ultralytics platform
Smart annotation, dataset management, and built-in analytics. Everything you need to go from raw data to training, all on Ultralytics Platform.
17.8K
Total models
34.4M
Created images
167.7M
Created annotations
Ultralytics Platform gives you the tools to build high-quality labelled datasets faster. From smart annotation to precise manual editing, every tool is designed to reduce annotation time without sacrificing quality.
SAM-powered smart annotation: Generate precise masks, bounding boxes, or oriented boxes with a click.
Manual annotation tools: A full suite of drawing tools for all five detection tasks.
Inline class creation: Create and organize classes directly during annotation.
Pose: Use templates or custom-make keypoints for your pose estimation projects.
Upload images, videos, or ZIP archives directly to the platform. Import datasets already labelled in YOLO or COCO format, or start from scratch with raw, unannotated images. Your data is processed, validated, and ready to annotate in seconds.
Understand your dataset inside and out before you start training. Visualize class distributions, spot split imbalances, review annotation location heat maps, and check image dimension spreads, all from built-in charts that update automatically as your dataset evolves.
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Annotate
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Train
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Deploy
Yes. Ultralytics Platform accepts datasets labeled in YOLO format and COCO format, the two most widely used annotation standards in computer vision. If your data was labeled in another tool like CVAT or Roboflow, that exports to either format, you can upload it directly and start training immediately.
Computer vision models are trained on labelled datasets, learning to associate visual patterns with the annotation labels in your data. The quality, size, and balance of your training data directly influences how well trained models perform. Ultralytics Platform connects your annotation workflow directly to cloud training, no tool switching required.
Ultralytics Platform supports YOLO format and COCO format for dataset import, with automatic format detection on upload. If you've annotated data in an open-source tool like CVAT, LabelImg, or LabelMe, export your labels in YOLO or COCO format and they'll be parsed automatically. You can export annotations from the platform in Ultralytics NDJSON format.
Manual annotation involves human annotators drawing labels directly on images using an annotation tool. Smart annotation uses AI algorithms, like Segment Anything (SAM), an open-source model developed by Meta, to pre-label images with minimal human input. Most production workflows combine both: smart annotation for speed, manual review for accuracy.
What is image annotation? Image annotation is the process of labelling images to identify objects, features, or regions within them. It is the foundational step in training computer vision models for tasks like object detection, image segmentation, image classification, and pose estimation. Annotation types vary by use case and include bounding boxes, polygons, masks and keypoints. It's a process carried out in both open-source tools and dedicated commercial platforms.
Join thousands of teams building production-ready computer vision models on Ultralytics.
