Dataset annotation is better with teams: you can move more quickly, keep everyone on the same page, track annotation progress, and easier provide examples of what the ultimate dataset should look like.

In this post, we'll walkthrough an example workflow of annotating data with your team using Roboflow Pro.

The recommended workflow is: (1) inviting our team members (2) uploading datasets components for the team members (3) sharing the datasets with your team (4) annotating images (5) merging the individually labeled datasets into a final dataset.

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Inviting Your Team Members  

First, we need to invite our team members to join our team on Roboflow.

Invite team members in Roboflow.
Invite team members in Roboflow.

Visit to add your colleagues.

Uploading Images for Annotation

As a next step, we'll create datasets (yes, plural!) for annotation. Specifically, we'll create one dataset per person on our team that is going to be labeling images. Each dataset will be each individual team member's responsibility to annotate. (Once we've labeled all the data in all datasets, we'll merge them.) Refer to our documentation on creating a dataset.

When creating a dataset, we recommend naming each dataset with the person responsible for labeling that portion. For example, if I (Joseph) were labeling chess images with a team member (Brad), I would name the datasets "Chess Pieces - Joseph" and "Chess Pieces - Brad."

Sharing Datasets with Your Team

Once the datasets are uploaded, they need to be shared with your team. Open a dataset, click "Team Sharing" on the left-hand side, and check the box for the team with which you want to share the dataset. We recommend sharing all of the individual datasets with your team members so everyone can see examples from their colleagues.

Roboflow Team sharing - Check the box for the team with which you want to share.
Check the box for the team with which you want to share a dataset.

Annotating Images

Once images are uploaded and the datasets are shared, it's time to annotate images. Be sure to refer to our guide on best practices for labeling data:

Seven Tips for Labeling Images for Computer Vision
Creating a high quality dataset for computer vision is essential to havingstrong model performance. In addition to collecting images that are as similarto your deployed conditions as possible, labeling images carefully andaccurately is essential. Check out the video version of this article on…
How to Label Images for Object Detection

And the video version:

How to Label Images for Object Detection. Subscribe to our YouTube.

Merging Labeled Datasets

Once each team member has fully labeled their respective dataset, we want to merge these datasets into one dataset that contains all labeled images. To do so, simply check the boxes for the datasets we seek to merge and click "Merge Datasets" in the upper right hand corner.

Merged image datasets in Roboflow
Note that after merging our datasets, the merged dataset contains our 20 labeled images from the two 10 image components.

The resulting dataset will contain all de-duplicated images and their labels. Note that the individual dataset components also remain.

Advanced hotkeys to speed up your annotation workflow

And that's it! We can continue to follow this workflow as we add more images by adding images to only that individual labeler's dataset or creating a new dataset altogether. Happy annotating!