Bounding Box Image Annotation
Budget: $10 – $30 USD
I have a small batch of images—fewer than one-hundred—that need clean, consistent object-detection labelling. For each image you will draw tight, non-overlapping bounding boxes around every instance of the target classes I will supply once we start. Accuracy matters more than speed; missed objects or sloppy boxes will be rejected.
Preferred workflow is any modern tool that can export to COCO JSON or Pascal-VOC XML, as these formats plug straight into my training pipeline. If you normally use LabelImg, CVAT, Supervisely, or similar, that’s perfect.
Deliverables
• Annotated dataset in COCO JSON or Pascal-VOC XML (your choice, just stay consistent).
• A quick text report summarising class counts and any edge cases flagged during labelling.
I will run a spot-check on at least 20 % of the images; corrections will need turning around before final sign-off. If you’re comfortable with precise annotation work and can commit to clear communication, this should be a fast, straightforward project.
Preferred workflow is any modern tool that can export to COCO JSON or Pascal-VOC XML, as these formats plug straight into my training pipeline. If you normally use LabelImg, CVAT, Supervisely, or similar, that’s perfect.
Deliverables
• Annotated dataset in COCO JSON or Pascal-VOC XML (your choice, just stay consistent).
• A quick text report summarising class counts and any edge cases flagged during labelling.
I will run a spot-check on at least 20 % of the images; corrections will need turning around before final sign-off. If you’re comfortable with precise annotation work and can commit to clear communication, this should be a fast, straightforward project.