training data
Labelbox integrates with your ML pipeline by hosting or connecting to your training data (labeled and unlabeled). Supports both private cloud and on-premise data sources.
Distribute work to your internal team and outsourced services
Label data with internal and external teams simultaneously. Review annotations collaboratively. Keep track of activity and progress.
Ensure accurate and consistent training data
Catch bad labels in real time using the consensus system to automaticaly identify and correct inconsistent annotations. Set up review workflows to correct annotations and ensure accuracy of the training data.
Export your training data to standard ML formats with one click
Move your labeled data into a training environment seamlessly by exporting to popular ML formats including COCO, Pascal VOC, Tensorflow, JSON XY and CSV.
Extensible and Developer Friendly
Set up ingest, ETL, and egress integrations between your ML pipeline and Labelbox. Connect your ML models to supercharge labeling productivity and orchestrate active learning. Labelbox is API-first so you can do just about anything!
from graphqlclient import GraphQLClient
client = GraphQLClient('https://api.labelbox.com/graphql')
client.inject_token('Bearer <API_KEY_HERE>')
data = client.execute('
mutation {
createDataRow(
data: {
rowData: "<DATA_THAT_NEEDS_TO_BE_LABELED>",
dataset: {
connect: {
id: "<DATASET_ID_HERE>"
}
},
organization: {
connect: {
id: "<INSERT_YOUR_ORGANIZATION_ID_FROM_ABOVE_HERE>"
}
},
}
)
}
')
from graphqlclient import GraphQLClient
client = GraphQLClient('https://api.labelbox.com/graphql')
client.inject_token('Bearer <API_KEY_HERE>')
data = client.execute('
mutation {
createLabel(
data: {
label:"<INSERT_LABEL_DATA_STRING_HERE>",
secondsToLabel: 42,
dataRow: {
connect: {
id: "<INSERT_DATA_ROW_ID_HERE>"
}
},
project: {
connect: {
id: "<INSERT_PROJECT_ID_HERE>"
}
},
type: {
connect: {
name: "Any"
}
}
}) {
id
label
}}
')
from graphqlclient import GraphQLClient
client = GraphQLClient('https://api.labelbox.com/graphql')
client.inject_token('Bearer <API_KEY_HERE>')
data = client.execute('
mutation{
createPrediction(data:{
label:"<should-be-the-exact-same-as-label.label>",
predictionModelId:"<from-the-prediction-model-you-created-earlier>",
projectId:"<any-project-id>",
dataRowId:"<any-datarow-id>",
}){
id
}
}
')
from graphqlclient import GraphQLClient
client = GraphQLClient('https://api.labelbox.com/graphql')
client.inject_token('Bearer <API_KEY_HERE>')
data = client.execute('
query {
project(where:{id: "<INSERT_PROJECT_ID_HERE>"}) {
labels(first: 5){
id
label
createdBy{
id
email
}
type {
id
name
}
secondsToLabel
agreement
dataRow {
id
rowData
}
}
}
}
')
Customers ♥ Labelbox
Labelbox has become essential to our process. It is the beginning of every single deep learning exercise we do. The only thing that comes before Labelbox is recording the data.
Gal Ozery
AI Product Owner, Genius Sports
Labelbox is very well integrated as a part of our overall system design. Labelbox has helped us efficiently collect and analyze data through their strong infrastructure support. We’re able to get the labels we need for all different types of video and image analysis.
Stephen Krotosky
Manager, Applied Machine Learning