The fastest way to annotate computer vision datasets

Yes. Ultralytics Platform accepts datasets labeled in YOLO and COCO formats, the two most widely used annotation standards in computer vision. If your data was labeled in another tool like CVAT or Roboflow and exported to either format, you can upload it directly and start training immediately.

Computer vision models are trained on labeled datasets, learning to associate visual patterns with the annotation labels in your data. The quality, size, and balance of your training data directly influence how well trained models perform. Ultralytics Platform connects your annotation workflow directly to cloud training with 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, such as 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 and manual review for accuracy.

Image annotation is the process of labeling 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.