Roboflow + SuperAnnotate
Bring images and labels from SuperAnnotate into Roboflow, then train a model on them and deploy it to the cloud or your own hardware.
How to connect SuperAnnotate to Roboflow
5 stepsExport from SuperAnnotate
From your image project, prepare a download and choose COCO as the format. The SuperAnnotate JSON format also works.
Create a Roboflow project
Choose object detection for boxes or instance segmentation for polygons.
Upload the export
Drag the images and the annotation file onto the upload page. Roboflow reads the format and keeps your classes.
Generate a version
Pick preprocessing and augmentation steps and generate a dataset version.
Train and deploy
Train a model such as RF-DETR, then deploy it with the Serverless Hosted API or on your own hardware with Roboflow Inference.
What teams build with SuperAnnotate
Keep labeling where it is
Your labeling team stays in SuperAnnotate and sends finished exports to Roboflow for training.
Compare models on the same labels
Train more than one architecture on one dataset version and compare the results.
Find what the model misses
Use model evaluation in Roboflow to see which classes need more labeled examples, then send that work back to your labelers.
Questions
COCO is a good default because many tools read it. Roboflow also reads SuperAnnotate's own JSON format.
Yes. Upload each new export to the same Roboflow project and generate a new version when you are ready to retrain.
Yes. Roboflow Annotate supports boxes and polygons, with Label Assist to pre-label images using a model.
Why are companies choosing Roboflow?
The end-to-end platform for vision AI: create datasets, train models and deploy to production.
Flexible deployment modes
Move from a scalable cloud API to edge hardware like NVIDIA Jetson without managing complex dependencies.
Integrated workflow builder
Combine multiple AI models, custom business logic and third-party integrations into one production-ready pipeline in a low-code editor.
Model monitoring and alerts
Dashboards track confidence, inference time and request volume for your deployed models, and email alerts tell your team when confidence suddenly drops.
