12 integrations
Annotation and Data integrations
Bring images and labels in from the tools your team already uses.
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AWS S3You can add upload images stored in S3 for use in building datasets for models in Roboflow.
CVATPre-label images in CVAT with a Roboflow Universe model, then bring the finished labels into Roboflow to train and deploy your own model.
FastdupDownload a Roboflow dataset, run fastdup to find duplicates, outliers and other problem images, then upload the cleaned set back to train on.
LabelImgUpload the Pascal VOC XML or YOLO files LabelImg saves to Roboflow, then train and deploy a model on your labels.
LabelMeUpload LabelMe JSON files with your images to Roboflow to train detection and segmentation models on your polygons and boxes.
LabelboxUpload a Labelbox JSON export to Roboflow to convert it, train a model on it and deploy the model.
Make SenseConnect a Roboflow model to Make Sense so it draws suggested boxes as you open each image, then upload the finished labels to Roboflow.
SageMaker Ground TruthUpload the output manifest from a SageMaker Ground Truth labeling job to Roboflow, then train, convert or relabel the dataset without a conversion script.
Scale AIUpload the JSON that Scale AI returns for completed annotation tasks to Roboflow, then train and deploy a model on those labels.
SuperAnnotateBring images and labels from SuperAnnotate into Roboflow, then train a model on them and deploy it to the cloud or your own hardware.
SuperviselyExport a Supervisely project and upload it to Roboflow to train, compare and deploy models on the same labels.
VoTTYou can export the data annotated in VoTT into Roboflow for use in generating a dataset with preprocessing and augmentations, and for use in model training.
Don’t see your tool?
Roboflow imports and exports the common annotation formats and has a REST API and Python SDK, so most stacks connect without a dedicated integration.