This guide walks through Roboflow's team annotation workflow from start to finish. Invite team members with role-based access, upload images into batches, and divide the work with labeling jobs. AI tools such as Label Assist and Auto Label with SAM 3 cut down manual drawing, while Review Mode and Annotation Insights keep quality high and track progress across your team.
Dataset annotation is better with teams: you can move more quickly, keep everyone on the same page, track annotation progress, and catch labeling mistakes before they reach your model.
In this post, we'll walk through how to annotate data with your team using Roboflow. Over 750,000 datasets and 575 million images have been labeled with Roboflow.
How To Annotate Images with Your Team
The recommended workflow is: (1) inviting your team members, (2) uploading images to a shared project, (3) assigning labeling jobs, (4) annotating images with AI assistance, (5) reviewing and approving labels before they enter your dataset. Annotation jobs handle assignment, review, and progress tracking inside a single project, so there's no need to split work into separate datasets and merge them later.
Invite Your Team Members
First, we need to invite our team members to join our workspace on Roboflow.
Invite team members in Roboflow.
Visit https://app.roboflow.com/invite to add your colleagues. When you invite someone, you can give them a role: Admin, Labeler, or Reviewer. Role-based access keeps your data safe when you add labelers from inside your organization or from outside labeling teams.
Upload Images for Annotation
Next, upload your images to a single project. Refer to our documentation on adding data.
Images you upload are organized into batches. You don't have to label everything you upload: batches let you store raw images from the real world and decide later which ones are worth annotating and training on. Sampling images your model will see in production is a great way to spot edge cases early.
Assign Labeling Jobs
Once images are uploaded, divide the work by assigning labeling jobs from the Assign Images tab.
For each job, you choose the images, a labeler, and a reviewer. If I were labeling dog images with a team member, I'd assign half the batch to myself and half to my colleague. Each of us gets a notification when work lands on our plate.
You can also add labeling instructions to a batch before assigning it.
Written instructions attached to the job are the easiest way to keep classes consistent when several people label at once. And if someone isn't in your workspace yet, you can invite them and assign them a job in one step.
The labeling jobs board gives you an at-a-glance view of every job as it moves through the annotation process, and you can reassign jobs to different team members as needed. For more detail, read our team collaboration docs.
Annotate Images
With jobs assigned, it's time to label. Be sure to refer to our guide on best practices for labeling data:
Additionally, your team doesn't need to draw every box by hand. Roboflow Annotate includes a suite of AI labeling tools:
- Label Assist uses a trained model (one of your own, or a public model) to suggest annotations, which Roboflow reports can cut human labeling time by up to 95%.
- Smart Polygon creates segmentation masks in one click, powered by Meta AI's Segment Anything, including SAM 3.
- Auto Label uses foundation models like SAM 3 to label thousands of images from text prompts, so your team reviews labels instead of drawing them from scratch.
Review and Approve Labels
Labeled images flow through review before they're added to your dataset, so quality issues get caught early instead of after training.
In Review Mode, a reviewer can approve or reject each labeled image and send rejected images back to the labeler for rework. Team members can leave comments directly on images to discuss labeling decisions, and every image keeps a full annotation history, so you can see who changed what and revert if needed.
Approved images are added to the dataset; everything else stays in the job until it's fixed. The result is one clean, deduplicated dataset ready for training.
Track Progress
The jobs board shows where every job stands, and you can filter statistics by labeler. Annotation Insights adds labeling-operation metrics on top: images annotated, time spent, boxes drawn, and acceptance rate per team member. That makes it easy to spot who needs help and which parts of the dataset are slowing things down.
If your team can't keep up with labeling volume, Roboflow's managed labeling services can scale up the work with a trained workforce inside the same review workflow. Learn more about outsourcing your data labeling.
Cite this Post
Use the following entry to cite this post in your research:
Aarnav Shah. (Jun 1, 2026). How To Annotate Images with Your Team Using Roboflow. Roboflow Blog: https://blog.roboflow.com/team-image-annotation-workflow/