Creating custom training datasets

Create, label, and export an object-detection dataset with Label Studio.

💡 Reminder: All code and scripts for this project are executed in the cloud. Open the Google Colab Notebook and select Runtime > Run all to execute the setup.

A good dataset is like the perfect textbook for teaching a computer how the real world works. It needs to show a wide variety of examples, including messy and everyday situations, so the computer will not get confused when things are not perfect. Everything inside it must be organized and named exactly the same way, with no broken files or careless mistakes. It also needs to have a fair and equal amount of every topic you want the computer to learn, rather than just showing one thing over and over again. Most importantly, the information has to perfectly match the specific job you want the computer to do, so it learns exactly what it needs to succeed.

Build a labeled dataset for object detection or image segmentation. Use known examples to train a model for your use case.

Set up your workspace

Install and launch Label Studio before creating your dataset.

Choose your platform

Set up macOS

Set up Windows

Continue when Label Studio opens in your browser.

1

Create a project

Open Label Studio and create a project. Give it a clear name.

Select Data Import and upload your images. You can use your own photos or a public dataset.

Label Studio data import screen

Import images in batches of 100 when working with larger datasets.

2

Configure labels

Open Labeling Setup. Choose Object Detection with Bounding Boxes, then save the configuration.

Label Studio labeling template selection

Add a label for each object class. Save your changes when finished.

Label Studio label configuration

3

Label your images

Open the project and select the first image. Draw a bounding box around each target object.

Use each class shortcut to select its label. For example, press 1 for lavender, 2 for pink, and 3 for purple.

Select a region to resize or delete its bounding box.

Submit the label before moving to the next image.

4

Export the dataset

Open the project export options and select an output format.

Label Studio export options

Use YOLO with Images to include uploaded images in the export.