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Cloud data overview
Restrict data access by IP range
How to generate signed URLs
How to generate non-expiring signed URLs
Model-assisted labeling (import annotations)
Real-time human-in-the-loop labeling
Custom label interface
Creating your first project
Project setup script
Python SDK FAQ
Intro to the GraphQL API
Data types overview
Bulk import requests
Legacy vs new editor ontology
Legacy vs new editor JSON exports
Legacy vs new editor hotkeys
Model predictions (legacy)
September 25, 2020
August 21, 2020
August 6, 2020
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Table of Contents
Updated by Alex Cota
This page breaks down, step by step, how to create a basic project. To see our sample Python script for a complete project setup, see Project setup script.
Before you start
Make sure the API client is initialized:
from labelbox import Client
client = Client()
Basic project setup
Step 1 Build your project's foundation
In the rough hierarchical structure of Labelbox’s data objects, Projects and Datasets are considered "top-level" objects. They are the foundation upon which your labeling pipeline is structured. Because they are top-level, Projects and Datasets are created using the Labelbox Client directly.
create_project method to create and name your Project. You will be attaching your Datasets to your Project so name it accordingly.
project = client.create_project(name="<project_name>")
Within your project, use the
create_dataset method to create a Dataset, name it, and attach it to your Project.
dataset = client.create_dataset(name="<dataset_name>", projects=project)
Step 2 Add data to your project
There are two ways to create data rows within a dataset, in bulk and individually. For details on acceptable file types, see Data import overview.
create_data_row method accepts files individually and is a synchronous operation.
dataset = client.get_dataset("dataset_id")
data_row = dataset.create_data_row(row_data="http://my_site.com/photos/img_01.jpg")
You can also pass a string to a local file.
data_row = dataset.create_data_row(row_data="path/to/file.jpg")
For instructions on how to bulk upload data rows using the
create_data_rows method, see Data Rows.
Step 3 Select label editor and ontology
You have several options for selecting a labeling frontend and an ontology for your project. For some examples, see our Project setup script, a complete code sample that includes the steps above and more.