Precise bounding box labeling for object detection

We support all standard image formats including JPG, PNG, TIFF, BMP, and WebP. For output, we provide annotations in COCO JSON, YOLO TXT, Pascal VOC XML, CSV, and custom formats tailored to your ML framework. We also support direct integration with popular tools like Labelbox, Supervisely, and CVAT.

We employ a triple-layer quality assurance process: (1) trained annotators follow detailed guidelines, (2) peer reviewers validate every annotation, (3) automated consistency checks flag outliers. For critical projects, we can implement consensus annotation where multiple annotators label the same image and discrepancies are adjudicated by senior reviewers.

Yes. Our annotators are trained to draw tight bounding boxes around visible portions of objects, even when partially occluded or truncated by image boundaries. We can also add attributes like “truncated,” “occluded,” or “difficult” to help your model learn to handle these challenging cases, which is especially important for real-world deployment.

Bounding boxes are axis-aligned rectangles that enclose objects – fast to create and perfect for object detection models like YOLO and Faster R-CNN. For rotated objects, consider oriented bounding boxes. For pixel-perfect segmentation, use polygon annotation or semantic segmentation. We can help you choose the right annotation type for your use case.

We leverage AI-assisted pre-labeling to accelerate large projects. Our system generates initial bounding boxes using your existing model or our pre-trained models, then human annotators review and correct them. This hybrid approach reduces annotation time by up to 70% while maintaining 99.9% accuracy. We also scale our workforce dynamically to meet your deadline.